Functional imaging of peripheral vision and dorsal stream … › d76e ›...

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Department of Neurology

University of Helsinki

Functional imaging of peripheral vision and dorsal visual

stream in the human cerebral cortex

Linda Stenbacka

Brain Research Unit and Advanced Magnetic Imaging Centre,

Low Temperature Laboratory,

Aalto University

Academic Dissertation

To be publicly discussed by the permission of the Faculty of Medicine of the University of

Helsinki in the Lecture Hall S1, Aalto University, Otakaari 5A, on May 6th 2010 at 12 noon

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ISBN 978-952-92-7258-7 (nid.)

ISBN 978-952-10-6256-8 (PDF)

Picaset Oy

Helsinki 2010

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Supervisor:

Docent Simo Vanni

Brain Research Unit and Advanced Magnetic Imaging Centre,

Low Temperature Laboratory

Aalto University

Espoo Finland

Reviewers:

Dr. Iiro Jääskeläinen

Department of Biomedical Engineering and Computational Science

Faculty of Information and Natural Sciences

Aalto University

Espoo Finland

Docent Jyrki Mäkelä

Biomag Laboratory

Helsinki University Central Hospital

Helsinki, Finland

Opponent:

Professor Yves Trotter

Centre de Recherche Cerveau et Cognition

Faculté de Médecine de Rangueil

Toulouse France

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List of Contents

Abstract 6

Abbreviations 8

List of publications 9

1. Introduction 11

2. Review of the literature 14

2.1. Overview of neural transmission in central nervous system 14

2.2. Vision system 16

2.2.1. Visual information processing starts already in the retina 16

2.2.2. Large portion of cortex is sensitive primarily to visual stimulation 18

2.2.3. Properties of a neuron in primary visual cortex depends on

the signal and brain state 22

2.2.4. Extrastriate visual cortices are specialised 24

2.2.5. Information from peripheral and central visual field serves

partially different purposes 26

2.2.6. Functional visual areas V6 and V6A are related to visuomotor

processing 27

2.2.7. Guidance of saccades and spatial attention share a network

of brain regions 30

2.3. Magnetoencephalography 34

2.3.1. Principles 34

2.3.2. Source modelling 35

2.4. Functional magnetic resonance imaging 37

2.4.1. Principles 37

2.4.2. Data analysis 39

2.5. Retinotopic mapping 41

3. Aims of the study 43

4. Materials and methods 44

4.1. Subjects, stimuli, and tasks 44

4.1.1. Subjects 44

4.1.2. Visual stimuli and tasks 44

4.2. Measurements 46

4.2.1. fMRI measurements 46

4.2.2. MEG measurements 47

4.3. Data analysis and visualisation 48

4.3.1. Analysis of fMRI data 48

4.3.2. Analysis of MEG data 49

4.3.3. Statistics 49

4.4. Eye movement recordings 50

5. Experiments 51

5.1.Comparison of minimum current estimate and dipole

modelling in the analysis of simulated activity in

the human visual cortices (Study I) 51

5.1.1. Methods 51

5.1.2. Results 52

5.1.3. Discussion 53

5.2. fMRI of peripheral visual field representation (Study II) 54

5.2.1. Methods 54

5.2.2. Results 55

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5.3. Central luminance flicker can activate peripheral retinotopic

representation (Study III) 59

5.3.1. Methods 59

5.3.2. Results 60


5.4. Peripheral visual field representation activates during saccades

in darkness (Study IV) 63

5.4.1. Methods 63

5.4.2. Results 64


5.5. Topography of attention in the primary visual cortex (Study V) 68

5.5.1. Methods 68

5.5.2. Results 69


5.6. Motion sensitivity of human V6: A magnetoencephalography

study (Study VI) 71

5.6.1. Methods 71

5.6.2. Results 71


6. General discussion 74

6.1. Controlling methodological confounds in MEG and fMRI 74

6.2. Human visual area V6 75

6.3. Top-down modulation of V1 77

6.4. Peripheral visual field representation in human parieto-occipital

sulcus 78

7. Conclusions 81

8. Acknowledgements 84

9. References 86

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Abstract

Visual information processing in brain proceeds in both serial and parallel fashion

throughout various functionally distinct cortical areas. These areas can be organised

hierarchically according to neurons’ response profile and interareal connections.

Feedforward signals from the retina and hierarchically lower cortical levels are the major

activators of visual neurons, but top-down and feedback signals from higher level cortical

areas have a modulating effect on neural processing.

Modern imaging methods enable in vivo studies of human brain function. This thesis utilises

magnetoencephalography and functional magnetic resonance imaging for the measurement

of cortical responses during visual stimulation and oculomotor and cognitive tasks from

healthy volunteers. Magnetoencephalography measures the electromagnetic signal of neural

activation and provides high resolution in the temporal domain, whereas functional magnetic

resonance imaging detects the hemodynamic response to neural activation and is spatially

accurate but temporally bound to the hemodynamic delay. The use of both methods provides

temporally and spatially accurate knowledge of visual processing but also forces to consider

the limitations of the methods, the first aim of this work.

This thesis concentrates on hierarchically low level cortical visual areas in the human brain

and examines neural processing especially in the cortical representation of visual field

periphery. Previous evidence suggests that the visual field location could be one basis for the

division of visual encoding into the functionally segregated streams of cortical areas. The

second objective of my work was to develop methods for the stimulation of peripheral visual

field, to map the cortical representations of peripheral visual field in retinotopic visual areas,

and to study the functional properties of peripheral vision. The stimulation of peripheral

visual field enabled delineation of the putative human homologue of monkey visual area V6,

the third aim of this thesis. Substantial knowledge of brain function comes from animal

studies and the question of interspecies differences in visual cortical organisation arises

when the evidence from human neuroimaging is interpreted in the context of animal studies.

It is argued that homologous visual areas should have similar relative position and response

profile. This thesis aims to study the putative human V6. The fourth aim of my work was

examine the top-down modulation in hierarchically low cortical levels by measuring the

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effect of attention and voluntary movement on retinotopic visual areas in the medial surface

of occipital lobe.

Visual cortex forms a great challenge for the modelling of neuromagnetic sources because

multiple neighbouring visual areas have temporally highly overlapping responses. This

thesis shows that a priori information of source locations are needed for neuromagnetic

source modelling in visual cortex (study I). In addition, this work examines other potential

confounding factors in vision studies: The optical properties of the eye can lead to light

scatter which may result in erroneous responses in cortex outside the representation of the

stimulated region (study III), and eye movements and attention increase responses and thus

confound the quantitative interpretations of the BOLD signal if not controlled (studies IV

and V).

This thesis demonstrates that the peripheral visual field representation of low-level visual

areas extends to the anterior part of calcarine sulcus and to the posterior bank of parieto-

occipital sulcus and the peripheral vision is functionally related to eye-movement processing

and connected to rapid stream of cortical areas that encode visual motion (studies II and IV).

My results identify the putative human V6 region in the posterior bank of parieto-occipital

sulcus (study II) and show that human V6 activates during eye-movements and responds to

visual motion at short latencies (studies IV and VI). These findings contribute to the

evidence that the human homologue of monkey V6 is located in parieto-occipital sulcus and

suggest that human V6, like its monkey homologue, is related to fast processing of visual

stimuli and visually guided movements. In addition, my work demonstrates two different

forms of top-down modulation of neural processing in the hierachically lowest cortical

levels. First, I found responses during eye-movements that are related to dorsal stream

activation and may reflect motor processing or resetting signals that prepare visual cortex for

change in the environment (study IV). Second, I show local signal enhancement at the

cortical representation of the attended visual field region that reflects local feed-back signal

and may perceptionally increase the stimulus saliency (study V).

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Abbreviations

ANOVA Analysis of variance

BALC Brain ála Carte

BEM Boundary element model

BOLD Blood oxygenation level dependent

DLPFC Dorso-lateral prefrontal cortex

ECD Equivalent current dipole

EEG Electroencephalography

EPI Echo planar imaging

FEF Frontal eye field

FDR False discovery rate

fMRI Functional magnetic resonance imaging

FWE Family wise error

GLM General linear model

HRF Hemodynamic response function

IPS Intraparietal sulcus

IPS-1 – 4 Visual areas in intraparietal sulcus

LGN Lateral geniculate nucleus

LIP Lateral intraparietal area

LO-1 & 2 Visual areas in lateral occipital cortex

MCE Minimum current estimate

MEG Magnetoencephalography

mf Multifocal

mffMRI Multifocal fMRI

MIP Medial intraparietal area

MNE Minimum norm estimate

MNI Montreal neurologic institute

MRI Magnetic resonance imaging

PCC Posterior cingulate cortex

PET Positron emission tomography

PHC-1 & 2 Visual areas in parahippocampal cortex

PO Parieto-occipital

RF Radiofrequency

ROI Region of interest

SEF Supplementary eye field

SNR Signal-to-noise ratio

SPM Statistical parametric map

TMS Transcranial magnetic stimulation

V1 Primary visual area, striate cortex

V2–V7 Extrastriate visual areas 2-7

V6A, V3A, V3B Visual areas 6A, 3A and 3B

VIP Ventral intraparietal area

VO-1 & 2 Visual areas in ventral occipital cortex

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List of publications

This thesis is based on following six publications which will be referred with roman

numerals I – VI

I Stenbacka L, Vanni S, Uutela K and Hari R. Comparison of minimum current estimate and

dipole modeling in the analysis of the simulated activity on human visual cortices.

NeuroImage 2002 Aug;16(4):936-943

II Stenbacka L and Vanni S. fMRI of peripheral visual field representation. Clinical

Neurophysiology 2007 Jun;118(6):1303-1314

III Stenbacka L and Vanni S. Central luminance flicker can activate peripheral retinotopic

representation. NeuroImage 2007 Jan1;34(1):342-348

IV Stenbacka L and Vanni S. Peripheral visual field representation activates during saccades

in darkness. Submitted

V Simola J, Stenbacka L and Vanni S. Topography of attention in the primary visual cortex.

European Journal of Neuroscience 2009 Jan;29(1):188-196

VI von Pföstl V, Stenbacka L, Vanni S, Parkkonen L, Galletti C and Fattori P. Motion

sensitivity of human V6: A magnetoencephalography study. NeuroImage 2009

May1;45(4):1253-1263

The author’s contribution:

I was a principal author in studies I-IV. In study I, I was responsible for the analysis of the

data and the interpretation of the results. I had a major role in writing the manuscript and I

collaborated with the other authors on the study design and data collection. In studies II-IV, I

was responsible for the measurement and the analysis of the data and had a major role in the

writing of the manuscript. We interpreted the results and designed the studies together with

the second author. In studies V and VI, I collaborated with the other authors on the study

design, measurement of the data and writing of the manuscript.

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1. Introduction

Vision sense enables recognition of an object already at far distance and creation of spatially

accurate model of both the close and far visual environment. Seeing is essentially a cognitive

process. Brain builds a three-dimensional model of the visible world from the two-

dimensional light distribution that the relatively simple optic system of the eye has projected

onto the retina. This model and the resulting percept depend on the visual environment, input

from other senses, state of mind, and previous knowledge. The brain is able to renew the

model according to environmental changes, predict these changes, and create relevant

actions to them.

Visual processing in the central nervous system has traditionally been viewed as a diverse

and a hierarchical process. Light activates photoreceptors in the retina and the resulting

neural signal is processed in several consecutive steps first in various subcortical nuclei and

then in numerous cortical areas (Grill-Spector and Malach, 2004). The processing starts from

simple features such as contrast borders (Ferster and Miller, 2000) and finally primarily

visual processing turns into multisensory and guidance of motor actions (Andersen and

Buneo, 2002). In addition, different components of the visual signal are, to an extent,

processed in parallel (Ungerleider and Mishkin, 1982; Livingstone and Hubel, 1988; Lennie

and Movshon, 2005). However, accumulating evidence suggests that feedback from “higher-

level” visual areas and top-down signals from cognitive, multisensory, and motor systems

modulate the visual signal even in the earliest levels of the processing hierarchy and parallel

streams of visual processing are highly interactive (Bullier and Nowak, 1995; Albright and

Stoner, 2002; Gilbert and Sigman, 2007).

The visual system is the focus of large amount of research from cellular level to cognition

studies with wide range of animals from simple life forms, such as drosophila, to humans.

Previously examination on the impact of brain lesions as well as psychophysical

investigations have provided much information on the organisation of the human visual

system, but most knowledge on its neural basis has been acquired from electrophysiological

studies in nonhuman primates and cats. However, modern brain imaging methods developed

within the last decades enable detection of neural activity in the intact living brain, and

extend studies of human vision from detection of perception and action to measurement of

neural response. The imaging methods are based on different principles.

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Electroencephalography (EEG) and magnetoencephalography (MEG) measure the

electromagnetic signal from postsynaptic potentials directly, whereas functional magnetic

resonance imaging (fMRI) detects the hemodynamic response to neural activation, and

positron emission tomography (PET) measures either hemodynamic response or uptake of

labelled molecules by activated neurons.

All imaging methods have limitations. EEG and MEG are temporally very accurate but the

localisation of neural activation is ambiguous due to non-unique inverse problem. In

contrast, fMRI is spatially accurate but its temporal resolution is compromised by the

hemodynamic delay. In addition, neurovascular coupling is not fully understood (Logothetis

and Wandell, 2004). Studies I, II, and III explore some of the limitations which need to be

considered in experimental work. Study I compares two methods for estimating the neural

source in MEG and shows that a priori information of neuromagnetic sources is needed for

localisation of close simultaneous sources in visual cortex. Study II aims to overcome the

limitation of narrow visual field in fMRI and describes a method for wide visual field

mapping. Stimulation of peripheral visual field is a challenge in a narrow magnet bore where

visual display setup has limited the field of view. Study III suggests that light scatter in the

eye can form a significant confounding factor with high luminance contrast stimuli.

The visual cortex can be divided into functionally separate regions, even though the exact

delineation is still under debate (Wandell et al., 2007). Functional areas can be arranged

hierarchically on the basis of their interareal connections (Felleman and Van Essen, 1991).

Functional areas at the bottom of the hierarchy show clearest retinotopic organization and

retinotopic mapping can be used for their localisation. Study II mapped the medial occipital

retinotopic areas and their representation of the peripheral visual field. The use of peripheral

visual stimuli enabled the localisation of human visual area V6, whereas study III showed

that, in contrast to previous hypothesis, a central luminance flicker stimulus is not an

adequate functional localiser of V6. Studies IV and VI investigated V6 region and showed

activation of V6 during saccades (study IV) and demonstrated motion sensitivity and short

response latency of V6 (study VI). These results suggest that human V6, like its monkey

homologue, belongs to dorso-medial processing stream that controls visually guided

movements (Rizzolatti and Matelli, 2003).

Studies IV and V utilise the relatively good spatial resolution of fMRI to examine the impact

of top-down modulation on the hierarchically lowest cortical visual area, V1. In study V, the

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mapping of representations of multiple visual field locations shows the spread of visually

evoked responses in primary visual cortex during spatial attention to stimulus location. The

width of the response spread is in line with the top-down signal (Angelucci and Bullier,

2003). Study IV located eye movement –related neural activation to peripheral visual field

representations in the hierarchically earliest visual areas and showed different neural

responses in central and peripheral representations. Distribution of the responses and

simultaneous activation of dorsal stream cortical areas suggest a dorsal stream origin of the

responses.

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2. Review of the literature

2.1. Overview of the neural transmission in central nervous system

The following chapter is based on the book of Kandel, Schwartz, and Jessell (2000) and

reviews of Bullier (2004a), Callaway (2004), Gilbert and Sigman (2007), and Saalmann and

Kastner (2009). Neurons and supporting glial cells mainly form the central nervous system,

the former being responsible for signal transmission. Within neurons signals are transmitted

electrically whereas the signal of presynaptic action potential is transferred chemically

across the synaptic cleft to the postsynaptic neuron. Like in all cells, a potential difference

exists across the neural cell membrane, but in neurons the membrane potential can be

modulated and reversed. The membrane potential builds up from concentrations of ions

inside and outside of the neurons that aim to reach equilibrium between chemical and

electrical forces. The ions can move only through protein channels because the lipid bilayer

of the cell membrane is impermeable for the charged particles. The number of open ion

channels can change according to neural state and environment, resulting in

hyperpolarisation and depolarisation of the cell membrane. Temporal and spatial summation

of these potentials may result in action potential i.e. depolarisation of the axonal cell

membrane. In contrast to postsynaptic potentials, action potentials are discrete and the signal

is embedded within the frequency of action potentials.

The signal is processed in a network of neurons. Neural networks in the brain integrate

signals from multiple sources. On the other hand the signal diverges in cortical networks into

separate streams, functional areas, and local intra-areal neural circuits. According to the

classical feedforward model, visual processing begins from simple attributes and the input to

subsequent steps of neural processing is a result of the previous steps, with the response

properties of a neuron at the higher level reflecting a combination of those at the previous

levels. However, recent evidence has challenged this model. Currently it is believed that

response properties of a neuron result from an interaction between local circuits and

feedforward and feedback connections. Feedforward connections are thought to be the main

driving force of the neurons whereas feedback signal and top-down information have a

modulatory role. Thus, accumulating evidence suggests that initial brain state can modulate

neural processing. Analysis of visual information in turn modifies the brain state which

provides top-down source for the signal processing.

