Metabolic derangement is considered as a key underlying cause and contributor of metabolic encephalopathy, which is featured by cognitive decline, psychiatric symptoms, and severe coma. Clinical and preclinical studies have demonstrated that malnutrition, electrolyte derangements, toxic agents, and organ failure lead to metabolic disturbances including hyper/hypoglycaemia, hyperammonaemia, vitamin deficiency, hyper/hypokalaemia, hyper/hyponatraemia, and hyper/hypoferraemia. These metabolic stressors result in cognitive impairment and changed mental health. Because metabolic imbalance is often attributed to abnormalities in multiple factors rather than one factor, its distinction and management are critical. In cases of severe metabolic imbalances, it is recommended to identify such abnormalities using multiple strategies. Given that impaired brain function is difficult to recover, long-term dietary regulation and proper management are required by considering underlying diseases or conditions. Hence, by exploring the connections between metabolic imbalances (e.g., glucose, ammonia, indoxyl sulfate, electrolyte ions, and vitamins), and cognitive and mental health, this review provides evidence that emphasises the critical importance of management of metabolic health. Comprehending psychoneurological impairment caused by metabolic disturbances highlights the potential overlap with cognitive/mental symptoms of underlying conditions. Therefore, identifying the key pathological mechanisms of metabolic stress-induced psychoneurological impairment provides valuable insignts into the development of effective management and therapeutic approaches.
While sleep disturbances are recognized as early markers of Alzheimer's disease (AD), the practical application of gold-standard polysomnography (PSG) for long-term monitoring is limited. This study aims to establish a novel methodological framework for the time-series analysis of sleep data collected via consumer-grade wearables and to explore whether this approach can detect differentiated signals across various stages of cognitive impairment. Daily sleep patterns of thirteen participants (5 healthy controls, 4 with aMCI, and 4 with mild AD) were monitored over three months using the Fitbit Charge 2. Rather than relying on aggregate nightly averages, we implemented a time-resolved analysis across 10-minute intervals to examine the temporal dynamics of sleep architecture. The proposed analysis revealed distinct, group-dependent temporal signatures. Specifically, the aMCI and AD groups exhibited shorter deep sleep during the early phase of the night, reduced REM sleep approximately three hours after sleep onset, and consistently elevated levels of light sleep and wake after sleep onset (WASO). These findings demonstrate that time-series analysis of wearable sleep data presents the potential to identify candidate digital phenotypes associated with cognitive decline. This study supports the feasibility of using longitudinal, dynamic sleep monitoring as an exploratory analytical framework warranting further validation for the detection of pathophysiological changes in older adults, shifting the focus from simple detection to the identification of candidate temporal sleep features. All reported findings are exploratory in nature and require replication in larger, independent cohorts.
Selenium-binding protein 1 (SELENBP1), previously implicated in several neurological and psychiatric disorders, was recently reported to be altered in the brains of individuals with Alzheimer's disease (AD). However, the cellular specificity of SELENBP1 in AD pathogenesis, including its role in amyloid-beta, remains unclear. Given the prominent role of microglia in amyloid-driven neuroinflammation and the hippocampal regional vulnerability in early AD, clarifying how SELENBP1 is regulated at both regional and cell-type-specific levels is essential. In this study, we examined age- and genotype-dependent changes in Selenbp1 expression in the hippocampus and prefrontal cortex of non-transgenic and 5XFAD mice at 1.5, 3, and 6 months of age by using western blot and immunofluorescence analyses. Western blot analyses revealed robust age-dependent increases in Selenbp1 expression in both regions, with no statistically significant genotype-dependent differences. However, immunofluorescence analyses showed that Selenbp1 levels were selectively increased in amyloid-vulnerable hippocampal subregions, including the dentate gyrus, dorsal subiculum, and retrosplenial cortex. Selenbp1 expression was expressed in microglia and was largely absent from neurons or astrocytes. These findings indicate that Selenbp1 elevation under AD-like conditions is region- and cell-type-specific, reflecting microglial responses detectable only through spatially resolved analysis. Therefore, Selenbp1 may represent a microglial molecular signature associated with early amyloid pathology.
Understanding the brain requires mapping not only neuronal circuits but the full landscape of intercellular communication. Here we introduce "signaling connectomics", a systems framework for interpreting divergent signaling context. It charts brain-wide signaling across synaptic, neuromodulatory, immune, glial, and vascular pathways. By integrating targeted perturbations with multiplexed molecular and imaging readouts, this approach infers causal signaling networks that operate beyond conventional synapses. It challenges circuit-centric models by emphasizing how dynamic receptor landscapes, extracellular cues, and non-synaptic interactions collectively shape neural function and dysfunction. Combining cell-type-specific optogenetics, biosensors, and spatial transcriptomics, signaling connectomics enables systematic mapping of intercellular signaling dynamics and cross-talk in vivo, providing a multidimensional view of how the brain coordinates activity across diverse cell types. Ultimately, this framework provides conceptual and methodological foundations for linking multicellular signaling to circuit-level plasticity and behavior via explicit causal chains.
