2006 Scientific Report fmri Summary

Overview

A fundamental challenge for the DBPs is defining circuitry the subserves major cognitive operations that are dysfunctional in schizophrenia and other neuropsychiatric disorders. Structural MRI can define the location, volume and shape of key nodes in networks. fMRI defines activation foci that an individual engages while performing a cognitive task. DTI can define the anatomic tracts that constitute the circuitry of interest. Methods are needed for data integration, visualization and quantitiative measurment across these modalities.

• NAMIC teams have made progress in several key areas related to fMRI during the prior funding period including fMRI software implementation, development of novel analytic methods relating functional, structural and diffusion brain imaging data, and preliminary applications in several collaborative projects.

fMRI Algorithm and Software Development

fMRI Statistics Software Infrastructure

• The GE team is working to provide ITK and Slicer based tools for processing fMRI data. Currently, the scope of this effort focuses on the data processing that occurs after the alignment of the time sequence acquisition. As such, the effort is centered on the statistical analysis of fMRI and includes infrastructure for data representation, massively univariate processing, hypothesis testing and segmentation.

Implementation of fMRI analysis software in Slicer 2

• The Slicer fMRIEngine software module team includes Wendy Plesniak, Haiying Liu, Carsten Richter, Steven Pieper, Sandy Wells and Cindy Wible (Harvard). This module has been released in Slicer 2.6. Key features include loading of pre-processed fMRIData, specification of the stimulus schedule for blocked, event-related and mixed designs, signal modeling, contrast definition, and GLM-based analysis of results. Output includes statistics on activation-based regions of interest, and timecourse plotting on individual voxels and on ROIs. In Slicer's visualization environment, statistical parametric maps of brain activation can be superimposed on high resolution anatomical scans of the same subject, and this data can also be combined with DTI and other data on the same subject. The modular design permits extension to alternate algorithms for activation detection. An in-depth tutorial for use of this module is being refined and will be tested in our local community and will subsequently be made available to the broader user community. Support for Ising Priors is also being added to the module currently.

Spatial Regularization for fMRI Detection

• An important NAMIC theme is the integration of structural and functional imaging data to enhance the information yield of each method. We recognized that detection of brain activation in fMRI studies could be enhanced by incorporating spatial priors. A team from MIT (Polina Golland, Wanmei Ou) and Harvard (Steve Pieper, Sandy Wells, Wendy Plesniak, Carsten Richter) developed a method that employs Markov Random Fields (MRF) as spatial smoothing priors to address the low signal-to-noise ratio of BOLD fMRI signals. This approach developed by NAMIC extends the MRF prior to include anatomical information. The anatomical prior, in the form of a segmented MRI scan, biases the activation detection towards the gray matter and inhibits smoothing of the activation maps across tissue boundaries. This has the potential to significantly increase statistical power in fMRI experiments. We have validated the method on a set of fMRI scans and are currently working on implementation of the detection algorithm in Slicer. Our plans include releasing the code into the ITK library to make the method available to a broader community.

Path of interest (POI) analysis for integrating fMRI and DTI

• To test study hypotheses regarding abnomal cognitive circuitries, we identified the need for a tool to find optimal and alternative paths between regions of interest in a tensor image. A prototype version of the POI tool was developed by a team from Harvard (Josh Snyder, David Tuch) and Dartmouth (Andrew Saykin, John West) with DTI coordinate system support from Harvard (Gordon Kindlmann, Raul San Jose, Steve Pieper). The POI tool accepts fMRI activation foci or structural ROIs as input to drive the identification of POIs. The output is eventually to include probability density images, path statistics and extracted quantitiative information regarding identified paths. Output can now be visualized in 3D Slicer and future plans include incorporating the POI algorithms into the Slicer DTI analysis module and ROI drawing tools. The prototype POI tool was tested on a 1.5T 12 diffusion direction DTI dataset from Dartmouth. We are now planning to test this tool on new 32 diffusion direction 3.0T data from Dartmouth.

Conformal Flattening for fMRI Visualization

• This project is directed towards development of new flattening methods for better visualizing neural activity from fMRI scans. Conformal mappings are used to map the cortical surface onto a sphere in an angle preserving manner. To date, the team from Georgia Tech and Harvard has developed code for conformal flattening that has been incorporated into Slicer.

fMRI project links

1. Neural Substrates of Working Memory in Schizophrenia: A Parametric 3-Back Study (Dartmouth, Harvard, UCI?)
2. Brain Activation during a Continuous Verbal Encoding and Recognition Task in Schizophrenia (Dartmouth, Harvard)
3. Fronto-Temporal Connectivity in Schizophrenia during Semantic Memory (Dartmouth, Harvard)
4. Imaging Phenotypes in Schizophrenics and Controls (UCI, Toronto)
5. Attentional Circuits in Schizophrenia as revealed by fMRI and PET (UCI)

Key Investigators

• BWH: Martha Shenton, Marek Kubicki, Wendy Plesniak, Sandy Wells, Carsten Richter, Haiying Liu, Cindy Wible, Ron Kikinis
• Dartmouth: Andrew Saykin, Robert Roth, John West, Laura Flashman, Thomas McAllister, Nancy Koven, J.C. Pendergrass
• GE: Jim Miller
• Georgia Tech: Steven Haker, Allen Tannenbaum
• Harvard: Dave Tuch, Josh Snyder, Gordon Kindlmann, Raul San Jose
• Isomics: Steve Pieper, H Liu
• Kitware: Karthik Krishnan
• MIT: Polina Golland, Wanmei Ou
• Toronto: James Kennedy
• UCI: Steven Potkin, James Fallon, Jessica Turner

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