A Computational Cognitive Biomarker for Early-Stage Huntington's Disease.
Huntington's disease (HD) is genetically determined but with variability in symptom onset, leading to uncertainty as to when pharmacological intervention should be initiated. Here we take a computational approach based on neurocognitive phenotyping, computational modeling, and classification, i...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0148409 |
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| author | Thomas V Wiecki Chrystalina A Antoniades Alexander Stevenson Christopher Kennard Beth Borowsky Gail Owen Blair Leavitt Raymund Roos Alexandra Durr Sarah J Tabrizi Michael J Frank |
| author_facet | Thomas V Wiecki Chrystalina A Antoniades Alexander Stevenson Christopher Kennard Beth Borowsky Gail Owen Blair Leavitt Raymund Roos Alexandra Durr Sarah J Tabrizi Michael J Frank |
| author_sort | Thomas V Wiecki |
| collection | DOAJ |
| description | Huntington's disease (HD) is genetically determined but with variability in symptom onset, leading to uncertainty as to when pharmacological intervention should be initiated. Here we take a computational approach based on neurocognitive phenotyping, computational modeling, and classification, in an effort to provide quantitative predictors of HD before symptom onset. A large sample of subjects-consisting of both pre-manifest individuals carrying the HD mutation (pre-HD), and early symptomatic-as well as healthy controls performed the antisaccade conflict task, which requires executive control and response inhibition. While symptomatic HD subjects differed substantially from controls in behavioral measures [reaction time (RT) and error rates], there was no such clear behavioral differences in pre-HD. RT distributions and error rates were fit with an accumulator-based model which summarizes the computational processes involved and which are related to identified mechanisms in more detailed neural models of prefrontal cortex and basal ganglia. Classification based on fitted model parameters revealed a key parameter related to executive control differentiated pre-HD from controls, whereas the response inhibition parameter declined only after symptom onset. These findings demonstrate the utility of computational approaches for classification and prediction of brain disorders, and provide clues as to the underlying neural mechanisms. |
| format | Article |
| id | doaj-art-ee4a4bf929c34bc5a6787cf325d306a6 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ee4a4bf929c34bc5a6787cf325d306a62025-08-20T03:10:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01112e014840910.1371/journal.pone.0148409A Computational Cognitive Biomarker for Early-Stage Huntington's Disease.Thomas V WieckiChrystalina A AntoniadesAlexander StevensonChristopher KennardBeth BorowskyGail OwenBlair LeavittRaymund RoosAlexandra DurrSarah J TabriziMichael J FrankHuntington's disease (HD) is genetically determined but with variability in symptom onset, leading to uncertainty as to when pharmacological intervention should be initiated. Here we take a computational approach based on neurocognitive phenotyping, computational modeling, and classification, in an effort to provide quantitative predictors of HD before symptom onset. A large sample of subjects-consisting of both pre-manifest individuals carrying the HD mutation (pre-HD), and early symptomatic-as well as healthy controls performed the antisaccade conflict task, which requires executive control and response inhibition. While symptomatic HD subjects differed substantially from controls in behavioral measures [reaction time (RT) and error rates], there was no such clear behavioral differences in pre-HD. RT distributions and error rates were fit with an accumulator-based model which summarizes the computational processes involved and which are related to identified mechanisms in more detailed neural models of prefrontal cortex and basal ganglia. Classification based on fitted model parameters revealed a key parameter related to executive control differentiated pre-HD from controls, whereas the response inhibition parameter declined only after symptom onset. These findings demonstrate the utility of computational approaches for classification and prediction of brain disorders, and provide clues as to the underlying neural mechanisms.https://doi.org/10.1371/journal.pone.0148409 |
| spellingShingle | Thomas V Wiecki Chrystalina A Antoniades Alexander Stevenson Christopher Kennard Beth Borowsky Gail Owen Blair Leavitt Raymund Roos Alexandra Durr Sarah J Tabrizi Michael J Frank A Computational Cognitive Biomarker for Early-Stage Huntington's Disease. PLoS ONE |
| title | A Computational Cognitive Biomarker for Early-Stage Huntington's Disease. |
| title_full | A Computational Cognitive Biomarker for Early-Stage Huntington's Disease. |
| title_fullStr | A Computational Cognitive Biomarker for Early-Stage Huntington's Disease. |
| title_full_unstemmed | A Computational Cognitive Biomarker for Early-Stage Huntington's Disease. |
| title_short | A Computational Cognitive Biomarker for Early-Stage Huntington's Disease. |
| title_sort | computational cognitive biomarker for early stage huntington s disease |
| url | https://doi.org/10.1371/journal.pone.0148409 |
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