Objective: The present study aims to demonstrate a possible application of the analysis of functional brain connectivity from brain connectivity matrices.
 Specifically, we aim to: 
– Differentiate between the healthy gait state and the involuntary freezing state in Parkinson’s disease
– to show that our methodology is also capable of differentiate the voluntary rest state and the involuntary freezing state

Background: Freezing of gait (FOG)  is the most common gait disorder in advanced Parkinson’s disease (PD), with a great impact on the quality of life and morbidity of these patients. Current therapies are partially effective, likely reflecting their multifaceted and enigmatic underlying pathophysiology. One of the strengths of Electroencephalography (EEG) is its ability to assess brain function while walking; Its millisecond temporal resolution may allow more accurate study of brain dynamics during freezing episodes.

Method: In this experimental study, 3 patients with Parkinson’s Disease and FOG, in off medical, performed two series of timed up and go (TUG) tasks: Regular TUG and trials including voluntary stops with verbal instructions to suddenly stop movement. The EEG time series of each individual has moments of involuntary freezing state, healthy gait state and voluntary rest state. Brain connectivity matrices were used, using Pearson correlations, Spearman correlations, Granger causality and mutual information to feed a machine learning algorithm.

Results: In the most cases, the connectivity matrix method presents excellent performance in discriminating patients in different brain states. We obtained the results in the Table 1. In Figure 4, we calculated the area under the curve (AUC) (1.0) = 0.83 for the naive bayes classifier. In all cases, our tests showed that the use of connectivity matrices is a reliable technique for differentiating distinct brain information.

Conclusion: This study adds relevant insights into possible changes in brain connectivity in FOG. Differentiating brain connectivity from FOG and voluntary arrest may help in future brain-machine interface technologies and in the prognostic assessment of future rehabilitations in FOG.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/connectivity-matrices-for-differentiating-voluntary-stopping-states-and-involuntary-freezing-states-in-parkinsons-disease/