Biology subjects

Yoshihara, Y.

Publications and source records attributed to Yoshihara, Y..

10 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

Structural regeneration and functional recovery of the olfactory system of zebrafish following brain injury

Olfactory dysfunction is a common outcome of brain injuries, negatively affecting quality of life. The mammalian nervous system has limited capacity for spontaneous olfactory recovery, making it challenging to study olfactory regeneration and recovery in adults. In contrast, zebrafish are an ideal model for such studies due to its extensive and lifelong regenerative abilities. In this work, we describe a model of excitotoxic injury in the olfactory bulb using quinolinic acid (QA) lesions in adult zebrafish. We observed extensive neurodegeneration in both the olfactory bulb and olfactory epithelium, including a reduction of bulbar volume, neuronal death, and impaired olfactory function. Recovery mechanisms involved tissue remodeling, cell proliferation, neurogenesis, leading to full restoration of olfactory function by 21 days. This study provides a model to further investigate the effects of excitotoxicity on olfactory dysfunction, and highlights zebrafishs remarkable regenerative abilities, providing insights into potential therapeutic strategies for restoring olfactory function following brain injuries.

neuroscience↗

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

An atlas and database of neuropeptide gene expression in the adult zebrafish forebrain

Zebrafish is a useful model organism in neuroscience; however, its gene expression atlas in the adult brain is not well developed. In the present study, we examined the expression of 38 neuropeptides, and glutamatergic neuron marker gene mix (slc17a6a, slc17a6b, slc17a7a, and slc17a7b) in the adult zebrafish brain using in situ hybridization. The results are summarized as an expression atlas in 19 coronal planes of the forebrain. Furthermore, the scan data of all sections were made publicly available as a database. Based on these data, we performed detailed neuroanatomical analyses of the hypothalamus. By scrutinizing and comparing the expression patterns of neuropeptides, we found that several regions described as one nucleus in the reference zebrafish brain atlas contain two or more subregions with significantly different neuropeptide/neurotransmitter expression profiles, and we proposed them as novel subnuclei. Subsequently, the expression data obtained in this study were compared with those in mice, and a cluster analysis was performed to examine the similarities. As a result, several nuclei in zebrafish and mice were clustered in close vicinity: zebrafish ventral part of the anterior part of the parvocellular preoptic nucleus (PPav)/magnocellular preoptic nucleus (PM) and mouse paraventricular hypothalamic nucleus (Pa), zebrafish posterior part of the parvocellular preoptic nucleus (PPp) and mouse medial preoptic area (MPA), zebrafish dorsal part of the ventral zone of periventricular hypothalamus (Hvd)/anterior tuberal nucleus (ATN) and mouse ventromedial hypothalamic nucleus (VMN). The present expression atlas, database, and anatomical findings will contribute to future neuroscientific research using zebrafish. Key pointsO_LIThe expression of 38 neuropeptides and GABAergic/glutamatergic neuronal marker genes in adult zebrafish forebrain was examined and compiled as an atlas. C_LIO_LIAll scanned brain section data were published as a database. C_LIO_LIBased on the expression data obtained, multiple subnuclei in the zebrafish hypothalamus were proposed, and comparisons with the mouse hypothalamus were conducted. C_LI

neuroscience↗

bioRxiv2023

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P<0.05, family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

neuroscience↗

bioRxiv2018

Respiration-phased switching between sensory inputs and top-down inputs in the olfactory cortex

Olfactory perception depends on respiration phases: olfactory cortex processes external odor signals during inhalation whereas it is isolated from the external odor world during exhalation. Olfactory cortex pyramidal cells receive the sensory signals via bottom-up pathways terminating on superficial layer (SL) dendrites while they receive top-down inputs on deep layer (DL) dendrites. Here we asked whether olfactory cortex pyramidal cells spontaneously change the action modes of receiving olfactory sensory inputs and receiving top-down inputs in relation to respiration phases. Current source density analysis of local field potentials recorded in three different olfactory cortex areas of waking immobile rats revealed {beta}- and {gamma}-range fast oscillatory current sinks and a slow current sink in the SL during inhalation, whereas it showed {beta}- and {gamma}-range fast oscillatory current sinks and a slow current sink in the DL during exhalation. Sensory deprivation experiments showed that inhalation-phased olfactory sensory inputs drove the inhalation-phased fast oscillatory potentials in the SL but they drove neither the inhalation-phased slow current sink in the SL nor the exhalation-phased slow current sink in the DL. The results indicate that independent of inhalation-phased olfactory sensory inputs, olfactory cortex pyramidal cells spontaneously generate a slow depolarization in the SL dendrites during inhalation, which may selectively boost the concomitant olfactory sensory inputs to elicit spike outputs. In addition, the pyramidal cells spontaneously generate slow depolarization in the DL dendrites during exhalation, which may assist top-down inputs to elicit spike outputs. We thus hypothesize that the olfactory cortical areas coordinately perform inhalation/exhalation-phased switching of input biasing: inhalation phase is the time window for external odor signals that arrive in the SL dendrites, whereas exhalation phase is assigned to boost top-down signals to the DL dendrites that originate in higher brain centers.

