<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>24(1)</volume><submitter>Anbarasi J</submitter><pubmed_abstract>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 biomarke</pubmed_abstract><journal>BMC neurology</journal><pagination>364</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11438080</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.</pubmed_title><pmcid>PMC11438080</pmcid><pubmed_authors>Anbarasi J</pubmed_authors><pubmed_authors>Ganesh M</pubmed_authors><pubmed_authors>Kumari R</pubmed_authors><pubmed_authors>Agrawal R</pubmed_authors></additional><is_claimable>false</is_claimable><name>Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.</name><description>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 biomarke</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Sep</publication><modification>2026-06-05T08:38:42.489Z</modification><creation>2025-04-06T16:25:18.348Z</creation></dates><accession>S-EPMC11438080</accession><cross_references><pubmed>39342171</pubmed><doi>10.1186/s12883-024-03864-0</doi></cross_references></HashMap>