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Glial cells have an important homeostatic role. Astrocytes support neurons by providing

lactate molecules as fuel for citric acid cycle of neurons and by being part of

neurotransmitter cycle. Oligodendrocytes form myelin sheet around the axons to increase the

conduction velocity. Brain tissue has a strong local autoregulation of the blood flow. The

increased activation of the nerve cells results in increased demand of glucose and oxygen.

The blood flow normally responds to increased demands. Cells’ energy storage (adenosine

triphosphate) is utilised in synaptic transmission and maintenance of ion concentration, and

largest portion of energy consumption is attributed to the post-synaptic effects of

neurotransmitters. Chemical vasodilatators including adenosine and neurotransmitters

glutamate and GABA are released from the activated neurons. Nitric oxide mediates

vasodilatation that propagates in retrograde fashion into upstream arterioles. In addition,

local neural circuits regulate the diameters of arterioles and thus the perfusion rate.

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2.2. Vision system

2.2.1. Visual information processing starts already in the retina

Light enters the eye via pupil and is refracted when it travels through the cornea and lens. In

emmetropic eye the refraction focuses light onto the retina. However, the refraction power of

cornea and lens and the length of the eye may be imbalanced, and lens inhomogeneities

affect the light refraction. A large pupil increases these distortions, whereas a small pupil

aperture results in light diffraction. These mechanisms result in blurred retinal image

(Wandell, 1995). In addition, light is scattered when it passes cornea and lens and hits the

retina (Vos, 2003). As a consequence of intraocular scattering of light the retinal

illumination does not optically correspond to the direction of light (Ijspeer et al., 1990).

Scattering of bright light causes a phenomenon called disability glare; a veil of light and

lower contrast elsewhere. Intraocular scattering and disability glare increase with age and

can be noticed for example when driving a car in the dark.

Neural visual signal is generated in the retina. The retina contains a complex network of

cells; photoreceptor cells are located in the outermost layer and the inner layers contain

neurons. Thus, light travels through neural layers before it reaches photoreseptors. Light

changes conformation of visual pigment molecules of photoreceptors, which results in neural

signal (Kandel et al., 2000). Human retina contains four types of photoreceptors and visual

pigment molecules. Pigment molecules in the rods are sensitive to all wavelengths within

visible light spectra already at very low intensities. Three types of pigment molecules in the

cones have a different wavelength sensitivity profile which enables colour perception. All

photoreceptors make distinct connections and they are unevenly distributed across the retina.

Rods are relatively more common towards the periphery and are totally absent in the fovea,

whereas cones are more abundant in central retinal regions. S-type cones are sensitive to

shortest wavelengths and are located relatively sparsely outside the foveal region, whereas l-

and m-type cones which are sensitive to longer wavelengths compose the fovea and are

unevenly distributed outside the fovea (Wandell, 1995; Dacey, 2000; Gegenfurtner and

Kiper, 2003).

Retina belongs to central nervous system. It forms a network in which signals pass through

bipolar cells to ganglion cells. The retinal network converts input from the photoreceptors

into information on the spatial and temporal contrasts of the light intensity (Field and

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Chichilnisky, 2007). Bipolar and ganglion cells have circular receptive fields consisting of a

centre and a surround that oppose each other (Kuffler, 1953). Ganglion cell responses show

several non-linearities which represent for example as light adaptation (Purpura et al., 1990).

Retina comprises of multiple ganglion cell types and the relative density of different

ganglion cells varies according to retinal eccentricity. Each ganglion cell type has its own

characteristic structure, connectivity, physiological properties, and central projections and

each type feeds anatomically and functionally distinct parallel visual pathways (Dacey,

1994, 2000; Field and Chichilnisky, 2007).

From the retina the neural signals travel via the visual nerve, formed from the axons of the

retinal ganglion cells. Axons from the nasal hemiretina cross, at the optic chiasm, to the

contralateral hemisphere, resulting in a representation of the contralateral visual field.

Retinotopy is defined as orderly presentation of visual representation that follows the

topography of the visual field. Retinotopy is present in the retina and it is conserved later in

visual system where the information from different parts of the retina is processed in parallel

(Wandell, 1995).

The next synapse in the afferent visual pathway is in the lateral geniculate nucleus (LGN) of

the thalamus. The neurons in the LGN have similar circular centre-surround receptive fields

as ganglion cells, and they behave non-linearly which can, for example, dampen responses to

increases in stimulus luminance in order to maintain neural sensitivity even at a wide range

luminance values (Garandini et al., 2005). The LGN is anatomically separated as magno-,

parvo-, and konio-cells are arranged into distinct layers. Receptive fields of parvo-cells are

smaller and they are more sensitive to high spatial frequencies. Parvo-cells are also sensitive

to red-green contrasts whereas the magno-system, responds with shorter delays and more

transiently to visual stimulation and is more sensitive to low stimulus contrasts (Livingstone

and Hubel, 1988). The third, less studied cell group, the konio-cells, relay both low-acuity

visual information and blue-yellow contrast and have extrastriate projections and

connections with superior colliculus (Hendry and Reid, 2000). Importantly, the feedforward

signal is modified in the LGN, indicating that it is not a mere relay nucleus. In fact, the

majority of LGN input comes from feedback sources and cortical feedback signal have been

shown to modulate neural responses in the LGN (Saalmann and Kastner, 2009).

LGN receives approximately 90 % of the retinal signal. From the LGN the visual input is

transmitted to the cortex. The main destination is the primary visual cortex, V1, in the medial

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part of the occipital lobe. The remaining 10 % of the retinal signal is projected to subcortical

structures such as the superior colliculus, suprachiasmatic nucleus, nucleus of the optic tract,

pretectum, and the nucleus of the accessory optic tract (Bullier, 2004a).

2.2.2. Large portion of cortex is sensitive primarily to visual stimulation

The human visual cortex covers approximately a fourth of the neocortex, and it includes

occipital lobe, significant portions of parietal and temporal lobes, and regions in frontal lobe

(Van Essen, 2004). Cortex can be divided into separate functional visual areas. The primary

visual cortex, V1, receives visual input from the thalamus and feeds the signal to extrastriate

cortical areas. At some extent extrastriate visual areas are specialised for the encoding of

specific stimulus attributes and they form separate streams of processing (Grill-Spector and

Malach, 2004). The dorsal stream is primarily devoted to visuo-motor transformations and

spatial processing whereas the ventral stream is devoted to the analysis of form, colour and

objects (Ungerleider and Mishkin, 1982). This concept of functional specialisation is

supported by neurological cases in which a lesion in a cortical region results in specific

visual deficits and from observation that stimulation of a cortical region specifically affects

behaviour and perception. A lesion of the dorsal stream areas may cause simultanagnosia,

meaning inability to detect more than one object at time, deficiency in visuomotor tasks such

as optic ataxia and apraxia, and deficiencies in spatial perception such as neglect. In contrast,

a ventral stream lesion may result in visual agnosia, a disorder of object recognition (Pisella

et al., 2006). However, several areas can be essential for a given visual function and one

region can participate in a wide range of functions.

Abundant connections link cortical functional areas. Feedforward and feedback connections

originate from and terminate to different cortical layers and they are determined according to

laminar pattern of the axon terminals (Barone et al., 2000). In addition to cortico-cortical

links, connections can operate via cortico-thalamo-cortical circuits through lateral geniculate

nucleus, pulvinar, and reticular nucleus and via superior colliculus (Guillery and Sherman,

2002; Shipp, 2002; Callaway, 2004; Cappe et al., 2009). Visual areas of the macaque are

believed to be hierarchically organised according to the laminar pattern of inter-areal

connections (Felleman and Van Essen, 1991). The majority of neurons in the higher levels of

the hierarchy are sensitive to visually complex and even multisensory or motor stimuli and

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they are activated at relatively long latencies (Bullier, 2004b). However, some V1 neurons

also seem to be sensitive for higher-order computations (Lee et al., 1998) and the latencies of

lower and higher order areas are highly overlapping (Schmolesky et al., 1998).

Because visual areas serve partially different functions, they are sensitive to different

stimuli. These different response profiles are utilised in localising and mapping of visual

areas. Localisers often use contrasts between responses to different stimuli such as visual

motion and stationary stimuli to determine stimulus preference of neurons. However,

preferential sensitivity to a specific stimulus attribute does not provide a complete view to

the function of the area and the localiser response may originate from several functional

areas. Retinotopic mapping provides additional means for delineation of different areas. It is

well established that the early and intermediate steps of the visual processing hierarchy in the

human brain are organized in a retinotopic manner while the retinotopy of higher level areas

is under debate. Malach and colleagues (Levy et al., 2001) suggested that object areas on

high level of the hierarchy are organised according to eccentricity whereas Wandell and co-

workers (2005) proposed that these regions also have a retinotopic organization. Lately,

several higher-level topographically organized areas have been delineated with mapping

protocols that combine visual stimulation with attention or eye movements and these studies

suggests that topographic organization extends to high levels in visual cortex.

Figure 1 shows a map representing the current view of visuotopically and spatiotopically

organised areas in human cortex. Topographic areas form clusters in which the

representations of the same visual field eccentricities are next to each other in neighbouring

areas and the areas serve similar functional purposes (Wandell et al., 2005). Magnification

factor describes the relationship between visual field coordinates and cortical locations by

providing an approximation of the spatial extent of the cortex devoted for every degree of

visual field eccentricity (Daniel and Whitteridge, 1961; Virsu and Rovamo, 1979). The

proportions of visual field representations vary across visual areas but typically the foveal

representation is magnified. For example, Duncan and Boynton (2003) estimate that the

representation of the central 10 degrees covers 50 % of the cortical area of V1.

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Figure 1. Visuotopically arranged functional areas in human cortex according to current knowledge.

The areas have been drawn at approximate locations according to previous literature and results of

study II. Functional areas V1, V2, V3 and V6 in medial occipital cortex are defined according to

mappings in study II. Ventral occipital areas hV4, VO-1, VO-2, PHC-1, and PHC-2 are drawn

according to results of Brewer and co-workers (2005) and Arcaro and co-workers (2009). Areas

V3A, V3B, LO-1 and LO-2 are placed according to mappings of Larsson and Heeger (2006). Area

V7 is placed according to data of Tootell and co-workers (1998). Location of V5 is adopted from the

results of Tootell and co-workers (1995). Parietal areas IPS-1, IPS-2, IPS-3, and IPS-4 are located

according to results of Swisher et al. (2007), Schluppeck et al. (2005), and Silver et al. (2005).

Proposed human VIP is drawn according to Sereno and Huang (2006). Saygin and Sereno (2008)

located spatiotopic map approximately at PRR? (parietal reach region?). PRR? region activates

during reaching (Filimon et al., 2009). Locations of FEF and dorsolateral prefrontal cortex (DLPFC)

are adopted from the results of Hagler and Sereno (2006).

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Table 1. Retinotopic and spatiotopic visual areas presented in FIG 1.

Cortical region Areas

Medial occipital cortex V1, V2, V3, V6

Cuneus V3A, V3B

Ventral occipital and temporal lobes hV4, VO-1, VO-2, PHC-1, PHC-2

Lateral occipital cortex LO-1, LO-2, V5

Parietal lobe V7, IPS-1, IPS-2, IPS-3, IPS-4, PRR?, VIP

Frontal lobe FEF, DLPFC

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2.2.3. Properties of a neuron in primary visual cortex depends on the signal and brain state

Primary visual cortex located around calcarine sulcus is the first stage of visual cortical

information processing and the first region in which the signal from both eyes is integrated.

V1 has been extensively studied in both animals and humans and the similarity between the

species is well established. The following presents results from electrophysiological

recordings, most of them from the V1 of the macaque monkey.

V1 has a clear retinotopy. In addition to this retinotopic organization, cells with similar

functional properties are to some extent grouped together (Hubel and Wiesel, 1963; Hubel

and Wiesel, 1968). Optimal stimulus varies between neurons, and V1 neurons are tuned for

stimulus features such as orientation (Hubel and Wiesel, 1959, 1962), direction of motion

(Hubel and Wiesel, 1959; Hubel and Wiesel, 1968), spatial frequency (Campbell et al.,

1969), wavelength (Gouras, 1970), and vertical and horizontal binocular disparities (Durand

et al., 2002; Durand et al., 2007). Majority of V1 neurons are jointly tuned for multiple

stimulus features (Grunewald and Skoumbourdis, 2004).

Neurons in the primary visual cortex have been divided into simple and complex cells

(Hubel and Wiesel, 1962). By definition, the receptive field of the simple cell contains a

region sensitive for increments and decrements of light and there is signal summation within

and antagonism between these regions. The response of a simple cell is linear and

predictable from the stimulus. Other cells are classified as complex cells. However, even the

responses of simple cells show nonlinearities such as modulation of the response by the

stimulus outside the classical receptive field (Blakemore and Tobin, 1972). Because of such

nonlinearities, a clear dichotomy between simple and complex cells may not exist at all;

rather, there might be a continuum of cells with varying degree of receptive field complexity

(Garandini et al., 2005).

In contrast to LGN, most receptive fields in V1 are not circular. Angelucci and co-workers

(2003) reviewed the receptive field properties of V1 neurons. The classical receptive field is

defined with a small high-contrast flashing or moving stimuli. However, visual information

is summated over a region extending beyond the classical receptive field. This summation

area depends on the stimulus contrast and it is larger for low-contrast stimuli. High contrast

summation area is considered as the centre of the receptive field, whereas the surround of the

receptive field consists of an additional low contrast summation field and a modulatory

23

surround. The extent of summation field for low contrast stimuli is in line with the horizontal

connections in V1 whereas the width of the whole modulatory region equals the receptive

field size in higher order areas such as V2, V3 or V5.

A stimulus presented far from the classical receptive field can modulate responses of V1

neurons by adding significant non-linearity to the neuron’s response. This modulation of V1

responses depends on stimulus context and it can be either facilitatory or suppressive. Such

contextual modulation has been linked to various perceptual phenomena (Albright and

Stoner, 2002) such as figure-ground segregation (Lamme, 1995), feature pop-up (Kastner et

al., 1997), and brightness perception (Rossi et al., 1996; Rossi and Paradiso, 1999). In

addition to modulation related to stimulus context, information from other sensory

modalities or top-down modulation from higher areas affect neural processing in V1 by

providing modulation related to the behavioural context. Behavioural context may modulate

neural processing even before the onset of a target stimulus (Li et al., 2004), but stimulus

context also affects responses rapidly, already less than ten milliseconds after the start of the

response (Hupé et al., 2001b).

Behavioural contexts which enhance neural signals include cognitive processes such as

attention and learning (Motter, 1993; Gilbert et al., 2000; Sharma et al., 2003; Li et al.,

2004). Perceptually, detection of salient stimuli and spatial working memory correlate with

contextual modulation (Supèr et al., 2001a, 2001b). In addition, oculomotor activity is

reflected in V1 responses. Neural activity in V1 is increased before the execution of saccades

at the location of saccade target (Supèr et al., 2004) and viewing distance and gaze direction

also modulate V1 responses (Trotter et al., 1992; Trotter and Celebrini, 1999). This

modulation is utilised in spatial perception; both gaze direction and vertical disparity provide

frames for horizontal disparity signal to generate 3D egocentric coordinates (Trotter et al.,

2004).

Both local horizontal connections and interareal connections may mediate centre-surround

interaction but cortico-cortical feedback connections are crucial for differentiation of low

salience stimuli from the background (Hupé et al., 1998; Hupé et al., 2001a). Bullier and

colleagues (2001; 2004b) hypothesised that the visual signal is transmitted to higher-order

areas via fast conducting fibres (Bullier and Nowak, 1995) for a first-pass analysis. This is

followed by feedback signals which can guide the ongoing feedforward signal processing in

lower level areas. Bullier et al (2001) proposed that feedback connections interact with

24

feedforward and horizontal connections in non-linear fashion to control the response gain

and that different stimuli utilise different sets of connections. Schwabe and co-workers

(2006) presented a feedforward feedback network model that could explain surround

suppression and facilitation and thus contextual modulation in V1. In their model a higher-

level neuron which provides feedback to lower-order areas is monosynaptically connected

with interneurons with a high threshold and gain. These interneurons are both excitatory and

inhibitory, and excitatory feedback connections can suppress responses via inhibitory

interneurons.

2.2.4. Extrastriate visual cortices are specialised

The following chapter provides a brief summary of the organisation of the human

extrastriate visual cortex. Adjacent to V1 are areas V2 and V3 (Sereno et al., 1995). Both V2

and V3 contain discontinuous contralateral hemifield maps. The maps are divided

approximately along the horizontal meridians. The lower and upper visual field quadrants

are represented dorsally and ventrally of the calcarine sulcus, respectively. The functional

role of V2 and V3 remains unsettled, but most likely they contribute V1 in processing of

local visual features (Boynton and Hegdé, 2004; Sincich and Horton, 2005).

