TY - JOUR
T1 - Statistical methods for dissecting interactions between brain areas
AU - Semedo, João D.
AU - Gokcen, Evren
AU - Machens, Christian K.
AU - Kohn, Adam
AU - Yu, Byron M.
N1 - Funding Information:
We thank A. Jasper and T. Verstynen for valuable discussions. This work was supported by Simons Collaboration on the Global Brain 543009 (C.K.M.), 542999 (A.K.), 543065 (B.M.Y.), 364994 (A.K., B.M.Y.), NIH U01 NS094288 (C.K.M.), NIH EY028626 (A.K.), Irma T. Hirschl Trust (A.K.), NIH R01 HD071686 (B.M.Y.), NIH CRCNS R01 NS105318 (B.M.Y.), NSF NCS BCS 1533672 and 1734916 (B.M.Y.), NIH CRCNS R01 MH118929 (B.M.Y.), and NIH R01 EB026953 (B.M.Y.).
Publisher Copyright:
© 2020 The Author(s)
PY - 2020/12
Y1 - 2020/12
N2 - The brain is composed of many functionally distinct areas. This organization supports distributed processing in the brain, 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 inter-areal 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.
AB - The brain is composed of many functionally distinct areas. This organization supports distributed processing in the brain, 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 inter-areal 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.
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U2 - 10.1016/j.conb.2020.09.009
DO - 10.1016/j.conb.2020.09.009
M3 - Review article
C2 - 33142111
AN - SCOPUS:85094857262
SN - 0959-4388
VL - 65
SP - 59
EP - 69
JO - Current Opinion in Neurobiology
JF - Current Opinion in Neurobiology
ER -