Biology subjects

Okada, N.

Publications and source records attributed to Okada, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Asymmetric Neurogenomic States Emerge in Winners and Losers After Social Competition

Winner and loser effects can influence future aggressive behavior following social competition, but their neurogenomic basis remains poorly understood. Using Betta splendens, we integrated behavioral analyses, whole-brain RNA sequencing, and genomic coexpression network analyses to investigate how social experiences influence enduring behavioral and molecular states. Winners showed increased attack frequency, whereas losers prolonged aggressive latency, indicating effects on distinct behavioral aspects. Winners and losers initially shared acute responses in terms of both gene expression and network coordination but subsequently diverged through outcome-specific patterns of network reorganization, despite few persistent differentially expressed genes. This divergence was asymmetric and involved multiple components: immune-related networks showed notable contrasts, with winners displaying anticipated postconflict coordination but losers instead showing discoordination, whereas neuroendocrine and purinergic networks diverged over distinct temporal scales. These findings highlight transcriptomic network-level remodeling as a potential key feature of the winner-loser effect.

animal behavior and cognition↗

bioRxiv2026

Microtubule occupancy at kinetochores links checkpoint silencing with mitotic memory

The spindle assembly checkpoint (SAC) promotes faithful chromosome segregation by delaying mitosis until all kinetochores attach to spindle microtubules. Paradoxically, a p53-dependent memory mechanism--the mitotic stopwatch" --blocks daughter cell proliferation after unusually prolonged mitoses. How the SAC coordinates with the mitotic stopwatch remains unknown. Here, we found that microtubule occupancy at kinetochores is a cornerstone linking SAC silencing with mitotic memory. By combining live-cell with super-resolution microscopy, FRAP, laser microsurgery, biochemistry and molecular perturbations in Indian muntjac fibroblasts, we show that SAC silencing at kinetochores is gradual, non-uniform and confined to highly-localized microtubule attachments. Augmin promotes timely SAC silencing with high microtubule occupancy at kinetochores, whereas MPS1/CDK1 inhibition bypasses this requirement. Conversely, low microtubule occupancy delays SAC silencing, increases segregation errors and blocks daughter cell proliferation due to mitotic stopwatch surveillance. Thus, timely SAC silencing with high microtubule occupancy avoids "bad memories" of mitosis to allow daughter cell proliferation.

cell biology↗

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↗

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↗

bioRxiv2024

Live-cell imaging under centrifugation characterized the cellular force for nuclear centration in the Caenorhabditis elegans embryo

Organelles in cells are appropriately positioned, despite crowding in the cytoplasm. However, our understanding of the force required to move large organelles, such as the nucleus, inside the cytoplasm is limited, in part owing to a lack of accurate methods for measurement. We devised a novel method to apply forces to the nucleus of living, wild-type Caenorhabditis elegans embryos to measure the force generated inside the cell. We utilized a centrifuge polarizing microscope (CPM) to apply centrifugal force and orientation-independent differential interference contrast (OI-DIC) microscopy to characterize the mass density of the nucleus and cytoplasm. The cellular forces moving the nucleus toward the cell center increased linearly at [~]14 pN/m depending on the distance from the center. The frictional coefficient was [~]1,100 pN s/m. The measured values were smaller than previously reported estimates for sea urchin embryos. The forces were consistent with the centrosome-organelle mutual pulling model for nuclear centration. Frictional coefficient was reduced when microtubules were shorter or detached from nuclei in mutant embryos, demonstrating the contribution of astral microtubules. Finally, the frictional coefficient was higher than a theoretical estimate, indicating the contribution of uncharacterized properties of the cytoplasm.

biophysics↗

bioRxiv2023

A potential pathway that links intron retention with the physiological recovery by a Japanese herbal medicine

Our previous experiments with klotho mice (i.e., klotho knockout mice) showed that aberrant intron retention (IR) occurs in a pre-symptomatic state of aging and can be restored to the normal state by administration of Juzentaihoto (JTT), a formula of the Japanese herbal medicine (referred to as kampo; Okada et al., 2021). To explain that this phenomenon is not just a specific example observed in klotho mice, but is due to a universal cause that may be common in a pre-symptomatic state, we re-analyzed the data for genes that were differentially expressed in the klotho liver in response to JTT. Our data showed that AMPK signaling was activated in these mice, resulting in inhibition of anabolic pathways including protein synthesis and activation of catabolic pathways including lipolysis in various organs. These data were consistent with the observation that klotho mice appeared to be in a perpetual state of pseudo-starvation, i.e., based on their overall metabolic profile. The abnormally increased incidence of IR (IncIR) in the liver of klotho mice could be classified into three types according to their transcriptional status, revealing that each of all the three types of IncIR was caused by AMPK activation, a consequence of the pseudo-starvation state exhibited by these mice. Furthermore, we showed that both the transcriptional drift variance and the IR drift variance can be useful for visually assessing recovery from the pseudo-starvation state of klotho mice after administration of JTT. With these data, we propose a novel pathway linking intron retention to changes in the physiological state such as starvation, resulting in the maintenance of cellular homeostasis.

