Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders
The study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Here our focus is on the disruption in neuropsychiatric di...
| Egile Nagusiak: | , |
|---|---|
| Formatua: | Journal article |
| Hizkuntza: | English |
| Argitaratua: |
Elsevier
2014
|
| _version_ | 1826283275468931072 |
|---|---|
| author | Deco, G Kringelbach, M |
| author_facet | Deco, G Kringelbach, M |
| author_sort | Deco, G |
| collection | OXFORD |
| description | The study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Here our focus is on the disruption in neuropsychiatric disorders (pathoconnectomics) and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales. We review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics. This raises great expectations that whole-brain modeling and computational connectomics may provide an entry point for understanding brain disorders at a causal mechanistic level, and that computational neuropsychiatry can ultimately be leveraged to provide novel, more effective therapeutic interventions, e.g., through drug discovery and new targets for deep brain stimulation. |
| first_indexed | 2024-03-07T00:56:28Z |
| format | Journal article |
| id | oxford-uuid:883e10c9-af80-491c-94b3-c070f14a24e9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:56:28Z |
| publishDate | 2014 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:883e10c9-af80-491c-94b3-c070f14a24e92022-03-26T22:15:56ZGreat expectations: using whole-brain computational connectomics for understanding neuropsychiatric disordersJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:883e10c9-af80-491c-94b3-c070f14a24e9EnglishSymplectic Elements at OxfordElsevier2014Deco, GKringelbach, MThe study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Here our focus is on the disruption in neuropsychiatric disorders (pathoconnectomics) and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales. We review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics. This raises great expectations that whole-brain modeling and computational connectomics may provide an entry point for understanding brain disorders at a causal mechanistic level, and that computational neuropsychiatry can ultimately be leveraged to provide novel, more effective therapeutic interventions, e.g., through drug discovery and new targets for deep brain stimulation. |
| spellingShingle | Deco, G Kringelbach, M Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders |
| title | Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders |
| title_full | Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders |
| title_fullStr | Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders |
| title_full_unstemmed | Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders |
| title_short | Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders |
| title_sort | great expectations using whole brain computational connectomics for understanding neuropsychiatric disorders |
| work_keys_str_mv | AT decog greatexpectationsusingwholebraincomputationalconnectomicsforunderstandingneuropsychiatricdisorders AT kringelbachm greatexpectationsusingwholebraincomputationalconnectomicsforunderstandingneuropsychiatricdisorders |