Ventral occipital cortex responds to colour stimuli (Lueck et al., 1989). The division of this

cortical region into functional areas has been under debate. Hadjikhani and co-workers

suggested that adjacent to ventral V3 is a representation of the upper visual field quadrant

belonging to V4 and adjacent to which is a representation of the whole contralateral visual

field, named V8 (Hadjikhani et al., 1998). In contrast, Wandell and colleagues located a

whole contralateral representation next to ventral V3; this was named hV4. Next to hV4 they

located two additional ventral areas, VO-1 and VO-2 (Brewer et al., 2005). Ventral occipital

cortex and temporal cortex around fusiform and parahippocampal gyri are related to

processing of objects, faces, and scenes (Grill-Spector and Malach, 2004), and the optimal

stimulus becomes more complex further up the hierarchy (Lerner et al., 2001).

Representations of different object categories have been suggested to form local clusters of

highly specialised neurons (Kanwisher et al., 1997). Such clusters may be distributed and

overlapping (Haxby et al., 2001) or arranged according to eccentricity (Levy et al., 2001). It

is possible that responses to different objects are distributed over several retinotopic areas

25

that have different sensitivity profiles and eccentricity weightings. Recently, two new

retinotopically organised areas (PHC-1 and PHC-2) which are particularly sensitive to

scenes but respond to other objects as well were found near parahippocampal gyrus (Arcaro

et al., 2009).

Lateral occipital cortex contains regions that are sensitive to visual motion and objects. Zeki

and co-workers located a motion-sensitive area in the lateral occipital cortex of humans

which was named as the human homologue of the monkey area V5 (Zeki et al., 1991;

Watson et al., 1993). V5 has a role in integration of points moving to the same direction and

in discriminating a moving target from the background (Born and Bradley, 2005). Posterior

and medial to V5 is the LO-complex which is preferentially activated by objects than

texture-stimuli (Malach et al., 1995) and responds to object structure from a wide variety of

cues (Kourtzi and Kanwisher, 2000). Two retinotopic areas, named LO-1 and LO-2, have

been located in the region of LO-complex (Larsson and Heeger, 2006).

Visual areas in the dorsal occipital cortex are selective for stimulus orientation and they are

also sensitive to motion. Dupont and co-workers (1997) localised sensitivity to kinetic

contours in the lateral occipital lobe but the relationship of these responses to retinotopic

areas is unsettled. Lateral cuneus contains the retinotopic area V3A (DeYoe et al., 1996;

Tootell et al., 1997) and its adjacent area V3B. Anterior to V3A is area V7 (or IPS-0)

(Tootell et al., 1998). Visual motion sensitivity has also been detected in the human parieto-

occipital sulcus in a region that supposedly contains the human homologue of the monkey

area V6 (Pitzalis et al., 2006; Pitzalis et al., 2010).

Parietal lobe contains several functional areas which are related to visuospatial and

visuomotor processing, which are arranged in eyecentric coordinates, and which are strongly

connected with frontal cortex (Andersen and Buneo, 2002). Colby and Goldberg (1999)

reviewed functional properties of the monkey parietal cortex. Medial intraparietal area (MIP)

of monkey functions in reaching and grasping movements and lateral intraparietal area (LIP)

is related to control of eye movements. In contrast, ventral intraparietal area (VIP) may play

a role in the guidance of head movements. Sereno and colleagues (2001) were the first to

locate a a spatiotopically organised area in the human intraparietal sulcus which they

proposed to be a homologue of LIP in the monkey brain. Subsequent studies have revealed

several contralateral visual field maps along the intraparietal sulcus using visual stimuli

(Swisher et al., 2007) as well as tasks involving delayed saccades (Schluppeck et al., 2005)

26

and covert shifts of attention (Silver et al., 2005). The superior part of the postcentral sulcus

contains maps for tactile and near-face visual stimuli, suggestive of a homologue of monkey

area VIP (Sereno and Huang, 2006). In addition, several areas in frontal lobe are related to

visual signal processing. Dorsolateral prefrontal cortex is activated during visual working

memory tasks and the frontal eye fields which are involved in eye-movements and saliency

maps are both topographically arranged (Hagler and Sereno, 2006).

2.2.5. Information from peripheral and central visual fields serves partially different

purposes

The fovea, covering 1-2 degrees of the visual field, and the macula, covering the central 5

degrees, form the retinal part of the detailed central vision. Retinal parts exterior to the

macula are considered peripheral. Peripheral and central vision can be partially separated.

Structural and functional differences between central and peripheral vision begin at the

retina. Distribution of photoreceptors varies according to eccentricity and receptive fields of

bipolar and ganglion cells are the smallest in the fovea and increase in size in the periphery

(Dacey, 2000; Field and Chichilnisky, 2007).

Response profiles in early retinotopic areas directly reflect photoreceptor distribution

(Hadjikhani and Tootell, 2000). The sizes of receptive fields and the distributions of parallel

processing streams are also reflected in the properties of V1 neurons. The receptive field size

in striate cortex increases towards the periphery (Hubel and Wiesel, 1974) and neurons

become more selective for low spatial frequencies (Xu et al., 2007; Henriksson et al., 2008).

Even the properties of stereopsis have been adapted to retinal position. In the periphery,

neurons are sensitive to both vertical and horizontal disparity whereas neurons in the central

representations encode only horizontal disparity (Durand et al., 2007). In addition to

response tuning, modulations related to stimulus and behavioural context differ according to

eccentricity. The response gains of peripheral neurons show stronger suppressive

modulation; the surround facilitation is diminished and the suppression is less orientation

and frequency specific (Xing and Heeger, 2000). Attention increases spatial integration in

the periphery but decreases it in the central representations (Roberts et al., 2007).

27

The distinction between central and peripheral vision is also manifested in the extrastriate

cortex. In the monkey, dorsal stream areas receive relatively more projections from the

peripheral visual field representations whereas ventral stream areas are more densely

connected to the central representations (Ungerleider and Desimone, 1986; Colby et al.,

1988; Boussaoud et al., 1990; Baizer et al., 1991; Lewis and Van Essen, 2000; Gattass et al.,

2005). In fact, the input to peripheral and central representations can significantly vary even

within the same functional area (Palmer and Rosa, 2006). Furthermore, the peripheral

representations receive multisensory input. For example, peripheral V1 is connected with

auditory cortex (Falchier et al., 2002) and dorsal stream areas in parietal and frontal lobe

receive multisensory connections (Schall et al., 1995; Lewis and Van Essen, 2000). In

addition, relative visual field representations vary between functional areas in the human

brain. For example, the dorsal stream area V6 is strongly biased towards the periphery

(Pitzalis et al., 2006) whereas the ventral stream area hV4 has an extended central visual

field representation (Wade et al., 2002). Lesion studies support the association between the

peripheral vision and the dorsal stream. Lesion to the parieto-occipital cortex containing

dorsal stream areas results in optic ataxia which manifests in the peripheral vision (Pisella et

al., 2006) and impairs perception in the periphery (Pisella et al., 2009).

The connections and the response profiles suggest different functional role for the central

and the peripheral vision. The central vision in the ventral stream is important for object

recognition whereas the peripheral vision in the dorsal stream may be more important for

detecting sudden changes in the environment. A large field of view integrated with

multisensory input (Wang et al., 2008) would provide information and motor connections

would provide the means for controlling visually guided movements. However, the central

and the peripheral visions are segregated also within the ventral and dorsal stream. For

example, in humans reaching towards peripheral and central visual field activate different

cortical regions (Prado et al., 2005) and cortical regions sensitive to different object

categories may have different bias towards the centre and periphery (Levy et al., 2001).

2.2.6. Functional visual areas V6 and V6A are related to visuomotor processing

In the eighties a new area, PO complex, was defined on histological grounds in the anterior

bank of parieto-occipital sulcus of macaque monkey (Colby et al., 1988) and a functional

28

visual area V6 was discovered in the same location (Zeki, 1986). Later Galletti and co-

workers examined the function of the macaque’s parieto-occipital cortex and showed visual

responses, eye-position related modulation and oculomotor activity in that cortical region

(Galletti et al., 1991; Galletti et al., 1995). They defined two densely interconnected

functional areas in the anterior bank and at the bottom of the parieto-occipital sulcus of the

macaque, V6 and V6A, and argued that PO includes both V6 and V6A in the macaque brain

(Galletti et al., 1996; Galletti et al., 2005). Histologically V6 is an occipital area whereas

V6A belongs to the parietal cortex (Luppino et al., 2005). Following chapters review

literature concerning the visual areas V6 and V6A of the macaque monkey, studies of the

human homologue of V6/V6A, and the functional properties of the human parieto-occipital

region.

Monkey V6 is a retinotopically organized visual area with smaller emphasis on central

visual field than in areas V1-V5 leaving more cortex sensitive for peripheral stimulation

(Galletti et al., 1999b). Its neurons are especially sensitive to stimulus orientation and

motion, and eye position modulates the responses (Galletti et al., 1996). Monkey V6 receives

direct connections from the layer IVb of the primary visual cortex, thus it has a relatively

strong afferent connection from the magnocellular system (Galletti et al., 2001). Anatomical

tracing studies have shown that connections between V1 and V6 are concentrated in V1

periphery (Shipp et al., 1998; Galletti et al., 2001). In addition to V1, V6 has strong

reciprocal connections with areas V3, V3A, V5 and V6A and weaker connections with V2

and parietal lobe (Galletti et al., 2001).

In contrast to V6, area V6A is not retinotopic. The receptive fields of neurons are large and

may differ several degrees from each other between neighbouring neurons (Galletti et al.,

1999a). Area V6A contains both visually responsive and non-responsive neurons. As in area

V6, visually-driven neurons are sensitive to orientation and motion whereas visually non-

responsive neurons respond to saccades and eye and hand position (Galletti et al., 1996).

Area V6A receives connections from V6 and sends projections to dorsal stream areas in

parietal lobe and premotor cortex in frontal lobe (Shipp et al., 1998). Neurons in V6A have

been shown to respond during reaching and grasping (Fattori et al., 2004), and these neurons

are coded both according to the retinotopic position of the target of reaching or the spatial

direction of reaching (Fattori et al., 2005; Marzocchi et al., 2008).

29

Rizzolatti and Matelli (2003) presented a hypothesis of the division of dorsal stream into two

distinct functional systems. Ventro-dorsal (dorso-lateral) stream extends from area V5 to

lateral parietal lobe and its function concerns spatial perception and both the guiding and the

understanding of motor actions. Area V6 is a central node in dorso-dorsal (dorso-medial)

stream which extends to medial parietal lobe and frontal lobe and controls visually guided

actions. V6 is rich in cells that are able to distinguish the real motion of stimulus from self-

induced motion signals which can result from eye movements (Galletti and Fattori, 2003),

and some neurons in V6A code information in ego-centric coordinate frame of reference

which remains anchored in the subject despite eye movement (Galletti et al., 1995).

Functionally, V6 is involved in detecting motion in the whole visual field, the analysis of

flow fields resulting from self motion, and selecting peripheral targets in during visual search

(Galletti et al., 1999b; Galletti and Fattori, 2003). V6A has an important role in controlling

visually guided hand movements (Galletti et al., 2003).

The first proposals of the human homologue of monkey V6 came from MEG studies. Visual

and saccade-related responses in the posterior bank of parieto-occipital sulcus were

suggested to originate from human V6 (Jousmäki et al., 1996; Portin et al., 1998).

Luminance flicker stimulus produced especially strong responses with no foveal

magnification (Portin et al., 1998; Portin and Hari, 1999). The first parieto-occipital MEG

responses emerged early, less than 100 ms after the stimulus onset (Tzelepi et al., 2001;

Vanni et al., 2001) in line with fast magnocellular input to the area.

Pitzalis and co-workers (2006) located a new retinotopic area in the posterior bank of the

dorsal part of human PO-sulcus with fMRI. This area had a complete contralateral visual

field representation and it was located anterior to the peripheral representation of dorsal V2

and V3. On the basis of location and retinotopy they referred to this area as the human

homologue of monkey V6. Other groups suggested a location of human V6 and V6A in PO

sulcus on the basis of visual (Dechent and Frahm, 2003; Stiers et al., 2006), visuomotor (de

Jong et al., 2001), and oculomotor (Law et al., 1998; Bristow et al., 2005) responses.

However, they did not use retinotopic mapping in the localisation of V6 and responses from

peripheral visual field representations in V1-V3 may have confounded their results. In

addition, the separation of V6 and V6A may be complicated due to dense connections and

partially similar response profile. I use the term V6/V6A complex whenever the separation

of areas is ambiguous.

30

The functional roles of human PO cortex suggested by imaging and lesion studies are in line

with the properties of monkey V6 and V6A. A lesion in dorsal PO sulcus results in motion

blindness (Blanke et al., 2003) and in an impairment to detect frequency-doubling stimuli

(Castelo-Branco et al., 2006), suggestive of motion sensitivity and magnocellular

connections. Electrical stimulation of PO cortex evokes the perception of motion (Richer et

al., 1991). Visual motion activates human PO sulcus (Dupont et al., 1994; Mercier et al.,

2009) and human PO sulcus is activated when the perception of stimulus motion changes to

self motion (Kleinschmidt et al., 2002). PO responses have also been associated with the

calibration of visual motion signal during eye movements (Galati et al., 1999; Tikhonov et

al., 2004) which would require a contribution from real-motion cells. In addition, gaze

direction and vergence angle modulate PO responses (Deutschländer et al., 2005; Quinlan

and Culham, 2007), PO responses have been detected during saccades (Bodis-Wollner et al.,

1997; Dejardin et al., 1998) and visually guided pointing (de Jong et al., 2001), all

homologous response properties to monkey V6/V6A complex.

Posterior parietal cortex in the region of parieto-occipital junction may contain the human

homologue of V6A. This region is activated during visually guided reaching towards a

peripheral target (Prado et al., 2005; Filimon et al., 2009) and its lesion causes optic ataxia.

Ataxic symptoms are worst when pointing towards periphery and less marked when pointing

to the centre of the visual field or body parts (Karnath and Perenin, 2005). The symptoms

originate from deficiency in transformation between dynamic gaze-centered and arm-

centered coordinates (Khan et al., 2005). In monkeys V6A has a role in coordinate

transformation during reaching and grasping movements (Galletti et al., 1997; Galletti et al.,

2003) and lesion in V6A of the monkey produces short term impairment in visually guided

reaching (Battaglini et al., 2003). This may connect V6A lesion with optic ataxia in humans.

2.2.7. The guidance of saccades and spatial attention share a network of brain regions

Attention and saccades share many common features. A salient visual stimulus draws

attention in a bottom-up manner, and attention can also be directed in endogenous and

voluntary fashion (Kastner et al., 2001). Stimulus-driven and voluntary attention are guided

by different cortical networks (He et al., 2007). Competing stimuli show mutual suppression

demonstrating a bottom-up mechanism whereas top-down control may direct attention

31

towards spatial location, features, and objects (Kastner and Ungerleider, 2000). Like

attention, saccades are evoked with both salient stimulus in bottom-up manner and voluntary

top-down mechanisms. Stimulus driven pro-saccades aim to place a target-of-interest at

foveal vision whereas volitional saccades actively explore the environment. These saccade

types are mediated with slightly different network of brain areas (McDowell et al., 2008).

According to the premotor theory of attention, target selection for eye movements and

spatial attention are coupled (Rizzolatti et al., 1987). The activation of the similar network of

cortical regions during both saccade and attention tasks supports the hypothesis (Corbetta,

1998; Corbetta et al., 1998). Figure 2 shows approximate cortical regions activating during

saccades and the covert shifts of attention. Parietal cortex shows robust responses during

both conditions. Parietal cortex has a role in coordinate transformation (Merriam et al., 2003)

and it supposedly provides spatial information for saccades and attention shifts (Gaymard et

al., 1998). In addition, both saccades and attention activate frontal lobe in the region of

frontal and supplementary eye fields. Gaymard et al (1998) and McDowell et al (2008)

reviewed the roles of brain regions in guidance of saccades. Frontal eye field (FEF) is

located in precentral sulcus anterior to primary hand area. Electrical stimulation of FEF

elicits saccades at low threshold and this region is involved in the preparation and triggering

of saccades. Supplementary eye field (SEF) belongs to supplementary motor area. It is

located in superior frontal gyrus and it is involved in the temporal control of action

sequences. Dorsolateral prefrontal cortex (DLPFC) around inferior frontal sulcus inhibits

stimulus-driven saccades and is related to the coordination of voluntary action and spatial

working memory (Faw, 2003). Superior colliculus is a central node in saccade control and it

is connected to saccade generator in the brainstem. In addition, striatum, the part of cortico-

basal ganglia-cortical loop, and cerebellar vermis function in the motor control of saccades.

