Explore the vast range of titles available.

To investigate the quantitative characteristics and major subtypes of local structural connectomes for medial temporal lobe (MTL) parcellations. The Q-Space Diffeomorphic Reconstruction (QSDR) method was used to track white matter fibers for the ROIs within MTL based on the integrating high-resolution T1 structural MR imaging and diffusion MR imaging of 100 adult Chinese individuals. Graph theoretical analysis was employed to construct the local structural connectome models for ROIs within MTL and acquire the network parameters. These connectivity matrices of these connectomes were classified into major subtypes undergoing hierarchical clustering. (1) In the local brain connectomes, the overall network features exhibited a low characteristic path length paired with moderate to high global efficiency, suggesting the effectiveness of the local brain connectome construction. The amygdala connectomes exhibited longer characteristic path length and weaker global efficiency than the ipsilateral hippocampus and parahippocampal connectomes. (2) The hubs of the amygdala connectomes were dispersed across the ventral frontal, olfactory area, limbic, parietal regions and subcortical nuclei, and the hubs the hippocampal connectomes were mainly situated within the limbic, parietal, and subcortical regions. The hubs distribution of the parahippocampal connectomes resembled the hippocampal structural connectomes, but lacking interhemispheric connections and connectivity with subcortical nuclei. (3) The subtypes of the brain local structural connectomes for each ROI were classified by hierarchical clustering, The subtypes of the bilateral amygdala connectomes were the amygdala-prefrontal connectome; the amygdala-ipsilateral or contralateral limbic connectome and the amygdala-posterior connectome. The subtypes of the bilateral hippocampal connectomes primarily included the hippocampus-ipsilateral or contralateral limbic connectome and the anterior temporal-hippocampus-ventral temporal-occipital connectome in the domain hemisphere. The subtypes of the parahippocampal connectomes exhibited resemblances to those of the hippocampus. We have constructed the brain local connectomes of the MTL parcellations and acquired the network parameters to delineate the hubs distribution through graph theory analysis. The connectomes can be classified into different major subtypes, which were closely related to the functional connectivity.

Objectives Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune inflammatory disease of the central nervous system. Accumulating evidence suggests there is a distinct pattern of brain lesions characteristic of NMOSD, and brain MRI has potential prognostic implications. However, the question of how the brain lesions in NMOSD are associated with its distinct clinical course remains incompletely understood. Here, we aimed to investigate the association between neurological impairment and brain lesions via brain structural disconnection. Methods Twenty patients were diagnosed with NMOSD according to the 2015 International Panel for NMO Diagnosis criteria. The white matter lesions were manually drawn section by section. Whole-brain structural disconnection was estimated, and connectome-based predictive modeling (CPM) was used to estimate the patient’s Expanded Disability Status Scale score (EDSS) from their disconnection severity matrix. Furthermore, correlational tractography was performed to assess the fractional anisotropy (FA) and axial diffusivity (AD) of white matter fibers, which negatively correlated with the EDSS score. Results CPM successfully predicted the EDSS using the disconnection severity matrix (r = 0.506, p = 0.028; q 2  = 0.274). Among the important edges in the prediction process, the majority of edges connected the motor to the frontoparietal network. Correlational tractography identified a decreased FA and AD value according to EDSS scores in periependymal white matter tracts. Discussion Structural disconnection-based predictive modeling and local connectome analysis showed that frontoparietal and periependymal white matter disconnection is predictive and associated with the EDSS score of NMOSD patients. Keypoints: The structural disconnection-based predictive modeling showed that frontoparietal white matter disconnection is predictive of disability in NMOSD patients. Correlational tractography identified decreased fractional anisotropy value according to EDSS in the periependymal local connectome.

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how the local connectome has both the sensitivity and the specificity to be used as a phenotypic marker for subject-specific attributes.