Objective: This study investigated longitudinal clinical, structural and functional brain alterations in Parkinson’s disease patients with freezing of gait (PD-FoG) and in those developing (PD-FoG-converters) and not developing FoG (PD-non-converters) over two years. Moreover, we explored if any clinical and/or MRI metric predict FoG development.

Background: FoG is a multifactorial phenomenon and the pathophysiological mechanisms underlying FoG development are still debated.

Method: Thirty PD-FoG, 11 PD-FoG-converters and 11 PD-non-converters were followed for two years. Thirty healthy controls were included at baseline. Participants underwent clinical and MRI visits. Cortical thickness, basal ganglia volumes and functional network graph metrics were evaluated at baseline and over time. In PD groups, correlations between baseline MRI and clinical worsening were tested. A ROC curve analysis investigated if baseline clinical and MRI measures, selected using a stepwise model procedure, could differentiate PD-FoG-converters from PD-non-converters.

Results: At baseline, PD-FoG patients showed widespread cortical/subcortical atrophy, while PD-FoG-converters and non-converters presented atrophy in sensorimotor areas and basal ganglia relative to controls. Over time, PD-non-converters accumulated cortical thinning of sensorimotor/temporal areas, and atrophy of basal ganglia without significant clinical changes. PD-FoG-converters showed worsening of disease severity, executive functions, and mood together with an accumulation of occipital atrophy, similarly to PD-FoG. At baseline, PD-FoG-converters relative to controls and PD-FoG showed higher global and parietal clustering coefficient and global local efficiency. Over time, PD-FoG-converters showed reduced parietal clustering coefficient, PD-non-converters showed increased sensorimotor path length, while PD-FoG patients showed stable graph metrics. Stepwise prediction model including dyskinesia, postural instability and gait disorders (PIGD) scores and parietal clustering coefficient was the best predictor of FoG conversion. Combining clinical and MRI data, ROC curves provided the highest classification power to predict the conversion (AUC= 0.95, 95%CI: 0.86-1).

Conclusion: Our findings suggested that altered functional graph metrics of the parietal lobe together with the presence of dyskinesia and high PIGD score could be markers of FoG development in PD patients.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/mri-biomarkers-of-freezing-of-gait-development-in-in-parkinsons-disease/