Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

• Published in: NeuroImage (Orlando, Fla.) Vol. 181; pp. 16 - 29
• Main Authors: Wu, Ye, Zhang, Fan, Makris, Nikos, Ning, Yuping, Norton, Isaiah, She, Shenglin, Peng, Hongjun, Rathi, Yogesh, Feng, Yuanjing, Wu, Huawang, O'Donnell, Lauren J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2018; Elsevier Limited
• Subjects: Adult; Annotations; Attention deficit hyperactivity disorder; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - pathology; Cingulum; Cortex; Data processing; Depressive Disorder, Major - diagnostic imaging; Depressive Disorder, Major - pathology; Diffusion MRI; Diffusion Tensor Imaging - methods; Emotions; Female; Fiber clustering; Hemispheric laterality; Humans; Identification; Limbic System - diagnostic imaging; Limbic System - pathology; Male; MDD; Mental depression; Methods; Nerve Fibers, Myelinated - pathology; Neural Pathways - diagnostic imaging; Neural Pathways - pathology; Pyramidal Tracts - diagnostic imaging; Pyramidal Tracts - pathology; Sensorimotor Cortex - diagnostic imaging; Sensorimotor Cortex - pathology; Sensorimotor system; Statistical analysis; Statistics; Substantia alba; Thalamus - diagnostic imaging; Thalamus - pathology; Tractography; White matter; White Matter - diagnostic imaging; White Matter - pathology; Young Adult; Diffusion MRI; Fiber clustering; MDD; White matter; Tractography
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.06.019

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. •An AAFC method to identify anatomical tracts from the whole brain tractography.•Compute a tract anatomical label for each tract cluster in an automated fashion.•Leverage group-wise fiber geometry and cortical termination information.•Application to study emotional processing and sensorimotor areas in MDD.