Back to Search Start Over

Longitudinal prognosis of Parkinson’s outcomes using causal connectivity

Authors :
Cooper J. Mellema
Kevin P. Nguyen
Alex Treacher
Aixa X. Andrade
Nader Pouratian
Vibhash D. Sharma
Padraig O'Suileabhain
Albert A. Montillo
Source :
NeuroImage: Clinical, Vol 42, Iss , Pp 103571- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Despite the prevalence of Parkinson’s disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson’s disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1*1* A summary of all acronyms used in this paper is presented in Appendix A. Supplementary Data. and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

Details

Language :
English
ISSN :
22131582
Volume :
42
Issue :
103571-
Database :
Directory of Open Access Journals
Journal :
NeuroImage: Clinical
Publication Type :
Academic Journal
Accession number :
edsdoj.1627f5a542ec411cbc80d232aae49bbb
Document Type :
article
Full Text :
https://doi.org/10.1016/j.nicl.2024.103571