bioinformatics↗

bioRxiv2023

Myeloid-derived suppressor cells exacerbate poly(I:C)-induced lung inflammation in mice with renal injury and older mice

Viral pneumonia is a global health burden with a high mortality rate, especially in the elderly and in patients with underlying diseases. Recent studies have found that myeloid-derived suppressor cells (MDSCs) are abundant in these patient groups; however, their roles in the progression of viral pneumonia remain unclear. In this study, we observed a substantial increase in MDSCs in a mouse model of renal ischemia/reperfusion (I/R) injury and in older mice. When intranasal polyinosinic- polycytidylic acid (poly(I:C)) administration was used to mimic viral pneumonia, we found that mice with renal I/R injury exhibited more severe lung inflammation than sham mice when challenged with poly(I:C). In addition, MDSC depletion attenuated lung inflammation in mice with I/R injury. Similar results were obtained in older mice compared with those in young mice. Furthermore, we found that the adoptive transfer of in vitro-differentiated MDSCs exacerbated poly(I:C)-induced lung inflammation. Taken together, these experimental results suggest that the increased proportion of MDSCs in mice with renal I/R injury and in older mice exacerbates poly(I:C)-induced lung inflammation. These findings have important implications for the treatment and prevention of severe lung inflammation caused by viral pneumonia.

immunology↗

bioRxiv2023

HDAC1-3 inhibition reduces CCR2 expression and immunosuppressive function of myeloid-derived suppressor cells

Myeloid-derived suppressor cells (MDSCs) play a critical role in cancer progression and resistance, making them significant targets for cancer immunotherapy. Although epigenetic regulation by histone deacetylases (HDACs) regulates cell fate and function, the specific roles of HDACs in modulating MDSCs remain poorly understood. We aimed to examine the effects and underlying mechanisms of HDAC on MDSCs using various HDAC inhibitors. HDAC1-3 inhibitors were found to reduce the expression of CCR2, a chemokine receptor that mediates the migration of monocytic (M-)MDSCs to tumors and attenuated the immunosuppressive activity of MDSCs. In an orthotopic hepatocellular carcinoma (HCC) murine model, HDAC1-3 inhibitors reduced the infiltration of M-MDSCs, increased the number of natural killer cells in tumors, and suppressed tumor growth. Our results also suggest that HDAC1-3 inhibitors potentiate the antitumor effects of anti-programmed cell death protein 1 antibodies. To elucidate the molecular mechanisms underlying the inhibition of MDSCs by HDAC1-3 inhibitors, ATAC-seq and RNA-seq analyses were performed. We identified 115 genes that were epigenetically upregulated by HDAC1-3 inhibitors, related to transcriptional regulation and ubiquitination. HDAC1-3 inhibitors further reduced CCR2 protein expression by enhancing ubiquitination-mediated degradation. Our findings reveal a novel mechanism of action of HDAC1-3 inhibitors in MDSCs and suggest a potential combination strategy with immunotherapy for the clinical translation of HCC.

immunology↗

bioRxiv2023

Comprehensive analysis of nasal IgA antibodies induced by intranasal administration of the SARS-CoV-2 spike protein

