Translational PsychiatryNeurosciencePeer-reviewed journal article
Electrophysiological signatures of anxiety in Parkinson's disease
Sahar YassineiD, Sourour Almarouk, Ute GschwandtneriD, Manon AuffretiD, Peter FuhriD, Marc VériniD, Mahmoud HassaniD
Abstract
Anxiety is a common non-motor symptom in Parkinsons disease (PD) occurring in up to 31% of the patients and affecting their quality of life. Despite the high prevalence, anxiety symptoms in PD are often underdiagnosed and, therefore, undertreated. To date, functional and structural neuroimaging studies have contributed to our understanding of the motor and cognitive symptomatology of PD. Yet, the underlying pathophysiology of anxiety symptoms in PD remains largely unknown and studies on their neural correlates are missing. Here, we used resting state electroencephalography (RS-EEG) of 68 non-demented PD patients with or without clinically-defined anxiety and 25 healthy controls (HC) to assess spectral and functional connectivity fingerprints characterizing the PD-related anxiety. When comparing the brain activity of the PD anxious group (PD-A, N=18) to both PD non-anxious (PD-NA, N=50) and HC groups (N=25) at baseline, our results showed increased fronto-parietal delta power and decreased frontal beta power depicting the PD-A group. Results also revealed hyper-connectivity networks predominating in delta, theta and gamma bands against prominent hypo-connectivity networks in alpha and beta bands as network signatures of anxiety in PD where the frontal, temporal, limbic and insular lobes exhibited the majority of significant connections. Moreover, the revealed EEG-based electrophysiological signatures were strongly associated with the clinical scores of anxiety over the course of the disease. We believe that the identification of the electrophysiological correlates of anxiety in PD using EEG is conducive toward more accurate prognosis and diagnosis and can ultimately support the development of new therapeutics strategies.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 11
- FWCI
- 2.93
- Field percentile
- top 9%
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