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Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.

ABSTRACT: The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning algorithm that has been successfully applied to large-scale data analysis across vast biomedical disciplines, though rarely in psychiatric research or for application to longitudinal data. When provided with 795 raw and composite scores primarily from baseline measures, Random Forest regression prediction explained 50.8% (5000-run average, 95% bootstrap confidence interval [CI]: 50.3-51.3%) of the variance in proportion of time spent remitted. Machine performance improved when only the most predictive 24 items were used in a reduced analysis. Consistently high-ranked predictors of longitudinal remission included Yale-Brown Obsessive Compulsive Scale (Y-BOCS) items, NEO items and subscale scores, Y-BOCS symptom checklist cleaning/washing compulsion score, and several self-report items from social adjustment scales. Random Forest classification was able to distinguish participants according to binary remission outcomes with an error rate of 24.6% (95% bootstrap CI: 22.9-26.2%). Our results suggest that clinically-useful prediction of remission may not require an extensive battery of measures. Rather, a small set of assessment items may efficiently distinguish high- and lower-risk patients and inform clinical decision-making.

SUBMITTER: Askland KD

PROVIDER: S-EPMC5466447 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.

Askland Kathleen D KD Garnaat Sarah S Sibrava Nicholas J NJ Boisseau Christina L CL Strong David D Mancebo Maria M Greenberg Benjamin B Rasmussen Steve S Eisen Jane J

International journal of methods in psychiatric research 20150521 2

The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning algorithm that has been successfully applied to large-scale data analysis across vast biomedical disciplines, though rarely in psychiatric research or for application to longitudinal data. When provided with 795 raw and composite scores primarily fr ...[more]

PMID: 25994109

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Project description:The authors assessed the functioning of mesolimbic and striatal areas involved in reward-based spatial learning in unmedicated adults with obsessive-compulsive disorder (OCD).Functional MRI blood-oxygen-level-dependent response was compared in 33 unmedicated adults with OCD and 33 healthy, age-matched comparison subjects during a reward-based learning task that required learning to use extramaze cues to navigate a virtual eight-arm radial maze to find hidden rewards. The groups were compared in their patterns of brain activation associated with reward-based spatial learning versus a control condition in which rewards were unexpected because they were allotted pseudorandomly to experimentally prevent learning.Both groups learned to navigate the maze to find hidden rewards, but group differences in neural activity during navigation and reward processing were detected in mesolimbic and striatal areas. During navigation, the OCD group, unlike the healthy comparison group, exhibited activation in the left posterior hippocampus. Unlike healthy subjects, participants in the OCD group did not show activation in the left ventral putamen and amygdala when anticipating rewards or in the left hippocampus, amygdala, and ventral putamen when receiving unexpected rewards (control condition). Signal in these regions decreased relative to baseline during unexpected reward receipt among those in the OCD group, and the degree of activation was inversely associated with doubt/checking symptoms.Participants in the OCD group displayed abnormal recruitment of mesolimbic and ventral striatal circuitry during reward-based spatial learning. Whereas healthy comparison subjects exhibited activation in this circuitry in response to the violation of reward expectations, unmedicated OCD participants did not and instead over-relied on the posterior hippocampus during learning. Thus, dopaminergic innervation of reward circuitry may be altered, and future study of anterior/posterior hippocampal dysfunction in OCD is warranted.

Dataset Information

Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.

Publications

Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.

Similar Datasets

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