No abstract

Background: Among motor symptoms of Parkinson’s disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III is an agent-based score making reproducible measurements and follow-up challenging. Objective: Using a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the guidelines of the gold-standard MDS-UPDRS III. Methods: We adapted and applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton for a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols acquired in the Movement Disorder unit of Avicenne University Hospital. Results: We developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines. Conclusion: We developed a deep learning-based tool to precisely analyze movement parameters allowing to reliably score bradykinesia for parkinsonian patients in a MDS-UPDRS manner.

To better understand how the brain allows primates to perform various set of tasks, the ability to simultaneously record the activity of the brain at multiple temporal and spatial scales is challenging but necessary. In non-human primates, combined fMRI and electrophysiological recordings have not disentangle the contributions of spiking activity to the neurovascular response. Here, we combined functional ultrasound imaging (fUS) of cerebral blood volume (CBV) and recording of single-unit activities (SUA) in visual and fronto-medial cortices of behaving macaques. We computed task-induced and SUA-induced CBV activation maps. We demonstrate that SUA provides a significant estimate of the neurovascular response below the typical fMRI voxel spatial resolution of 2mm3. Furthermore, our results also show that single unit and CBV activities are statistically uncorrelated during the resting states but correlate during behaving tasks. Conversely, during the resting states, CBV activities across known connected brain areas are correlated but decorrelate at the onset of the tasks as expected if participating in the default mode network (DMN). These results have important implications for interpreting functional imaging findings collected with fMRI or fUS while one constructs inferences of spiking activities during resting-state or while primates perform tasks.