  Parkinsons disease (PD) is a movement disorder and the second most common
neurodengerative disease but despite its relative abundance, there are no
clinically accepted neuroimaging biomarkers to make prognostic predictions or
differentiate between the similar atypical neurodegenerative diseases Multiple
System Atrophy and Progressive Supranuclear Palsy. Abnormal connectivity in
circuits including the motor circuit and basal ganglia have been previously
shown as early markers of neurodegeneration. Therefore, we postulate the
combination patterns of interregional dysconnectivity across the brain can be
used to form a patient-specific predictive model of disease state and
progression in PD. These models, which employ connectivity calculated from
noninvasively measured functional MRI, differentially predict between PD and
the atypical lookalikes, predict progression on a disease-specific scale, and
predict cognitive decline. Further, we identify the connections most
informative for progression and diagnosis. When predicting the one-year
progression in the Movement Disorder Society-sponsored revision of the Unified
Parkinson's Disease Rating Scale (MDS-UPDRS) and Montreal Cognitive assessment
(MoCA), mean absolute errors of 1.8 and 0.6 basis points in the prediction are
achieved respectively. A balanced accuracy of 0.68 is attained when
distinguishing idiopathic PD versus the lookalikes and healthy controls. We
additionally find network components strongly associated with the prognostic
and diagnostic tasks, particularly incorporating connections within deep
nuclei, motor regions, and the Thalamus. These predictions, using an MRI
modality readily available in most clinical settings, demonstrate the strong
potential of fMRI connectivity as a prognostic biomarker in Parkinsons disease.
