Biology subjects

Takamura, M.

Publications and source records attributed to Takamura, M..

9 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

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

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↗

bioRxiv2023

Single cell transcriptomics identifies adipose tissue CD271+ progenitors for enhanced angiogenesis in limb ischemia

Therapeutic angiogenesis using mesenchymal stem/stromal cell grafts have shown modest and controversial effects in preventing amputation for patients with critical limb ischemia. Through single-cell transcriptomic analysis of human tissues, we identified CD271+ progenitors specifically from subcutaneous adipose tissue (AT) as having the most prominent pro-angiogenic gene profile distinct from other stem cell populations. AT-CD271+ progenitors demonstrated robust in vivo angiogenic capacity, over conventional adipose stromal cell grafts, characterized by long-term engraftment, augmented tissue regeneration, and significant recovery of blood flow in a xenograft model of limb ischemia. Mechanistically, the angiogenic capacity of CD271+ progenitors is dependent on functional CD271 and mTOR signaling. Notably, the number and angiogenic capacity of CD271+ progenitors was strikingly reduced in insulin resistant donors. Our study highlights the identification of AT-CD271+ progenitors with in vivo superior efficacy for limb ischemia. Furthermore, we showcase comprehensive single-cell transcriptomics strategies for identification of suitable grafts for cell therapy. HIGHLIGHTSO_LIAdipose tissue stromal cells have a distinct angiogenic gene profile among human cell sources. C_LIO_LICD271+ progenitors in adipose tissue have a prominent angiogenic gene profile. C_LIO_LICD271+ progenitors show superior therapeutic capacities for limb ischemia. C_LIO_LICD271+ progenitors are reduced and functionally impaired in insulin resistant donors. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/527726v2_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1b5e1aorg.highwire.dtl.DTLVardef@bf2c34org.highwire.dtl.DTLVardef@ef0e68org.highwire.dtl.DTLVardef@9aee1f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2022

Comparing Deep Learning Models for Age Prediction Based on the Resting State fMRI Dataset from the "Brain Dock" Service in Japan

In Japan, many hospitals provide the unequaled service of medical check-up called the "Brain Dock"; however, there is a paucity of studies aimed at leveraging functional magnetic resonance imaging (fMRI) in hospitals. We obtained the resting-state fMRI (rs-fMRI) scans of about 695 patients who accessed this service for preventive medicine against Alzheimers Disease. In this study, we created deep learning models for age prediction with the ultimate aim of arriving at standard protocols for introducing rs-fMRI into clinical settings, particularly in Brain Docks. With that view, an assemblage of modeling conditions was attempted, changing multiple parameter values, features based on data extraction methods (region of interest-wise mean blood-oxygen-level-dependent lump-sum time series models or dynamic functional connectivity models), deep learning algorithms (Transformer, Multi-task Transformer, and unidirectional or bidirectional long short-term memory models), and different atlas-dependent brain region segmentation methods (including the Automated Anatomical Labeling and Harvard-Oxford atlases). As a result, a robust and highly significant correlation was obtained between actual and predicted ages from all types of methodologies. In addition, we determined that some conditions had a relatively large impact on prediction performance based on extended comparisons. The accuracy decreased, particularly according to the choice of atlases but with the same modeling conditions. Notwithstanding, we found that atlases based on intrinsic functional connectivity provided significant prediction accuracy even with a small number of regions to a similar extent as networks lowered spatial granularity. Moreover, we found that multi-task learning with other phenotype data (related to gender differences) was possible, but did not improve the prediction accuracy as much as expected. Despite these limitations, our results could provide a hopeful prospect of introducing fMRI into the field of neuro-clinical practice. HighlightsO_LIAge prediction modeling was performed using the rs-fMRI data of a Japanese "Brain Dock" service targeting mainly elderly people, which made prediction more challenging. C_LIO_LIA robust and highly significant correlation was obtained from all types of methodologies changing data extraction methods, deep learning algorithms, and brain atlases. C_LIO_LIAge prediction was successful even with a very small number of regions exclusively based on intrinsic functional connectivity networks, although there was a difference in the achievement of modeling. C_LI

neuroscience↗

bioRxiv2021

Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

Neurofeedback (NF) aptitude, which refers to an individuals ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation systems characteristics in task-free resting-state brain activity.

neuroscience↗

bioRxiv2020

Common brain networks between major depressive disorder and symptoms of depression that are validated for independent cohorts

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable major depressive disorder (MDD) brain network markers which would distinguish patients from healthy controls (a classifier) or would predict symptom severity (a prediction model) based on resting state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD brain network markers. The classifier achieved 70% generalization accuracy, and the prediction model moderately well predicted symptom severity for an independent validation dataset with 449 participants from 4 different imaging sites. Finally, we found common 2 functional connections between those related to MDD diagnosis and those related to depression symptoms. The successful generalization to the perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

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

A survey of public attitudes towards third-party reproduction in Japan in 2014

Objective: The objective of this study was to examine public attitudes towards third-party reproduction and the disclosure of conception through third-party reproduction.\n\nMethods: We conducted the web-based survey for the public attitude towards third-party reproduction in February 2014. Twenty-five hundred people were recruited with equal segregation of age (20s, 30s, 40s, and 50s) and gender. We analyzed the association between gender, age, infertility, and ethical view using a questionnaire regarding donor sperm, donor oocyte, donor embryo, gestational surrogacy, and disclosure to offspring.\n\nResults: Of the respondents, 36.2% approved and 26.6% disapproved with gamete or embryo donation. The frequency of those who approved was the lowest in females in the 50-59 years age group, and was significantly higher in males or females with infertility. Secondly, 40.9% approved and 21.8% disapproved with gestational surrogacy. The frequency of those who approved gestational surrogacy was higher in males or females with infertility. Thirdly, 46.3% of respondents agreed and 20.4% disagreed with \"offspring have the right to know their origin\". Those who disagreed were primarily in the 50-59 age group of both genders, and disagreement was significantly higher in the infertility group compared with non-infertility group.\n\nConclusion: In this study, public attitudes were affected by gender, age, and experience of infertility. These study findings are important in understanding the attitude towards third-party reproduction and disclosure to the offspring. Respondents having indecisive attitudes were >30%, which might indicate an increased requirement for information and education to enhance the discussion on the ethical consensus on third-party reproduction in Japan.

scientific communication and education↗