Wiecki Thomas V, Antoniades Chrystalina A, Stevenson Alexander, Kennard Christopher, Borowsky Beth, Owen Gail, Leavitt Blair, Roos Raymund, Durr Alexandra, Tabrizi Sarah J, Frank Michael J
Cognitive, Linguistic & Psychological Sciences, Brown, Providence, United States of America.
Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, Level 6 West Wing, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, United Kingdom.
PLoS One. 2016 Feb 12;11(2):e0148409. doi: 10.1371/journal.pone.0148409. eCollection 2016.
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.
亨廷顿舞蹈症（HD）由基因决定，但症状出现存在变异性，这导致在何时应开始药物干预方面存在不确定性。在此，我们采用一种基于神经认知表型分析、计算建模和分类的计算方法，试图在症状出现前提供HD的定量预测指标。一大组受试者——包括携带HD突变的症状前个体（症状前HD）、早期有症状个体以及健康对照——执行了反扫视冲突任务，该任务需要执行控制和反应抑制。虽然有症状的HD受试者在行为指标[反应时间（RT）和错误率]上与对照组有显著差异，但症状前HD组没有如此明显的行为差异。RT分布和错误率通过基于累加器的模型进行拟合，该模型总结了所涉及的计算过程，并且与前额叶皮层和基底神经节更详细的神经模型中确定的机制相关。基于拟合模型参数的分类显示，一个与执行控制相关的关键参数区分了症状前HD组和对照组，而反应抑制参数仅在症状出现后下降。这些发现证明了计算方法在脑部疾病分类和预测中的实用性，并为潜在的神经机制提供了线索。
