Large, High-Dimensional Data Sets in Functional Neuroimaging
Transcript of Large, High-Dimensional Data Sets in Functional Neuroimaging
©2012 Mark Cohen, all rights reserved www.brainmapping.org
Large, High-Dimensional Data Sets in Functional Neuroimaging
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Goals of Functional Neuroimaging■ Identify Regional Specializations of the Brain
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Goals of Functional Neuroimaging■ Identify Regional Specializations of the Brain
❏ Basic Science Questions❏ Medical or Surgical Intervention
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Language
Topology
Velocity
Location
Color
Identity
Direction
Texture
Vision
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Goals of Functional Neuroimaging■ Identify Regional Specializations of the Brain■ Understand Network Connectivity
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Physical Temporal Information
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Goals of Functional Neuroimaging■ Identify Regional Specializations of the Brain■ Understand Network Connectivity
■ Understand Multiple Levels of Organization
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Levels of Understanding
fMRI-EEG
Fiber Tracingregional connectivity
fMRI - functional nuclei or processing centers
EEG & Autoradiographycell assemblies
Multi-unit Recordinglocal circuits: columns, retina…
Single Unit Electrophysiologyaction potentials, chemomodulation
Crystallography, Chromatography (etc…)transmitters, ion channels, membrane proteins
fMRI-EPhys
The Big Problem isn’t sparse...
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Goals of Functional Neuroimaging■ Identify Regional Specializations of the Brain■ Understand Network Connectivity
■ Understand Multiple Levels of Organization■ Understand the Structure of Human Cognition
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Neuroimaging Tools
■ Positron Emission Tomography (PET)■ functional MRI (fMRI)
■ Electro-encephalography (EEG)■ Magneto-encephalography (MEG)
■ Near Infrared Spectroscopy (NIRS)■ …
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Positron Emission Tomography (PET)
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outside inside
Note: Recon similar to CT
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Positron Emission Tomography (PET)
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Phelps, Mazziotta, et al.
–
=
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Tractography
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40 µm
up to 1m
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fMRI
explores intensity variations in MR signal
intensity variations reflect venous [O2]
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Traditional MRI Analysis - Model Driven
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Task Timing
Observed Signals
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Traditional MRI Analysis - Model Driven
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HemodynamicResponse Model
z=5
z=1.5
Signal Model
Task Model
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Model-Free MRI Analysis
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Independent Components Analysis (ICA)
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http://www.fmrib.ox.ac.uk/fslcourse/lectures/melodic.pdf
Spatial ICA for fMRI
# ICs
Time
# ICs Location (space)
IC Spatial Maps
Time
Location (space)
Scan #k
fMRI ImageData
impose spatialindepedencedata are decomposed into a set of
spatially-independent maps and a set of time courses.
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ICA Exposes Functional Networks
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EEG: Hans Berger
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EEG vs. Magnetoencephalogaphy (MEG)
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EEG vs. Magnetoencephalogaphy (MEG)
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EEG
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Creutzfeld Jacob (prion) disease Left parieto-posterior temporal spikes during drowsiness
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EEG
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with Steve Sands and Massoud Akhtari
Somatosensory Evoked Potentials
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EEG/MEG■ EEG/MEG is generally difficult to interpret■ Physicians frequently fail to detect abnormalities
■ Electrical features are ambiguous■ Estimation of electrical sources from scalp Voltage is
underconstrained
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Near Infrared Spectroscopy (NIRS)
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Neuroimaging Tools - Data Size/subject
■ Positron Emission Tomography (PET)❏ 128 x 128 x 12 ≈ 2E5 samples
■ functional MRI (fMRI)❏ 64 x 64 x 20 x 200 ≈ 1.6E7 samples, but...
■ Electro-encephalography (EEG)
■ & Magneto-encephalography (MEG)❏ 256 x 250samples/s x 600s ≈ 3.8E7 samples
■ Near Infrared Spectroscopy (NIRS)❏ 32 x 250 samples/s x 600s ≈ 4.8E6 samples
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Brain Reading
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Postulate: All interesting behavioral, affective, mental or cognitive states are the expression of, or reflected in, neural “activity”
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Machine Learning in fMRI
Haxby, et al., Science 293:2426
Resulting maps are difficult to interpret.
