Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.
Classification
DTI
Machine learning
Macroscale connectomics
Neurological
Neuropsychiatric
Personalized treatment
Prediction
Regression
dMRI
fMRI
Journal
BMC neurology
ISSN: 1471-2377
Titre abrégé: BMC Neurol
Pays: England
ID NLM: 100968555
Informations de publication
Date de publication:
28 Sep 2024
28 Sep 2024
Historique:
received:
03
05
2024
accepted:
16
09
2024
medline:
29
9
2024
pubmed:
29
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.
Identifiants
pubmed: 39342171
doi: 10.1186/s12883-024-03864-0
pii: 10.1186/s12883-024-03864-0
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
364Informations de copyright
© 2024. The Author(s).
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