neuroscience↗

bioRxiv2018

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

When collecting large neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent the greatest barrier when acquiring multi-site neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multi-site, multi-disorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. Effects on resting-state functional MRI connectivity because of both bias types were greater than or equal to those because of psychiatric disorders. Furthermore, our findings indicated that each site can sample only from among a subpopulation of participants. This result suggests that it is essential to collect large neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using traveling-subject dataset and achieved the reduction of the measurement bias by 29% and the improvement of the signal to noise ratios by 40%.

neuroscience↗

bioRxiv2018

Overlapping but asymmetrical relationships between schizophrenia and autism revealed by brain connectivity

(Abstract included 248 words)Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. To address this issue, we took advantage of dual (ASD and SSD) classifiers that discriminate patients from their controls based on resting state brain functional connectivity. An SSD classifier using sophisticated machine-learning algorithms that automatically selected SSD- specific functional connections was applied to Japanese datasets including adult patients with SSD in a chronic stage. We demonstrated good performance of the SSD classification for independent validation cohorts. The generalizability was tested by USA and European cohorts in a chronic stage, and one USA cohort including first episode schizophrenia. The specificity was tested by two adult Japanese cohorts of ASD and major depressive disorder, and one European cohort of attention-deficit hyperactivity disorder. The weighted linear summation of the classifiers functional connections constituted the biological dimensions representing neural liability to the disorders. Our previously developed robust ASD classifier constituted the ASD dimension. Distributions of individuals with SSD, ASD and healthy controls were examined on the SSD and ASD biological dimensions. The SSD and ASD populations exhibited overlapping but asymmetrical patterns on the two biological dimensions. That is, the SSD population showed increased liability on the ASD dimension, but not vice versa. Furthermore, the two dimensions were correlated within the ASD population but not the SSD population. Using the two biological dimensions based on resting-state functional connectivity enabled us to quantify and visualize the relationships between SSD and ASD.

neuroscience↗

bioRxiv2018

The Claustrum Coordinates Cortical Slow-Wave Activity

During sleep and awake rest, the neocortex generates large-scale slow-wave activity. Here we report that the claustrum, a poorly understood subcortical neural structure, coordinates neocortical slow-wave generation. We established a transgenic mouse line allowing genetic and electrophysiological interrogation of a subpopulation of claustral glutamatergic neurons. These claustral excitatory neurons received inputs from glutamatergic neurons in a large neocortical network. Optogenetic activation of claustral neurons in vitro induced excitatory post-synaptic responses in most neocortical neurons, but elicited action potentials primarily in inhibitory interneurons. Optogenetic activation of claustral neurons in vivo induced a Down-state featuring a prolonged silencing of neural acticity in all layers of many cortical areas, followed by a globally synchronized Down-to-Up state transition. These results demonstrate a crucial role of the claustrum in synchronizing inhibitory interneurons across the neocortex for spatiotemporal coordination of brain state. Thus, the claustrum is a major subcortical hub for the synchronization of neocortical slow-wave activity.

neuroscience↗

bioRxiv2017

A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

Individual differences in cognitive function have been shown to correlate with brain-wide functional connectivity, suggesting a common foundation relating connectivity to cognitive function across healthy populations. However, it remains unknown whether this relationship is preserved in cognitive deficits seen in a range of psychiatric disorders. Using machine learning methods, we built a prediction model of working memory function from whole-brain functional connectivity among a healthy population (N = 17, age 19-24 years). We applied this normative model to a series of independently collected resting state functional connectivity datasets (N = 968), involving multiple psychiatric diagnoses, sites, ages (18-65 years), and ethnicities. We found that predicted working memory ability was correlated with actually measured working memory performance in both schizophrenia patients (partial correlation,{rho} = 0.25, P = 0.033, N = 58) and a healthy population (partial correlation,{rho} = 0.11, P = 0.0072, N = 474). Moreover, the model predicted diagnosis-specific severity of working memory impairments in schizophrenia (N = 58, with 60 controls), major depressive disorder (N = 77, with 63 controls), obsessive-compulsive disorder (N = 46, with 50 controls), and autism spectrum disorder (N = 69, with 71 controls) with effect sizes g = -0.68, -0.29, -0.19, and 0.09, respectively. According to the model, each diagnosiss working memory impairment resulted from the accumulation of distinct functional connectivity differences that characterizes each diagnosis, including both diagnosis-specific and diagnosis-invariant functional connectivity differences. Severe working memory impairment in schizophrenia was related not only with fronto-parietal, but also widespread network changes. Autism spectrum disorder showed greater negative connectivity that related to improved working memory function, suggesting that some non-normative functional connections can be behaviorally advantageous. Our results suggest that the relationship between brain connectivity and working memory function in healthy populations can be generalized across multiple psychiatric diagnoses. This approach may shed new light on behavioral variances in psychiatric disease and suggests that whole-brain functional connectivity can provide an individual quantitative behavioral profile in a range of psychiatric disorders.

neuroscience↗