Morphometry BIRN

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The LONI Pipeline Processing Environment (UCLA) applies a sequence of processes to identify the lobar regions of the brain for each subject and align the BWH Probabilistic Atlas to each subject space. Briefly, subject PD volumes are stripped of non-brain tissue using the logic of the Brain Surface Extractor (Shattuck et al., 2001) and the Brain Extration Tool (Smith, 2002), ensuring no extraneous tissue is left in the brain volumes. Separate 252 parameter 5th order polynomial warping fields are used to align each subject brain to the BWH Probabilistic Atlas and a common atlas space. The BWH Probabilistic Atlas is resampled to each individual subject for anatomical segmentations to be performed by the BWH EM algorithm. The common atlas contains a lobar labeling from a subgroup of subjects. These labels are resampled to each individual subject brain to identify the corresponding cortical lobes (e.g. frontal, parietal, and temporal). The common atlas space was generated previously from a similar, but separate, set of data from the Duke Archives. It was derived using a tensor averaging (Woods, 2003) of the individual subject's linear alignment transformations to each other. This common space is a linear least distant space from the contributing subjects in which a non-linear 252 parameter warp is used to carefully align gross anatomy within the space (Rex et al., 2003). This space is ideal for identifying gross anatomic structures, such as the lobes of the brain.

UCLAAIR Registration

and Lobar Analysis

Multi-site Imaging Research in the Analysis of Depression (MIRIAD)

Duke University (NIRL): M. Payne, J. MacFall, B. Boyd, R. Krishnan, D. McQuoid, E. FlintBrigham and Women's Hospital (SPL): S. Pieper, K. Pohl, N. Weisenfeld, J. Sacks, S. Warfield, M. Shenton, R. Kikinis

UCLA (LONI): D. Rex, A. Toga; UCSD (BIRN-CC): P. Papadopoulos, J. Grethe; The Brain Morphometry BIRN: www.nbirn.net

Introduction:

Conclusion

The Biomedical Informatics Research Network (BIRN) project is a nationwide effort by NIH supported research sites to merge data grid and computational cyberinfrastructure into the workflows of biomedical research. The Morphometry BIRN is building on the BIRN infrastructure by integrating data and analysis methodology drawn from the participating sites. The Multi-site Imaging Research in the Analysis of Depression (MIRIAD) project, diagrammed below, applies sophisticated image processing of a dataset of MRI scans of a longitudinal study of elderly subjects. The subjects include patients with clinical depression and age-matched controls. Some of these depression patients go on to develop dementia.

The BIRN MIRIAD Project demonstrates the benefits of pooling algorithms and analysis techniques from multiple leading research centers. This collaboration allows researchers to extract clinically-relevant morphometric data from longitudinal imaging studies and explore the structural basis for debilitating neuropsychiatric diseases. This multi-site collaboration enabled a more detailed analysis than had previously been possible on this retrospective data set. The BIRN infrastructure provides a medium through which a geographically distributed group of researchers are able to work jointly on projects to analyze, visualize, and interpret large imaging data sets in the context of a rich clinical history.

Fifty depressed subjects and 50 normal controls were recruited and scanned with both Proton Density and T2 weighted MRI protocols. Various clinical assessments were administered to establish subjects' scores on depression and other ratings and scales. Diagnoses were assigned based on the DSM-IV diagnostic manual. The scanning protocol was repeated every two years and the clinical assessments every year. In order to satisfy HIPAA requirements for the protection of subject privacy, all requisite identifiers were removed from the image header, such as medical record number, name, date of birth etc. The MRN or equivalent identifier was replaced by the BIRN ID, which is a uniquely defined 8 digit random number used to track the subject's data while preserving anonymity. Subject data was shared following an anonymization protocol approved by the Duke Institutional Review Board.

DukeArchives

Goals: To integrate brain morphometry tools from multiple sites (BWH and UCLA), analyze MRI structural data from one site (Duke), and measure volume changes in cortical and subcortical gray matter that correlate with various clinical measures in depression and age-matched controls.

MIRIAD Data Flow1 Retrospective data upload from Duke2 Lobar analysis and Registration of Atlas to Subjects3 Anatomical Segmentation4 Comparison to Clinical History

DukeClinical Analysis

High PerformanceComputing

Statistical Analysis. Morphometric results across the subject population are statistically analyzed with respect to the neuropsychiatric scales collected for the individuals. As an example, early results for the lobar analysis show that: (1) Depressed subjects have smaller parietal lobe fractional volume both at baseline and Year2 compared to controls (p < 0.02); (2) Subjects who responded to therapy showed a trend to larger fractional temporal lobe volume (p < .08) compared to non-responders while there was no such trend in the frontal lobe (p < 0.6). The comprehensive lobar analysis made possible by the BIRN infrastructure indicates that there are major lobar architectural changes associated with unipolar major depression in elderly subjects. Analyses for the other variables are underway.

Parallel Computing. The BWH segmentation tools were adapted to run on a high performance compute cluster located at UCSD (SunFire V60x 128 Node Grid Computing Cluster). Application of this compute resource to the MIRIAD data allowed rapid iteration of new algorithmic approaches applied to the entire data set.

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Morphometry BIRN

Anatomical Segmentation. To enhance the outcome of the Expectation Maximization Segmentation process (developed at BWH in collaboration with MIT), an Intensity Normalization process is applied to the image data. This alters, in a highly constrained way, the intensity profile of a given image to meet that of a model. The EM Segmentation algorithm distinguishes different tissue classes by using several different pulse sequences, in this case T2-weighted and Proton Density scans. By an iterative process which alternately takes into account intensity profiles of tissue classes and field inhomogeneities, a segmentation of grey matter, white matter and CSF is obtained. The probabilistic atlas provides subdivision of these structures into anatomically meaningful substructures.

BWHIntensity Normalizationand EM Segmentation

Tissue Classes: 3D Slicer's EM Segmentation algorithm uniquely identifies each MRI voxel with one of the following anatomical labels: CSF, White Matter, Gray Matter, and Left and Right: Amygdala, Hippocampus, Parahippocampus, Thalamus, Posterior Insula Cortex, Anterior Insula Cortex,Inferior Temporal Gyrus, Superior Temporal Gyrus, Temporal Lobe.

UCLA

Brighamand Women’s

Hospital Johns Hopkins

University

Brain Imaging

and Analysis Center