Both attention and saccades modulate visual processing in the low and intermediate levels of

the visual cortical hierarchy. Top-down attention affects neural signals in the visual cortex in

various ways. Spatial attention enhances neural responses in the corresponding retinotopic

representations (Tootell et al., 1998; Watanabe et al., 1999) whereas attention to a specific

stimulus attribute increases activation selectively to the corresponding stimulus feature

(Watanabe et al., 1998). In addition, attention may result in an increase of baseline activity

(Kastner et al., 1999) and response sensitivity (Reynolds and Chelazzi, 2004). Activity in the

primary visual cortex is also influenced by saccade processing. Despite the temporal

32

limitation of fMRI, previous studies suggested that BOLD responses to visual stimuli are

suppressed at the time of saccades even though saccades in darkness results in positive

responses (Sylvester et al., 2005; Sylvester and Rees, 2006; Vallines and Greenlee, 2006).

Response decrement is associated with the suppression of perception during saccades (Burr

et al., 1994; Vallines and Greenlee, 2006) and signal increases have been suggested to

represent efference copies from the movement planning and execution (Sylvester and Rees,

2006). In monkeys, signal increases are associated with sensory memory for visual

continuity during eye movements (Khayat et al., 2004), spatially triggered update signals

after eye position changes (Nakamura and Colby, 2002), and disparity related response

changes which are utilised in 3D vision (Trotter and Celebrini, 1999; Trotter et al., 2004).

Several studies have found connections between visual cortex and the fronto-parietal

attention and saccade network. These suggest a strong physiological top-down connection

from FEF and parietal cortex to retinotopic visual cortex. In monkeys, electrical stimulation

of FEF modulates response gain in extrastriate cortex (Moore and Armstrong, 2003) and

increases visually evoked responses in V1 (Ekstrom et al., 2008). In humans transcranial

magnetic stimulation (TMS) of FEF caused increased blood flow in parieto-occipital cortex

(Paus et al., 1997). Recently, TMS of right FEF and intraparietal sulcus resulted in BOLD

signal changes in the low-level retinotopic areas even in the absence of visual stimulation

(Ruff et al., 2006; Ruff et al., 2008; Ruff et al., 2009).

In addition, visual cortex has a role in saccade generation. Electrical microstimulation of the

primary visual cortex facilitates or interferes saccades depending on the stimulated layer

(Tehovnik et al., 2005). Saccadic eye movements increase neural responses in the saccade

target representation before the actual eye movement (Supèr et al., 2004). V1 has also been

suggested to serve oculomotor structures in target selection for saccades depending on

whether the target is part of the figure or background (Supèr, 2006). In humans, signal

increase in the representation of saccade target may be related to the spatial guidance of

saccades (Geng et al., 2008).

33

Figure 2. The network of cortical regions related to the processing of saccades and the covert shifts

of attention in human brain. The coloured regions are placed at the approximate positions of the

activations reported by Corbetta and co-workers (1998). In their review Corbetta and co-workers

presented the activations during saccade and attention tasks, but they did not delineate neighbouring

functional areas. Thus the activations may overlap with the neighbouring functional areas. Yellow

indicates parietal and frontal regions that were activated during both saccades and covert shifts of

attention. Green colour shows occipital regions that were more strongly activated with covert

attention task and red indicates regions activated with eye-movement task. In original data by

Corbetta et al, saccades activated lateral occipital cortex in left hemisphere but not in right

hemisphere.

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2.3. Magnetoencephalography

2.3.1. Principles

Magnetoencephalography (MEG) is a noninvasive brain imaging method. It measures

neuronal activity directly and provides excellent, in the range of millisecond, temporal

resolution. Following overview summarises the basic principles of MEG method and MEG

source modelling. It is primarily based on the reviews by Hämäläinen and co-workers (1993)

and Hari (1999).

Electrical responses of neurons create magnetic field that can be measured with MEG.

Postsynaptic potential changes result in small electric currents called primary currents within

dendrites. Volume currents that flow passively through the whole conducting medium (e.g.,

the brain) complete the current loop. MEG signal results from postsynaptic currents most

likely in the cortical pyramidal cells. Apical dendrites of pyramidal cells are parallel to each

other and are oriented perpendicular to cortex surface. Thus simultaneous postsynaptic

potentials in several dendrites form dipolar current field perpendicular to cortex surface.

Neuromagnetic signals are typically 50-500 fT, which requires activation of approximately

10 000 -50 000 neurons (Murakami and Okada, 2006). Action potentials result in two

current dipoles and thus quadrupolar field which decreases more as a function of distance

than dipolar field. In addition, longer lasting postsynaptic currents summate temporally more

effectively than fast action potentials.

Measurement of weak magnetic fields requires sophisticated technology and

superconducting SQUID based sensors. Measurements are run in a magnetically shielded

room to prevent artefacts resulting from the earth’s magnetic field, radio-frequency fields

and ferromagnetic objects. In addition, physiological artefacts hamper MEG measurements.

Electric activity of the heart, muscle activity, eye movements and blinks create strong

magnetic signals, and during the measurements the subject must be still and avoid eye

movements and blinks. Eye movements are measured during the measurement and the

contaminated epochs are rejected. The configurations of neuromagnetic sensors help to

control artefacts. In planar gradiometers, the figure-of-eight construction, with the two loops

in opposite directions; result in sensitivity to sources near the coil. Homogenous fields

resulting from distinct far-away sources induce similar opposite current in both loops that

35

attenuate each other. Evoked neural responses are differentiated from spontaneous brain

activity and random noise by averaging several hundred responses.

2.3.2. Source modelling

Forward solution refers to the calculation of magnetic fields from electric currents. Cell

membrane level phenomena are discarded form the electromagnetic model and the whole

brain is considered as a conductor for the forward model. Moreover, because all tissues are

almost equally transparent to the magnetic field, a single layer conductor model is often

sufficient. The brain can be approximated with a homogenous sphere when the sphere radius

has been fitted to the curvature of the brain surface. For spherically symmetric volume

conductor only tangential component of the primary current produces magnetic field outside

the conductor due to symmetry reasons. In this model, the activities in brain sulci are

oriented tangentially to surface and create the neuromagnetic signal. The spherical conductor

model is computationally simple and reasonably accurate. Brain-shaped piecewise

homogenous conductor, boundary element model (BEM), is also used and it provides better

approximation in cortical regions where the brain surface is not spherical.

Neuromagnetic inverse problem refers to the estimation of underlying current sources on the

basis of the measured magnetic field. Due to non-uniqueness of the inverse problem the

current distribution inside the conductor cannot be uniquely defined from the

electromagnetic field outside. Nevertheless, a reasonable source model can be formed on the

basis of constrains, which may include the source distribution and statistics, sensor statistics,

and functional and anatomical a priori information. The source model aims to minimise the

difference between the measured magnetic field and the field obtained with the forward

calculations. Several methods of source modelling have been developed and they either

assume distributed or point-like sources. However, it is important to remember that all

methods provide only a model of brain activation. Moreover, the distribution of the modelled

sources reflects the chosen method rather than the actual extent of brain activation.

The electric source can be assumed to be point-like and modelled with an equivalent current

dipole (ECD)(Williamson and Kaufman, 1981). ECDs are defined with a fixed location and

usually fixed orientation and variable amplitude. When the noise is assumed to have

36

Gaussian distribution, dipole parameters can be approximated with a least square search i.e.

the minimization of the difference between the measured and calculated magnetic field

(Tuomisto et al., 1983). Complicated magnetic field pattern can be explained with several

ECDs in time-varying multidipole model (Scherg, 1990). ECD models require the definition

of source number and approximate location and are thus dependent of the educated guess of

the user. Number and location of ECDs can be approximated on the basis of the magnetic

field pattern or a prior knowledge of likely sources. In addition, BOLD responses can be

used as seeds for ECDs (Vanni et al., 2004).

Another approach to source modelling, based on general linear model, is to make minimal a

priori assumptions and to find the smallest current distribution at each time point that can

explain the data. Minimum norm estimate (MNE) was the first method based on that

principle (Hämäläinen and Ilmoniemi, 1984, 1994). It assumes that currents are normally

distributed and selects the current distribution with the smallest Euclidean norm. MNE

favours superficial sources, but this can be opposed with depth weightings. In addition,

MNE produces smooth and extended responses. Minimum current estimate (MCE) assumes

exponential a priori distribution of currents, minimises the currents as L1 norm and produces

more focal source estimate (Matsuura and Okabe, 1995; Uutela et al., 1999).

Current methods provide activity time course estimates for every cortical location and have

some advantages over the ECD method. Whereas ECDs are defined on individual basis,

current distribution estimates provide opportunity for normalization of the responses and

group level analysis. By normalising the current estimates with the noise, current

distributions can be treated like statistical parametric maps and displayed as dynamic

statistical parametric maps (Dale et al., 2000). In addition, anatomical constrains are used to

improve the results of the analysis (Dale and Sereno, 1993). Because neural currents are

oriented perpendicular to cortex, a cortex surface model provides an anatomical constraint to

the inverse problem. In practice, a loose orientation constraint provides better results because

it is less sensitive to segmentation and coregistration errors than a strict constraint (Lin et al.,

2006).

37

2.4. Functional magnetic resonance imaging

2.4.1. Principles

Functional magnetic resonance imaging (fMRI) is the most widely used functional brain

imaging method. It provides safe and relatively non-invasive way to measure hemodynamic

response to neural activation. It is spatially very accurate but temporally bound to

hemodynamic delay. The following overview is mostly based on the book of Huettel and co-

workers (2004).

Nuclear magnetic resonance of paramagnetic matter forms the basis of magnetic resonance

imaging. Atoms with odd number of protons form small magnet dipoles. These magnet

dipole vectors can also be explained as the vector of precession of a nuclear spin. When

exposed to a strong magnetic field a small part of randomly oriented magnet dipoles align

parallel to the field and start to precess around the direction of the field. The frequency of the

precession, Larmor frequency, depends on the matter and the magnetic field strength. The

1H isotope of hydrogen creates the most of the magnetic resonance signal from living

tissues. The orientation of magnetic dipoles parallel to the external magnetic field requires

lower energy and the system emits energy when it reaches new equilibrium. This equilibrium

and thus the magnetisation depend on the magnetic field strength and the temperature.

The creation of magnetic resonance signal requires application of the second

electromagnetic field. This radiofrequency (RF) pulse oscillates at the Larmor frequency and

tilts the magnet dipoles and thus the magnetization vector. When the second pulse is off and

the magnetization is subject to static field only the magnetization vector returns to

equilibrium and emits energy. The time the system requires to reach the equilibrium is called

T1 relaxation time. In biological tissue T1 relaxation time roughly corresponds to water

content. In the end of the RF pulse all magnetic dipoles are precessing in synchrony

producing strong signal, but interactions between spins results in de-phasing. T2 relaxation

time describes the time of de-phasing and it varies between tissues. Magnetic field

inhomogeneities affect the de-phasing of the rotation described with T2* effective relaxation

time.

A change in transverse magnetization results in measurable MR signal. Magnetization along

the main field depends on T1 relaxation whereas transverse magnetization depends on initial

38

magnetization, loss of magnetization due to T2 decay, and the phase of magnetization vector.

A MR image shows the spatial distribution of one spin related property that changes

transverse magnetization and it consists of discrete volume samples, voxels. However, a

measured MR signal is the sum of transverse magnetization within the whole excited sample

and spatial encoding schemes are required for the calculation of signal from each particular

voxel. A MR image formation is based on the introduction of spatially varying gradient

magnetic fields that alter the precession frequency and the accumulated phase of

magnetization vector depending on spatial location, thus producing different MR signal from

each location. Three orthogonal gradients are often used in anatomical images, whereas two

dimensional imaging sequences with one-dimensional excitation pulse to select a slice and 2-

D encoding scheme to resolve spatial distribution within a slice are used in fMRI.

Any signal changing in time or space can be constructed from the series of components in

temporal and spatial frequency domain. The spatial frequencies of a MR image are described

in k-space, which is a Fourier transformation of the image space. One point in k-space

represents the MR signal under corresponding gradient fields. At the centre of the k-space

magnetization vectors of all image voxels are at the same phase and it determines the signal-

to-noise ratio of the image, whereas high spatial frequencies of the image are described in

the periphery of k-space corresponding to MR signal when the phase of magnetization vector

differs between voxels.

Discovery of blood oxygenation level dependent (BOLD) signal enabled utilization of MR

in functional brain studies (Ogawa et al., 1990). Deoxygenated haemoglobin has higher

magnetic susceptibility than oxygenated haemoglobin, which causes dephasing of spins, and

thus T2* weighted MR pulse sequences show more signal where blood is highly oxygenated.

Both changes in blood flow and oxygen consumption of tissue affect the deoxyhemoglobin

content. Hemodynamic response function (HRF) describes the change of MR signal

triggered by neural activation. In the beginning, a local transient signal decrease has been

detected which probably reflects increased oxygen consumption. Later, compensatory blood

flow increases more than oxygen consumption resulting in decreased deoxyhemoglobin

content and increased MR signal. Signal starts to increase approximately two seconds after

the onset of neuronal activity and rises to plateau six to nine seconds after the start of

continuous activity. Finally, after the cessation of neural response, blood flow returns to

normal more quickly than blood volume and signal decreases under baseline.

39

Logothetis and co-workers (2001) showed in simultaneous electrophysiological and fMRI

measurements that local field potentials correlate with BOLD response more than the spiking

activity of neurons. The local field potentials reflect mainly synaptic potentials but also

membrane oscillations and spike after-potentials, and thus local field potentials and BOLD

signal reflect the aspects of input signal and local intracortical processing (Logothetis and

Wandell, 2004). However, the relationship between neural activity and BOLD response

varies in different brain regions due to local anatomic and neural circuit properties. In

addition, BOLD signal can fluctuate without correspondence to local neural activity (Sirotin

and Das, 2009). These variations of signal implicate that BOLD signal should only be

compared within a voxel during one experiment and balanced design.

2.4.2. Data analysis

Following chapter describes the analysis of fMRI signal and is based on the book by

Frackowiak and colleagues (2003). Preprosessing of measured raw data is essential for a

good signal-to-noise ratio, but the required preprocessing steps depend on the chosen method

for response analysis. Correction of the subject motion is most often recommended. For the

motion correction, translation and rotation parameters are defined for all volumes compared

to one. In addition, the timing differences between slices can be corrected. Statistical testing

with open anatomical hypothesis requires spatial smoothing. Data is smoothed by

convolving it with three dimensional Gaussian kernel to render the error distribution more

normal, to enable use of Gaussian random field theory for the correction of multiple

comparisons, and decrease the variance between subjects for the analysis of multiple

subjects. In addition, spatial normalisation is required for multisubject analysis. Spatial

normalisation includes both linear and nonlinear transformations to fit the volumes to a

template volume, such as Montreal Neurological Institute (MNI) brain.

Several approaches to data analysis have been developed. Correlation analysis calculates

covariance and correlation coefficients between the stimulus and the data. Anatomically

closed method examines BOLD signal within a region-of-interest (ROI). General linear

model (GLM) based methods are perhaps the most widely used for anatomically open

hypothesis. These methods explain the measured data as a linear combination of explanatory

variables and error term. To obtain explanatory variables in practice, a design matrix is

40

created according to study protocol and convolved with a hemodynamic response function to

take the time difference between the stimulus and the hemodynamic response in to account.

Additional regressors can be included into the design matrix to model confounding elements

and long- and short-range temporal correlations are controlled. The time-course of the model

is fitted to the data with a least square method separately for each voxel. The fitting

procedure provides parameter estimates for each condition at every voxel within the volume

and parameter estimates are combined to give contrast estimates for the effects of interest.

Voxel values of the contrast estimates are assumed to follow a statistical distribution

(student-t or F distribution) under null hypothesis. Statistical testing is conducted separately

for each voxel and the results are displayed as noise-normalised statistical parametric maps.

These statistical parameric maps show the significance of the results as the probability that

they could happen under null hypothesis. Correction for multiple comparisons controls type I

error due to very large number of statistical tests. Gaussian random field theory takes spatial

dependence of voxel values into account for Family wise error (FWE) correction of false

positive whereas False discovery rate (FDR) correction controls the probability of type I

error (Benjamini and Hochberg, 1995; Genovese et al., 2002). GLM approach allows

statistical inference of multiple subjects. Random effect analysis controls variability both

within and between subjects and provides inference of population (Friston et al., 1999)

41

2.5. Retinotopic mapping

Visual field mapping has many applications both in research and in clinical practice. Most

importantly retinotopic mapping provides a way to define the borders of a subset of

functional cortical areas. Because functional borders do not typically follow anatomical

landmarks and locations of functional areas varies between subjects, the knowledge of the

exact location of a functional area enables more accurate comparisons across subjects and

data analysis in group level (Wandell et al., 2007). Retinotopic mapping has implications

also in studies concerning other sensory modalities as some multimodal areas are organized

in visuotopic coordinates (Sereno et al., 2001). The relative representations of different parts

of the visual field within an area provide non-direct information about the function of the

area. The knowledge of the functional area borders, its neighbouring areas and the internal

representation enables the interspecies comparison. Finally, retinotopic mapping is useful in

clinical practise as quantitative analysis of visual field maps can be used in studies of visual

system pathologies for example in case of retinal injury, hemianopia, and preoperative

cortical mapping (Wandell et al., 2007).

