Biology subjects

Takagishi, H.

Publications and source records attributed to Takagishi, H..

5 recordsLinked to original sources

bioRxiv2025

Unsupervised feature computation-based feature selection robustly extracted resting-state functional connectivity patterns related to mental disorders

Research on biomarkers for predicting psychiatric disorders from resting-state functional connectivity (FC) is advancing. While the focus has primarily been on the discriminative performance of biomarkers by machine learning, identification of abnormal FCs in psychiatric disorders has often been treated as a secondary goal. However, it is crucial to investigate the effect size and robustness of the selected FCs because they can be used as potential targets of neurofeedback training or transcranial magnetic stimulation therapy. Here, we incorporated approximately 5,000 runs of resting-state functional magnetic resonance imaging from six datasets, including individuals with three different psychiatric disorders (major depressive disorder [MDD], schizophrenia [SCZ], and autism spectrum disorder [ASD]). We demonstrated that an unsupervised feature-computation-based feature selection method can robustly extract FCs related to psychiatric disorders compared to other conventional supervised feature selection methods. We found that our proposed method robustly extracted FCs with larger effect sizes from the validation dataset compared to different types of feature selection methods based on supervised learning for MDD (Cohens d = 0.40 vs. 0.25), SCZ (0.37 vs. 0.28), and ASD (0.17 vs. 0.16). We found 78, 69, and 81 essential FCs for MDD, SCZ, and ASD, respectively, and these FCs were mainly thalamic and motor network FCs. The current study showed that the unsupervised feature-computation-based feature selection method robustly identified abnormal FCs in psychiatric disorders consistently across datasets. The discovery of such robust FCs will contribute to understanding neural mechanisms as abnormal brain signatures in psychiatric disorders. Furthermore, this finding can aid in developing precise therapeutic interventions, such as neurofeedback training or transcranial magnetic stimulation therapy.

neuroscience↗

bioRxiv2024

Association Between Relational Mobility and DNA Methylation in Oxytocin Receptor Gene: A Social Epigenetic Study

DNA methylation is a type of epigenetic modification known to exhibit fluctuations in response to environmental factors. The association of macrosocial factors, such as interpersonal mobility, on methylation has seldom been investigated. This study aimed to examine the association of relational mobility, defined as the extent to which individuals can form and replace social relationships, on the DNA methylation of oxytocin receptor genes. DNA was extracted from the buccal cells of 95 adult participants (50 men and 45 women) and subjected to microarray analysis of DNA methylation using Illumina EPIC v2.0. The findings indicate that the oxytocin receptor genes methylation level was higher in individuals residing in low relational mobility social environments. The CpG site associated with relational mobility is an enhancer region, indicating that social environments with low relational mobility exert a suppressive effect on the transcriptional efficiency of the oxytocin receptor gene. Significance StatementThe association between DNA methylation of the oxytocin receptor gene and relational mobility was examined in 95 adults in their 20s to 60s, and found that those living in social environments with lower levels of relationship mobility had higher rates of DNA methylation of the oxytocin receptor gene. This study is a novel approach to a problem discussed in the social sciences using new analytical techniques in epigenomics.

genetics↗

bioRxiv2024

Computational Mechanisms of Neuroimaging Biomarkers Uncovered by Multicenter Resting-State fMRI Connectivity Variation Profile

Resting-state functional connectivity (rsFC) is increasingly used to develop biomarkers for psychiatric disorders. Despite progress, development of the reliable and practical FC biomarker remains an unmet goal, particularly one that is clinically predictive at the individual level with generalizability, robustness, and accuracy. In this study, we propose a new approach to profile each connectivity from diverse perspective, encompassing not only disorder-related differences but also disorder-unrelated variations attributed to individual difference, within-subject across-runs, imaging protocol, and scanner factors. By leveraging over 1500 runs of 10-minute resting-state data from 84 traveling-subjects across 29 sites and 900 participants of the case-control study with three psychiatric disorders, the disorder-related and disorder-unrelated FC variations were estimated for each individual FC. Using the FC profile information, we evaluated the effects of the disorder-related and disorder-unrelated variations on the output of the multi-connectivity biomarker trained with ensemble sparse classifiers and generalizable to the multicenter data. Our analysis revealed hierarchical variations in individual functional connectivity, ranging from within-subject across-run variations, individual differences, disease effects, inter-scanner discrepancies, and protocol differences, which were drastically inverted by the sparse machine-learning algorithm. We found this inversion mainly attributed to suppression of both individual difference and within-subject across-runs variations relative to the disorder-related difference by weighted-averaging of the selected FCs and ensemble computing. This comprehensive approach will provide an analytical tool to delineate future directions for developing reliable individual-level biomarkers.

neuroscience↗

bioRxiv2023

Uncovering the links between physical activity and prosocial behaviour: A functional near-infrared spectroscopy hyperscanning study on brain connectivity and synchrony

The prevalence of sedentary lifestyles in modern society raises concerns about their potential association with poor brain health, particularly in the lateral prefrontal cortex (LPFC), which is crucial for human prosocial behaviour. Here, we show the relationship between physical activity and prosocial behaviour, focusing on potential neural markers, including intra-brain functional connectivity and inter-brain synchrony in the LPFC. Forty participants, each paired with a stranger, underwent evaluation of neural activity in the LPFC using functional near-infrared spectroscopy hyperscanning during eye-to-eye contact and an economic game. Results showed that individuals with exercise habits and more leisure-time physical activity demonstrated greater reciprocity, less trust, longer decision-making time, and stronger intra-brain connectivity in the dorsal LPFC and inter-brain synchrony in the ventral LPFC. Our findings suggest that a sedentary lifestyle may alter human prosocial behaviour by impairing adaptable prosocial decision-making in response to social factors through altered intra-brain functional connectivity and inter-brain synchrony.

neuroscience↗

bioRxiv2023

Multimodal imaging for identifying brain markers of human prosocial behavior

How is the high degree of prosocial behavior that characterizes humans achieved? Here, we examined the structural and functional basis of the human brain with prosocial behavior using multimodal brain imaging data and 15 economic games. We identified that stronger interhemispheric connectivity, greater corpus callosum volume, higher functional segregation and integration, and fewer myelin maps combined with a thicker cortex were strongly associated with prosocial behavior. These associations were found especially in the social brain regions. This suggests that the strength of functional/structural connectivity between the left and right hemispheres, the strength of modular and efficient networks, and the high number of non-myelinated cells (i.e., dendrites, spines, synapses, and glia) are strongly associated with higher prosocial behavior in humans, particularly in the social brain regions.

neuroscience↗