Comparison of brain functional connectivity methods for the diagnosis of Parkinson’s disease using resting state fMRI

Background
In the absence of validated biomarkers, the early diagnosis of Parkinson’s disease (PD), the second most common neurodegenerative disorder worldwide [1], is challenging and is prone to low accuracy [2]. Recent evidence suggests that the average pattern of functional connectivity (FC) between the basal ganglia and cerebral cortex assessed in the resting state using functional magnetic resonance imaging (rs-fMRI) might discriminate between mild PD and healthy controls with 85% overall accuracy [3].
Goal
We will test if this finding can be replicated in our population. We will also compare the diagnosis accuracy of this approach, which depicts an average pattern of connectivity during the whole scanning period, with that of dynamic FC that investigates the spontaneous fluctuations of the pattern of connectivity over the scanning period [4].
Methods
We are currently processing and analyzing rs-fMRI data prospectively acquired on a 3T MRI in 39 patients with PD (mean disease duration 5.4 years; mean Hoehn and Yahr stage 1.5) and 39 healthy controls matched for age, gender and levels of education. For dynamic FC we will compare two different methods [4], one that use slice-time windows to capture brain dynamics with another that captures spatial co-activation patters (CAPs) at specific time points.
Conclusion
The selected methods will be further validated in a new cohort of de novo drug-naïve PD patients.
[1] Tessitore, A., et al. Sensorimotor connectivity in Parkinson’s disease: the role of functional neuroimaging. Frontiers in neurology 5 (2014).
[2] Adler et al. Low clinical diagnostic accuracy of early vs advanced Parkinson disease. Clinicopathologic study. Neurology 2014;83:406–412
[3] Szewczyk-Krolikowski, K., et al. Functional connectivity in the basal ganglia network differentiates PD patients from controls. Neurology 83.3 (2014): 208-214.
[4] Hutchison, M., et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80 (2013): 360-378.