Modern imaging methods provide means for retinotopic mapping in human cortex even

though the need for good spatial resolution limits the choice of usable methods. Even though

some mappings have been done with PET (Fox et al., 1987; Shipp et al., 1995), fMRI is the

most used method for retinotopic mapping (Engel et al., 1994; Sereno et al., 1995; DeYoe et

al., 1996; Warnking et al., 2002; Dougherty et al., 2003). Several ways for stimulus

presentation have been developed. The earliest approach was simply to stimulate different

parts of visual field one after another (Schneider et al., 1993). Later, the widely used phase-

encoded method was developed (Engel et al., 1994; Sereno et al., 1995). Method uses two

types of stimuli. A rotating wedge attached to fixation point provides information about the

visual field representation in polar direction and expanding or contracting ring around

fixation point about the representation in eccentricity direction. Stimulus phase is Fourier

transformed into visual field location, which in turn corresponds to cortex regions according

to retinotopic organization.

In my work I use mainly multifocal (mf) mapping method for determination of visuotopic

maps. It is based on the simultaneous stimulation of multiple visual field regions and on the

measurement of multiple local visual field responses in parallel. The timing of each region is

linearly independent from the other regions and thus the correlation between the stimulus

42

and the response pattern reflects directly the underlying neural system. The principle of

parallel stimulation of several regions was presented in maximal length shift register (m-

sequence) stimuli (Sutter, 2001), Andrew James (2003) introduced a mf-design with pulses

of pattern reversal resulting in large visually evoked potential amplitudes, and Vanni and co-

workers (2005) developed a multifocal stimulus for fMRI (mffMRI, Figure 3). In contrast to

phase-encoded retinotopic mapping, where a travelling wave of activation along the cortex

gives a continuum of phases in the response, mffMRI produces multiple discrete local

retinotopic BOLD responses that can directly be used as regions of interest for further

analysis. In addition, the standard general linear model based tools can be used in mf-data

analysis and the localization and the quantification of the signal is more straightforward even

though non-linear neural interactions may also result in signal loss (Pihlaja et al., 2008).

Compared to block designs, mf-technique enables simultaneous stimulation of several

locations, and therefore it requires considerably shorter measurement time.

Figure 3. One image from multifocal stimulus

Correct localization of close retinotopic areas or different visual field representations form a

challenge for EEG and MEG due to relatively poor spatial resolution of the methods.

Excellent temporal resolution would enable separation and localisation based on latency

differences. However, timings of early and intermediate retinotopic areas are highly

overlapping (Schmolesky et al., 1998) which hampers the separation. Both anatomical and

functional MRI results has been utilised as constrains for MEG and EEG source localisation,

in order to improve signal separation originating from different retinotopic areas (Di Russo

et al., 2001; Vanni et al., 2004; Hagler et al., 2009).

43

3. Aims of the study

This work concentrates on visual processing on human cerebral cortex in functional areas

which are at low-level in the hierarchy of visual cortices. Especially, the following

experiments examine the neural processing around parieto-occipital sulcus which

corresponds to human V6 and peripheral visual field representation in low-order visual

areas. Brain signals are measured with fMRI and MEG. In addition, this work approaches

some of the methodological problems of MEG and fMRI.

The aim of the work was to:

examine some of the limitations of MEG and fMRI methods in studies of human visual

processing (Studies I, II, and III).

map the peripheral visual field representations in retinotopic areas (Study II).

localise human V6 and study the properties of its neurons (Studies II, III, IV, and VI).

examine the effect of motor and attention tasks on early visual processing, which should

reflect top-down modulation, and especially to detect the spatial distribution of the

responses in the retinotopic cortex, including visual area V6 (Studies IV and V).

44

4. Materials and methods

4.1. Subjects, stimuli and tasks

4.1.1. Subjects

In study I ten volunteers from laboratory staff analysed the simulated MEG data with two

different methods. Studies II - VI comprised healthy adult volunteers. Some of the subjects

were measured in several experiments. For example, cortical surface maps of retinotopic

areas were made for six subjects in study II, and these maps were used also in studies III and

IV. Table 2 summarises number of subjects attending in different studies and experiments.

All subjects gave their written informed consent before participation in the studies, and the

studies were accepted by the Ethics Committee of Hospital District of Helsinki and Uusimaa.

Study Aim of the study Exp

1

Exp

2

Exp

3

Exp

4

I compare MEG source localisation with ECD and MCE 10

II map peripheral visual field representation, locate V6 12 5 6 2

III find cortical regions sensitive to luminance flicker 11 4

IV examine the effect of saccades in retinotopic areas 11 6 5 2

V examine the effect of spatial attention in V1 19

VI locate and study V6 with MEG 10

Table 2. The number of subjects in different studies and experiments and the aims of the studies

4.1.2. Visual stimuli and tasks

Visual stimuli were presented in studies II, III, V, and VI. All stimulus images were

generated with MatlabTM

(Mathworks Inc), and their timing was controlled with

PresentationTM

(Neurobehavioral systems Inc.). All visual stimuli were gray scale images at

photopic luminance levels. In study II, we developed a method for mapping the retinotopic

representations of wide visual field, 100 degrees and 40 degrees diameter for horizontal and

vertical dimensions, respectively. To achieve wide field in the narrow magnet bore, the

subjects viewed the stimuli at very short distance with the help from an optical aid. The

multifocal stimuli comprised five to 48 discrete regions. In study III the stimulus was

designed for comparison of the responses in retinotopic areas to same stimulus with different

45

surround. We showed luminance flicker and reversing checkerboard stimuli with dark and

light surround. The stimuli were viewed through small apertures in front of the eyes. The

surrounds of the stimuli were constructed with two masks, one black, coarse and dark and

another white and brightly illuminated. Study IV explored the effect of saccades on early

visual cortices. Activations were examined together with retinotopic mapping data from

study II. No visual stimuli were used, but the subjects were asked to perform predefined eye-

movement tasks in darkness. The darkness was complete, none of the subjects reported any

light after the full dark adaptation. In study V we used both event related fMRI, attention

task, and 60-region mffMRI, which served as an efficient functional localizer for V1

regions-of-interest. The attention task contained four regions from the mapping stimulus, one

in each visual field quadrant. Study VI was designed to describe the temporal properties of

human V6 with MEG. Peripheral and moving stimulus in upper visual field quadrant

enabled differentiation of neuromagnetic signal from V6.

Figure 4 visualises the stimulus systems used in fMRI and MEG experiments. Stimulus

images in fMRI studies were produced by a data projector with three micromirrors

(VistaPro, Electrohome Ltd) outside a magnet room. A customized objective with 350 mm

focal length was covered by 0.6 log unit neutral density filter to reduce the excessive

luminous flux. Images went through a RF-shield tube and were reflected by a surface mirror

into the magnet bore. In studies III and V a semitransparent back-projection screen was

located in the back of the head coil at 30 to 35 cm distance from the eyes, whereas in study II

the screen was located over the subjects’ foreheads at eight cm distance from the eyes. In all

fMRI studies the subjects lay on their backs and viewed the screen via a surface mirror.

Similar stimulus projection system was also used in MEG study (study VI). A data projector

with three micromirrors was located outside a magnetically shielded room. Stimulus images

were projected to a semitransparent screen via two surface mirrors. The screen was in front

of the subjects at 30 cm distance.

46

Figure 4. A) The stimulus presentation system used in the fMRI measurements (studies III and V,

The projection screen was above the subjects forehead in study II). B) The head coil and the surface

mirror used in fMRI measurements. C) The stimulus presentation system used in the MEG

measurement (study VI)

4.2. Measurements

4.2.1. fMRI measurements

All fMRI measurements were performed in Advanced Magnetic Imaging Centre (AMI-

centre) with a 3-tesla MRI scanner (SignaTM

, General Electric Ltd.) using a standard GE

single-channel head coil or an eight-channel head coil with exciteTM

that provides better

signal-to-noise ratio, especially at a brain surface. The functional imaging sequence was

T2*-weighted gradient-echo echo-planar imaging (EPI). Magnetic field inhomogeneities

were corrected with the default shimming for the entire brain. The imaging parameters

varied in different studies. Table 3 shows the measurement parameters for studies II – V.

Data was always acquired from the dorsal part of the brain. The measurement slices were

situated approximately orthogonal to PO sulcus. In studies II, III and IV the slices covered

the whole occipital and parietal lobes and the dorsal parts of the frontal and temporal lobes

and the cerebellum whereas in study V the slices fully covered only the occipital lobe. This

partial coverage of the brain enabled a better spatial resolution and faster sampling rate in the

focus areas.

47

At the end of each functional session, a 3-min anatomical T1-weighted image was acquired.

This image enabled the normalisation of the data into a standard anatomical space and

coregisteration of the different data sets within one subject. The parameters for all these

anatomical images were 1.5 mm slice thickness, field of view 23 cm, 128 x 128 matrix

resulting in 1.8-mm in-plane resolution. The segmentation of cortex for the surface oriented

analysis required higher resolution anatomical images, and the images with 0.9 mm slice

thickness, 256 x 256 matrix and 0.9 mm in-plane resolution were measured for six subjects.

Study TR

(ms)

FOV

(mm2)

Voxel

(mm3)

n.o. slices TE

(ms)

flip angle

(deg)

II 2000 190x190 3x3x3 27 30 60

III 2000 190x190 3x3x3 27 40 90

IV 2000 190x190 3x3x3 27 40 & 30 90 & 60

V 1800 160x160 2.5x2.5x2.5 24 30 60

Table 3. Measurement parameters in studies II - V

4.2.2. MEG measurement

MEG data for study VI were measured in Brain Research Unit of Low Temperature

Laboratory with a 306 channel Vectorview neuromagnetometer provided by Elekta

Neuromag Ltd. The measurement channels are composed from three channels in 102

locations covering the whole head. In each location are two orthogonally oriented

gradiometers that measure the spatial gradient of magnetic field and one magnetometer

detecting the strength of the magnetic field. The head position was measured in respect to

measurement helmet. The subjects sat in a magnetically shielded room. The remaining

artefacts were removed with the signal-space separation method. The neuromagnetic signals

were filtered to 0.1 – 200 Hz and sampled at 600 Hz. Vertical and horizontal eye movements

were recorded and the epochs contaminated with saccades or blinks were rejected on line.

We measured and averaged 200-300 epochs for each subject to obtain good signal-to-noise

ratio.

48

4.3. Data analysis and visualization

4.3.1. Analysis of fMRI data

Data in fMRI studies were analysed with Matlab based SPM2 software provided by the

Wellcome department of Imaging Neuroscience, London, UK (Frackowiak et al., 2003).

Head movements and timing errors between slices were corrected with spatial and temporal

realignment functions. The examination of the results in individual level required the co-

registration of functional and anatomical images, and the data were normalized into a

standard anatomical space before the group analysis. We used both FWE and FDR

corrections for multiple comparisons, and generalization of the results from the sample to the

population with the second level statistics. The resulting statistical t-maps were visualized

either over the anatomical images of the subjects or over a standard anatomical image.

We used Matlab-based Brain ála Carte (BALC) toolbox provided by INSERM unit

594/Universitè Joseph Fourier, Grenoble, France, for surface oriented analysis (Warnking et

al., 2002). BALC segments cortex along the border of white and grey matter and creates 3-D

model cortex. The statistical t-maps obtained from SPM-analysis were projected on this 3-D

model which was then unfolded onto 2-D surface.

Signal changes in a given brain location were calculated in studies II, III, IV, and V. In

studies II, III, and IV the regions-of-interest (ROI) for signal calculations were marked on

the cortex surface of V1 and V2. For study IV, we determined additional ROIs covering

statistically significant activations in brain from the group data. In study V the ROIs were

defined with multifocal sequences i.e. responses to one multifocal region in V1 corresponded

to one ROI. The location of ROI in V1 was confirmed with the restrictions of size and

distance from the local maximum. SPM calculates regression coefficients, β-estimates, for

each voxel separately, which are saved as 3-D volumes for each regressor of the design

matrix and for mean signal. The global mean signal is normalised during the estimation but

the mean signal in any particular voxel likely deviates from the global mean. We calculated

the percentage signal change by dividing the parameter value for the effect of interest with

parameter value for the constant term for all voxels within ROI separately and multiplying

with 100. Then the signal changes in all voxels within ROI were averaged. The calculation

of an average signal between voxels assumes that behaviours of the voxels are homogenous.

49

This assumption is reasonable when we measure retinotopic representation or other rather

inaccurate parameter.

4.3.2. Analysis of MEG data

In study I volunteers analysed simulated visual MEG data with Vectorview XFit toolbox for

ECD modelling and MCE toolbox for distributed modelling. They reported location,

orientation, timing and amplitude of the modelled sources and these parameters were

analysed further. Analysis of MEG data in study VI was performed with MNE toolbox

utilising surface orientation constraints provided by Athinoula A. Martinos Center for

Biomedical Imaging, Harvard University/MIT/MGH, Charlestown, Massachusetts. The

analysis was performed at both individual and group level. Activation ROIs were selected

from the individual data, and the timing of activations within ROIs were examined. The

group level analysis was utilised to confirm the individual results.

4.3.3. Statistics

A reletively small sample size forced us to use non-parametric methods for statistical testing.

In study I we used Wilcoxon signed-rank test for paired data to see whether the results

obtained with two methods differed significantly. The Wilcoxon signed rank test allows non-

parametric testing of the order of data points in paired samples, without need to assume

normal distribution of the variables. In studies II and III the BOLD responses were

quantified with statistical parametric mapping and the results were displayed at statistical t-

maps. No additional statistic was applied on the description of the results. In study IV we

examined the regional BOLD signals in addition to SPM analysis. We identified the

functional ROIs in V1 and V2 and quantified the signal change. Statistical significance of

the treatment across the subject group was evaluated with non-parametric Wilcoxon signed

rank test. In addition, we determined ROIs from the group data and calculated the correlation

coefficients between different ROIs. In study V we did not calculate statistical parametric

maps, but the whole analysis relied on the detection of signal changes at predefined ROIs.

Repeated measures of analysis of variance (ANOVA) were used for response quantification.

Conditions, visual field regions, and locations were used as regressors in the analysis of the

50

strength and the extent of response modulation. Post hoc paired samples t-tests examined the

difference between conditions. ANOVA was utilised also in the analysis of eye movement

measurements. In study VI the MEG data were presented as dynamic statistical parametric

maps and the functional ROIs were selected on the basis of these maps. The differences in

onset latencies between ROIs were tested with ANOVA, and post-hoc Tukey-Kramer test

revealed the significance of the differences between ROIs.

4.4. Eye movement recordings

Eye movements were recorded in studies IV and V with MRI compatible iViewXTM

MRI-

LR video-oculography system, provided by SensoMotoric Instruments (SMI) GmbH,

Germany. In study IV, complete darkness during the fMRI data measurement was essential

and therefore the eye movement recordings were performed before or after the measurement

in separate runs. In study V the eye movement recordings were performed simultaneously

with the fMRI measurements. The eye was illuminated with infrared light and recorded with

charge-coupled device camera, sensitive for infrared light. The eye positions were sampled

at 50 Hz or 60 Hz with an accuracy of approximately 1 degree. The positions of pupil and

corneal reflections indicated the position of the eye in relation to coordinate frame. This

coordinate frame was defined before the measurement with calibration of five to nine eye

positions.

51

5. Experiments

5.1. Comparison of minimum current estimate and dipole modeling in the analysis of

simulated activity in the human visual cortices (Study I)

The aim of the study I was to compare two different approaches to MEG source modelling.

Even though the localisation accuracy and source separation has been studied extensively in

different conductor models and source and noise conditions, the performance of data

analysis methods applied by different users had not been compared before.

5.1.1. Methods

Ten volunteers analysed the simulated MEG data with both multidipole modelling and

minimum current estimate methods. Figure 5 summarises the simulations 1-4. We created

four simulations; three with six sources of different amplitude and temporal activation

envelope and one with ten sources aiming to imitate better real measured data. Six sources

were situated in the average positions of human retinotopic areas V1-V5 (Hasnain et al.,

1998), and three more anterior sources were placed in well-known positions of MEG

responses. Background noise was obtained from a real measurement. We gave instructions

for source selection in both dipole modelling and MCE and, because the subjects were not

familiar with MCE, a short lesson of the method before the analysis. Subjects performed the

data analysis independently and reported the location, orientation, and timing of the

modelled sources. We examined the proportions of the modelled sources from the simulated

sources and the distances and the latency differences between the modelled and the

simulated sources within each simulation. The results were considered against the properties

of the simulations. In addition, we examined the number of false positives i.e. modelled

sources that did not correspond to any of the simulated sources.