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Optimal Basis Selection
■ Efficient Machine Learning Dimensions Are:❏ Independent Measures (foot size + shoe size adds little)❏ Sparse - Ideally the minimum number needed to
categorize the data❏ Too Many Dimensions Results in Errors!
■ For Scientific Applications Dimensions Ideally Reflect Real Sample Properties and are Explanatory
■ What are the Right Dimensions for Neuroscience?
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A (perhaps naïve) Model of Cognition
■ Multiple Networks are Concurrently Active■ Many Such Networks are Common Across People
■ Current Cognitive State Reflects the Contributions of all Currently Active Networks
■ Perhaps:❏ Current Cognitive State is the sum of Active Network Activity
CS = α1N1 +α2N2 +α 3N3 +…+α j N j .Where:
CS is the current cognitive stateNk is one among many networksαk is the “activity” level of the corresponding network CS and α are functions of time
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ICA Exposes Functional Networks
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IC Dictionary Elements
“Categorization and Generation of group-wide independent components in fMRI using clustering.” A Anderson1, J Bramen, A Lenartowicz, P Douglas, C Culbertson, A Brody, MS Cohen. OHBM 2010
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IC’s as Classifier Dimensions
Network 3
Cognitive State Instance Network 1
Network 2
CS = α1N1 +α2N2 +α 3N3 +…+α j N j .
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“I believe that the infidels must die.”
Belief
Why should belief and disbelief gate emotion and behavior in this way?
Why should uncertainty not do so?
“I believe a sandwich would be tasty now”
Beliefs are actions in potentia
Sam Harris
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“I believe that the infidels must die.”
Belief
Beliefs are actions in potentia
Sam Harris
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Operationalized Belief■ Autobiographical
You own a toaster oven.
■ Ethical
It is good to help people in need.
■ Factual
Sugar is sweet.
■ Geographical
Nevada borders California.
■ Mathematical
(45/3) + 25 = 40
■ Religious
Jesus was actually born of a virgin.
■ Semantic
“gigantic” means “huge”
S Harris, SA Sheth and MS Cohen, Annals of Neurology, 63(2): p. 141-147. 2008
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Belief Detector
PK Douglas, S Harris, A Yuille and MS Cohen, “Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.” NeuroImage, 56(2): p. 544-553. 2011.
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Disbelief
Channel 8 Channel 200Channel 19 Channel 145Belief
Disbelief
Belief
Diagnostic Non-informative
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Wavelet Spectrogram: Optimal Sampling Schedule
■ Spectrogram Power Sampled at 20 ms Intervals■ Highly Ranked Channels Determine Feature Time Points■ Optimal “disbelief” Time Point 500 ms After Belief
❏ Consistent with Behavioral Data
time
frequency
time time time
frequency
Belief
Disbelief
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1 sec
Pamela Douglas, Edward LauAgatha Lenartowicz, Wei Li
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Concurrent EEG and fMRI
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1 sec
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Observable signals
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Activity Observablewith fMRI
Activity Observablewith EEG
fMRI Signal
EEG Signal
Filter Processe.g. hrf
Noise Noise
Noise
CommonNeural
Substrate
Noise
Filter Processe.g. coherentactivity only
based on a figure by Dan Ruan
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Multimodal Imaging
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α
β
■ The observed EEG is the linear sum of the underlying electrical activity:❏ Synchronous Activity (SA)❏Asynchronous Activity (AA)
■ The magnitude signal, |EEG|, is:
■ fMRI signal is (probably) a function of the sum:
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EEG & fMRI Signal Strength
EEG = k SA + AA( )
fMRI = f SA+AA( ).
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EEG-fMRI Coupling - A Variety of Mechanisms?
Xia HongjingOrganization for Human Brain Mapping 2012
5s
5s
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Tomographic EEG Projection
Wei Li, Edward LauPamela Douglas, Agatha Lenartowicz,
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What is Sparse in Functional Neuroimaging?
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What is Sparse in Functional Neuroimaging?■ Pixel-level analysis■ Axonal (fiber) connections
■ Number of Brain States■ Blood flow responses w.r.t. driving functions
■ Resolvable Electrical Sources■ Number of “meaningful” networks
■ Shared Sources in Multimodal data
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