Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders
To survive in an ever-changing environment, the brain must seamlessly integrate a rich stream of incoming information into coherent internal representations that can then be used to efficiently plan for action. The brain must, however, balance its ability to integrate information from various source...
| Asıl Yazarlar: | , , , |
|---|---|
| Materyal Türü: | Journal article |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
Royal Society
2017
|
| _version_ | 1826266943983714304 |
|---|---|
| author | Lord, L Stevner, A Deco, G Kringelbach, M |
| author_facet | Lord, L Stevner, A Deco, G Kringelbach, M |
| author_sort | Lord, L |
| collection | OXFORD |
| description | To survive in an ever-changing environment, the brain must seamlessly integrate a rich stream of incoming information into coherent internal representations that can then be used to efficiently plan for action. The brain must, however, balance its ability to integrate information from various sources with a complementary capacity to segregate information into modules which perform specialized computations in local circuits. Importantly, evidence suggests that imbalances in the brain's ability to bind together and/or segregate information over both space and time is a common feature of several neuropsychiatric disorders. Most studies have, however, until recently strictly attempted to characterize the principles of integration and segregation in static (i.e. time-invariant) representations of human brain networks, hence disregarding the complex spatio-temporal nature of these processes. In the present Review, we describe how the emerging discipline of whole-brain computational connectomics may be used to study the causal mechanisms of the integration and segregation of information on behaviourally relevant timescales. We emphasize how novel methods from network science and whole-brain computational modelling can expand beyond traditional neuroimaging paradigms and help to uncover the neurobiological determinants of the abnormal integration and segregation of information in neuropsychiatric disorders.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. |
| first_indexed | 2024-03-06T20:46:38Z |
| format | Journal article |
| id | oxford-uuid:36219c3c-aee8-42d8-8448-1add5c49ed6f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:46:38Z |
| publishDate | 2017 |
| publisher | Royal Society |
| record_format | dspace |
| spelling | oxford-uuid:36219c3c-aee8-42d8-8448-1add5c49ed6f2022-03-26T13:35:58ZUnderstanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disordersJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:36219c3c-aee8-42d8-8448-1add5c49ed6fEnglishSymplectic Elements at OxfordRoyal Society2017Lord, LStevner, ADeco, GKringelbach, MTo survive in an ever-changing environment, the brain must seamlessly integrate a rich stream of incoming information into coherent internal representations that can then be used to efficiently plan for action. The brain must, however, balance its ability to integrate information from various sources with a complementary capacity to segregate information into modules which perform specialized computations in local circuits. Importantly, evidence suggests that imbalances in the brain's ability to bind together and/or segregate information over both space and time is a common feature of several neuropsychiatric disorders. Most studies have, however, until recently strictly attempted to characterize the principles of integration and segregation in static (i.e. time-invariant) representations of human brain networks, hence disregarding the complex spatio-temporal nature of these processes. In the present Review, we describe how the emerging discipline of whole-brain computational connectomics may be used to study the causal mechanisms of the integration and segregation of information on behaviourally relevant timescales. We emphasize how novel methods from network science and whole-brain computational modelling can expand beyond traditional neuroimaging paradigms and help to uncover the neurobiological determinants of the abnormal integration and segregation of information in neuropsychiatric disorders.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. |
| spellingShingle | Lord, L Stevner, A Deco, G Kringelbach, M Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders |
| title | Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders |
| title_full | Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders |
| title_fullStr | Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders |
| title_full_unstemmed | Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders |
| title_short | Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders |
| title_sort | understanding principles of integration and segregation using whole brain computational connectomics implications for neuropsychiatric disorders |
| work_keys_str_mv | AT lordl understandingprinciplesofintegrationandsegregationusingwholebraincomputationalconnectomicsimplicationsforneuropsychiatricdisorders AT stevnera understandingprinciplesofintegrationandsegregationusingwholebraincomputationalconnectomicsimplicationsforneuropsychiatricdisorders AT decog understandingprinciplesofintegrationandsegregationusingwholebraincomputationalconnectomicsimplicationsforneuropsychiatricdisorders AT kringelbachm understandingprinciplesofintegrationandsegregationusingwholebraincomputationalconnectomicsimplicationsforneuropsychiatricdisorders |