Bonaiuto, [email protected]
Synthetic Brain Imaging: A Computational Interface Between Electrophysiology and
Neuroimaging
James Bonaiuto (work with Michael Arbib, USC)
Andersen Laboratory
California Institute of Technology
Bonaiuto, [email protected]
The Challenge of Data Integration and Interpretation
Two of the major problems facing cognitive neuroscientists are data interpretation and comparison across modalities
Neuroimaging studies are typically designed to test some conceptual model of the interactions between the brain regions involved in a task.
The results are usually evaluated using an ad-hoc verbal analysis and compared to neurophysiological data.
?≈
Rizzolatti et al (1995) Buccino et al (2004)
Bonaiuto, [email protected]
From Neural Activity to BOLD Response
Neural activity Signalling Vascular response
Vascular tone (reactivity)Autoregulation
Metabolic signalling
BOLD signal
glia
arteriole
venule
B0 field
Synaptic signalling
Blood flow,oxygenationand volume
BOLD signal ≠ Neuron firing
(figure byRichard Wise)
Bonaiuto, [email protected]
Synthetic Brain Imaging
Synthetic Brain Imaging was developed to address the disconnect between experimental modalities
Computational models based on neurophysiological data are used to generate simulated neuroimaging signals: regional cerebral blood flow (rCBF) blood oxygen level-dependent (BOLD) responses
This technique has been used to successfully predict: A projection from the PFC to the anterior intraparietal region AIP (Arbib, Fagg & Grafton, 2002)
Populations of sound contour-selective cells in secondary auditory cortex (Husain et al., 2004)
Bonaiuto, [email protected]
It depends on the data the model is intended to address Early synthetic brain imaging approaches used firing rate models (Arbib et al., 1995; Horwitz et al,. 1998)
We require the simplest possible model that can produce a wide range of firing patterns
What is the Appropriate Neural Model?
(Izhikevich, 2004)
Bonaiuto, [email protected]
Neurophysiology and fMRI
Under some conditions spiking activity and CBF dissociate
In general, local field potential (LFP) is a better predictor of BOLD than spiking activity
LFP most directly reflects synaptic rather than spiking activity
We therefore require a model with realistic synaptic activity
(Goense & Logothetis, 2008)
(Lauritzen et al.,
2003)
Bonaiuto, [email protected]
We use the sum of synaptic conductances as the measure of neural activity
Several mechanisms coexist to regulate blood flow neuron-astrocyte pathway (Koehler et al., 2006) vasomotor GABAergic interneurons (Cauli et al., 2004) nitric oxide diffusion (Metea & Newman, 2006)
We use a generic blood flow-inducing signal that subsumes neurogenic and diffusive components (Friston et al., 2000) A linear function of neural activity with signal decay and autoregulatory feedback from blood flow
Neurovascular Coupling
AMPA NMDA GABAa GABAbu t g t g t g t g t
0
0
1in
b f
u t u fdb b
dt u
=gain parameteru0=baseline synaptic activityb=decay time constantfin=blood flowf=feedback time constant
Bonaiuto, [email protected]
Vascular Signal Generation: Balloon Model
We use Friston & Buxton’s balloon model to simulate the vascular response to neural activity and generate simulated PET or fMRI signals
Bonaiuto, [email protected]
A New Synthetic Brain Imaging Model
Izhikevich neurons with realistic synaptic dynamics and noise
Total synaptic conductance for all synapses in a voxel used to generate to a generic blood flow-inducing signal
Use normalized blood flow-inducing signal as input to the Balloon model
Bonaiuto, [email protected]
Basic Network Architecture
Pyramidal Neuron Firing Rate (Hz)
{Response latency
– Populations of pyramidal neurons and inhibitory interneurons– Center-surround connectivity implements winner-take-all dynamic– Depending on the input, there can be a considerable latency
before the network settles on a stable winner
Membrane Potential (mV)
0
100
1 100Neuron
0
2.0
Tim
e (s
)
-80
30
0
-50
Bonaiuto, [email protected]
Example 1: Random Dot Motion Discrimination
The random dot motion
direction discrimination task is
commonly used to study
perceptual decision-making Task: saccade in the net movement direction of a field of randomly moving dots Stimulus coherence: percentage of dots moving in the same direction
This task is useful in demonstrating the power of synthetic brain imaging because A well-defined network of brain regions is involved (MT, LIP, FEF) There exists neural recording, microstimulation, behavioral and imaging data using the task in humans and non-human primates
Bonaiuto, [email protected]
Model Derivation
