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Peter A. Bandettini, Ph.D.
Section on Functional Imaging Methods
Laboratory of Brain and Cognitionhttp://fim.nimh.nih.gov
&
Functional MRI Facilityhttp://fmrif.nimh.nih.gov
Functional MRI: Patterns, Fluctuations and a Focus on the
Individual
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Overview of fMRI
Functional Contrast:Blood volumeBlood flow/perfusionBlood oxygenation
Spatial resolution: Typical: 3 mm3
Upper: 0.5 mm3
Temporal resolution:Minimum duration: < 16 msMinimum onset diff: 100 ms to 2 sec
Sensitivity:tSNR = 40/1 to 120/1 fCNR = 1/1 to 6/1
Interpretability issues:Neurovascular coupling, vascular sampling, blood, physiologic noise, motion and other artifacts, etc..
task task
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What fMRI Is Currently Being Used For
Research Applications-map networks involved with specific behavior, stimulus, or performance-characterize changes over time (seconds to years)-determine correlates of behavior (response accuracy, etc…)-characterization of groups or individuals
Clinical Research-clinical population characterization (probe task or resting state)-assessment of recovery and plasticity-attempts to characterize (classify) individuals
Clinical Applications-presurgical mapping (CPT code in place as of Jan, 2007)
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“fMRI” or “functional MRI”
Scopus: Articles or Reviews Published per Year
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Methodology
Interpretation Applications
Technology
Coil arraysHigh field strengthHigh resolutionNovel functional contrast
Functional Connectivity AssessmentMulti-modal integrationPattern classification Real time feedbackTask design (fMRIa…)
Fluctuations DynamicsSpatial patterns
Basic NeuroscienceBehavior correlation/predictionPathology assessment
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Yacoub et al. PNAS 2008
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• Volume (gadolinium)
• BOLD• Perfusion (ASL)• CMRO2
• Volume (VASO)• Neuronal
Currents• Diffusion
coefficient• Temperature
fMRI Contrast
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1.Patterns
2.Fluctuations
3.Individual Focus
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1. Patterns
•Classical fMRI analysis: What’s activated during a task?
•Pattern-information analysis: Does a pattern carry a particular
kind of information?
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Ventral temporal category representations
Object categories are associated with distributed representations in ventral temporal cortex
Haxby et al. 2001
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Yacoub et al. PNAS 2008
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Boynton (2005), News & Views on Kamitani & Tong (2005) and Haynes & Rees (2005)
Methodology
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Kamitani & Tong (2005)
Lower spatial frequency clumping
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Visual Stimuli
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responsepatterns
stimuli...
...
ROI in Brain
dissimilaritymatrix
00
00
00
00
00
00
96
96
compute dissimilarity (1-correlation across space)
96
Dissimilarity MatrixCreation
N. Kriegeskorte, et al
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Human IT(1000 visually most responsive voxels)
Human Early Visual Cortex(1057 visually most responsive voxels)
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average of 4 subjectsfixation-color task316 voxels
average of 2 monkeysfixation task
>600 cells
N. Kriegeskorte, et al (in review)
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Sources of time series fluctuations:
•Blood, brain and CSF pulsation
•Vasomotion
•Breathing cycle (B0 shifts with lung expansion)
•Bulk motion
•Scanner instabilities
•Changes in blood CO2 (changes in breathing)
•Spontaneous neuronal activity
2. Fluctuations
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Rest: seed voxel in motor cortex
Activation: correlation with reference function
B. Biswal et al., MRM, 34:537 (1995)
Resting State Correlations
Methodology
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Goldman, et al (2002), Neuroreport
BOLD correlated with 10 Hz power during “Rest”
Positive
Negative
10 Hz power
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Methodology
Resting state networks identified with ICA
M. DeLuca, C.F. Beckmann, N. De Stefano, P.M. Matthews, S.M. Smith, fMRI resting statenetworks define distinct modes of long-distanceinteractions in the human brain. NeuroImage, 29, 1359-1367
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Decoding human brain activity duringreal-world experiences
Hugo J. Spiers and Eleanor A. MaguireTICS, 2007
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2. Focus on the Individual
High sensitivity and resolution lends itself to individual assessment with fMRI
• Diagnosis of disorder• Assessment of therapy• Cognitive strategy• Real time fMRI for therapy
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SC NL KB
JL HG EE
CC BK BB
Individual activations from the left hemisphere of the 9 subjects
group Individual Differences in Brain Activations During Episodic
RetrievalMiller et al., 2002
Courtesy, Mike Miler, UC Santa Barbara and Jack Van Horn, fMRI Data Center, Dartmouth University
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group Individual Differences in Brain Activations During Episodic
RetrievalMiller et al., 2002
KBNLSC
HGJL
BBBKCC
EE
Individual activations from the right hemisphere of the 9 subjects
Courtesy, Mike Miler, UC Santa Barbara and Jack Van Horn, fMRI Data Center, Dartmouth University
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Group Analysis of Episodic Retrieval
Subject SC
Subject SC 6 months later
These individual patterns of activations are stable over time
Courtesy, Mike Miler, UC Santa Barbara and Jack Van Horn, fMRI Data Center, Dartmouth University
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Emerging Technology
High resolution fMRIPattern effect processing / classificationFluctuation characterizationMultimodal integration
How to best process, compare, and use these data?
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Challenges and Opportunities in Non-Invasive Human Brain Imaging: From Molecules to Circuits
1. Individual assessment using fMRI- Resting state fluctuations, calibrated fMRI, or pattern effect mapping- Impact would be on diagnosis, assessment of therapy, decisions on therapy, and
non-clinical.- A major obstacle is the wide variation in the data. - Proposed Solution: Higher resolution, signal to noise, calibration methods,
classification methods, and multi-modal integration2. High field, high resolution, pattern effect assessment, resting state, and multi-
modal integration are just emerging. - Combination of high sensitivity, high resolution, better processing, and better
multi-modal integration are emerging- Impact: The information obtained will be significantly more than “blobs of
activation” or parametric changes with task modulation- Obstacle: Not enough methods focused grants or competitions- Proposed solution: More grants (or competitions) to develop this methodology.