Article

Toward discovery science of human brain function.

Department of Radiology, New Jersey Medical School, Newark, NJ 07103, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 03/2010; 107(10):4734-9. DOI: 10.1073/pnas.0911855107
Source: PubMed

ABSTRACT 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 www.nitrc.org/projects/fcon_1000/.

0 Followers
 · 
331 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Abnormal interhemispheric functional connectivity correlates with several neurologic and psychiatric conditions, including depression, obsessive-compulsive disorder, schizophrenia, and stroke. Abnormal interhemispheric functional connectivity also correlates with abuse of cannabis and cocaine. In the current report, we evaluated whether tobacco abuse (i.e., cigarette smoking) is associated with altered interhemispheric connectivity. To that end, we examined resting state functional connectivity using magnetic resonance imaging (MRI) in short term tobacco deprived and smoking as usual tobacco smokers, and in non-smoker controls. Additionally, we compared diffusion tensor imaging (DTI) in the same subjects to study differences in white matter. The data reveal a significant increase in interhemispheric functional connectivity in sated tobacco smokers when compared to controls. This difference was larger in frontal regions, and was positively correlated with the average number of cigarettes smoked per day. In addition, we found a negative correlation between the number of DTI streamlines in the genual corpus callosum and the number of cigarettes smoked per day. Taken together, our results implicate changes in interhemispheric functional and anatomical connectivity in current cigarette smokers.
    Frontiers in Human Neuroscience 03/2015; 9. DOI:10.3389/fnhum.2015.00116 · 2.90 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a "big data" problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.
    01/2015; 4:13. DOI:10.1186/s13742-015-0045-x
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Approximately one-third of patients with major depressive disorder (MDD) do not achieve remission after various treatment options and develop treatment resistant depression (TRD). So far, little is known about the pathophysiology of TRD. Studies in MDD patients showed aberrant functional connectivity (FC) of three "core" neurocognitive networks: the salience network (SN), cognitive control network (CCN), and default mode network (DMN). We used a cross-sectional design and performed resting-state FC MRI to assess connectivity of the SN, CCN, and both anterior and posterior DMN in 17 severe TRD, 18 non-TRD, and 18 healthy control (HC) subjects. Relative to both non-TRD and HC subjects, TRD patients showed decreased FC between the dorsolateral prefrontal cortex and angular gyrus, which suggests reduced FC between the CCN and DMN, and reduced FC between the medial prefrontal cortex and precuneus/cuneus, which suggests reduced FC between the anterior and posterior DMN. No significant differences in SN FC were observed. Our results suggest that TRD is characterized by a disturbance in neurocognitive networks relative to non-TRD and HC.
    Frontiers in Psychiatry 03/2015; 6:28. DOI:10.3389/fpsyt.2015.00028

Full-text (3 Sources)

Download
94 Downloads
Available from
Jun 2, 2014