Biology subjects

Axelsson, J.

Publications and source records attributed to Axelsson, J..

4 recordsLinked to original sources

bioRxiv2025

The potential of ensemble-based automated sleep staging on single-channel EEG signal from a wearable device

Machine-learning-based sleep staging models have achieved expert-level performance on standard polysomnographic (PSG) data. However, their application to EEG recorded by wearable devices remains limited by non-conventional referencing montage and the lack of benchmarking against PSG. Here, we tested whether an ensemble of state-of-the-art automatic staging algorithms can reliably classify sleep from a customized configuration of the ZMax headband, adapted to record a single fronto-mastoid EEG channel. A total of 35 nights of simultaneous ZMax and PSG recordings were acquired in a home setting, amounting to 250.02 hours of analysable data from 10 healthy participants. PSG data were scored according to AASM criteria by two independent experts from different sleep centres, with discrepancies resolved to obtain a consensus hypnogram. ZMax signal was processed using four machine-learning algorithms (YASA, U-Sleep, SleepTransformer, DeepResNet), whose predictions were further combined into a final ensemble scoring through soft-voting. The ensemble scoring achieved almost perfect agreement with human consensus staging (night-level mean {+/-} SD; accuracy = 88.83% {+/-} 2.84%, Cohens {kappa} = 84.10% {+/-} 4.52%, and Matthews Correlation Coefficient = 84.54% {+/-} 4.23%). It showed excellent predictive accuracy for REM (F1-score = 93.99%), N3 (89.53%), N2 (87.93%), and wakefulness (86.37%), with lower performance for N1 (53.20%). These findings support the deployment of an ensemble scoring approach based on state-of-the-art sleep staging algorithms on ultra-minimal, mastoid-referenced EEG setups. This paradigm opens the way to the integration of data from modern wearable technologies into traditional PSG-based sleep research, overcoming longstanding barriers to ecological and large-scale sleep monitoring.

neuroscience↗

bioRxiv2023

Validation of the sleep EEG headband ZMax

Polysomnography (PSG) is the gold standard for recording sleep. However, the standard PSG systems are bulky, expensive, and often confined to lab environments. These systems are also time-consuming in electrode placement and sleep scoring. Such limitations render standard PSG systems less suitable for large-scale or longitudinal studies of sleep. Recent advances in electronics and artificial intelligence enabled wearable PSG systems. Here, we present a study aimed at validating the performance of ZMax, a widely-used wearable PSG that includes frontal electroencephalography (EEG) and actigraphy but no submental electromyography (EMG). We analyzed 135 nights with simultaneous ZMax and standard PSG recordings amounting to over 900 hours from four different datasets, and evaluated the performance of the headbands proprietary automatic sleep scoring (ZLab) alongside our open-source algorithm (DreamentoScorer) in comparison with human sleep scoring. ZLab and DreamentoScorer compared to human scorers with moderate and substantial agreement and Cohens kappa scores of 59.61% and 72.18%, respectively. We further analyzed the competence of these algorithms in determining sleep assessment metrics, as well as shedding more lights on the bandpower computation, and morphological analysis of sleep microstructural features between ZMax and standard PSG. Relative bandpower computed by ZMax implied an error of 5.5% (delta), 4.5% (theta), 1.6% (alpha), 0.5% (sigma), 0.8% (beta), and 0.2% (gamma), compared to standard PSG. In addition, the microstructural features detected in ZMax did not represent exactly the same characteristics as in standard PSG. Besides similarities and discrepancies between ZMax and standard PSG, we measured and discussed the technology acceptance rate, feasibility of data collection with ZMax, and highlighted essential factors for utilizing ZMax as a reliable tool for both monitoring and modulating sleep.

neuroscience↗

bioRxiv2021

Dopamine and reward-related vigor in younger and older human participants

Vigor reflects how motivated one is to respond to a stimulus. We previously showed that humans are more vigorous when more reward is available on average, and that this relationship is modulated by the dopamine precursor levodopa. Dopamine signalling and probabilistic reward learning degrade with age, so the relationship between vigor and reward should change with age. We test this and assess whether the relationship between vigor and reward correlates with D1 dopamine receptor availability measured using Positron Emission Tomography. We measured response times of 30 older and 30 younger subjects during an oddball discrimination task where rewards varied systematically between trial. Reward rate had a similar impact on the vigor of both groups. We observed a weak positive association across subjects between ventral striatal dopamine receptor availability and effect of average reward rate on response time, which was in the opposite direction to our prediction. Overall, the effect of reward on response vigor is similar between younger and older humans and is weakly sensitive to dopamine D1 receptor availability.

neuroscience↗

bioRxiv2020

No metabolic "default mode" of human brain function

The finding of reduced activity in the default mode network (DMN) during externally focused cognitive control has been highly influential to our understanding of human brain function, but deactivations have also prompted major questions of interpretation. Using hybrid functional PET-MR imaging, this study shows that fMRI task activations and deactivations do not reflect antagonistic patterns of synaptic metabolism. FMRI activations were accompanied by concomitant increases in metabolism during cognitive control, but, unlike the BOLD response, metabolism in the core DMN did not change between rest and task. Metabolic increases along the borders of the DMN during task performance further revealed a set of regions that guide engagement and suppression of neighboring networks during cognitive control. Collectively, dissociations between metabolism and BOLD signal specific to the DMN reveal functional heterogeneity in this network and demonstrate that BOLD deactivations during cognitive control should not be interpreted to reflect reduced synaptic activity.

neuroscience↗