Biology subjects

Kasai, K.

Publications and source records attributed to Kasai, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Structure of an active FlhA ring reveals allosteric coupling between substrate specificity and export engine activation

The bacterial flagellar type III secretion system transports structural subunits in a defined order to assemble the flagellum. FlhA is the core component of this nanomachine; its transmembrane domain functions as an ion-driven export engine and its cytoplasmic domain connected by a flexible linker forms a nonameric ring that controls substrate selection. The coupling mechanism of these activities has remained unclear because the transmembrane domain has resisted structural analysis. Here we identify linker deletions that autonomously activate the export gate and capture a functionally active, hook-type protein export state of the entire ring complex. Using cryo-electron microscopy, we determine the structure of the FlhA({Delta}331-347) ring at 2.73 [A] resolution. The structure shows linker-mediated docking of the cytoplasmic ring to the transmembrane ring to stabilize the entire ring while sterically excluding filament-type substrate binding. These findings reveal how the FlhA ring allosterically couples substrate specificity to ion-driven export during flagellar assembly.

microbiology↗

bioRxiv2025

Towards On-Site Paleogenomics: Application and Perspective of Nanopore Sequencing with Ancient DNA

Ancient DNA (aDNA) research has greatly advanced understanding of past populations, yet progress in this field is still limited by two fundamental issues: dependence on fixed laboratory infrastructure and ethical considerations surrounding the cross-border transfer of archaeological biological specimens. In this study, we present the first successful application of Oxford Nanopore Technologies (ONT) sequencing to authentic aDNA from ancient human remains dating to the Early Jomon period. Our results demonstrate that nanopore platforms can recover characteristic postmortem damage signatures, generate genome-wide information with time-stamped sequencing data, and produce population genetic inferences consistent with Illumina short-read data. Crucially, the ONT sequencing has enabled the first demonstration of time-resolved aDNA analysis. This enables key genetic metrics, such as biological sex inference, to be determined within the first 60 minutes of sequencing. The portability and operational simplicity of ONT devices provide a practical basis for on-site paleogenomics, facilitating the generation of genomic data directly at archaeological sites, in museums, or at local research institutions. This capability is particularly significant in regions where the export of biological specimens entails administrative procedures, often spanning several months or even years. By enabling local archaeologists and anthropologists to independently generate and interpret genomic data, on-site nanopore sequencing can accelerate global research alliance, and promote equitable scientific authorship and continuing dialogue on sample sovereignty and collaborative research practices between geneticists and field researchers. Together, our findings establish nanopore sequencing as a socially sustainable and viable tool for paleogenomics, offering new pathways for the rapid, field-deployable and ethical process of archaeological remains.

genomics↗

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↗

bioRxiv2025

Chronic social defeat causes dysregulation of systemic glucose metabolism via the cerebellar fastigial nucleus

Chronic psychological stress leads to hyperglycemia through the endocrine and sympathetic nervous systems, which contributes to the development of type II diabetes mellitus (T2DM). Higher plasma corticosteroids after stress is one well-established driver of insulin resistance in peripheral tissues. However, previous studies have indicated that only a fraction of patients with depression and post-traumatic disorder (PTSD) who develop T2DM exhibit hypocortisolism, so corticosteroids do not fully explain psychological stress-induced T2DM. Here, we find that chronic social defeat stress (CSDS) in mice enhances gluconeogenesis, which is accompanied by a decrease in plasma insulin, an increase in plasma catecholamines, and a drop in plasma corticosterone levels. We further reveal that these metabolic and endocrinological changes are mediated by the activation of neurons projecting from the cerebellar fastigial nucleus (FN) to the medullary parasolitary nucleus (PSol). These neurons are crucial in shifting the bodys primary energy source from glucose to lipids. Additionally, data from patients with depression reveal correlations between the presence of cerebellar abnormalities and both worsening depressive symptoms and elevated HbA1c levels. These findings highlight a previously unappreciated role of the cerebellum in metabolic regulation and its importance as a potential therapeutic target in depression, PTSD, and similar psychological disorders.

neuroscience↗

bioRxiv2024

From Monomers to Oligomers: Structural Mechanism of Receptor-Triggered MyD88 Assembly in Innate Immune Signaling

