Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses.

High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.

Exposure to high-altitude hypoxia, with reduced oxygen levels, can replicate brain function changes akin to aging and Alzheimer's disease. In our work, we propose high-altitude hypoxia as a possible reversible model of human brain aging. We gathered EEG data at high altitude and sea level, investigating the impact of hypoxia on brainwave patterns and connectivity. Our findings revealed that high-altitude exposure led to slower and noisier brain oscillations and produced altered brain connectivity, resembling some remarkable changes seen in the aging process. Intriguingly, these changes were linked to age, even when hypoxia's effects were considered. Our research unveils how high-altitude conditions emulate brain patterns associated with aging and neurodegenerative conditions, providing valuable insights into the understanding of both normal and impaired brain function.

Odor cueing of declarative memories during sleep enhances coordinated spindles and slow oscillations.

Long-term memories are formed by repeated reactivation of newly encoded information during sleep. This process can be enhanced by using memory-associated reminder cues like sounds and odors. While auditory cueing has been researched extensively, few electrophysiological studies have exploited the various benefits of olfactory cueing. We used high-density electroencephalography in an odor-cueing paradigm that was designed to isolate the neural responses specific to the cueing of declarative memories. We show widespread cueing-induced increases in the duration and rate of sleep spindles. Higher spindle rates were most prominent over centro-parietal areas and largely overlapping with a concurrent increase in the amplitude of slow oscillations (SOs). Interestingly, greater SO amplitudes were linked to a higher likelihood of coupling a spindle and coupled spindles expressed during cueing were more numerous in particular around SO up states. We thus identify temporally and spatially coordinated enhancements of sleep spindles and slow oscillations as a candidate mechanism behind cueing-induced memory processing. Our results further demonstrate the feasibility of studying neural activity patterns linked to such processing using olfactory cueing during sleep.

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.

The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.