Biology subjects

Holst, S. C.

Publications and source records attributed to Holst, S. C..

5 recordsLinked to original sources

bioRxiv2025

Neurologically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies

BackgroundBrain-age gap (BAG), the difference between predicted age and chronological age, is studied as a biomarker for the natural progression of neurodegeneration. The BAG captures brain atrophy as measured with structural Magnetic Resonance Imaging (MRI). Electroencephalography (EEG) has also been explored as a functional means for estimating brain age. However, EEG studies showed mixed results for BAG including a seemingly paradoxical negative BAG, i.e. younger predicted age than chronological age, in neurological populations. ObjectivesThis study critically examined brain age estimation from spectral EEG power as common measure brain activity in two of the largest public EEG datasets containing neurological cases alongside controls. MethodsEEG recordings were analyzed from individuals with neurological conditions (n=900, TUAB data; n=417 MCI & n=311 dementia, CAU data) and controls (n=1254, TUAB data; n=459, CAU data). ResultsWe found that age-prediction models trained on the reference population systematically under-predicted age in people with neurological conditions replicating a negative BAG for diseased brain activity. Inspection of age-related trends along the EEG power spectra revealed complex frequency-dependent alterations in neurological groups underlying the seemingly paradoxical negative BAG. ConclusionsThe utility of brain age as an interpretable biomarker relies on the observation from structural MRI that progressive neurodegeneration often broadly resembles accelerated aging. This assumption can be violated for functional assessments such as EEG spectral power and, potentially, different neurological and psychiatric conditions or therapeutic effects. The sign of the BAG may not meaningfully be interpreted as a deviation from normal aging.

neuroscience↗

bioRxiv2023

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

EEG is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications including brain-computer interfaces, epilepsy monitoring and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, focusing on the age prediction task. With cross-validated R2 scores between 0.3 - 0.5, prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.

neuroscience↗

bioRxiv2020

Sleep-specific changes in physiological brain pulsations

Sleep is known to increase the convection of interstitial brain metabolites along with cerebrospinal fluid (CSF). We used ultrafast magnetic resonance encephalography (MREGBOLD) to quantify the effect of sleep on physiological (vasomotor, respiratory and cardiac) brain pulsations driving the CSF convection in humans. Transition to electroencephalography verified sleep occurred in conjunction with power increase and reduced spectral entropy (SE) of physiological brain pulsations. During sleep, the greatest increase in spectral power was in very-low frequency (VLF < 0.1 Hz) waves, followed by respiratory and cardiac brain pulsations. SE reduction coincided with decreased vigilance in awake state and could robustly (ROC 0.88, p < 0.001) differentiate between sleep vs. awake states, indicating the sensitivity of SE of the MREGBOLD signal as a marker for sleep level. In conclusion, the three physiological brain pulsation contribute to the sleep-associated increase in glymphatic CSF convective flow in an inverse frequency order. HighlightsO_LIBrain tissue contains almost no connective tissue, this enabling pressure waves to initiate long-distance brain pulsations C_LIO_LIBrain pulsations are induced by vasomotion, respiration, and the cardiac cycle C_LIO_LISleep strikingly increases spectral power and decreases spectral entropy of brain pulsations, especially for the very low frequency vasomotor waves C_LIO_LISpectral entropy of brain pulsations detected by MREG is a sensitive measure of vigilance, resembling the corresponding entropy changes detected by scalp EEG C_LI

neuroscience↗

bioRxiv2019

Haplotype of the astrocytic water channel AQP4 modulates slow wave energy in human NREM sleep

Cerebrospinal fluid (CSF) flow through the brain parenchyma is facilitated by the astrocytic water channel aquaporin 4 (AQP4). Homeostatically regulated electroencephalographic (EEG) slow waves are a hallmark of deep non-rapid-eye-movement (NREM) sleep and have been implicated in the regulation of parenchymal CSF flow and brain clearance. The human AQP4 gene harbors several single nucleotide polymorphisms (SNPs) associated with AQP4 expression, brain-water homeostasis and neurodegenerative diseases. To date, their role in sleep-wake regulation is unknown. To investigate whether functional variants in AQP4 modulate human sleep, nocturnal EEG-recordings and cognitive performance were investigated in 123 healthy participants genotyped for a common eight-SNP AQP4-haplotype. We show that this AQP4-haplotype is associated with distinct modulations of NREM slow wave energy, strongest in early sleep and mirrored by changes in sleepiness and reaction times during extended wakefulness. The study provides the first human evidence for a link between AQP4, deep NREM sleep and cognitive consequences of prolonged wakefulness.

neuroscience↗

bioRxiv2018

Dynamic changes in cerebral and peripheral markers of glutamatergic signaling across the human sleep-wake cycle

Both sleep and glutamatergic signaling in the brain are tightly controlled and homeostatically regulated. Sleep homeostasis is reliably reflected by predictable changes in brain electrical activity in waking and sleep, yet the underlying molecular mechanisms remain elusive. Current hypotheses posit that recovery sleep following prolonged waking restores efficient functioning of the brain, for example by keeping glutamatergic signaling in a homeostatic range. We recently provided evidence in humans and mice that metabotropic glutamate receptors of subtype-5 (mGluR5) contribute to the brains coping mechanisms with sleep deprivation. Here we combined in 31 healthy men, proton magnetic resonance spectroscopy to measure the levels of glutamate (Glu), GLX (glutamate-to-glutamine ratio) and GABA ({gamma}-amino-butyric-acid) in basal ganglia (BG) and dorsolateral prefrontal cortex, simultaneous positron emission tomography to quantify mGluR5 availability with the novel radioligand, [18F]PSS232, and quantification in blood plasma of the mGluR5-regulated proteins, fragile-X mental retardation protein (FMRP) and brain-derived neurotrophic factor (BDNF). All measurements were conducted at the same circadian time in baseline, following sleep deprivation and after recovery sleep. We found that Glu and GLX in BG (pall < 0.01), but not in prefrontal cortex, and the plasma concentration of FMRP (p < 0.02), were increased after sleep loss and tended to normalize following recovery sleep (pall < 0.1). Furthermore, a night without sleep enhanced whole-brain and striatal mGluR5 availability and was normalized by recovery sleep (pall < 0.05). By contrast, other brain metabolites and plasma BDNF levels were not altered. The findings demonstrate convergent changes in distinct markers of glutamatergic signaling across prolonged wakefulness and recovery sleep in humans. They warrant further studies to elucidate the underlying mechanisms that link the homeostatic regulation of sleep and glutamatergic system activity in health and disease.\n\nOne-sentence summarySleep-dependent recovery of wakefulness-induced changes in, cerebral glutamatergic signaling\n\nMajor subject areaNeuroscience; Human Biology & Medicine

neuroscience↗