Power and distribution of evoked gamma oscillations in brain aging and cognitive performance

Modern neurophysiological techniques indexing normal or abnormal brain aging

Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.

Early cerebral amyloid-β accumulation and hypermetabolism are associated with subtle cognitive deficits before accelerated cerebral atrophy

AIMS

Alzheimer's disease (AD) is characterized by the accumulation of amyloid beta (Aβ) in the brain. The deposition of Aβ is believed to initiate a detrimental cascade, including cerebral hypometabolism, accelerated brain atrophy, and cognitive problems-ultimately resulting in AD. However, the timing and causality of the cascade resulting in AD are not yet fully established. Therefore, we examined whether early Aβ accumulation affects cerebral glucose metabolism, atrophy rate, and age-related cognitive decline before the onset of neurodegenerative disease.

METHODS

Participants from the Metropolit 1953 Danish Male Birth Cohort underwent brain positron emission tomography (PET) imaging using the radiotracers [11C]Pittsburgh Compound-B (PiB) (N = 70) and [18F]Fluorodeoxyglucose (FDG) (N = 76) to assess cerebral Aβ accumulation and glucose metabolism, respectively. The atrophy rate was calculated from anatomical magnetic resonance imaging (MRI) scans conducted presently and 10 years ago. Cognitive decline was examined from neurophysiological tests conducted presently and ten or 5 years ago.

RESULTS

Higher Aβ accumulation in AD-critical brain regions correlated with greater visual memory decline (p = 0.023). Aβ accumulation did not correlate with brain atrophy rates. Increased cerebral glucose metabolism in AD-susceptible regions correlated with worse verbal memory performance (p = 0.040).

CONCLUSIONS

Aβ accumulation in known AD-related areas was associated with subtle cognitive deficits. The association was observed before hypometabolism or accelerated brain atrophy, suggesting that Aβ accumulation is involved early in age-related cognitive dysfunction. The association between hypermetabolism and worse memory performance may be due to early compensatory mechanisms adapting for malfunctioning neurons by increasing metabolism.

Itaconate: A promising precursor for treatment of neuroinflammation associated depression

Neuroinflammation triggers the production of inflammatory factors, influences neuron generation and synaptic plasticity, thus playing an important role in the pathogenesis of depression and becoming an important direction of depression prevention and treatment. Itaconate is a metabolite secreted by macrophages in immunomodulatory responses, that has potent immunomodulatory effects and has been proven to exert anti-inflammatory effects in a variety of diseases. Microglia are mononuclear macrophages that reside in the central nervous system (CNS), and may be the source of endogenous itaconate in the brain. Itaconate can directly inhibit succinate dehydrogenase (SDH), reduce the production of NOD-like receptor thermal protein domain associated protein 3 (NLRP3), activate nuclear factor erythroid-2 related factor 2 (Nrf2), and block glycolysis, and thereby improving the depressive symptoms associated with the above mechanisms. Notably, itaconate also indirectly ameliorates the depressive symptoms associated with some inflammatory diseases. With the optimization of the structure and the development of new delivery systems, the application value and therapeutic potential of itaconate have been significantly improved. Dimethyl itaconate (DI) and 4-octyl itaconate (4-OI), cell-permeable derivatives of itaconate, are more suitable for crossing the blood-brain barrier (BBB), exhibiting therapeutic effects in the research of multiple diseases. This article provides an overview of the immunomodulatory effects of itaconate and its potential therapeutic efficacy in inflammatory depression, focusing on the promising application of itaconate as a precursor of antidepressants.