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A comprehensive protocol for manual segmentation of the human claustrum and its sub-regions using high-resolution MRI

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arxiv 2010.06423 v1 pith:X7PJF2PN submitted 2020-10-13 q-bio.QM eess.IV

classification q-bio.QMeess.IV
keywords delineateprotocolbrainhumanincludingclaustrumcomprehensiveepilepsy
verification ladder T0 review T1 audit T2 compute T3 formal
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The claustrum (Cl) is a thin grey matter structure located in the center of each brain hemisphere. Cl has been hypothesized as a central hub of the brain for multisensory/sensorimotor integration, consciousness, and attention. Accumulating evidence has suggested that Cl might be important in the development of severe neurological and psychiatric symptoms including epileptic seizures and psychosis. However, the specifics of the roles of Cl in human epilepsy and psychosis are largely unknown, primarily due to methodological limitations related to the thin morphology of Cl that is challenging to delineate accurately using conventional methods. The goal of this work is to develop noninvasive multimodal neuroimaging methods to delineate Cl anatomy by utilizing a large healthy adult high resolution (0.7mm3) T1-weighted MRI collected as part of the Washington University-Minnesota Consortium Human Connectome Project (WU-Minn HCP). We developed a comprehensive manual segmentation protocol to delineate Cl based on a cellular level brain atlas. The protocol involves detailed guidelines to delineate the three subregions of Cl, including the dorsal, ventral, and temporal Cl that can be parcellated based on a geometric method. As demonstrated in a representative result, Cl is large in its anterior-posterior, and the dorsal-ventral extent. Also, the volume is comparable to that of the amygdala. It is required to assess the reliability of the protocol so that it can be used for future anatomical studies of neuropsychiatric disorders, including epilepsy and schizophrenia.

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Cited by 1 Pith paper

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  1. A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A SynthSeg-based deep learning method segments the claustrum from ultra-high-resolution ex vivo MRI and generalizes to typical in vivo scans across contrasts and resolutions.

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