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Investigation into Local White Matter Abnormality in Emotional Processing and Sensorimotor Areas using an Automatically Annotated Fiber Clustering in Major Depressive Disorder

Institution:
1Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China.
2Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Elsevier Science
Publication Date:
Nov-2018
Journal:
Neuroimage
Volume Number:
181
Pages:
16-29
Citation:
Neuroimage. 2018 Nov 1;181:16-29.
PubMed ID:
29890329
PMCID:
PMC6415925
Keywords:
Diffusion MRI, Fiber clustering, MDD, Tractography, White matter
Appears in Collections:
NAC, NCIGT, SPL
Sponsors:
U01 CA199459 /NH/NIH HHS/United States
P41 EB015902 /NH/NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
R01 MH074794 /NH/NIH HHS/United States
P41 EB015898 /NH/NIH HHS/United States
U01 CA199459/CA/NCI NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Wu Y., Zhang F., Makris N., Ning Y., Norton I., She S., Peng H., Rathi Y., Feng Y., Wu H., O'Donnell L.J. Investigation into Local White Matter Abnormality in Emotional Processing and Sensorimotor Areas using an Automatically Annotated Fiber Clustering in Major Depressive Disorder. Neuroimage. 2018 Nov 1;181:16-29. PMID: 29890329. PMCID: PMC6415925.
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This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.