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Updated: Sep 10, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
A transdiagnostic approach to non-motor symptoms in Parkinson's disease: exploring internalizing, bipolar spectrum,
Ardıl Bayram Şahin1,2,3, Mahmoud Mahmoudi1,2, Yasemin Kadandır4
1Graduate School of Health Sciences, Koç University, Istanbul, Türkiye.
Background:
Parkinson's disease (PD) is associated with diverse non-motor symptoms (NMS), including depression, anxiety, anhedonia, bipolar-spectrum features, and impulsive-compulsive behaviors (ICBs), which substantially impair quality of life. Although these domains may share dopaminergic and frontostriatal mechanisms, they are often studied separately. This study adopted a transdiagnostic, person-centered approach to identify clinically meaningful NMS profiles in PD.
Methods:
In this cross-sectional study, 126 patients with PD and no dementia diagnosis from two university centers completed validated assessments of ICBs, impulsivity, anhedonia, depression, anxiety, and bipolar-spectrum symptoms. K-means clustering was applied to standardized variables, and cluster validity was evaluated using multiple stability metrics.
Results:
The four-cluster solution demonstrated the best validity and identified Low-Symptom (44.3%), Moderate Internalizing (29.4%), Bipolar-Impulsive (16.7%), and Severe Internalizing-Impulsive (9.5%) profiles. Clusters differed significantly across depression, anxiety, anhedonia, bipolar-spectrum symptoms, ICBs, and impulsivity. The Bipolar-Impulsive group was significantly younger, whereas the Moderate Internalizing profile was older and predominantly female. The Severe Internalizing-Impulsive cluster showed the highest psychiatric burden, motor severity, disease stage, dopaminergic treatment load, and amantadine use. No significant between-cluster differences emerged in global cognitive performance.
Conclusion:
These findings support a transdiagnostic model in PD in which affective dysregulation and impulsivity interact with disease-related factors to form distinct neuropsychiatric phenotypes. Recognizing these profiles may facilitate earlier identification of vulnerable patients and more individualized neuropsychiatric management strategies beyond a solely dopaminergic framework.
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