Cargando…

Statistical methods for dissecting interactions between brain areas

The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availabilit...

Descripción completa

Detalles Bibliográficos
Autores principales: Semedo, João D, Gokcen, Evren, Machens, Christian K, Kohn, Adam, Yu, Byron M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935404/
https://www.ncbi.nlm.nih.gov/pubmed/33142111
http://dx.doi.org/10.1016/j.conb.2020.09.009
_version_ 1783661000366489600
author Semedo, João D
Gokcen, Evren
Machens, Christian K
Kohn, Adam
Yu, Byron M
author_facet Semedo, João D
Gokcen, Evren
Machens, Christian K
Kohn, Adam
Yu, Byron M
author_sort Semedo, João D
collection PubMed
description The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of interareal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions.
format Online
Article
Text
id pubmed-7935404
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-79354042021-03-05 Statistical methods for dissecting interactions between brain areas Semedo, João D Gokcen, Evren Machens, Christian K Kohn, Adam Yu, Byron M Curr Opin Neurobiol Article The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of interareal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions. 2020-11-01 2020-12 /pmc/articles/PMC7935404/ /pubmed/33142111 http://dx.doi.org/10.1016/j.conb.2020.09.009 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Semedo, João D
Gokcen, Evren
Machens, Christian K
Kohn, Adam
Yu, Byron M
Statistical methods for dissecting interactions between brain areas
title Statistical methods for dissecting interactions between brain areas
title_full Statistical methods for dissecting interactions between brain areas
title_fullStr Statistical methods for dissecting interactions between brain areas
title_full_unstemmed Statistical methods for dissecting interactions between brain areas
title_short Statistical methods for dissecting interactions between brain areas
title_sort statistical methods for dissecting interactions between brain areas
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935404/
https://www.ncbi.nlm.nih.gov/pubmed/33142111
http://dx.doi.org/10.1016/j.conb.2020.09.009
work_keys_str_mv AT semedojoaod statisticalmethodsfordissectinginteractionsbetweenbrainareas
AT gokcenevren statisticalmethodsfordissectinginteractionsbetweenbrainareas
AT machenschristiank statisticalmethodsfordissectinginteractionsbetweenbrainareas
AT kohnadam statisticalmethodsfordissectinginteractionsbetweenbrainareas
AT yubyronm statisticalmethodsfordissectinginteractionsbetweenbrainareas