fmri in namic

NA-MICNational Alliance for Medical Image Computing http://na-mic.org

fMRI in NAMIC

Facilitator: Polina Golland

Presenters: Jim Fallon and Andy Saykin

National Alliance for Medical Image Computing http://na-mic.org

fMRI and NAMIC

• NAMIC Core 1 projects focus on structure– Anatomical– DTI

• Many of us are interested in fMRI– Core 1: analysis– Core 3: tool for study of the disease

• Potential for new collaborations


fMRI Status Update

• Basic analysis functions in ITK (GE/Kitware)• User Interface in Slicer (BWH)

• Advanced detection/analysis– MIT/BWH – anatomically guided fMRI detection– UC Irvine – localization of activation peaks– Other Core 1 groups

• Integrated visualization of anatomy & function


Our goals

• Not to replicate existing analysis tools

• To identify problems that are – important to Core 3– interesting to Core 1

• Use NAMIC to create new collaborations


Our findings

• Some of the “problems” have already been “solved”

• Many items on the “wish list” are in reach– Especially with help of Core 2

• There are some really hard and interesting problems


No Smoothing Gaussian MRF

Anatomically guided fMRI detection

With anatomy

Wanmei Ou, MIT


Quality control for fMRI• Spatiotemporal browser designed for quality control

during preprocessing of single subject time series data or contrasts– Easy loading of raw scan formats– Easy navigation through time & space– Quantify signal to noise– Identify temporal spikes optional smoothing– Identify spatial distortion

• B0 field map and phantom optional adjustment

• Also feature to identify outliers in group data

• Tom Nichols at U. Michigan has something like this tool in Matlab. Core 2?


Managing fMRI findings• fMRI activation cluster utility

– Need to create functional ROI (fROI) label maps for use in subsequent analyses

– Assuming user has created a thresholded activation map

• Ability to choose activation clusters to include in the label map

• User should be able to choose label values and provide a name in a text field for each cluster

– Extract data from these clusters• Individual time series or for group data

• Core 2?


Outline of this discussion

• Presentations (15-20min)– Jim Fallon – Andy Saykin

• Questions (15-20min)– Ask the speakers more detailed questions

• Discussion/brainstorming on how we might solve these problems


Major Themes

• Integration with anatomical and DTI:– Anatomically accurate and precise integration of all

modalities, including fMRI, DTI, into a single analysis framework

• Characterizing fMRI activation areas:– Invent new ways to describe active areas and how

they change from an experiment to an experiment. This ties into population analysis of activation.


Jim Fallon

National Alliance for Medical Image Computing http://na-mic.org Fallon

Occip

Heschl’s

Frontal pole

7

ITG

STG

CB

DMPFC

DLPFC

VMPFC

LOF

IFG

Critical samples in BOLD


Anterior ViewAnterior-Inferior View

Variability in population


DLPFCBA 46

BA 7

SLF-2


McCarthy, 2004


beta map fBIRN phantom sensorimotor task

Activation patterns mixture model (Kim, et al, 2005)

Thresholded voxels (p<0.05)

Add 20% “gutter region” around each strictly defined area (e.g., DLPFC) to capture “rogue” functional activations in different subject and patientpopulations…”DLPC PLUS”


Andy Saykin


fMRI Specific Applications• Tool for assessment of test-retest reproducibility

of fMRI experiments– Simple approach would be calculating intraclass correlation

coefficients for voxels and ROIs• Useful but limited value because of fluctuations in exact peak and

distribution of activation foci

– A more sophisticated approach would include identification and extraction of key spatiotemporal features

• Prior knowledge could be used to inform regarding importance• Reproducible features could be quantified

• A related tool would provide an analysis of longitudinal stability and change– Consider reliable change index approach applied to activation

maps


Multimodality Integration

• Registration of fMRI, DTI and anatomic MR– individual and group data

• Easy mapping between atlas space and native scan space– Permit warping from native space to atlas space or

vice versa

• Automated parcellation of cortical surface and subcortical gray matter structures– Generate label maps– Extract quantitative data from labeled ROIs or fROIs

• e.g. examine atrophy within functionally derived ROI


Multimodality Integration - II

• Integrate measures of connectivity– Voxel by voxel and labeled ROI measures of

connectivity within single subject time series• Resting & Task-induced connectivity

• Changes in strength of connectivity over time

– important for learning and habituation experiments

– Relation to existing work • PLS, SEM, DCM, POI, other?

– Visualization tool to display strength of connectivity including functional and neuroanatomic (tractography)


Questions and Discussion


Major Themes

• Integration with anatomical and DTI:– Anatomically accurate and precise integration of all

modalities, including fMRI, DTI, into a single analysis framework

• Characterizing fMRI activation areas:– Invent new ways to describe active areas and how

they change from an experiment to an experiment. This ties into population analysis of activation.

fmri in namic

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