Research article

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

Philippe Pinel^1,2,3 , Bertrand Thirion⁴ , Sébastien Meriaux⁵ , Antoinette Jobert^1,2,3 , Julien Serres⁶ , Denis Le Bihan⁵ , Jean-Baptiste Poline⁵ and Stanislas Dehaene^1,2,3,7

¹  INSERM U562/ IFR 49, Cognitive Neuroimaging Unit, Gif-sur-Yvette, France

²  CEA, DSV/I2BM, NeuroSpin Center, Cognitive Neuroimaging Laboratory (LCOG), Gif-sur-Yvette, France

³  Université Paris-Sud / IFR 49, Cognitive Neuroimaging Laboratory, Gif-sur-Yvette, France

⁴  INRIA Futurs, NeuroSpin Center, Computer Assisted Neuroimaging Laboratory (LNAO), Gif-sur-Yvette, France

⁵  CEA, DSV/I2BM, NeuroSpin Center, Gif-sur-Yvette, France

⁶  CNRS UMR 6152 / Université de la Méditerranée, Laboratoire Mouvement et Perception Marseille, France

⁷  Collège de France, Paris, France

author email corresponding author email

BMC Neuroscience 2007, 8:91doi:10.1186/1471-2202-8-91

Published:	31 October 2007

Abstract

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.