52

Figure 5. Time-courses and amplitudes of simulated sources in study I

5.1.2. Results

Table 4 summarises the number of found sources and the distances and the timing

differences between the modelled and simulated sources in simulations 1, 2, and 4. Subjects

found approximately the same number of sources with both methods and defined the sources

with both good temporal (< 8 ms) and spatial (8 mm) accuracy, even though none of the

subjects was able to locate all sources in any of four simulations. The deep location and

radial orientation diminished the signal-to-noise ratio and thus hampered the source

localisation. In addition, the other simultaneously active sources affected source separation

and localisation. When the sources were not active simultaneously in simulation 1, dipole

modelling was more accurate both in time and space. The differences between methods (2

mm and 1 ms) were small even though they were statistically significant. When sources were

more overlapping in time in simulations 2 and 3 the dipole model and MCE performed

equally, but MCE analysis resulted in larger number of sources emerging from noise than

ECD analysis. In simulation 2, where the sources were more overlapping in time and every

second source had double amplitude, the stronger sources were located with both methods,

but the localisation accuracy was lowered by the weaker partially simultaneous sources. In

simulation 3, where the sources were strongly overlapping in time, the six sources merged

into two or three clusters according to source orientations. This merging of sources was seen

with both methods and it made impossible to connect a modelled source to a particular

simulated source. In simulation 4, both methods were equal in the analysis of the data that

imitated real visual evoked response.

53

Dipole modelling MCE

Simulation located

sources (%)

localisation

error (mm)

timing

error (ms)

located

sources (%)

localisation

error (mm)

timing

error (ms)

I 65 ± 12 4.4 ± 1.9 0.8 ± 0.3 62±13 6.2 ± 1.1 1.6 ± 0.9

II 58 ± 8 7.8 ± 1.8 7.8 ± 3.0 55±12 7.6 ± 2.5 5.4 ± 1.2

IV 54 ± 8 7.2 ± 1.0 5.7 ± 3.1 60± 2 7.2 ± 0.9 7.7 ± 1.5

Table 4. The results from simulations 1, 2, and 4. Mean number and standard deviations of reported

sources and localisation and timing errors. The results of simulation 3 are not presented because the

merging of the overlapping activations did not allow the identification of one simulated source from

the responce pattern.

5.1.3. Discussion

An ideal method for MEG source modelling would be fast, accurate and require as little user

interaction as possible. Dipole modelling relies on subjective selection of the detector

channels whereas MCE is sensitive to false positives. Especially subjects who localised more

simulated sources were prone to select also noise sources. As expected, the orientation and

the strength of the simulated sources strongly affected the responses, and the sources

overlapping in time form a challenge for MEG. Both dipole model and MCE performed well

when sources were active separately, but neither of the methods was able to differentiate

close simultaneous sources, a situation which is typical in visual cortex. Thus neither of the

methods was able separate the components of the real visually evoked response i.e. to find

and localise temporally overlapping sources in different visual areas. Functional or

anatomical a priori information would be required before MEG signals can be associated

with a correct functional area. However, our simulated sources were local current dipoles

which may have provided advantage for dipole modelling over MCE method.

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5.2. fMRI of peripheral visual field representation (Study II)

The aim of study II was to map peripheral visual field representations in the medial surface

of occipital lobe and delineate the medial retinotopic areas as completely as possible. We

developed a method to stimulate wide visual field in a narrow magnet bore and tested

different multifocal mapping stimuli.

5.2.1. Methods

The projection screen was placed 8 cm from the subjects’ eyes, above their foreheads. The

subjects viewed the stimuli with + 10 diopter lenses. To diminish the need of converge

Fresnel prisms were applied onto the lenses. This stimulus presentation system resulted in

100 degrees of horizontal and 40 degrees of vertical visual field. According to Duncan and

Boynton (2003), 50 degree radius visual field corresponds to about 90 % of the surface area

of the primary visual cortex whereas typical 15 degree corresponds only to 60 % of the

primary visual cortex. Two sources of aberration emerged in our setup. Due to spherical

aberration the lenses increased the stimulus size nonlinearly, resulting in increase of the

image towards the periphery. However, the increment was easily measured with a light from

a point source (laser pointer) revealing the true extent of the stimulated visual field. In

addition, the prisms reflected the minor part of the light also to the hemiretina ipsilateral to

the stimulation.

We constructed four different multifocal stimuli and compared the results with historical

phase-encoded mapping from the same subjects with stimuli subtending up to 30 degrees of

visual field. The retinotopic visual areas in medial occipital lobe were delineated with a

stimulus covering horizontal and vertical meridians, and two stimuli covering 45 degrees of

polar angle in each visual field quadrant and full polar cycle gave information about

structure of the areas. A monocular stimulus, where the prisms were not necessary, was

constructed to control the reflections from the prisms. The data analysis was made with

SPM2 and with separate analysis algorithms and the results were examined in both group

and individual level, in 3-D and on cortical surface. The cortical surface analysis was made

with BALC.

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5.2.2. Results

Our simple and relatively comfortable optical method allowed stimulation of the visual field

up to 40-50 degrees of eccentricity and delineation of the peripheral representation of the

retinotopic areas in medial occipital surface. Multifocal technique produces multiple discrete

local responses and thus the individual results can easily be examined both in 3-D and 2-D.

In contrast to mffMRI, phase-encoded mapping requires segmentation of cortex because it

utilises visual field sign for area delineation and visual field sign is defined from the polar

and radial phase gradient separately for each cortical location. 3-D analysis is a great

advantage especially in clinical conditions as the segmentation of grey matter or white and

grey matter border for cortical surface model is laborious. In addition, group level statistics

could be calculated from the 3-dimensional data. Because the multifocal technique relies on

the general linear model, the data from predefined retinotopic representations can be easily

averaged across subjects. However, the intersubject variability of area locations hampers the

group level analysis. New methods with more robust surface segmentation and inter-subject

analysis in 2-D surface might provide better results.

Figure 6 visualises the results of group level analysis. The group level analysis showed that

occipital retinotopic areas extended dorsally to parieto-occipital sulcus and ventrally

throughout the posterior brain to anterior calcarine sulcus up to brainstem structures. In

addition to responses in the medial surface of occipital lobe representing areas V1, V2, and

V3, we found responses in the lateral part of occipital lobe likely corresponding to human

V5 (Zeki et al., 1991; Watson et al., 1993). Separate upper visual field representations were

found in dorsal lateral cuneus and in medial cuneus. In lateral cuneus the most central

stimulus evoked responses, whereas medial cuneus activated only for the most peripheral,

more than 30 degrees of eccentricity, stimulus. The lateral cuneal activation is likely to

correspond to V3a (Tootell et al., 1997). The Talairach coordinates of the medial cuneal

responses were x = -12 y = -77 z = 37 and x = 18 y = -77 z = 34, which were close to the

proposed location of human V6 (Pitzalis et al., 2006).

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Figure 6. The group level results projected on the right hemisphere of a template brain. The

multifocal stimulus comprised a checkerboard wedge in each visual field quadrant. The most central

part of the stimulus (visualised with yellow) extended approximately from one to 12 degrees of

eccentricity, the middle part (red) subtended degrees from 12 to 30 and the most peripheral one

(green) subtended to approximately 50 degrees of eccentricity. On the left are the results from

stimulation of the left upper visual field and the image on the right shows responses to the lower left

visual field stimulus.

The results of individual analysis were in line with the findings of the group level statistics

and multifocal and phase-encoded method yielded similar responses on the cortical surface.

Figure 7 visualises the results of one representative subject on her right occipital surface.

Wide multifocal stimuli activated medial cortex at larger extent than the narrower phase-

encoded stimulus. V1 responded extensively to all multifocal stimuli, but extrastriate cortex

showed less activation when the multifocal stimulus covered the whole field. In addition, a

stimulus in horizontal meridian did not activate border between dorsal V2 and V3. Only the

most peripheral stimulus along the horizontal meridian activated the dorsal extrastriate

cortex and this activation overlapped with the separate upper visual field response in parieto-

occipital sulcus.

Other subjects, whose data were analysed on cortical surface, showed similar responses as

the representative subject. On cortical surfaces the responses extended to anterior calcarine

sulcus, lingual gyrus, and PO sulcus with some individual variability. The same separate

upper visual field representations in lateral and medial cuneus emerged in individual and

group analysis. The separate representations of peripheral upper visual field in medial cunei

were located in posterior bank of PO-sulcus anterior and lateral to peripheral V2d and V3.

The mean Talairach coordinates of the medial cuneal responses were x = -12 ± 6 y = -74 ± 7

z = 30 ± 8 and x = 12 ± 6 y = -71 ± 5 z = 28 ± 5. Calculation of mean magnification factor in

V1 showed that the inverse relationship between cortical magnification factor M and visual

field eccentricity E was 1/M = 0.0592E + 0.0310. In addition, the surface analysis revealed

57

that when the stimulus regions were next to each other extrastriate areas did not activate but

the activations were abundant when the stimulus regions were separated. In addition, the

results showed asymmetric activation in dorsal and ventral V2 / V3 as the horizontal

meridian stimulus failed to activate dorsal V2/V3 border. This result suggests that the

division of the visual field to upper and lower representation for ventral and dorsal V2 and

V3 is below the horizontal meridian.

Figure 7. The responses to central phase-encoded stimulus and wide field multifocal (mf) stimuli for

one representative subject projected over the segmented and unfolded cortex surface of her right

occipital lobe. The upper row visualises results of phase-encoded mapping and stimulation of

horizontal and vertical meridians. The area borders are drawn according to meridian data. Lower row

shows responses to different multifocal stimuli. On the left are results to “full field” stimulus at

different polar angles and in the middle at different eccentricities. On the right are the responses to

left upper visual field stimulus showing an additional activation in the dorsal part of parieto-occipital

sulcus (marked with V6).

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5.2.3. Discussion

We mapped retinotopic visual areas up to 50 degrees of eccentricity both in 3-D for group

and individual level analysis and on cortical surface for individual analysis. Compared to

classical methods with eccentricities from 10 to 15 degrees (Engel et al., 1994) we cover

almost the whole extent of V1. In line with previous histological results (Rademacher et al.,

1993; Amunts et al., 2000), peripheral visual field representation in V1 extended to PO-

sulcus and anterior calcarine sulcus. Almost complete delineation of retinotopic areas

enabled localization of parieto-occipital peripheral visual field representations.

We found an additional response to peripheral upper visual field stimulus in the posterior

bank of the PO sulcus in both individual and group data. The location of this response

maximum was approximately at one centimeter distance from previously proposed position

of the human homologue of area V6 (Pitzalis et al., 2006) and it was clearly separate from

the other upper visual field representations in medial and lateral occipital surface. In line

with previous MEG and fMRI results (Jousmäki et al., 1996; Portin et al., 1998; Vanni et al.,

2001; Pitzalis et al., 2006), we suggest that the peripheral upper visual field representation

belongs to human V6.

Surface oriented analysis enabled calculation of human magnification factor which was in

line with previous calculations from more central data (Grüsser, 1995; Sereno et al., 1995;

Engel et al., 1997; Duncan and Boynton, 2003). The lack of horizontal meridian responses in

dorsal V2 and V3 may indicate the division of dorsal and ventral retinotopic areas below the

horizontal meridian as suggested previously (Vanni et al., 2004) whereas the lack of

extrastriate responses related to stimulus regions next to each other suggests the existence of

nonlinear interactions in extrastriate areas. Pihlaja and co-workers (2008) recently showed

that neural surround modulation in V1 attenuates response to central stimuli when the

surround is stimulated simultaneously. Large receptive fields in extrastriate areas could

predispose to this modulation.

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5.3. Central luminance flicker can activate peripheral retinotopic representation (Study

III)

Originally study III aimed at localization of cortical regions that are more sensitive to

luminance flicker than to other visual stimuli. Previous results had been suggested that

human V6 is particularly sensitive to luminance (Portin et al., 1998; Dechent and Frahm,

2003). Retinotopic mapping and second control experiment were included to confirm that

possible luminance sensitivity arises from a separate area.

5.3.1. Methods

Two visual stimulus systems were designed for study III. First, we aimed to maximise the

contrast between the stimulus and the surround and to exclude all light reflections from the

stimulus surround. This is the desired setup in studying a response to luminance stimuli,

where one wants to exclude aberrant indirect stimulation of the retina. The subjects wore a

black matte mask which covered the peripheral parts of the visual field, and the interior of

the head coil was covered with the same cloth to diminish light reflections. In the second

setup, the aim was to minimise the contrast of light scattered inside the eye. The subjects

wore a white mask which was illuminated with bright light. Light was directed inside the

magnet bore with two bundles of optic fibers, one for each eye. The idea to illuminate

stimulus surround was adopted from the studies of cortical blindness (Barbur et al., 1994;

Weiskrantz et al., 1995). The parameters for the visual stimulus viewed through a 30-deg

aperture inside the mask was similar in both measurements and it consisted of luminance

flicker (black = 0.4 cd/m², white = 25 cd/m², flickering at 4 Hz), checkerboard pattern

reversal (reversing at 8 Hz, no change in the mean luminance), and rest (a fixation point with

a grey background 11 cd/m²) blocks in interleaved order. The data were analysed with SPM2

and BALC. Both pattern reversal and luminance flicker blocks were contrasted against rest

and the results were compared both in group and individual level and in 3D and on cortical

surface. Responses on cortical surface were localised in relation to retinotopic visual areas.

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Figure 8. The results of one representative subject projected on his left medial occipital surface. The

responses to pattern reversal stimulus (pattern-rest, pFWE < 0.05) and the responses to luminance

flicker that extend beyond the pattern responses [(luminance-rest, pFWE < 0.05) excluding (pattern-

rest, p<0.05)] with both dark and illuminated stimulus surround. The borders between functional

areas have been defined according to the results of study II.

5.3.2. Results

Figure 8 visualises the results of one representative subject on his medial cortical surface.

Retinotopic areas V1-V3 responded strongly to the pattern reversal stimulus and luminance

flicker activated cortex outside the pattern-related responses only when the stimulus

surround is dark. This additional luminance response was located in the peripheral visual

field representations of the areas V1-V3. Figure 9 shows the signal changes related to all

stimulus conditions at the different eccentricities of V1. Responses to pattern reversal were

stronger in stimulus representations, but the responses to luminance flicker exceeded the

pattern responses in the periphery. Analysis of all data confirmed the results of the

representative subject. Both individual and group level results showed that the responses to

luminance flicker with dark stimulus surround reached further anteriorly than the responses

to pattern reversal. The mapping of retinotopic areas up to 50 degrees of eccentricity

confirmed that these additional luminance responses were located mainly in the peripheral

visual field representation of V1 and some activation was also detected in the peripheral

visual field representations of V2 and V3. When the illumination around the stimulus was

increased in the second experiment, these peripheral responses to luminance flicker

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disappeared whereas the responses to pattern reversal stimulus behaved similarly in both

conditions.

Figure 9. The perceptual signal changes in left V1 of the same subject as presented in Figure 8. The

lines represent signal increases related patter-rest and luminance-rest contrasts during both dark and

illuminated stimulus periphery. I marked regions-of-interest on V1 surface and calculated the mean

signals within the ROIs. I delineated V1 according to the results of study II and placed the ROIs at

regular intervals.

5.3.3. Discussion

Against a previous proposal (Dechent and Frahm, 2003), our results showed that luminance

responses in PO sulcus originate from the peripheral visual field representations in visual

areas V1, V2, and V3 and not from a separate luminance sensitive functional area.

Moreover, we did not find any cortical region more sensitive to luminance flicker than to

pattern reversal stimuli. Thus a central luminance flicker stimulus cannot be used for

localisation of human V6 with fMRI. We suggest that the luminance responses in peripheral

representations result from intraocular scattering of light even though light reflection may

have a minor contribution to our responses. When the mean luminance difference between

the stimulus and the surround of the stimulus is large, scattered light has high contrast and it

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can stimulate the retina outside the region which optically corresponds to the stimulus. The

intensity of stray light decreases with inverse square relationship with increasing angular

distance from the light source (Vos 2003). Because low contrast gives relatively high signal

in human V1 (Boynton et al., 1996), possibly enhanced by weighting of magnocellular

processing stream in the periphery, scattered luminance contrast could result in spread of the

response with linear amplitude decay as a function of distance detected in our study.

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5.4. Peripheral visual field representation activates during saccades in darkness (Study

IV)

Previous studies have reported eye-movement related activation in human parieto-occipital

region (Bodis-Wollner et al., 1997; Dejardin et al., 1998). Study IV aimed at localisation of

the medial occipital responses during saccades in relation to retinotopic areas and retinotopic

positions. In addition, we aimed to explore the functional role of these responses.

