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The dysregulation of the autonomic nervous system (ANS) activity notably contributes to the onset and progression of numerous diseases, including lifestyle-related and psychiatric disorders. This necessitates the development of effective nonpharmacological methods for regulating ANS function for therapeutic purposes and disease prevention. This study examined how the presence or absence of the inaudible high-frequency component (HFC) of sounds—which activates deep-brain structures—affects the ANS regulatory function. Under the N-back task condition, which requires concentration, exposure to sounds with HFC resulted in significantly higher sympathetic and parasympathetic nervous activities compared to sounds without HFC. Conversely, under the relaxation condition, the sounds with HFC significantly suppressed sympathetic nervous activity relative to sounds without HFC. Therefore, sounds with HFC may flexibly adjust the sympathetic and parasympathetic nervous activities based on situational demands.

Accurate interoceptive processing in decision-making is essential to maintain homeostasis and overall health. Disruptions in this process have been associated with various psychiatric conditions, including depression. Recent studies have focused on nutrient homeostatic dysregulation in depression for effective subtype classification and treatment. Neurophysiological studies have associated changes in appetite in depression with altered activation of the mesolimbic dopamine system and interoceptive regions, such as the insular cortex, suggesting that disruptions in reward processing and interoception drive changes in nutrient homeostasis and appetite. This study aimed to explore the potential of computational psychiatry in addressing these issues. Using a homeostatic reinforcement learning model formalizing the link between internal states and behavioral control, we investigated the mechanisms by which altered interoception affects homeostatic behavior and reward system activity via simulation experiments. Simulations of altered interoception demonstrated behaviors similar to those of depression subtypes, such as appetite dysregulation. Specifically, reduced interoception led to decreased reward system activity and increased punishment, mirroring the neuroimaging study findings of decreased appetite in depression. Conversely, increased interoception was associated with heightened reward activity and impaired goal-directed behavior, reflecting an increased appetite. Furthermore, effects of interoception manipulation were compared with traditional reinforcement learning parameters (e.g., inverse temperature β and delay discount γ ), which represent cognitive-behavioral features of depression. The results suggest that disruptions in these parameters contribute to depressive symptoms by affecting the underlying homeostatic regulation. Overall, this study findings emphasize the importance of integrating interoception and homeostasis into decision-making frameworks to enhance subtype classification and facilitate the development of effective therapeutic strategies.

Transcranial direct current stimulation (tDCS) is a procedure to polarize human brain. It has been reported that tDCS over the hand motor cortex transiently improves the performance of hand motor tasks. Here, we investigated whether tDCS could also improve leg motor functions. Ten healthy subjects performed pinch force (PF) and reaction time (RT) tasks using the left leg before, during and after anodal, cathodal or sham tDCS over the leg motor cortex. The anodal tDCS transiently enhanced the maximal leg PF but not RT during its application. Neither cathodal nor sham stimulation changed the performance. None of the interventions affected hand PF or RT, showing the spatial specificity of the effect of tDCS. These results indicate that motor performance of not only the hands but also the legs can be enhanced by anodal tDCS. tDCS may be applicable to the neuro-rehabilitation of patients with leg motor disability.

In this study, we propose a deep-learning technique for functional MRI analysis. We introduced a novel self-supervised learning scheme that is particularly useful for functional MRI wherein the subject identity is used as the teacher signal of a neural network. The neural network is trained solely based on functional MRI-scans, and the training does not require any explicit labels. The proposed method demonstrated that each temporal volume of resting state functional MRI contains enough information to identify the subject. The network learned a feature space in which the features were clustered per subject for the test data as well as for the training data; this is unlike the features extracted by conventional methods including region of interests (ROIs) pooling signals and principal component analysis. In addition, applying a simple linear classifier to the per-subject mean of the features (namely “identity feature”), we demonstrated that the extracted features could contribute to schizophrenia diagnosis. The classification accuracy of our identity features was comparable to that of the conventional functional connectivity. Our results suggested that our proposed training scheme of the neural network captured brain functioning related to the diagnosis of psychiatric disorders as well as the identity of the subject. Our results together highlight the validity of our proposed technique as a design for self-supervised learning.

Brain–computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure. •Brain–computer interface (BCI) success rate varies between users.•We investigated the relationship between BCI success rate and brain structure.•BCI performance correlated with gray matter volume of somatosensory and motor areas.

