TY - JOUR
T1 - Assessing the Relationship Between Body Mass Index and Neural Activity of Prefrontal Cortex in Overweight Adults Using EEG-Resting State Data
T2 - A Wavelet Transform Analysis
AU - Al-Hiyali, Mohammed Isam
AU - Ishak, Asnor Juraiza
AU - Al-Quraishi, Maged Saleh Saeed
AU - Mahmood, Sarmad Nozad
N1 - Publisher Copyright:
© 2025, Semarak Ilmu Publishing. All rights reserved.
PY - 2025/3
Y1 - 2025/3
N2 - Neuroscientific evidence suggests that weight gain may be associated with changes in brain lobes' volume and function, as well as impulsive behaviour related to eating. However, it remains unclear whether impulsivity behaviour in overweight subjects is linked to abnormal activity in the resting state. To address this question, we propose a novel method to assess the relationship between different levels of body mass index (BMI) and neural activity of the prefrontal cortex (PFC) using electroencephalography (EEG) resting state data. EEG signals recorded during open-eye resting state from 36 subjects were divided into two groups based on BMI: overweight and normal weight subjects. We applied wavelet transform technique to compute the power for decomposed EEG bands and extracted coherence maps to assess the functional connectivity of the PFC. The one-way analysis of variance (ANOVA) was employed to assess the difference in EEG variables between the study groups. The results show a significant increase in the power of the sub-Theta band (4.49-5.34) Hz in overweight subjects compared to normal weight subjects (p-value = 0.001), as well as dysfunctional connectivity between left-right prefrontal sites in the overweight group with decreasing coherence function. These outcomes suggest that the specific PFC-EEG signals observed in overweight individuals are consistent with EEG patterns seen in other impulsivity-related diseases. Therefore, our findings reveal a specific EEG pattern in overweight adults that could be potentially utilized in developing neurotherapybased treatment methods for overweight management.
AB - Neuroscientific evidence suggests that weight gain may be associated with changes in brain lobes' volume and function, as well as impulsive behaviour related to eating. However, it remains unclear whether impulsivity behaviour in overweight subjects is linked to abnormal activity in the resting state. To address this question, we propose a novel method to assess the relationship between different levels of body mass index (BMI) and neural activity of the prefrontal cortex (PFC) using electroencephalography (EEG) resting state data. EEG signals recorded during open-eye resting state from 36 subjects were divided into two groups based on BMI: overweight and normal weight subjects. We applied wavelet transform technique to compute the power for decomposed EEG bands and extracted coherence maps to assess the functional connectivity of the PFC. The one-way analysis of variance (ANOVA) was employed to assess the difference in EEG variables between the study groups. The results show a significant increase in the power of the sub-Theta band (4.49-5.34) Hz in overweight subjects compared to normal weight subjects (p-value = 0.001), as well as dysfunctional connectivity between left-right prefrontal sites in the overweight group with decreasing coherence function. These outcomes suggest that the specific PFC-EEG signals observed in overweight individuals are consistent with EEG patterns seen in other impulsivity-related diseases. Therefore, our findings reveal a specific EEG pattern in overweight adults that could be potentially utilized in developing neurotherapybased treatment methods for overweight management.
KW - EEG-resting state
KW - Neurotherapy
KW - Overweight
KW - Prefrontal cortex neural activity
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85194844376&partnerID=8YFLogxK
U2 - 10.37934/araset.45.1.137153
DO - 10.37934/araset.45.1.137153
M3 - Article
AN - SCOPUS:85194844376
SN - 2462-1943
VL - 45
SP - 137
EP - 153
JO - Journal of Advanced Research in Applied Sciences and Engineering Technology
JF - Journal of Advanced Research in Applied Sciences and Engineering Technology
IS - 1
ER -