5.4.1. Methods

Study IV comprised two experiments and two supplementary experiments that were all

carried out in complete darkness. In the first experiment eleven subjects made self paced

horizontal 10 degree saccades approximately twice per second. In the second measurement a

subgroup of six subjects made both horizontal and vertical saccades with small and large

amplitude. The second experiment was designed to examine whether the distribution of the

saccade-related responses in retinotopic areas reflected the distribution of the saccade target

in visual field. The supplementary experiments were designed to study whether the saccade-

related responses actually reflected covert shifts of attention or deviation of gaze. In the

supplementary measurements five subjects either made saccades or covertly shifted attention

in darkness and two subjects fixated on the left or on the right. Block design was used in all

measurements. The results of eleven subjects from the first measurement were analysed

individually and on group level to localize significant saccade-related responses in 3D brain.

The 3D analysis included the calculation of correlation coefficients between the signals from

the activated regions. In addition, all data from the subjects attending to two or more of the

experiments were analysed individually on cortical surfaces with BALC, and the responses

were localised in relation to retinotopic areas. I obtained retinotopic maps, which comprised

almost complete delineation of areas V1-V3 and localisation of V6, from study II. I marked

several ROIs on segmented cortex surfaces in V1 and V2 and calculated the mean signal

change during saccade and attention conditions at different eccentricities.

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Figure 10. The responses of one representative subject projected on her right medial occipital

surface. On the right are the responses during different saccade conditions. Upper left corner shows

the retinotopic data from study II (experiment 2). Colours mark different eccentricities, and the area

borders have been defined according to the responses to stimuli in horizontal and vertical meridians.

Lower left corner shows the responses to peripheral upper visual field stimulation from study II

(experiment 1). The circle marks the proposed region of human V6. PO and CA indicate parieto-

occipital and calcarine sulci.

5.4.2 Results

3D analysis in group and individual level showed activation in the well known cortical and

subcortical network for saccades including responses in anterior calcarine sulcus and the

ventral part of parieto-occipital sulcus. Correlations between the signals in different regions

were highly significant (correlation coefficient more than 0.7). Surface analysis revealed that

the occipital saccade-related responses were located mainly in the peripheral visual field

representation of V1, V2, and V3. Figure 10 visualises the results of one representative

subject projected on her segmented cortical surface. All saccade conditions result in

significant signal in the periphery, but the signal-to-noise ratio is stronger for large amplitude

saccades. Calculation of BOLD signal change in different V1 locations confirmed the

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peripheral weighting of the responses (Figure 11). The direction of the saccades, horizontal

vs. vertical, did not affect the magnitude or the distribution of the responses, whereas the

large amplitude saccades resulted in clearer peripheral weighting and the decrease of signal

in central representations. Saccades also activated the dorsal part of parieto-occipital sulcus.

The dorsal PO-responses overlapped with the putative location of human V6 described in

study II. Supplementary experiments 3 and 4 suggested that the responses during saccades in

peripheral representations did not reflect shift of spatial attention or deviation of gaze.

Figure 11. The average signals in representations of different eccentricities in V1. I marked the ROIs

at cortical surface of both hemispheres for all six subjects who attended both experiments 1 and 2. I

calculated the signals related to the effects of interest individually and then averaged the signals

between subjects. A) visualises the mean saccade-related signal increase in experiment 1 overlaid to

the visually evoked signals from experiment 2 of study II. B) shows the mean signal changes (±

S.E.M.) in experiment 2.

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5.4.2. Discussion

Our results showed saccade-related responses in peripheral visual field representations in

retinotopic areas that are at low hierarchical level. Cortical regions in PO sulcus have been

shown to respond during eye movements (Dejardin et al., 1998; Law et al., 1998), and our

results extend the knowledge by localising the responses in the peripheral representations.

We suggest that the responses in early visual areas result from top-down signal because the

experiment conditions did not contain any visual bottom-up stimulus. The simultaneous

activation of dorsal stream areas and the lack of activation in ventral stream suggest the

dorsal stream origin of the top-down signal. Experiment 2 showed that the responses in

periphery did not reflect the retinotopic representation of saccade target, and supplementary

experiments 3 and 4 suggested that the peripheral responses reflected movement processing

and not spatial attention of deviation of gaze. However, the results from experiment 2

suggest that two simultaneous processes may modulate saccade-related BOLD responses.

The peripheral signal increase may be combined with another process that increases signal at

the eccentricity of saccade target.

Monkey studies provided the first direct link from higher order dorsal stream areas to low

level visual areas (Moore and Armstrong, 2003). In humans, TMS stimulation of right

frontal eye field and intraparietal sulcus have resulted in BOLD signal increases in

hierarchically low-level retinotopic areas (Ruff et al., 2006; Ruff et al., 2008; Ruff et al.,

2009). Stimulation of FEF caused signal increases in peripheral visual field representations

and decreases in central representations. Our data agrees with these findings; all saccade

conditions activated FEF region and peripheral V1, V2, and V3, and the large amplitude

saccades decreased signals in central V1, thus proposing that FEF could be a source of our

occipital responses. However, because in monkeys FEF does not directly project to V1

(Schall, 1997) and modulation of V1 response after stimulation of FEF is gated with a

bottom-up signal (Ekstrom et al., 2008) the activation may reach low-level retinotopic areas

via a network of parietal and extrastriate areas and subcortical structures.

In addition to responses in peripheral V1, V2 and V3, saccades also activated dorsal PO-

sulcus in the proposed location of human V6 (Pitzalis et al., 2006). In monkeys V6 is the

central node of dorso-medial stream whose function is to control movements on line

(Rizzolatti and Matelli, 2003). Simultaneous activation of peripheral visual field

representations and dorsal stream areas associate peripheral vision with dorsal stream

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function. Dense connections between peripheral representations of low-level areas and

dorso-medial areas in monkey brain (Lewis and Van Essen, 2000; Gattass et al., 2005)

support the functional association in our human data. The peripheral signal increment is

supposedly related to motor processing reflecting corollary disharge signal of the motor plan

and command (Sommer and Wurtz, 2008) in accordance with the proposal of Sylvester and

Rees (2006). It may also reflect more non-specific resetting signal in line with results of Jack

and co-workers (2006).

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5.5. Topography of attention in the primary visual cortex (Study V)

The aim of Study V was to examine the effect of attention to visual BOLD responses in V1.

Previously spatial attention has been shown to modulate V1 responses (Tootell et al., 1998).

We utilised good spatial accuracy of fMRI as we assumed that the spatial extent of the

BOLD response would provide information about the underlying mechanism of the response

modulation. Recent models and data show that the local spread of activation is most likely

limited to the extent of monosynaptic connections (Angelucci and Bullier, 2003), and thus

the large spread of responses indicates feedback from hierarchically higher cortical areas

where neurons have larger receptive fields.

5.5.1. Methods

We measured nineteen subjects. First we detected a grid of sixty functional ROIs in V1 for

each individual subject with multifocal fMRI. Four target regions in the middle of each

visual field quadrant were selected from the multifocal stimulus regions. During the event

related attention paradigm, the subjects covertly shifted attention to each of the target regions

while fixating at the centre and reported the letter at the target. The changes of fixation mark

guided the task. Eye movements were recorded simultaneously with the fMRI measurement

to confirm the spatial accuracy of the results and to reject epochs contaminated with

saccades.

Multifocal stimulus was black and white checkerboard pattern to obtain as strong response

as possible whereas the visual stimulus for attention task comprised a change in spatial noise

in four target regions and a letter inside the regions. We compared the topographies of

BOLD signal change when the target region was attended and unattended. After the

definition of spatial clusters, we quantified the responses as the percentage of signal change

within each cluster. The statistical significance of the signal change was tested with repeated

measures ANOVA. In addition, we used least square search to fit two 2-D Gaussian models

to responses that gave estimates for the offset from baseline, the amplitude, and the width of

the function. The first set of parameters explained the unattended data and the second set of

parameters described the portion of attended data that remained unexplained by the first set.

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5.5.2. Results

Subjects showed good co-operation and performed the detection task reasonably well. They

detected approximately 75 % of the letters correctly with less than one second response time.

The eye-movement recordings did not show any attention effects. Neither performance of

the subjects nor the number of saccades was different in relation to visual field quadrant.

Figure 12 visualises the average BOLD signal changes in each ROI in attended and

unattended conditions. Spatial attention increased BOLD signal in the lower visual field. In

the upper visual field the difference between attended and unattended condition was not

significant. Attention increased signal on average 20 % in the target region in the lower

visual field whereas the increment was only 3 % in the upper visual field. Furthermore there

was a significant distance effect showing the strongest attention responses at the target and

the decrease of attention responses at the near surround of the target. Fit of Gaussian

function revealed that the attention-related signal increments spread to the target

surroundings with approximately 12.5 mm and 10.2 mm radius for the upper and the lower

visual field, respectively. The sensory responses spread only 3.9 mm and 4.2 mm

Figure 12. The mean signal changes in ROIs of right V1. The green spots mark fovea and red and

the white lines horizontal and vertical meridians, respectively. On the left are the corresponding

regions of the multifocal stimuli. The dark grey regions were targets in the attention experiment. In

the middle are the signal changes related to covert attention elsewhere and covert attention to the

region. The nodes of the grids represent centres of the ROIs. Within each grid the lower row

represents the central visual field and the periphery is represented in the upper rows. On the right are

the signal differences between attended and unattended conditions.

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5.5.3. Discussion

We observed an increment of V1 BOLD signal in response to spatial attention. Moreover the

attentional modulation of the signal spread to target surrounds. The spread of the BOLD

activations would correspond to the local spread of neural response. The spread of

unattended visual response corresponded to 4 mm of cortex, whereas the responses to

attended stimulus extended more than 10 mm of cortex. The observed effect can not result

from the suppression of unattended region because the location of attention target did not

modulate it. It is unlikely that the response spread would result from point-spread of the

BOLD signal because a point spread in 4 tesla magnet is approximately 4 mm (Parkes et al.,

2005).

According to the recent models of visual cortex architecture in monkey brain, the extra

classical surround modulation depends on the feedforward-feedback connections (Angelucci

et al., 2002; Schwabe et al., 2006). In our results the attention-related response spread that

exceeded the sensory spread and apparently horizontal connections more than twofold is

most likely modulated via feedback connections. According to Angelucci and co-workers

(2003) the feedback signal in V1 from V2 and V3 spread approximately 3 mm and 7 mm

radius, respectively. Assuming that the extends of signal spreads are similar with humans,

our results suggest that the attention modulation observed in this study originates from a

functional area with bigger receptive fields, perhaps an area in frontal or parietal lobe

previously associated with the guidance of spatial attention (Corbetta et al., 1993).

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5.6. Motion sensitivity of human V6: A magnetoencephalography study (Study VI)

Study VI was designed to examine human V6 with MEG. In addition, study VI aimed to

examine the visual areas that activate rapidly and are sensitive to motion stimulus. These

areas could respond to monkey dorsal stream areas. The excellent temporal resolution and

the reasonable spatial resolution of MEG enable differentiation of some functional areas

according to latency of activation and localisation of areas.

5.6.1. Methods

A visual stimulus was designed to match with the known sensitivity profile of monkey V6

(Galletti et al., 1996; Galletti et al., 1999b) and the organization of retinotopic areas in

human medial occipital cortex. The stimulus comprised 10-45 degrees of eccentricity in the

upper left visual quadrant. After the appearance it remained stable for 800 ms and then

drifted for another 800 ms. We measured and averaged approximately 200-300 epochs from

ten subjects. We performed the modelling of neuromagnetic signal sources with MNE

software at both individual and group level. We selected regions-of-interests showing

consistent activation across subjects and the location of V5 for further analysis. The

locations of V5 were defined according to previous fMRI V5 localiser experiments with the

same subjects or activation in the known position of human V5. The ROIs were selected

from the first 200 ms after the onset of static and moving stimulus, because, on the basis of

monkey and human studies (Galletti et al., 2001; Vanni et al., 2001), we expected V6

response at relatively short latency. The selected ROIs were relatively large to overcome the

source localisation ambiguity of the MEG. The time behaviours of the neuromagnetic signals

within the selected ROIs were examined and the latencies of the activations were compared.

5.6.2. Results

We localised six consistent activations and thus ROIs in both individual and group average

data. Figure 13 visualises the time-courses of selected activations. Four ROIs were located

on the medial surface over calcarine sulcus, parieto-occipital sulcus, precuneus, and the

region close to the posterior end of cingulate sulcus. On the lateral surface we located ROIs

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in temporo-occipital region and intraparietal sulcus. All of these ROIs were motion sensitive

and the latencies of the activation onsets were significantly longer for the stimulus motion

onset than for the stimulus onset. The region in cingulate gyrus showed only activation for

the motion stimulus. The ROI in parieto-occipital sulcus showed activation for both static

and motion stimuli with peak of activation for the onsets followed by sustained activations

of 300-400 ms. Latencies of the activations after the stimulus onset in calcarine sulcus, PO

sulcus and precuneus were approximately 80 ms. Temporo-occipital region and intraparietal

sulcus were activated significantly later.

Figure 13. The amplitudes and the time courses of neuromagnetic responses in all six ROIs. The

dashed line represents the stimulus onset. The stimulus started to move at 800 ms.

5.6.3. Discussion

We show responses for both moving and static stimuli in the posterior bank of parieto-

occipital sulcus. This region comprises the previously defined location of human V6 (Pitzalis

et al., 2006) and most likely our PO responses correspond to human V6 perhaps with

contribution from V6A. The upper visual field stimulus enabled the differentiation of V6

responses from the neighbouring dorsal V2 and V3 containing lower visual field

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representations. In line with previous monkey data (Galletti et al., 1996; Galletti et al.,

1999b), the peripheral stimulus strongly activated V6, and V6 also contains motion sensitive

neurons. A recent fMRI study confirmed the motion sensitivity of human V6 (Pitzalis et al.,

2010). The responses in V6 region appeared shortly after the stimulus onset in line with

direct magnocellular connections from V1 to V6 in monkey cortex (Galletti et al., 2001).

The response in V6 region was a part of a fast sequence of activations in medial cortex, and

it was followed by activations in precuneus and posterior part of cingulate sulcus. Cavanna

and Trimble (2006) reviewed the properties of precuneal cortex. It has connections with

visual cortex, FEF and SEF in frontal lobe and associative nuclei of thalamus. Posterior

precuneus is related to visually guided movements, attentive tracking, and shifting of

attention between targets whereas anterior precuneus activates during motor imaginery and

retrieval of episodic memory. Posterior cingulate cortex is related to consciousness and

vigilance, and it has high basic metabolic rate. The dorsal part of posterior cingulate cortex

has connections with premotor and parietal areas, and it functions in visuospatial orientation

topokinesia and navigation of the body (Vogt and Laureys, 2005; Vogt et al., 2006).

In monkeys, V6 is a central node of dorso-medial (also called dorso-dorsal) stream

controlling visually guided movements on line (Rizzolatti and Matelli, 2003). In our data the

fast activation sequence in medial cortex from V1 via V6 to precuneal and posterior

cingulate regions suggests that in humans as in monkeys V6 belongs to rapid dorso-medial

stream that conveys visual information towards premotor frontal cortex. In addition to

responses in the medial surface of the brain, we found temporo-occipital activation, most

likely corresponding to human V5, and activation in intraparietal sulcus. Our visual motion -

related IPS activation is in line with previous results showing motion sensitivity in several

human parietal areas (Sunaert et al., 1999). The latency of temporo-occipital activation was

delayed compared to PO-activation. The reason for the latency difference is unknown, but

possible explanations include different intra-areal population dynamics, contribution of other

functional areas to temporo-occipital activation, and differences in signal routing possibly

reflecting different speed of signal processing in dorso-medial and dorso-lateral streams

(Rizzolatti and Matelli, 2003).

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6. General discussion

6.1. Controlling methodological confounds in MEG and fMRI

Instead of measuring the activity of a single neuron, neuroimaging methods detect

electromagnetic or hemodynamic responses which reflect the activation of a large group of

neurons. This results in uncertainties that depend on the chosen method. Study I compared

two different softwares based on different algorithms for estimation the neuromagnetic

inverse problem, equivalent current dipoles and minimum current estimate. Neither method

was able to differentiate close, simultaneous activations. Anatomical and/or physiological

constrains are needed to improve the localisation accuracy even though the association of

responses to visual areas remains ambiguous. Anatomical constrains have been implemented

in the most recent MNE modelling method used in study VI which utilises individual cortex

surface models for inverse calculations. These constraints, however, seem to be insufficient

for linking responses to specific functional areas without a priori knowledge.

Imaging technology impose restrictions to experimental settings because no ferromagnetic

stimulus equipment can be used either in MEG or fMRI measurements and the equipment

must be suited for the shape of the device and the position of the subject. In study II we built

a stimulus presentation system for wide visual field stimulation. The optical aid with + 10

dioptre lenses and Fressnel prisms enabled comfortable fixation at short distance and thus

enabled stimulus presentation also in peripheral visual field. This has been a challenge for

fMRI because of the physical limits of the device interior. Previously Pitzalis and co-workers

(2006) mapped wide visual field by placing the visual stimuli close to the subjects’ eyes.

However, fixation to such a close target is difficult for emmetropic subjects. Our stimulus

system provided relatively good results; even the peripheral visual stimulus provided a good

signal-to-noise ratio and we were able to delineate peripheral parts of areas V1-V3. Future

development should improve resolution and control of peripheral aberrations.

