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From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428697/ https://www.ncbi.nlm.nih.gov/pubmed/36061504 http://dx.doi.org/10.3389/fnhum.2022.940842 |
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author | Li, Guoshi Yap, Pew-Thian |
author_facet | Li, Guoshi Yap, Pew-Thian |
author_sort | Li, Guoshi |
collection | PubMed |
description | As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term “mechanistic connectome.” The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders. |
format | Online Article Text |
id | pubmed-9428697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94286972022-09-01 From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis Li, Guoshi Yap, Pew-Thian Front Hum Neurosci Human Neuroscience As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term “mechanistic connectome.” The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders. Frontiers Media S.A. 2022-08-17 /pmc/articles/PMC9428697/ /pubmed/36061504 http://dx.doi.org/10.3389/fnhum.2022.940842 Text en Copyright © 2022 Li and Yap. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Human Neuroscience Li, Guoshi Yap, Pew-Thian From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis |
title | From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis |
title_full | From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis |
title_fullStr | From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis |
title_full_unstemmed | From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis |
title_short | From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis |
title_sort | from descriptive connectome to mechanistic connectome: generative modeling in functional magnetic resonance imaging analysis |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428697/ https://www.ncbi.nlm.nih.gov/pubmed/36061504 http://dx.doi.org/10.3389/fnhum.2022.940842 |
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