We used three connected WTA networks to simulate the MT-LIP-FEF network
Neural parameters were set using values from experimental data
Network parameters were set using a genetic algorithm that used the model’s
fit to neural
recording and
behavioral data
as the fitness
function
(Gold & Shadlen, 2007)
Bonaiuto, [email protected]
Results: Neural Activity
Stimulus Coherence3.2% 12.8% 51.2%
Pyramidal neurons in LIP converge on a population code centered on the chosen saccade direction
Response time was interpreted as the time taken for max firing rate in FEF to reach 100Hz (Hanes & Schall, 1996)
Bonaiuto, [email protected]
Results: Behavioral Measures
Response time and accuracy were fit to the same psychometric and chronometric functions used to analyze human data
Fitted parameters were within the range of human performance
Model Performance Human Behavioral Data
(Palmer et al., 2005)
Bonaiuto, [email protected]
Results: Microstimulation Simulations
Simulation Results Monkey Data
Control
MT Stim
LIP Stim
MT microstimulation biased decision process and reaction time LIP microstimulation had the same effect, but to a lesser extent The same results are found in monkey microstimulation experiments
(Hanks et al., 2006)
Bonaiuto, [email protected]
Results: Synthetic fMRI
MTLIPFEF
Synthetic fMRI Human fMRI
(Rees, Friston & Koch 2000)
The model replicated human fMRI data that only found a positive correlation between BOLD response and stimulus coherence in MT
In the model this is because intraregional processing (WTA) dominates LIP and FEF activity and is roughly the same at each coherence level
Bonaiuto, [email protected]
Example 1: Summary
A basic neural microcircuit was connected in a network based on anatomical considerations
Neural parameters were set using values from experimental data. Network parameters were set using a genetic algorithm that fit the firing rate and model behavior to neural recording and psychophysical data
The model was validated by replicating microstimulation and fMRI studies
Bonaiuto, [email protected]
Example 2: Reach Target Selection
We developed a model of the parieto-frontal reach circuit with each region based on macaque neurophysiological data and interregional connections constrained by tract-tracing studies
The output of the dorsal premotor region was decoded and used to control a simulated arm/hand
Bonaiuto, [email protected]
Synthetic PET: Comparison with Experimental Data
% change in rCBF in F2, F6, V6a, and PFC matches published neuroimaging data (Savaki et al., 1997, left)
Differences in activity in V4 highlight a PFC→V4 connection overlooked in model construction
Updated model activity closely matches published data (right)
0.0
0.8
0.4
Sig
nal %
Cha
nge
F6F2 LIP V6A PFC V4 F6F2 LIP V6A PFC V4
Bonaiuto, [email protected]
Example 2: Summary
A virtuous cycle of models and experiments
We used available connectivity and neural recording data to develop a model of reach target selection
Synthetic brain imaging was used to compare the global model activity to metabolic signals in the monkey brain
This comparison was used to update the model to include feedback connections from PFC to V4
Bonaiuto, [email protected]
Peigneux et al (2004) Looked at familiar vs novel gesture imitation Found that brain areas associated with gesture recognition and production were not more active for familiar vs novel imitation
Example 3: Imaging of a Cognitive Model of Apraxia
Red: visuo-gestural codingGreen: input praxiconBlue: output praxicon
Bonaiuto, [email protected]
Synthetic PET on a Cognitive Model of Apraxia
Bonaiuto, [email protected]
Example 3: Summary
A model of gesture recognition and imitation was used to generate PET predictions that conflict with those generated in an ad-hoc manner using a conceptual model
The model is simple, but nonetheless more complex than the conceptual one used by experimentalists
Data necessary to constrain the model do not exist – the model demonstrates one possibility
In this situation multiple competing models should be developed and used to determine an experiment that could disambiguate them
Bonaiuto, [email protected]
Summary
Synthetic imaging can bridge the gap between electrophysiology and neuroimaging
We gave three examples using this technique to Validate a neural model Refine a neural model Offer a novel interpretation of experimental data
Future studies could use this technique to generate predictors for fMRI analysis
Bonaiuto, [email protected]
Arbib LabRob Schuler
Itti LabDavid BergFarhan Baluch
FundingNSFSloan Foundation
Thank you
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