Brain connectivity fingerprinting as a predictive biomarker of art therapy outcomes in Parkinson’s disease

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Abstract Art therapy has emerged as a complementary approach to Parkinson’s Disease (PD), as it engages motor, cognitive, and emotional functions. However, individual responses to art therapy are highly variable and predictors of therapeutic efficacy are largely unknown. We hypothesized that the response heterogeneity may be related to individual patterns of brain activity and connectivity. Here, we combine functional connectomics, brain fingerprinting, and machine learning to identify such patterns and predict art therapy outcomes in PD. We mapped functional connectomes from high-resolution functional MRI of 23 patients with PD collected before a six-week art therapy protocol. We also assessed individual connectome fingerprints, examined their spatial specificity, and conducted meta-analytic functional decoding to link network topography with functional domains. Leveraging these network fingerprints, we computed topological measures and developed predictive models to identify patients most likely to benefit from art therapy, reaching an accuracy of 0.83 and a ROC-AUC of 0.80. Our results demonstrate that brain fingerprint-informed network measures can capture interindividual variability of therapy response, offering a data-driven, personalized approach to treatment. This study provides the first evidence that functional connectome fingerprints can guide personalized treatments in PD.

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