Toward discovery science of human brain function. Proc Natl Acad Sci USA

Department of Radiology, New Jersey Medical School, Newark, NJ 07103, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 03/2010; 107(10):4734-9. DOI: 10.1073/pnas.0911855107
Source: PubMed


Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at

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    • "Alternatively, functional connectivity can be measured by resting-state correlations under the assumption that the coupling strengths between distant brain regions is measurable by correlation between time series of BOLD signal fluctuations outside of an experimental context (Biswal et al., 1995; Buckner et al., 2013; Zhang and Raichle, 2010). It quantifies the correlative relationships between distant brain regions in subjects idling in the MRI scanner. "
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    • "Two criteria were used to remove biologically irrelevant components: (1) those representing known artifacts such as motion, and high-frequency noise; and (2) those with connectivity patterns not located mainly in gray matter. Networks of interest (NOI) were identified as anatomically and functionally classical resting-state networks (RSNs) [Biswal et al., 2010] upon visual inspection. The between-subject analysis was carried out using dual regression, a regression technique which back-reconstructs each group level component map at the individual subject level. "
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    • "the years that followed the publication of this paper, the idea to establish a " comprehensive structural description of the network of elements and connections forming the human brain " (Sporns et al., 2005) has gained rapidly growing interest in the field, yielding large-scale data-collection initiatives (Biswal et al., 2010; Nooner et al., 2012; Toga et al., 2012; Van Essen et al., 2013; 2012) as well as analyses (Glasser et al., 2013; Setsompop et al., 2013; Smith et al., 2013; Zuo et al., 2011), which illustrates the interest in structural connectivity databases of the human brain. Thus, in the past, several dMRIbased white matter atlases have been introduced (Mori et al., 2008) which were usually based on single subject data (Bürgel et al., 2006; Catani et al., 2002; Hagmann et al., 2003; Makris et al., 1997; Pajevic and Pierpaoli, 2000; Stieltjes et al., 2001; Wakana et al., 2004). "
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