EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes

Abstract

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The neurometabolic profiles of GABA and Glutamate as revealed by proton magnetic resonance spectroscopy in type 1 and type 2 diabetes

“…Despite the lack of clear neurophysiological markers, there is a strong epidemiologic bond between diabetes and the development of dementia, possibly related to glycemic control and insulin dysregulation [ 20 , 21 ]. Recent work shows similar cortical plasticity patterns between T2DM, cognitive function and Alzheimer’s Disease (AD), including abnormal long-term potentiation (LTP)-like plasticity mechanisms and Glutamatergic neurotransmission inferred by TMS studies [ 43 , 44 ] and diffuse oscillatory activity slowing reflected by shifts from higher to lower frequencies in EEG power analysis [ 45 ]. Also, a subset of the ACCORD-MIND RCT trial showed that an increase of 1% in HbA 1c levels was associated with lower cognitive and memory test scores [ 46 ].…”

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

“…In view of increasing life expectancy [16], NFT could improve the social well-being of the growing elderly population in the future. Previous classical NFT studies on healthy young adults showed significant changes in theta (4-8 Hz) [17,18], alpha (8)(9)(10)(11)(12)(13) Hz) [19][20][21], and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [21] band powers. These studies also reported significant improvements in the results of neuropsychological tests for the assessment of memory-related [17,19,21], attention [17,21], and visuospatial functions [20].…”

“…The RP measurement gives information on the normalized weight of each band in the spectral distribution. The spectral bands selected to assess the induced EEG changes were delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The lower (l f i ) and upper (u f i ) frequency limits of each band were specified according to the classical definitions of EEG bands [57].…”

Global research on artificial intelligence-enhanced human electroencephalogram analysis

“…Issues regarding mental disorders are still of interest to scholars (e.g., [270][271][272][273][274][275][276][277]), in particular, epilepsy, Alzheimer's disease, mild cognitive impairments, synchronization, and attention-deficit/hyperactivity disorders. For epilepsy, recent research is concerned with issues such as proposing a deep learning-driven EEG approach to detect epileptic seizures from EEG discharges [278], epilepsy lateralization through intra-hemispheric brain networks based on resting-state magnetoencephalography data [279], and EEG-based multiclass seizure type classification using CNNs and transfer learning [280].…”