Imagine trying to start walking, but your brain gets stuck like a broken record. That’s what happens to people with freezing of gait (FOG) in Parkinson’s disease. Scientists are still puzzled by this phenomenon, but a recent study has uncovered some clues. By analyzing electrical signals in the brain (measured by EEG), researchers found that certain patterns of synchronization between different brain regions were associated with FOG. One key finding was that the frontoparietal theta phase synchrony was unique to PD patients with FOG. Additionally, alpha phase synchrony was dominant in deeper brain networks of PD patients with FOG, while beta phase synchrony was more prominent in those without FOG. The study also revealed abnormal coupling between different frequency bands, suggesting disrupted communication within the brain. These findings shed light on the underlying mechanisms of FOG and could potentially lead to the development of EEG-based biomarkers for diagnosing and monitoring FOG in Parkinson’s disease. If you’re curious to learn more, dive into the full research article!

Freezing of gait (FOG) is a complex gait disturbance in Parkinson’s disease (PD), during which the patient is not able to effectively initiate gait or continue walking. The mystery of the FOG phenomenon is still unsolved. Recent studies have revealed abnormalities in cortical activities associated with FOG, which highlights the importance of cortical and cortical-subcortical network dysfunction in PD patients with FOG. In this paper, phase-locking value (PLV) of eight frequency sub-bands between 0.05 Hz and 35 Hz over frontal, motor, and parietal areas [during an ankle dorsiflexion (ADF) task] is used to investigate EEG phase synchronization. PLV was investigated over both superficial and deeper networks by analyzing EEG signals preprocessed with and without Surface Laplacian (SL) spatial filter. Four groups of participants were included: PD patients with severe FOG (N = 5, 5 males), PD patients with mild FOG (N = 7, 6 males), PD patients without FOG (N = 14, 13 males), and healthy age-matched controls (N = 13, 10 males). Fifteen trials were recorded from each participant. At superficial layers, frontoparietal theta phase synchrony was a unique feature present in PD with FOG groups. At deeper networks, significant dominance of interhemispheric frontoparietal alpha phase synchrony in PD with FOG, in contrast to beta phase synchrony in PD without FOG, was identified. Alpha phase synchrony was more distributed in PD with severe FOG, with higher levels of frontoparietal alpha phase synchrony. In addition to FOG-related abnormalities in PLV analysis, phase-amplitude coupling (PAC) analysis was also performed on frequency bands with PLV abnormalities. PAC analysis revealed abnormal coupling between theta and low beta frequency bands in PD with severe FOG at the superficial layers over frontal areas. At deeper networks, theta and alpha frequency bands show high PAC over parietal areas in PD with severe FOG. Alpha and low beta also presented PAC over frontal areas in PD groups with FOG. The results introduced significant phase synchrony differences between PD with and without FOG and provided important insight into a possible unified underlying mechanism for FOG. These results thus suggest that PLV and PAC can potentially be used as EEG-based biomarkers for FOG.

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