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Original Article Fatigue in PD is due to decreased efficiency of the frontal network: quantitative EEG analysis
Min Seung Kim1orcid , SangUk Park2orcid , Ukeob Park2orcid , Seung Wan Kang2,3orcid , Suk Yun Kang1corresp_iconorcid

DOI: [Accepted]
Published online: June 10, 2024
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1Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
2iMediSync, Inc., Seoul, South Korea
3National Standard Reference Data Center for Korean EEG, College of Nursing, Seoul National University, Seoul, South Korea
Corresponding author:  Suk Yun Kang, Tel: +82-31-8086-2310, Fax: +82-31-8086-2317, 
Received: 17 February 2024   • Revised: 22 March 2024   • Accepted: 5 June 2024
Min Seung Kim and SangUk Park contributed equally to this study as co-first authors.

Fatigue is a common, debilitating non-motor symptom of Parkinson’s disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could measure fatigue objectively and to expound on the pathophysiology of fatigue in PD.
We studied 32 de novo PD patients who underwent electroencephalography (EEG). We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectrum and graph including global efficiency (GE), characteristic path length (CPL), clustering coefficient (CCO), small worldness (SW), local efficiency (LE), degree centrality (DC), closeness centrality (CCE), and betweenness centrality (BC).
No significant differences in absolute and relative powers were seen between PD without and with fatigue (all ps > 0.02, Bonferroni-corrected). In network analysis, the brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD without fatigue (p= 0.0000, p = 0.0011, ps ≤ 0.0007, respectively, Bonferroni-corrected).
Our study suggests that PD patients with fatigue have less efficient networks in the frontal area compared with networks of those with PD without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD with fatigue is assumed to be compensation. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.

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