Biology subjects

Shimane, D.

Publications and source records attributed to Shimane, D..

2 recordsLinked to original sources

bioRxiv2026

Asynchronous cortical recovery from sleep inertia: region- and frequency-specific dynamics following deep NREM sleep

Sleep inertia (SI) is a transient state of impaired alertness and cognition after awakening, typically exacerbated by prior deep sleep. Despite its prevalence, the fine-grained temporal dynamics of brain activity during this transition remains poorly understood. We employed trial-by-trial behavioral assessment (20-second resolution) to map the recovery trajectories of 44 participants following awakenings from either shallow (N1/N2) or deep (N3) NREM sleep. We found that awakening from deep NREM sleep resulted in a significantly slower return to baseline working-memory performance. This behavioral deficit was mirrored by specific EEG signatures: frontal low-frequency activity (delta/theta) returned to baseline levels within minutes, whereas posterior high-frequency activity (alpha/beta) remained altered for a prolonged period. These findings reveal that the human brain reboots asynchronously following deep NREM sleep, with a "frontal-first, posterior-last" gradient that dictates the time course of behavioral restoration.

neuroscience↗

bioRxiv2022

Multi-modal deep neural decoding of visual object representation in humans

Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. However, it is unclear how neural activities in spatially distributed brain regions, especially in terms of temporal dynamics, represent visual objects. To address this issue, we explored the spatial and temporal organization of visual object representations using concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), combined with neural decoding using deep neural networks (DNNs). Visualization of the fMRI DNN revealed that visual categorization (faces or non-face objects) occurred in brain-wide cortical regions, including the ventral temporal cortex. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of the two DNNs improved the classification performance for both categorization and sub-categorization. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner.

neuroscience↗