Studies III-V show some potential confounding factors in vision studies. In study III

intraocular scatter resulted in considerable spread of responses which can be misleadingly

interpreted as greater local sensitivity of neurons to given stimulus. In study IV we show

eye-movement related responses, which may confound visual studies with uncontrolled eye

movements, whereas in study V attention modulated V1 responses and showed that subjects

were prone to saccades during attention off the fixation. These results underline the

75

importance of experimental planning. The properties of the stimuli must be carefully

balanced and eye movements and attention controlled for.

6.2. Human visual area V6

Previous electrophysiological studies have delineated and characterised visual area V6 in the

macaque and both neuromagnetic and fMRI studies have aimed to localise its human

homologue. We explored the human visual area V6 in studies II, III, IV and VI. Figure 14

visualises the locations of proposed V6 activations in studies II, IV and VI. In study II, a

separate peripheral upper visual field representation was located in the posterior bank of

parieto-occipital sulcus, anterior and lateral to the peripheral visual field representations of

dorsal V2 and V3. This peripheral upper visual field representation is in the proximity of a

new retinotopic area with complete contralateral visual field representation defined by

Pitzalis and co-workers (2006). In line with their proposal, and on the basis of its location

and responsiveness to peripheral visual field signal, we suggest that this upper visual field

representation belongs to the human homologue of monkey V6. The upper visual field

stimulus enabled the differentiation of V6 from peripheral representations in neighbouring

retinotopic areas.

Figure 14. The locations of human V6 according to studies II, IV and VI. The circle represents the

ROI used in study VI. The spots are located at the maxima of the PO responses in studies II and IV.

PO and CA mark parieto-occipital sulcus and calcarine sulcus.

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The peripheral visual field stimulation is essential for the localisation of V6 with fMRI when

checkerboard pattern reversal stimulus is used. In Study II, a central visual field pattern

stimulus did not evoke measurable responses in this region. Previous studies have used

central luminance flicker stimuli for V6 localisation (Dechent and Frahm, 2003; Stenbacka

et al., 2004). However, the results of Study III revealed that responses to luminance flicker

mainly arise from peripheral V1 and putative V6 activation results from indirect stimulation

of peripheral retina. Recently, Pitzalis and co-workers (2010) confirmed our results by

showing V6 activation during peripheral but not central luminance flicker. However, they

used central coherent motion stimulus to localise human V6.

In contrast to the proposed location of V6 in dorsal PO region, previous studies suggested

human V6 to be located in a more ventral part of PO sulcus (Dechent and Frahm, 2003;

Stiers et al., 2006). Studies II and III showed however that the more ventral part of PO sulcus

belongs to the peripheral V1. In the macaque, V6 feeds signals to area V6A, which functions

in controlling visually guided movements. Study II did not aim to locate a supposedly non-

retinotopic visuomotor human V6A with a visual retinotopic mapping stimulus. Previous

suggestions on the location of human V6A have varied from the ventral part of PO sulcus

(Dechent and Frahm, 2003) to superior parietal lobule (Simon et al., 2002) and parieto-

occipital junction (Prado et al., 2005; Filimon et al., 2009). Location of V6A at PO junction

is plausible assuming that the relative positions of neighbouring visual areas of the macaque

are conserved in the human brain. Furthermore, a lesion in human PO junction results in

similar misreaching symptoms as a lesion in monkey V6A (Battaglini et al., 2003; Karnath

and Perenin, 2005).

In study IV we found saccade-related responses in the posterior bank of the dorsal PO sulcus

at the proposed location of human V6. To my knowledge no saccade-related activity has

been found in macaque V6 even though responses are modulated by the eye position

(Galletti et al., 1996). The discrepancy between our results and the monkey studies may

result from methodological differences. BOLD signal is more sensitive to synaptic activity

than single cell recordings which measure action potentials (Logothetis and Wandell, 2004).

The discrepancy may also reflect differences between species. Alternatively, the saccade-

related responses may partially originate from human V6A, putatively located next to V6.

However, our results did show simultaneous saccade-related responses in peripheral V1-V3

and V6/V6A complex suggestive of connections between these regions and that these areas

77

belong to cortical network that is related to eye movements. In addition, the macaque V6

contains cells that are able to differentiate real stimulus motion from apparent motion

resulting from eye movements (Galletti and Fattori, 2003). These “real motion cells” need

information about eye movements and this may explain saccade-related activations detected

in study IV.

Study VI showed motion related activation in the human V6 region in line with monkey

single cell recordings showing orientation and direction sensitivity in V6 neurons (Galletti et

al., 1996). A recent fMRI study confirmed the sensitivity of human V6 to coherent motion

(Pitzalis et al., 2010). The V6 responses in study VI appeared at a short latency after

stimulus and stimulus motion onsets in line with previous studies (Tzelepi et al., 2001;

Vanni et al., 2001). Accordingly, monkey V6 receives direct and fast magnocellular input

from V1 (Galletti et al., 2001). After a short delay we detected activations also in precuneus

and PCC. Both precuneus and PCC have connections to motor areas in frontal cortex and

play a role in controlling visually guided movements. In monkeys, V6 is a central node of

the rapid dorso-medial stream which controls visually guided movements (Galletti et al.,

2003; Rizzolatti and Matelli, 2003). The fast activation sequence in medial cortex may

reflect dorso-medial stream in human brain that extends from V6 to parietal and frontal

lobes.

6.3. Top-down modulation of V1

Studies IV and V detected clear increases in the BOLD signal in V1 during motor and

cognitive tasks, and in both studies the BOLD increase resulted from top-down signal.

However, different processes may cause the BOLD response modulations in studies IV and

V. In the study IV neural activity increased diffusely in the peripheral representations,

whereas in study V visually evoked responses were locally enhanced and the response

increase may have reflected contextual modulation.

Functional and anatomical evidence has shown that contextual modulation in low-order

visual areas is mediated via feedback signal from intermediate areas (Bullier et al., 2001;

Angelucci and Bullier, 2003; Schwabe et al., 2006). In monkeys, spatial attention spreads

visually evoked neural activation (Ito and Gilbert, 1999) in relatively peripheral (six degrees

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of eccentricity) visual field representations (Roberts et al., 2007). Our results showed that

spatial attention resulted in a larger spread of visually evoked BOLD responses than is

commonly observed after sensory stimulation (Parkes et al., 2005; Tolias et al., 2005; Pihlaja

et al., 2008). Our attended stimulus was located at approximately at the same eccentricity as

the stimulus in study of Roberts et al. (2007), and we detected a BOLD response spread that

was in accordance with the attention-related neural spread observed in their study. The width

of the BOLD response spread suggests a top-down origin for the signal increase that is

similar to the mechanism of attentional enhancement of contextual modulation found in

monkey brain (Ito and Gilbert, 1999).

In study IV, saccadic eye-movements in darkness increased BOLD responses in the

peripheral visual field representations. The lack of bottom-up signal and simultaneous

activation of frontal and parietal dorsal-stream areas suggests that the peripheral activation

originates from a top-down signal from the dorsal-stream areas. Distribution of interareal

connections in macaque brain (Gattass et al., 2005) and previous human TMS studies (Ruff

et al., 2008; Ruff et al., 2009) support the association between peripheral response and dorsal

stream activation. More specifically, the activation of human V6 suggests that the dorso-

medial stream played a role in our results. The diffuse peripheral signal increase in low level

retinotopic areas may thus result from a top-down signal originating from the dorso-medial

stream. This top-down signal is concentrated in the peripheral visual field representations

due to connection anisometry and functionally reflects activation of perception–action

network.

6.4. Peripheral visual field representation in human parieto-occipital sulcus

We examined peripheral vision in studies II, IV, and VI. Stimulation system for wide visual

field developed in study II enabled the mapping of medial occipital retinotopic areas up to 50

degrees of eccentricity. The results showed that areas V1-V3 extended to the anterior part of

calcarine sulcus and the posterior bank of PO sulcus and suggested that human V6 with

relatively large peripheral visual field representation is located in the posterior bank of PO

sulcus. In addition, our results showed that the magnification factor describing the extent of

the visual field representation is similar both in central and peripheral V1.

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Results of studies IV and VI are in accordance with the association between peripheral

vision and dorsal stream. In study IV saccades in darkness activated both frontal and parietal

dorsal stream areas and peripheral visual field representations, suggesting a top-down signal

from higher order dorsal stream areas to periphery of V1 and V2. On the other hand,

activation of dorsal stream areas in occipital and parietal lobe after peripheral stimulation in

study VI, indicated feed-forward connections from peripheral retina to dorsal stream areas.

Previous knowledge from both human and monkey studies suggests that central and

peripheral vision serve partially different functions in the visual system. Study IV

demonstrates functional differences between central and peripheral vision by showing

peripheral eye-movement –related responses in low-order retinotopic visual areas. In

accordance with the study of Sylvester and Rees (2006), our results suggests that the

peripheral activation may reflect movement processing, more specifically corollary

discharge of a motor command (Sommer and Wurtz, 2008).

However, peripheral activation in study IV may also reflect more nonspecific top-down

processes. In monkey V1, enhanced activity and synchrony of neurons before stimulus

presentation facilitates stimulus detection (Supèr et al., 2003), whereas in humans, expecting

a stimulus increases BOLD signal in V1 (Kastner et al., 1999) and task changes can cause

BOLD signal increase in peripheral representations (Jack et al., 2006). Dorsal stream

activation during eye-movements could send a resetting signal to low-order visual areas

before the next trial. Thus the dorsal stream activation may prepare the visual cortex for a

change in the information flow.

Previous studies (Baizer et al., 1991; Falchier et al., 2002; Gattass et al., 2005; Roberts et al.,

2007) have suggested that the peripheral vision may be important for detecting sudden

changes in the environment which may lead to a redirecting of activation flow. According to

Bullier’s (2004b) proposal, the visual signal is first transmitted to the dorsal stream due to

the short latencies of magnocellular neurons. This is followed by feedback signals which

guide subsequent signal processing. Previous studies have provided evidence that the

feedback circuits may extend from the periphery to the center. Central visual field

representations contain information of the object presented in the peripheral visual field

(Williams et al., 2008) and the perception of self-movement due to peripheral motion

stimulus decreases visually evoked response in the central representations (Thilo et al.,

2003). A feedback signal would require a fast activation after the initial peripheral

80

stimulation. Consistent with this, a peripheral stimulus activates the parieto-occipital cortex

with a shorter latency than a central stimulus (Stephen et al., 2002) and stimulation of the

peripheral visual field in study VI resulted in a stream of activation at a short latency in the

dorso-medial stream areas in the human brain. Whether the peripheral vision and dorsal

stream have a role in global to local guidance of visual information processing remains to be

examined in the future studies.

81

7. Conclusions

This thesis investigated the processing of visual information in the human cortex. I measured

neuromagnetic signals and BOLD responses during visual stimulation as well as during

motor and cognitive tasks. Particularly, visual stimuli were designed to activate peripheral

visual field representations and cognitive and motor tasks were designed to activate dorsal

stream areas. The analysis of cortical responses concentrated on visual areas at low and

intermediate levels of the anatomical hierarchy.

Modern imaging methods provide enormous opportunities to neuroscience by enabling

studies of neural function in intact living human brain. This thesis examined some of the

problems that may arise in imaging studies of human visual processing. First I examined the

ambiguity of modelling neuromagnetic signal sources and showed, that even thought

nonsimultaneous sources can be located with good spatial and temporal accuracy a priori

information is needed to differentiate spatially close and temporally overlapping sources. In

addition, I attempted to overcome some of the physical limitations of stimulus design and

developed an optical system that enables stimulation of peripheral visual field in a narrow

magnet bore.

Peripheral vision is still relatively little studied and the second aim of my thesis was to study

peripheral vision in human brain. I mapped the peripheral visual field representation of low-

level retinotopic areas and showed functional differences between central and peripheral

vision. In addition, my results suggest that peripheral representations in low-level areas are

reciprocally connected with dorsal stream areas, especially within the dorso-medial stream.

In the monkey, the dorso-medial stream is involved in the processing of visuomotor actions.

My studies showed a rapid activation sequence and eye-movement related activation in the

medial and dorsal occipital and parietal lobes. This stream of areas could represent the

human dorso-medial stream suitable for a fast feed-forward-feedback analysis.

The third objective of my thesis was to study human V6. The results support previous

findings that human V6 is located anterior to peripheral lower visual field representation of

V2/V3 and is biased towards the peripheral visual field. My work shows that human V6 is

motion sensitive and that it is related to eye-movement processing. In the temporal domain it

is part of a fast sequence of activated areas, occupying the medial surface of occipital and

parietal lobes. This fast sequence may represent a dorso-medial stream in the human brain

82

that conveys information from the visual cortex to the parietal lobe, frontal eye fields and the

premotor cortex and controls visually guided movements.

Because animals are still widely used models for human brain function, the question of

interspecies differences in functional organisation of the visual cortex is fundamental. To

confirm interspecies homologue, the histology, relative position, functional profile,

retinotopy, and connections should be similar across species. My studies showed that this

putative human V6 has similar functional markers as its monkey homologue, such as a bias

towards the peripheral visual field, motion sensitivity and activation at short latency and

connections with the dorso-medial stream areas. These results, in line with previous function

and lesion studies, contribute to evidence that human homologue of monkey V6 is located in

the posterior bank of parieto-occipital sulcus.

The fourth aim of my thesis was to study the effect of top-down modulation in visual

processing. The results showed two examples of response modulation in hierarchically low-

level visual areas. Both of these modulations arose from top-down signals related to

cognitive tasks or motor behaviour. However, these modulations probably represent different

processes. One enhances the signal locally at the attended region and may increase the

stimulus saliency whereas the other is related to more non-specific dorsal stream activation

and may reflect motor processing or resetting signals that prepare visual cortex for change in

the environment.

84

8. Acknowledgements

This work was carried out in Brain Reserch Unit of Low Temperature Laboratory and

Advanced Magnetic Imaging Centre of Helsinki University of Technology (Aalto University

from 1.1.2010) and it was financially supported by the Academy of Finland, the Finnish

Gradute School of Neuroscience, Sigrid Juselius Foundation, Jenny and Antti Wihuri

Foundation, Finnish Medical Foundation and Oskar Öflund Foundation. I am most grateful

for all financial support and to the Director of Low Temperature Laboratory Professor

Mikko Paalanen and the Director of Brain Research Unit Professor Riitta Hari for providing

this excellent working environment.

I would like to thank my instructor Docent Simo Vanni for supervising and guiding my

journey in vision science and for all the hard work he has done. Thanks to past and present

members of the vision group especially to Dr. Linda Henriksson, Lauri Nurminen, and Jaana

Simola for friendship and to Dr. Juha Silvanto for his help with the language issues. I thank

Professor Riitta Hari for kind support and for teaching me to think and talk a little faster. I

would like thank the co-authors of my publications: Professor Claudio Galletti, Dr. Patrizia

Fattori, Professor Riitta Hari, Dr. Lauri Parkkonen, Jaana Simola, Veronika von Pföstl, Dr.

Kimmo Uutela, and Docent Simo Vanni for the opportunity to work with you in fruitful

collaborations. In addition, I wish to thank the preliminary examinators and the members of

the follow-up group Dr. Iiro Jääskeläinen, Docent Jyrki Mäkelä, and Professor Turgut

Tatlisumak for their effort and comments.

I want to express my deepest thanks to Marita Kattelus for friendly company and help with

the measurements and Dr. Antti Tarkiainen for his patience and help with the computer

matters. I thank the administrative forces of the Low Temperature Laboratory for helping me

with many practical issues, and Helge Kainulainen and Markku Korhonen for technical

support.

I spent many many years of my life working for my PhD and I want to thank people who

made these years so funny and memorable. Thank you Maarit Aro, Dr. Gina Caetano, Liisa

Helle, Jaana Hiltunen, Lotta Hirvenkari, Dr. Yevhen Hlushchuk, Annika Hultén, Dr. Jan

Kujala, Miiu Kujala, Hannu Laaksonen, Satu Lamminmäki, Mia Liljeström, Dr. Lauri

Parkkonen, Dr. Tiina Parviainen, Dr. Tommi Raij, Dr. Tuukka Raij, Pavan Ramkumar, Dr.

Hanna Renvall, Dr. Ville Renvall, Dr. Mika Seppä, Dr. Topi Tanskanen, Johanna Vartiainen,

85

Dr. Nuutti Vartiainen, and all others I forgot to mention. Special thanks to Sanna Malinen,

with whom I shared the office and many joys, sorrows, and jokes.

Thanks to my parents Anu and Nisse for support, my late grandmother Telma for her

enthusiasim and interest towards my work, and my dear old friends Mannis, Elina, Veera,

Katri, Tuure, Juri, Jone, Vappu, and Elina for great company. Finally I want to thank my

beloved family, my husband Jaska for his endless love and loyalty and our little Taru for

being so lovely and perfect.

Helsinki, April 2010

Linda Stenbacka

86

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