MyD88 plays a pivotal role in Toll-like receptor (TLR) and interleukin-1 family signaling through its oligomerization upon receptor activation, leading to downstream protein recruitment. The Toll/interleukin-1 receptor domain of MyD88 (TIRMyD88) is responsible for this receptor-mediated oligomerization, but the detailed mechanism involved remains elusive. We investigated the structure of TIRMyD88 oligomers and their interactions with TLRs. Cryoelectron microscopy revealed that tandemly arrayed TIRMyD88 subunits formed an antiparallel double-stranded filament that could further form rings and cylindrical filaments. Moreover, the self-assembly of TIRMyD88 in vitro was markedly accelerated by dimeric rather than monomeric receptor TIRs, possibly reflecting the signal initiation step in vivo. High-speed atomic force microscopy further captured the dynamic processes of oligomerization of TIRMyD88, in addition to its direct interaction with the receptor TIRs. Based on these results, a novel regulatory mechanism of TIRMyD88 oligomerization underlying the signal initiation step was revealed.

biophysics↗

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↗

bioRxiv2024

Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets

The objective diagnostic and stratification biomarkers developed with resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for mental disorders. Unfortunately, there are currently no widely accepted biomarkers, partially due to the large variety of analysis pipelines for developing them. In this study we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD) (1162 participants from eight imaging sites). We explored the combinations of options in four subprocesses of analysis pipelines: six types of brain parcellation, four types of estimations of functional connectivity (FC), three types of site difference harmonization, and five types of machine learning methods. 360 different MDD diagnostic biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four imaging sites) as a discovery dataset and evaluated with datasets from other projects acquired with heterogeneous protocols (449 participants from four imaging sites) for independent validation. To identify the optimal options regardless of the discovery dataset, we repeated the same procedure after swapping the roles of the two datasets. We found pipelines that included Glassers parcellation, tangent-covariance, no harmonization, and non-sparse machine learning methods tended to result in high classification performance. The diagnosis results of the top 10 biomarkers showed high similarity, and weight similarity was also observed between eight of the biomarkers, except two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other mental disorders (autism spectral disorder: ASD and schizophrenia: SCZ) and eight of the ten biomarkers showed sufficient classification performances for both disorders, except two pipelines that included Pearson correlation, ComBat harmonization and random forest classifier combination. HighlightsO_LIWe evaluated the analysis pipelines of rsFC biomarker development. C_LIO_LIFour subprocesses in them were investigated with two multi-site datasets. C_LIO_LIGlassers parcellation, tangent covariance, and non-sparse methods were preferred. C_LIO_LIThe weight patterns of eight of the top 10 biomarkers showed high commonality. C_LIO_LIEight of the top 10 pipelines were successful for developing SCZ/ASD biomarkers. C_LI

neuroscience↗

bioRxiv2023

Genome analysis of the Jomon dogs reveals the oldest domestic dog lineage in Eastern Eurasia

Dog is the oldest domesticated animal that established close relationships with humans. Due to its ancient origin, when, where, and whether a single or dual domestication event occurred is still under debate. The dogs in the Jomon period (Jomon dogs) in the Japanese archipelago had little change in morphology from 10,000 to 3,000 years ago. Therefore, we expected that the ancient genome of the Jomon dogs would provide a clue to reveal the characteristics of the ancient East Asian dogs. Here, we have sequenced the genomes of three 6000-year-old Jomon dogs, one 3000-4000-year-old Jomon dog, and four late 8th century dogs excavated in Japan. Our analyses suggest that the Jomon dogs are a distinct lineage from the previously known ancient dogs and are one of the oldest among the dogs in East Eurasian lineages. In addition, the genome of the Jomon dogs contained 9.5% of the genome of Japanese wolf ancestry due to a single introgression event. We estimated the proportion of the Jomon dog lineage genome in the genomes of dogs, which indicates that the genomic composition derived from the Jomon dog lineage is one of the major sources of modern dog genomes. Furthermore, we estimated the early admixture events of dogs in East Eurasia by analyzing the ancient genomes of the Jomon dogs. Due to the admixture events, the Jomon dog-derived genome has been one of the genomic sources of a wide range of modern dogs.

evolutionary biology↗

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

Statistical mining of triple-negative breast cancer-specific nanobodies among huge libraries from immunized alpacas

