Poster 1

[P-1-03] Electroencephalographic Network Features Associated with Symptom Severity in Individuals with Irritable Bowel Syndrome

*Toru Yasukawa¹, Yusuke Yamazato², Minori Machida², Jun Tayama³, Toyohiro Hamaguchi¹ (1.Saitama Prefectural University, Graduate School of Health, Medicine and Welfare(Japan), 2.Waseda University, Graduate School of Human Sciences(Japan), 3.Waseda University, Faculty of Human Sciences(Japan))

Keywords:

irritable bowel syndrome,EEG,brain network

This study aimed to clarify brain network characteristics associated with symptom severity in individuals with irritable bowel syndrome (IBS). IBS is a functional gastrointestinal disorder involving brain-gut dysregulation, and previous research suggests that abnormalities in resting-state brain networks may relate to symptom burden. Fourteen adult male participants with IBS symptoms underwent ten-minute eyes-closed resting-state electroencephalographic (EEG) recordings. From each dataset, ten 2-second segments were randomly selected. Wavelet transformation was applied to each EEG channel, and wavelet correlation coefficients were used to construct an EEG network, with electrodes as nodes and inter-channel correlations as edges. Two network metrics clustering coefficient and characteristic path length, were calculated and normalized against random networks. This study was approved by the Ethics Review Committee on Research with Human Subjects of Waseda University (2024-142). Simple linear regression showed that the normalized clustering coefficient was significantly and negatively associated with IBS severity (R² = 0.45, β = -0.67, p = 0.009). In contrast, the normalized characteristic path length showed no significant association (R² = 0.14, β = -0.38, p = 0.19). These findings suggest an increase in local connectivity within brain networks, as indicated by a higher clustering coefficient, may reflect decreased symptom severity in IBS. This provides preliminary evidence that EEG-derived network metrics, particularly the clustering coefficient, could serve as potential biomarkers for IBS-related brain dysfunction. Understanding such network patterns may also support the development of targeted EEG-based neurofeedback interventions.