Personalized brain network models for assessing structure–function relationships
[Display omitted] •Personalized brain network models combine brain structure with mathematical modeling.•Construction of models involves multiple different strategies and assumptions.•Computational models allow performance of in silico experiments otherwise impossible.•Models aide in the development...
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| Vydané v: | Current opinion in neurobiology Ročník 52; s. 42 - 47 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Elsevier Ltd
01.10.2018
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| ISSN: | 0959-4388, 1873-6882, 1873-6882 |
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| Abstract | [Display omitted]
•Personalized brain network models combine brain structure with mathematical modeling.•Construction of models involves multiple different strategies and assumptions.•Computational models allow performance of in silico experiments otherwise impossible.•Models aide in the development of personalized medical treatment strategies.
Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information derived from subject data. Here, we discuss recent work using personalized brain network models to study structure–function relationships in human brains. We describe the steps necessary to build such models and show how this computational approach can provide previously unobtainable information through the ability to perform virtual experiments. Finally, we present examples of how personalized brain network models can be used to gain insight into the effects of local stimulation and improve surgical outcomes in epilepsy. |
|---|---|
| AbstractList | [Display omitted]
•Personalized brain network models combine brain structure with mathematical modeling.•Construction of models involves multiple different strategies and assumptions.•Computational models allow performance of in silico experiments otherwise impossible.•Models aide in the development of personalized medical treatment strategies.
Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information derived from subject data. Here, we discuss recent work using personalized brain network models to study structure–function relationships in human brains. We describe the steps necessary to build such models and show how this computational approach can provide previously unobtainable information through the ability to perform virtual experiments. Finally, we present examples of how personalized brain network models can be used to gain insight into the effects of local stimulation and improve surgical outcomes in epilepsy. Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information derived from subject data. Here, we discuss recent work using personalized brain network models to study structure-function relationships in human brains. We describe the steps necessary to build such models and show how this computational approach can provide previously unobtainable information through the ability to perform virtual experiments. Finally, we present examples of how personalized brain network models can be used to gain insight into the effects of local stimulation and improve surgical outcomes in epilepsy.Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information derived from subject data. Here, we discuss recent work using personalized brain network models to study structure-function relationships in human brains. We describe the steps necessary to build such models and show how this computational approach can provide previously unobtainable information through the ability to perform virtual experiments. Finally, we present examples of how personalized brain network models can be used to gain insight into the effects of local stimulation and improve surgical outcomes in epilepsy. Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information derived from subject data. Here, we discuss recent work using personalized brain network models to study structure-function relationships in human brains. We describe the steps necessary to build such models and show how this computational approach can provide previously unobtainable information through the ability to perform virtual experiments. Finally, we present examples of how personalized brain network models can be used to gain insight into the effects of local stimulation and improve surgical outcomes in epilepsy. |
| Author | Nakuci, Johan Bansal, Kanika Muldoon, Sarah Feldt |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29704749$$D View this record in MEDLINE/PubMed |
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