Breast cancer can be classified into several types according to the expression patterns of human epidermal growth factor receptor 2 (Her2), oestrogen receptor (ER), and progesterone receptor (PgR) proteins. The prognosis of patients with tumors showing low Her2 expression and no ER and PgR expression--categorized as triple-negative breast cancer (TNBC)--is worst among these groups. Due to the lack of specific antibodies for TNBC, curative treatments for TNBC remain limited. Antibodies targeting TNBC have potential as diagnostic and therapeutic tools. Here, we generate a panel of nanobodies targeting TNBC cell lines by immunizing alpacas and subsequently panning the resulting phage libraries with TNBC cell lines. We show that several clones exclusively stain Her2-negative cells in tissues of breast cancer patients, and a few clones stain both Her2-positive and Her2-negative regions in these tissues. These clones can be applied to patient-specific therapies using drug-conjugated antibodies, radiolabelled antibodies, chimaera antigen receptor T cells, or drug delivery components, as well as to TNBC diagnosis.

cancer biology↗

bioRxiv2023

Normative modeling of brain morphometry in Clinical High-Risk for Psychosis

ImportanceThe lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in the majority of individuals at psychosis risk may be nested within the range observed in healthy individuals. ObjectiveTo quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high-risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design, Setting, and ParticipantsClinical, IQ and FreeSurfer-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1,340 CHR-P individuals [47.09% female; mean age: 20.75 (4.74) years] and 1,237 healthy individuals [44.70% female; mean age: 22.32 (4.95) years] from 29 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. Main Outcomes and MeasuresFor each regional morphometric measure, z-scores were computed that index the degree of deviation from the normative means of that measure in a healthy reference population (N=37,407). Average deviation scores (ADS) for CT, SA, SV, and globally across all measures (G) were generated by averaging the respective regional z-scores. Regression analyses were used to quantify the association of deviation scores with clinical severity and cognition and two-proportion z-tests to identify case-control differences in the proportion of individuals with infranormal (z<-1.96) or supranormal (z>1.96) scores. ResultsCHR-P and healthy individuals overlapped in the distributions of the observed values, regional z-scores, and all ADS vales. The proportion of CHR-P individuals with infranormal or supranormal values in any metric was low (<12%) and similar to that of healthy individuals. CHR-P individuals who converted to psychosis compared to those who did not convert had a higher percentage of infranormal values in temporal regions (5-7% vs 0.9-1.4%). In the CHR-P group, only the ADSSA showed significant but weak associations (|{beta}|<0.09; PFDR<0.05) with positive symptoms and IQ. Conclusions and RelevanceThe study findings challenge the usefulness of macroscale neuromorphometric measures as diagnostic biomarkers of psychosis risk and suggest that such measures do not provide an adequate explanation for psychosis risk. Key pointsO_ST_ABSQuestionC_ST_ABSIs the risk of psychosis associated with brain morphometric changes that deviate significantly from healthy variation? FindingsIn this study of 1340 individuals high-risk for psychosis (CHR-P) and 1237 healthy participants, individual-level variation in macroscale neuromorphometric measures of the CHR-P group was largely nested within healthy variation and was not associated with the severity of positive psychotic symptoms or conversion to a psychotic disorder. MeaningThe findings suggest the macroscale neuromorphometric measures have limited utility as diagnostic biomarkers of psychosis risk.

neuroscience↗

bioRxiv2021

Nanobodies recognizing conserved hidden clefts of all SARS-CoV-2 spike variants

We are in the midst of the historic coronavirus infectious disease 2019 (COVID-19) pandemic caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2). Although countless efforts to control the pandemic have been attempted--most successfully, vaccination1-3--imbalances in accessibility to vaccines, medicines, and diagnostics among countries, regions, and populations have been problematic. Camelid variable regions of heavy chain-only antibodies (VHHs or nanobodies)4 have unique modalities: they are smaller, more stable, easier to customize, and, importantly, less expensive to produce than conventional antibodies5, 6. We present the sequences of nine alpaca nanobodies that detect the spike proteins of four SARS-CoV-2 variants of concern (VOCs)--namely, the alpha, beta, gamma, and delta variants. We show that they can quantify or detect spike variants via ELISA and lateral flow, kinetic, flow cytometric, microscopy, and Western blotting assays7. The panel of nanobodies broadly neutralized viral infection by pseudotyped SARS-CoV-2 VOCs. Structural analyses showed that a P86 clone targeted epitopes that were conserved yet unclassified on the receptor-binding domain (RBD) and located inside the N-terminal domain (NTD). Human antibodies have hardly accessed both regions; consequently, the clone buries hidden crevasses of SARS-CoV-2 spike proteins undetected by conventional antibodies and maintains activity against spike proteins carrying escape mutations.

