A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging.


Journal

Journal of visualized experiments : JoVE
ISSN: 1940-087X
Titre abrégé: J Vis Exp
Pays: United States
ID NLM: 101313252

Informations de publication

Date de publication:
04 02 2022
Historique:
entrez: 21 2 2022
pubmed: 22 2 2022
medline: 8 4 2022
Statut: epublish

Résumé

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond its historical association with motor control. Structural and functional neuroimaging studies have further refined understanding of the functional neuroanatomy of the cerebellum beyond its anatomical divisions, highlighting the need for the examination of individual cerebellar subunits in healthy variability and neurological diseases. This paper presents a standardized pipeline for examining cerebellum grey matter morphometry that combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation (Automatic Cerebellum Anatomical Parcellation using U-Net Locally Constrained Optimization; ACAPULCO) and voxel-based registration of the cerebellum (Spatially Unbiased Infra-tentorial Template; SUIT) for volumetric quantification. The pipeline has broad applicability to a range of neurological diseases and is fully automated, with manual intervention only required for quality control of the outputs. The pipeline is freely available, with substantial accompanying documentation, and can be run on Mac, Windows, and Linux operating systems. The pipeline is applied in a cohort of individuals with Friedreich ataxia (FRDA), and representative results, as well as recommendations on group-level inferential statistical analyses, are provided. This pipeline could facilitate reliability and reproducibility across the field, ultimately providing a powerful methodological approach for characterizing and tracking cerebellar structural changes in neurological diseases.

Identifiants

pubmed: 35188124
doi: 10.3791/63340
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Video-Audio Media

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Rebecca Kerestes (R)

Department of Neuroscience, Central Clinical School, Monash University; rebecca.kerestes@monash.edu.

Shuo Han (S)

Department of Biomedical Engineering, The Johns Hopkins University.

Srinivas Balachander (S)

Department of Psychiatry, National Institute of Mental Health & Neuro Sciences (NIMHANS).

Carlos Hernandez-Castillo (C)

Faculty of Computer Science, Dalhousie University.

Jerry L Prince (JL)

Department of Electrical and Computer Engineering, The Johns Hopkins University; Department of Computer Science, The Johns Hopkins University.

Jörn Diedrichsen (J)

Brain and Mind Institute, Department for Statistical and Actuarial Sciences, Department for Computer Science, Western University.

Ian H Harding (IH)

Department of Neuroscience, Central Clinical School, Monash University; Monash Biomedical Imaging, Monash University.

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