Toward discovery science of human brain function
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 pr...
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| Format: | Article |
| Language: | en_US |
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National Academy of Sciences (U.S.)
2011
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| Online Access: | http://hdl.handle.net/1721.1/61013 |
| _version_ | 1826201499210874880 |
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| author | Gabrieli, Susan |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Gabrieli, Susan |
| author_sort | Gabrieli, Susan |
| collection | MIT |
| description | 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/. |
| first_indexed | 2024-09-23T11:52:24Z |
| format | Article |
| id | mit-1721.1/61013 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:52:24Z |
| publishDate | 2011 |
| publisher | National Academy of Sciences (U.S.) |
| record_format | dspace |
| spelling | mit-1721.1/610132022-10-01T06:37:26Z Toward discovery science of human brain function Gabrieli, Susan Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research at MIT Gabrieli, Susan Gabrieli, Susan 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/. National Institute of Mental Health (U.S.) (Grant R01MH083246) (Grant R01MH081218) National Institute of Drug Abuse (Grant R03DA024775) (Grant R01DA016979) Autism Speaks (Organization) National Institute of Neurological Disorders and Stroke (U.S.) (Grant (R01NS049176) 2011-02-22T23:00:47Z 2011-02-22T23:00:47Z 2010-02 2009-10 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/61013 Biswal, B. B. et al. “Toward discovery science of human brain function.” Proceedings of the National Academy of Sciences 107.10 (2010): 4734-4739. Web. en_US http://dx.doi.org/10.1073/pnas.0911855107 Proceedings of the National Academy of Sciences of the United States of America. (PNAS) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences (U.S.) PNAS |
| spellingShingle | Gabrieli, Susan Toward discovery science of human brain function |
| title | Toward discovery science of human brain function |
| title_full | Toward discovery science of human brain function |
| title_fullStr | Toward discovery science of human brain function |
| title_full_unstemmed | Toward discovery science of human brain function |
| title_short | Toward discovery science of human brain function |
| title_sort | toward discovery science of human brain function |
| url | http://hdl.handle.net/1721.1/61013 |
| work_keys_str_mv | AT gabrielisusan towarddiscoveryscienceofhumanbrainfunction |