Network Hyperexcitability in Early Alzheimer’s Disease: Is Functional Connectivity a Potential Biomarker?

Abstract

Network hyperexcitability (NH) is an important feature of the pathophysiology of Alzheimer’s disease. Functional connectivity (FC) of brain networks has been proposed as a potential biomarker for NH. Here we use a whole brain computational model and resting-state MEG recordings to investigate the relation between hyperexcitability and FC. Oscillatory brain activity was simulated with a Stuart Landau model on a network of 78 interconnected brain regions. FC was quantified with amplitude envelope correlation (AEC) and phase coherence (PC). MEG was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Functional connectivity was determined with the corrected AECc and phase lag index (PLI), in the 4–8 Hz and the 8–13 Hz bands. The excitation/inhibition balance in the model had a strong effect on both AEC and PC. This effect was different for AEC and PC, and was influenced by structural coupling strength and frequency band. Empirical FC matrices of SCD and MCI showed a good correlation with model FC for AEC, but less so for PC. For AEC the fit was best in the hyperexcitable range. We conclude that FC is sensitive to changes in E/I balance. The AEC was more sensitive than the PLI, and results were better for the thetaband than the alpha band. This conclusion was supported by fitting the model to empirical data. Our study justifies the use of functional connectivity measures as surrogate markers for E/I balance.

Analysis of the alpha activity envelope in electroencephalography in relation to the ratio of excitatory to inhibitory neural activity

“…We chose the EEG datasets provided by Miltiadous et al [ 37 – 39 ] and compared the Ap-An ratio between healthy participants and participants with dementia, in which the alpha oscillations associated with the excitatory and inhibitory functions might be destructed [ 26 – 29 , 51 ]. The present results showed a larger Ap-An ratio in the participants with dementia than in the healthy participants, suggesting that there were brain regions where the control of alpha wave amplitude differed between normal participants and patients with dementia.…”

Hub overload and failure as a final common pathway in neurological brain network disorders

“…Within the framework of the hub overload and failure scenario, it makes sense to interpret abnormal activation and hyperconnectivity of hubs as an early pathological phenomenon that should be treated to prevent subsequent structural damage to hub nodes. Furthermore, this scenario predicts that measures of hub functional hyperconnectivity could be potential biomarkers of hyperexcitability ( Cuesta et al, 2022 ; Ranasinghe et al, 2022 ; Stam et al, 2023 ).…”

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection

“…Not only can it confirm the diagnosis (it can also clarify the type of epilepsy), but it can have a role in making therapeutic decisions (e.g., whether to stop treatment in patients without seizures) as well as prognostic significance (e.g., evaluating critically ill patients for possible epileptic status or development of encephalopathy) ( Trinka and Leitinger, 2022 ). Apart from mentioned diseases, this method is also widely used in the early diagnosis of dementia ( Al-Qazzaz et al, 2014 ), Mb Alzheimer’s ( Stam et al, 2023 ), brain tumors ( Ajinkya et al, 2021 ), sleep disorders ( Kaskie and Ferrarelli, 2019 ; Steiger and Pawlowski, 2019 ), as well as the most severe neurodegenerative diseases ( Kidokoro et al, 2020 ). Artificial intelligence methods show immense potential in detection of different medical conditions.…”