Dataset Information

A Computational Cognitive Biomarker for Early-Stage Huntington's Disease.

ABSTRACT: 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.

SUBMITTER: Wiecki TV

PROVIDER: S-EPMC4752511 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Computational Cognitive Biomarker for Early-Stage Huntington's Disease.

Wiecki Thomas V TV Antoniades Chrystalina A CA Stevenson Alexander A Kennard Christopher C Borowsky Beth B Owen Gail G Leavitt Blair B Roos Raymund R Durr Alexandra A Tabrizi Sarah J SJ Frank Michael J MJ

PloS one 20160212 2

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 ...[more]

PMID: 26872129

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Project description:BackgroundIn Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.ObjectivesTo improve stratification of Huntington's disease individuals for clinical trials.MethodsWe used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.ResultsThe predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).ConclusionsThis study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Project description:Cognitive decline accompanying the clinically more salient motor symptoms of Huntington's disease (HD) has been widely noted and can precede motor symptoms onset. Less clear is how such decline bears on language functions in everyday life, though a small number of experimental studies have revealed difficulties with the application of rule-based aspects of language in early stages of the disease. Here we aimed to determine whether there is a systematic linguistic profile that characterizes spontaneous narrative speech in both pre-manifest and/or early manifest HD, and how it is related to striatal degeneration and neuropsychological profiles. Twenty-eight early-stage patients (19 manifest and 9 gene-carriers in the pre-manifest stage), matched with 28 controls, participated in a story-telling task. Speech was blindly scored by independent raters according to fine-grained linguistic variables distributed over 5 domains for which composite scores were computed (Quantitative, Fluency, Reference, Connectivity, and Concordance). Voxel-based morphometry (VBM) was used to link specific brain degeneration patterns to loci of linguistic decline. In all of these domains, significant differences were observed between groups. Deficits in Reference and Connectivity were seen in the pre-manifest stage, where no other neuropsychological impairment was detected. Among HD patients, there was a significant positive correlation only between the values in the Quantitative domain and gray matter volume bilaterally in the putamen and pallidum. These results fill the gap of qualitative data of spontaneous narrative speech in HD and reveal that HD is characterized by systematic linguistic impairments leading to dysfluencies and disorganization in core domains of grammatical organization. This includes the referential use of noun phrases and the embedding of clauses, which mediate crucial dimensions of meaning in language in its normal social use. Moreover, such impairment is seen prior to motor symptoms onset and when standardized neuropsychological test profiles are otherwise normal.

Dataset Information

A Computational Cognitive Biomarker for Early-Stage Huntington's Disease.

Publications

A Computational Cognitive Biomarker for Early-Stage Huntington's Disease.

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