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Keerativittayayut, R.

Publications and source records attributed to Keerativittayayut, R..

2 recordsLinked to original sources

Neural signatures of human psychological resilience driven by acute stress

Neurophysiological mechanisms underlying psychological resilience, the ability to overcome adversity1,2, have been extensively studied in animals. However, in comparison with animals, human resilience is unique in that it is underpinned by higher cognitive functions, such as self-confidence and a positive attitude to challenges3,4. Given these discrepancies, the neurophysiological mechanisms underlying human resilience remain unclear. To address this issue, we recorded multimodal responses after acute stress exposure over 1.5 hours using functional brain imaging and peripheral physiological measurements. Here, we showed that the degree of individual resilience is indexed by multiple changes in neural dynamics 1 hour after acute stress. Both functional magnetic resonance imaging and electroencephalography show that activity in the cortical salience network and power in high-beta and gamma oscillations increase in less resilient individuals. Contrastingly, activity in the cortical default mode network and spontaneous activity in the posterior hippocampus increase in more resilient individuals. Machine learning analysis confirmed that, 1 hour after stress exposure, the functional connectivity in the salience network was the most influential, followed by that in the default mode network, gamma power, high-beta power, and hippocampal activity. The neurophysiological dynamics for resilience do not occur as previously thought, but rather in a time-lagged manner against stress exposure. Our findings Shed light on a new approach to recovery from stress-induced deficits such as delayed neuromodulation after a stressful event.

neuroscience↗

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↗