TY - JOUR
T1 - Domain-Specific Prediction of Clinical Progression in Parkinson's Disease Using the Mosaic Approach
AU - Tahedl, Marlene
AU - Bogdahn, Ulrich
AU - Wimmer, Bernadette
AU - Hedderich, Dennis M.
AU - Kirschke, Jan S.
AU - Zimmer, Claus
AU - Wiestler, Benedikt
N1 - Publisher Copyright:
© 2025 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.
PY - 2025/1
Y1 - 2025/1
N2 - Purpose: Due to the highly individualized clinical manifestation of Parkinson's disease (PD), personalized patient care may require domain-specific assessment of neurological disability. Evidence from magnetic resonance imaging (MRI) studies has proposed that heterogenous clinical manifestation corresponds to heterogeneous cortical disease burden, suggesting customized, high-resolution assessment of cortical pathology as a candidate biomarker for domain-specific assessment. Method: Herein, we investigate the potential of the recently proposed Mosaic Approach (MAP), a normative framework for quantifying individual cortical disease burden with respect to a population-representative cohort, in predicting domain-specific clinical progression. Using MRI and clinical data from 135 recently diagnosed PD patients from the Parkinson's Progression Markers Initiative, we first defined an extremity-specific motor score. We then identified cortical regions corresponding to “extremity functions” and restricted MAP, respectively, and contrasted the explanatory power of the extremity-specific MAP to unrestricted MAP. As control conditions, domain-related but less specific general motor function and nondomain-specific cognitive scores were considered. We also tested the predictive power of the restricted MAP in predicting disease progression over 1 and 3 years using support vector machines. The restricted, extremity-specific MAP yielded higher explanatory power for extremity-specific motor function at baseline as opposed to the unrestricted, whole-brain MAP. On the contrary, for general motor function, the unrestricted, whole-brain MAP yielded higher power. Finding: No associations were found for cognitive function. The extremity-specific MAP predicted extremity-specific motor progression over 1 and 3 years above chance level. The MAP framework allows for domain-specific prediction of customized PD disease progression, which can inform machine learning, thereby contributing to personalized PD patient care.
AB - Purpose: Due to the highly individualized clinical manifestation of Parkinson's disease (PD), personalized patient care may require domain-specific assessment of neurological disability. Evidence from magnetic resonance imaging (MRI) studies has proposed that heterogenous clinical manifestation corresponds to heterogeneous cortical disease burden, suggesting customized, high-resolution assessment of cortical pathology as a candidate biomarker for domain-specific assessment. Method: Herein, we investigate the potential of the recently proposed Mosaic Approach (MAP), a normative framework for quantifying individual cortical disease burden with respect to a population-representative cohort, in predicting domain-specific clinical progression. Using MRI and clinical data from 135 recently diagnosed PD patients from the Parkinson's Progression Markers Initiative, we first defined an extremity-specific motor score. We then identified cortical regions corresponding to “extremity functions” and restricted MAP, respectively, and contrasted the explanatory power of the extremity-specific MAP to unrestricted MAP. As control conditions, domain-related but less specific general motor function and nondomain-specific cognitive scores were considered. We also tested the predictive power of the restricted MAP in predicting disease progression over 1 and 3 years using support vector machines. The restricted, extremity-specific MAP yielded higher explanatory power for extremity-specific motor function at baseline as opposed to the unrestricted, whole-brain MAP. On the contrary, for general motor function, the unrestricted, whole-brain MAP yielded higher power. Finding: No associations were found for cognitive function. The extremity-specific MAP predicted extremity-specific motor progression over 1 and 3 years above chance level. The MAP framework allows for domain-specific prediction of customized PD disease progression, which can inform machine learning, thereby contributing to personalized PD patient care.
KW - cortical thickness
KW - machine learning
KW - magnetic resonance imaging
KW - Parkinson's disease
KW - personalized medicine
UR - http://www.scopus.com/inward/record.url?scp=85214810631&partnerID=8YFLogxK
U2 - 10.1002/brb3.70289
DO - 10.1002/brb3.70289
M3 - Article
C2 - 39789902
AN - SCOPUS:85214810631
SN - 2162-3279
VL - 15
JO - Brain and Behavior
JF - Brain and Behavior
IS - 1
M1 - e70289
ER -