Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography
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Gabrieli_Connectivity-based segmentation.pdf
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Author(s)
Saygin, Zeynep M.
Osher, David E.
Augustinack, Jean
Fischl, Bruce
Gabrieli, John D. E.
Date Issued
March 2011
Journal
NeuroImage
Publisher
Elsevier
Citation
Saygin, Zeynep M., David E. Osher, Jean Augustinack, Bruce Fischl, and John D.E. Gabrieli. “Connectivity-Based Segmentation of Human Amygdala Nuclei Using Probabilistic Tractography.” NeuroImage 56, no. 3 (June 2011): 1353–1361.
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Author's final manuscript
Abstract
The amygdala plays an important role in emotional and social functions, and amygdala dysfunction has been associated with multiple neuropsychiatric disorders, including autism, anxiety, and depression. Although the amygdala is composed of multiple anatomically and functionally distinct nuclei, typical structural magnetic resonance imaging (MRI) sequences are unable to discern them. Thus, functional MRI (fMRI) studies typically average the BOLD response over the entire structure, which reveals some aspects of amygdala function as a whole but does not distinguish the separate roles of specific nuclei in humans. We developed a method to segment the human amygdala into its four major nuclei using only diffusion-weighted imaging and connectivity patterns derived mainly from animal studies. We refer to this new method as Tractography-based Segmentation, or TractSeg. The segmentations derived from TractSeg were topographically similar to their corresponding amygdaloid nuclei, and were validated against a high-resolution scan in which the nucleic boundaries were visible. In addition, nuclei topography was consistent across subjects. TractSeg relies on short scan acquisitions and widely accessible software packages, making it attractive for use in healthy populations to explore normal amygdala nucleus function, as well as in clinical and pediatric populations. Finally, it paves the way for implementing this method in other anatomical regions which are also composed of functional subunits that are difficult to distinguish with standard structural MRI.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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Creative Commons Attribution-Noncommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1016/j.neuroimage.2011.03.006
