BMC Neuroscience BioMed Central Research article Open Access Fast reproducible identification and large-scale databasing of individual functional cognitive networks Philippe Pinel*1,2,3, Bertrand Thirion4, Sébastien Meriaux5, Antoinette Jobert1,2,3, Julien Serres6, Denis Le Bihan5, Jean-Baptiste Poline5 and Stanislas Dehaene1,2,3,7 Address: 1INSERM U562/ IFR 49, Cognitive Neuroimaging Unit, Gif-sur-Yvette, France, 2CEA, DSV/I2BM, NeuroSpin Center, Cognitive Neuroimaging Laboratory (LCOG), Gif-sur-Yvette, France, 3Université Paris-Sud / IFR 49, Cognitive Neuroimaging Laboratory, Gif-sur-Yvette, France, 4INRIA Futurs, NeuroSpin Center, Computer Assisted Neuroimaging Laboratory (LNAO), Gif-sur-Yvette, France, 5CEA, DSV/I2BM, NeuroSpin Center, Gif-sur-Yvette, France, 6CNRS UMR 6152 / Université de la Méditerranée, Laboratoire Mouvement et Perception Marseille, France and 7Collège de France, Paris, France Email: Philippe Pinel* - [email protected]; Bertrand Thirion - [email protected]; Sébastien Meriaux - [email protected]; Antoinette Jobert - [email protected]; Julien Serres - [email protected]; Denis Le Bihan - [email protected]; Jean- Baptiste Poline - [email protected]; Stanislas Dehaene - [email protected] * Corresponding author Published: 31 October 2007 Received: 29 April 2007 Accepted: 31 October 2007 BMC Neuroscience 2007, 8:91 doi:10.1186/1471-2202-8-91 This article is available from: http://www.biomedcentral.com/1471-2202/8/91 © 2007 Pinel et al; licensee BioMed Central Ltd. 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. 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. Page 1 of 18 (page number not for citation purposes) BMC Neuroscience 2007, 8:91 http://www.biomedcentral.com/1471-2202/8/91 Background ety of cortical territories and to describe cerebral correlates Inter-subjects variability is a missing facet of the current associated with various level of cognitive processing, from neuroimaging literature [1-3], and until recently has been simple perceptual processing to higher-level cognitive viewed more as a nuisance for brain imaging studies than functions that require explicit learning and education. For as a relevant dimension to investigate the mechanisms of instance, recent advances in the genetics of dysphasia, dys- human cognition. Indeed, most of the published studies lexia, and dyscalculia have provided several candidate described the cerebral bases of various cognitive processes genes whose impact on inter-individual variability in the from voxel-based group analyses performed on the data normal population remains unknown. Considering some from 10–15 subjects. Group analysis of a small collection cognitive tasks that have been extensively described in the of brains assures that the description of these functional neuroimaging literature, we chose to include: a mental invariants may be extended to other healthy subjects. calculation task to investigate superior fronto-parietal net- However we usually do not know if a cerebral network works [24] and a language comprehension task which involved in a task is homogenous enough among the focuses on the inferior frontal and superior temporal healthy population to be analyzed in only one group or if lobes [25]. Using auditory and visual stimulation allows several groups have to be considered, nor how many sub- us to isolate cortical pathways associated with perceptual jects are required to correctly describe different sub- processing (superior temporal sulcus and occipito-tempo- groups [4] (This question was also recently addressed in ral cortex [26,27]) while the use of conjunction analysis [5] on the basis of the present database). Consequently, it across modalities also allows us to isolate correlates of is plausible that in many cases, especially in those involv- amodal processing (associative cortices, for instance). ing associative areas in complex tasks, we just capture the Finally, evolutionary and developmental models suggest common denominator of each individual cognitive circuit that some primitive mechanisms may be (partially) and lose a large amount of information. shared between hand/finger motor representation, speech language areas and correlates of mental arithmetic [28- Describing more completely the parts of cerebral net- 30], noticeably in frontal and parietal lobes. If such works used but not shared by all of our subjects require assumptions are verified, crossing analysis among these considering variability of brain activation, which may tasks may then help to dissect the task-related networks have various origins: ▪ Intra-subject inter-sessions variability into a more subtle functional parcellation, and enlighten due to movement artifacts, physiological noise, etc... [6,7] developmental issues of human brain organization. In ▪ Spatial variability caused by the shape and location of cor- brief, these considerations suggest that it would be partic- tical sulci [8] even for tasks requiring low-level processing. ularly valuable to obtain images of the cerebral substrates ▪ Biological factors such as sex [9,10], genotype [11-13], or of speech comprehension, reading, and calculation in a protein expression [14] ▪ Cognitive skills or difficulties, large number of subjects, associated with genetic, anatom- which may reflect heterogeneity of the healthy ('control') ical and behavioral data, in a highly standardized manner, population of volunteers [15]. ▪ Cognitive strategies spon- and at a low cost. taneously chosen by subjects to perform a task [16-18] or constrained by the protocol [19] ▪ Education and learning, As shown in Figure 1, we planed to acquire four types of that may locally modulate activation or structural anat- data: functional images and a high-resolution anatomical omy [15,20,21]. Exploring inter-individual variability scan for a fine description of sulci, grey and white matter, thus requires investigating various types of co-variation in as well as (not described here) behavioral and personal a multi-dimensional space. data, aimed to create a rough cognitive profile of the sub- ject, and DNA sampling (cheek swab). Recall that this Toward a multidimensional database data collection occurs within the constraints of this Characterizing this functional variability, particularly project: to be added to other running protocol, with a when considering the genetic level, ideally requires minimal cost of people and time. acquiring functional imaging data from hundreds of sub- jects and organizing these data into a large-scale database, A fast brain mapping sequence together with genetic, behavioral and biomorphological In the present research, our goal was to define a simple data. Databasing and analysis of structural magnetic reso- fMRI test, less than 5 minutes long, that could delineate, nance images has already resulted in probabilistic ana- in a subject-specific manner, those cerebral circuits. A tomical atlases [22,23]. However, a similar large scale functional sequence was added to each functional imag- description of functional networks is still lacking. ing session performed in our lab (Figure 1), taking advan- tage of the continuous flow of volunteers recruited for Given that we are in the early stages of exploration of the various protocols. Because we wanted to capture the max- causes of inter-individual variability,