molecular biology↗

bioRxiv2020

Promoter activity-based case-control association study on SLC6A4 highlighting hypermethylation and altered amygdala volume in male patients with schizophrenia

Associations between altered DNA methylation of the serotonin transporter (5-HTT)-encoding gene SLC6A4 and early life adversity, mood and anxiety disorders, and amygdala reactivity have been reported. However, few studies have examined epigenetic alterations of SLC6A4 in schizophrenia (SZ). We examined CpG sites of SLC6A4, whose DNA methylation levels have been reported to be altered in bipolar disorder, using three independent cohorts of patients with SZ and age-matched controls. We found significant hypermethylation of a CpG site in SLC6A4 in male patients with SZ in all three cohorts. We showed that chronic administration of risperidone did not affect the DNA methylation status at this CpG site using common marmosets, and that in vitro DNA methylation at this CpG site diminished the promoter activity of SLC6A4. We then genotyped the 5-HTT-linked polymorphic region (5-HTTLPR) and investigated the relationship among 5-HTTLPR, DNA methylation, and amygdala volume using brain imaging data. We found that patients harboring low-activity 5-HTTLPR alleles showed hypermethylation and they showed a negative correlation between DNA methylation levels and left amygdala volumes. These results suggest that hypermethylation of the CpG site in SLC6A4 is involved in the pathophysiology of SZ, especially in male patients harboring low-activity 5-HTTLPR alleles.

genetics↗

bioRxiv2020

Brain/MINDS Beyond Human Brain MRI Study: Multi-Site Harmonization for Brain Disorders Throughout the Lifespan

Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (FY2018 [~] FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. The Harmonization protocol (HARP) was established for five high-quality 3T scanners to obtain multimodal brain images including T1 and T2-weighted, resting state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, providing high reproducibility and sensitivity to subject-specific connectome. A total of 75 TS, as well as patients with various psychiatric and neurological disorders, are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a rigorous, prospective harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

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↗

bioRxiv2020

Patterns of functional and structural brain information are preserved over generations

There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks from parents to their children. Here, by employing an unusually large cohort dataset, we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic and behavioural phenotypes have effects on brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of parent-child similarity, and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.

neuroscience↗

bioRxiv2019

Functional connectome linking child-parent relationships with psychological problems in adolescence

Paternal- and maternal-child relationships are associated with partly distinct psychobehavioral problems, which often manifest differently between boys and girls. In order to understand neural mechanisms underlying complicated mappings between child-parent relationship and adolescents problems, we used a dataset of early adolescents (N=93) and separately estimated the effects of paternal- and maternal-child relationships on resting-state functional connectivity and problems in boys and girls. General linear models identified the effects of paternal- and maternal-child relationships in different sets of functional connectivity, which we termed functional brain connectomes associated with paternal- and maternal-child relationship (FBCp and FBCm), respectively. Subsequent connectome-based models using these FBCs significantly predicted aggressive behaviors only for boys and internalizing problems selectively only girls. Lastly, a causal discovery method identified causal paths from daughter-mother relationship to FBCm, and then to daughters internalizing problems. These observations highlight sex-dependent mechanisms linking child-parent relationship, brain, and psychobehavioral problems in early adolescence.

neuroscience↗

bioRxiv2018

Development of the Brief Personal Values Inventory for sense of values in the Japanese general population

The Portrait Values Questionnaire (PVQ) is a widely used questionnaire for assessing sense of values; however, it has limitations, especially for children. Therefore, the present study aims to develop a questionnaire for sense of values, called the Brief Personal Values Inventory (BPVI), consisting of simple questions and a smaller number of items compared to the PVQ. We first created 12 items for the BPVI and then tested their validity and reliability in 167 Japanese general population (81 males, mean age (SD) [range]: 23.4 (8.2) [15-57] years). Each of these items was correlated with one or more values in the PVQ-57. The BPVI items covered all higher-order values in Schwartzs theory (Openness to Change, Self-Enhancement, Conservation, and Self-Transcendence). In sum, the BPVI has an acceptable criterion-related validity and corresponds to higher-order values in Schwartzs value theory. The BPVI is suitable for a reliable and direct comparison of sense of values between children and adults, which may be useful for elucidating the developmental pathway of personal sense of values.

scientific communication and education↗