Learning brain dynamics for decoding and predicting individual differences.

Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Joyneel Misra, Srinivas Govinda Surampudi, Manasij Venkatesh, Chirag Limbachia, Joseph Jaja, Luiz Pessoa
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6d0c00fb0b7343f48a29e7416d64bc8c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6d0c00fb0b7343f48a29e7416d64bc8c
record_format	dspace
spelling	oai:doaj.org-article:6d0c00fb0b7343f48a29e7416d64bc8c2021-12-02T19:57:51ZLearning brain dynamics for decoding and predicting individual differences.1553-734X1553-735810.1371/journal.pcbi.1008943https://doaj.org/article/6d0c00fb0b7343f48a29e7416d64bc8c2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008943https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (∼90%) at the level of brain regions, and binary classification of experimental conditions (∼60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable to that of existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally, saliency maps and lesion analysis were employed to characterize brain-region/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. Our approach provides a framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.Joyneel MisraSrinivas Govinda SurampudiManasij VenkateshChirag LimbachiaJoseph JajaLuiz PessoaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1008943 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Joyneel Misra Srinivas Govinda Surampudi Manasij Venkatesh Chirag Limbachia Joseph Jaja Luiz Pessoa Learning brain dynamics for decoding and predicting individual differences.
description	Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (∼90%) at the level of brain regions, and binary classification of experimental conditions (∼60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable to that of existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally, saliency maps and lesion analysis were employed to characterize brain-region/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. Our approach provides a framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.
format	article
author	Joyneel Misra Srinivas Govinda Surampudi Manasij Venkatesh Chirag Limbachia Joseph Jaja Luiz Pessoa
author_facet	Joyneel Misra Srinivas Govinda Surampudi Manasij Venkatesh Chirag Limbachia Joseph Jaja Luiz Pessoa
author_sort	Joyneel Misra
title	Learning brain dynamics for decoding and predicting individual differences.
title_short	Learning brain dynamics for decoding and predicting individual differences.
title_full	Learning brain dynamics for decoding and predicting individual differences.
title_fullStr	Learning brain dynamics for decoding and predicting individual differences.
title_full_unstemmed	Learning brain dynamics for decoding and predicting individual differences.
title_sort	learning brain dynamics for decoding and predicting individual differences.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6d0c00fb0b7343f48a29e7416d64bc8c
work_keys_str_mv	AT joyneelmisra learningbraindynamicsfordecodingandpredictingindividualdifferences AT srinivasgovindasurampudi learningbraindynamicsfordecodingandpredictingindividualdifferences AT manasijvenkatesh learningbraindynamicsfordecodingandpredictingindividualdifferences AT chiraglimbachia learningbraindynamicsfordecodingandpredictingindividualdifferences AT josephjaja learningbraindynamicsfordecodingandpredictingindividualdifferences AT luizpessoa learningbraindynamicsfordecodingandpredictingindividualdifferences
_version_	1718375782137462784

Learning brain dynamics for decoding and predicting individual differences.

Ejemplares similares