The emergence of the occipital alpha rhythm on brain electroencephalogram (EEG) is associated with brain activity in the cerebral neocortex and deep brain structures. To further understand the mechanisms of alpha rhythm power fluctuation, we performed simultaneous EEGs and functional magnetic resonance imaging recordings in human subjects during a resting state and explored the dynamic relationship between alpha power fluctuation and blood oxygenation level-dependent (BOLD) signals of the brain. Based on the frequency characteristics of the alpha power time series (APTS) during 20-minute EEG recordings, we divided the APTS into two components: fast fluctuation (0.04-0.167 Hz) and slow fluctuation (0-0.04 Hz). Analysis of the correlation between the MRI signal and each component revealed that the slow fluctuation component of alpha power was positively correlated with BOLD signal changes in the brain stem and the medial part of the thalamus and anterior cingulate cortex, while the fast fluctuation component was correlated with the lateral part of the thalamus and the anterior cingulate cortex, but not the brain stem. In summary, these data suggest that different subcortical structures contribute to slow and fast modulations of alpha spectra on brain EEG.

Although stress significantly impacts on various metabolic syndromes, including diabetes mellitus, most stress management techniques are based on psychological and subjective approaches. This study examined how the presence or absence of the inaudible high-frequency component (HFC) of sounds, which activates deep-brain structures, affects glucose tolerance in healthy participants using the oral glucose tolerance test (OGTT). Sounds containing HFC suppressed the increase in glucose levels measured by incremental area under the curve in the OGTT compared with the otherwise same sounds without HFC. The suppression effect of HFC was more prominent in the older age group and the group with high HbA1c. This suggests that sounds with HFC are more effective in improving glucose tolerance in individuals at a higher risk of glucose intolerance.

In humans, the incisors and molars have distinct functions during mastication, analogous to the two main types of handgrip, the precision and power grips. In the present study, we investigated cortical activation and masticatory muscle activity during incisal and molar biting via simultaneous functional magnetic resonance imaging and electromyogram (EMG) recordings. We conducted recordings in 15 healthy adult participants while they performed incisal and molar biting tasks at three step-wise force levels using two custom-made splints. Regarding the results of the ROI analysis, we found a significantly stronger positive linear correlation between the blood oxygenation level dependent signal and EMG activity during molar biting than incisal biting, which was particularly prominent in the primary sensorimotor cortex and the cerebellum. We also found a significantly stronger negative linear correlation during incisal biting than molar biting, which was particularly prominent in the rostral cingulate motor area, superior frontal gyrus, and caudate nucleus. These findings indicate that molar biting enables powerful chewing: brain activity in several brain areas related to motor function was increased with increasing bite force levels, while incisal biting enables fine motor control: brain activity in several brain areas related to motor control was increased with reduced bite force levels.

Transcranial direct current stimulation (tDCS) is a non-invasive technique that modulates the neuronal membrane potential. We have previously documented a sustainable increase in extracellular dopamine levels in the rat striatum of cathodal tDCS, suggesting that cathodal tDCS enhances the neuronal excitability of the cortex. In the present study, we investigated changes in neuronal activity in the cerebral cortex induced by tDCS at the point beneath the stimulus electrode in anaesthetised rats in vivo. Multi-unit recordings were performed to examine changes in neuronal activity before and after the application of tDCS. In the cathodal tDCS group, multi-unit activity (indicating the collective firing rate of recorded neuronal populations) increased in the cerebral cortex. Both anodal and cathodal tDCS increased the firing rate of isolated single units in the cerebral cortex. Significant differences in activity were observed immediately following stimulation and persisted for more than an hour after stimulation. The primary finding of this study was that both anodal and cathodal tDCS increased in vivo neuronal activity in the rat cerebral cortex underneath the stimulus electrode.

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS) and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets, including 71 SZ patients and 71 HS. We created 16 3D-CAE models with different channels and convolutions to explore the effective range of hyperparameters for psychiatric brain imaging. The number of blocks containing two convolutional layers and one pooling layer was set, ranging from 1 block to 4 blocks. The number of channels in the extraction layer varied from 1, 4, 16, and 32 channels. The proposed 3D-CAEs were successfully reproduced into 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relation to clinical information. We explored the appropriate hyperparameter range of 3D-CAE, and it was suggested that a model with 3 blocks may be related to extracting features for predicting the dose of medication and symptom severity in schizophrenia.