Cargando…

Fast reproducible identification and large-scale databasing of individual functional cognitive networks

BACKGROUND: Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, an...

Descripción completa

Detalles Bibliográficos
Autores principales: Pinel, Philippe, Thirion, Bertrand, Meriaux, Sébastien, Jobert, Antoinette, Serres, Julien, Le Bihan, Denis, Poline, Jean-Baptiste, Dehaene, Stanislas
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2241626/
https://www.ncbi.nlm.nih.gov/pubmed/17973998
http://dx.doi.org/10.1186/1471-2202-8-91
_version_ 1782150524098838528
author Pinel, Philippe
Thirion, Bertrand
Meriaux, Sébastien
Jobert, Antoinette
Serres, Julien
Le Bihan, Denis
Poline, Jean-Baptiste
Dehaene, Stanislas
author_facet Pinel, Philippe
Thirion, Bertrand
Meriaux, Sébastien
Jobert, Antoinette
Serres, Julien
Le Bihan, Denis
Poline, Jean-Baptiste
Dehaene, Stanislas
author_sort Pinel, Philippe
collection PubMed
description BACKGROUND: Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level. RESULTS: 81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects. CONCLUSION: This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.
format Text
id pubmed-2241626
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-22416262008-02-13 Fast reproducible identification and large-scale databasing of individual functional cognitive networks Pinel, Philippe Thirion, Bertrand Meriaux, Sébastien Jobert, Antoinette Serres, Julien Le Bihan, Denis Poline, Jean-Baptiste Dehaene, Stanislas BMC Neurosci Research Article BACKGROUND: Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level. RESULTS: 81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects. CONCLUSION: This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes. BioMed Central 2007-10-31 /pmc/articles/PMC2241626/ /pubmed/17973998 http://dx.doi.org/10.1186/1471-2202-8-91 Text en Copyright © 2007 Pinel et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Pinel, Philippe
Thirion, Bertrand
Meriaux, Sébastien
Jobert, Antoinette
Serres, Julien
Le Bihan, Denis
Poline, Jean-Baptiste
Dehaene, Stanislas
Fast reproducible identification and large-scale databasing of individual functional cognitive networks
title Fast reproducible identification and large-scale databasing of individual functional cognitive networks
title_full Fast reproducible identification and large-scale databasing of individual functional cognitive networks
title_fullStr Fast reproducible identification and large-scale databasing of individual functional cognitive networks
title_full_unstemmed Fast reproducible identification and large-scale databasing of individual functional cognitive networks
title_short Fast reproducible identification and large-scale databasing of individual functional cognitive networks
title_sort fast reproducible identification and large-scale databasing of individual functional cognitive networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2241626/
https://www.ncbi.nlm.nih.gov/pubmed/17973998
http://dx.doi.org/10.1186/1471-2202-8-91
work_keys_str_mv AT pinelphilippe fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks
AT thirionbertrand fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks
AT meriauxsebastien fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks
AT jobertantoinette fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks
AT serresjulien fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks
AT lebihandenis fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks
AT polinejeanbaptiste fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks
AT dehaenestanislas fastreproducibleidentificationandlargescaledatabasingofindividualfunctionalcognitivenetworks