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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Available from: Sein Schmidt, Oct 04, 2015
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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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    ABSTRACT: Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies.
    Human Brain Mapping 01/2016; DOI:10.1002/hbm.22933 · 5.97 Impact Factor
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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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    ABSTRACT: The analysis of the structural architecture of the human brain in terms of connectivity between its sub-regions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute - Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. As a second experiment, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to prior literature findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) to perform fiber tracking directly within MNI space. Hence, it may be used for various purposes such as the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step towards a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level.
    NeuroImage 08/2015; DOI:10.1016/j.neuroimage.2015.08.048 · 6.36 Impact Factor
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    • "Data from 100 healthy adults (mean age = 21.4 years; 63% female) were obtained from the Cambridge dataset in the 1000 Functional Connectomes data repository (Biswal et al., 2010). Imaging was conducted on a General Electric 3T MRI (General Electric). "
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    ABSTRACT: The study of resting-state networks provides an informative paradigm for understanding the functional architecture of the human brain. Although investigating specialized resting-state networks has led to significant advances in our understanding of brain organization, the manner in which information is integrated across these networks remains unclear. Here, we have developed and validated a data-driven methodology for describing the topography of resting-state network convergence in the human brain. Our results demonstrate the importance of an ensemble of cortical and subcortical regions in supporting the convergence of multiple resting-state networks, including the rostral anterior cingulate, precuneus, posterior cingulate cortex, bilateral posterior parietal cortex, bilateral dorsal prefrontal cortex, along with the caudate head, anterior claustrum and posterior thalamus. In addition, we have demonstrated a significant correlation between voxel-wise network convergence and global brain connectivity, emphasizing the importance of resting-state network convergence in facilitating global brain communication. Finally, we examined the convergence of systems within each of the individual resting-state networks in the brain, revealing the heterogeneity by which individual resting-state networks balance the competing demands of specialized processing against the integration of information. Together, our results suggest that the convergence of resting-state networks represents an important organizational principle underpinning systems-level integration in the human brain.
    Brain Connectivity 05/2015; DOI:10.1089/brain.2015.0348
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