Intranasal vaccination is an attractive strategy for preventing COVID-19 disease as it stimulates the production of multimeric secretory immunoglobulin A (IgA), the predominant antibody isotype in the mucosal immune system, at the target site of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry. Currently, intranasal vaccine efficacy is evaluated based on the measurement of polyclonal antibody titers in nasal lavage fluid. However, how individual multimeric secretory IgA protects the mucosa from SARS-CoV-2 infection remains to be elucidated. To understand the precise contribution and molecular nature of multimeric secretory IgA induced by intranasal vaccines, we developed 99 monoclonal IgA clones from nasal mucosa and 114 monoclonal IgA or IgG clones from nonmucosal tissues of mice that were intranasally immunized with the SARS-CoV-2 spike protein. The nonmucosal IgA clones exhibited shared origins and common and unique somatic mutations with the related nasal IgA clones, indicating that the antigen-specific plasma cells in the nonmucosal tissues originated from B cells stimulated at the nasal mucosa. Comparing the spike protein binding reactivity, angiotensin-converting enzyme-2-blocking and in vitro SARS-CoV-2 virus neutralization of monomeric and multimeric secretory IgA pairs recognizing different epitopes showed that even non-neutralizing monomeric IgAs, which represents 70% of the nasal IgA repertoire, can protect against SARS-CoV-2 infection when expressed as multimeric secretory IgAs. We also demonstrated that the intranasal administration of multimeric secretory IgA delivered as prophylaxis in the hamster model reduced infection-induced weight loss. Our investigation is the first to demonstrate the function of nasal IgA at the monoclonal level, showing that nasal immunization can provide effective immunity against SARS-CoV-2 by inducing multimeric secretory IgAs at the target site of the virus infection.

immunology↗

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↗

bioRxiv2022

Fission yeast Dis1 is an unconventional TOG/XMAP215 that induces microtubule catastrophe to drive chromosome pulling

The shortening of microtubules attached to kinetochores is the driving force of chromosome movement during cell division. Specific kinesins are believed to shorten microtubules but are dispensable for viability in yeast, implying the existence of additional factors responsible for microtubule shortening. Here, we demonstrate that Dis1, a TOG/XMAP215 ortholog in fission yeast, promotes microtubule shortening to carry attached chromosomes. Although TOG/XMAP215 orthologs are generally accepted as microtubule polymerases, Dis1 promoted microtubule catastrophe in vitro and in vivo. Notably, microtubule catastrophe was promoted when the tip was attached to kinetochores, as they steadily anchored Dis1 at the kinetochore-microtubule interface. Engineered Dis1 oligomers artificially tethered at a chromosome arm region induced the shortening of microtubules in contact, frequently pulling the chromosome arm towards the vicinity of spindle poles in meiocytes. Thus, unlike Alp14 and other TOG/XMAP215 orthologs, Dis1 plays an unconventional role in promoting microtubule catastrophe, thereby driving chromosome movement.

cell biology↗

bioRxiv2022

NFYA promotes the anti-tumor effects of gluconeogenesis in hepatocellular carcinoma through the regulation of PCK1 expression

Reprogramming of glucose metabolism occurs in many human tumor types, and one of these, gluconeogenesis, is known to exhibit anti-tumor effects in hepatocellular carcinoma (HCC). The transcription factor NFYA regulates gluconeogenesis in the normal liver tissue, but the function of the NFYA-gluconeogenesis axis in cancer and the functional differences of NFYA splicing variants in the regulation of gluconeogenesis is still unclear. Here, we demonstrate that NFYAv2, the short-form variant, upregulates the transcription of a gluconeogenic enzyme PCK1. We further reveal that its regulation induces high ROS levels and energy crisis in HCC and promotes cell death. These indicate that the NFYAv2-gluconeogenesis axis has enhanced anti-tumor effects in HCC, suggesting that the axis may be a potential therapeutic target for HCC. Furthermore, Nfyav1-deficient mice, spontaneously overexpressing Nfyav2, had no increasing gluconeogenesis in the liver. Taken together, our results reveal NFYAv2-gluconeogenesis axis has anti-tumor effects and the potential for NFYAv2 to be a safer therapeutic target for HCC.

cancer biology↗

bioRxiv2022

NFYA promotes the malignant behavior of triple-negative breast cancer through the regulation of lipid metabolism

Two splicing variants exist in NFYA, which exhibits high expression in many human tumor types, and their expression balance is known to correlate with prognosis in breast cancer, but functional differences are still unclear. Here, we demonstrate that NFYAv1, the long-form variant, upregulates the transcription of ACACA and FASN, essential lipogenic enzymes, to enhance the malignant behavior of triple-negative breast cancer (TNBC). Loss of the NFYAv1-lipogenesis axis strongly suppresses the malignant behavior in vitro and in vivo, indicating that the NFYAv1-lipogenesis axis is essential for TNBC malignant behavior and that the axis might be a potential therapeutic target for TNBC. Furthermore, mice deficient in lipogenic enzymes such as Acly, Acaca, and Fasn exhibit embryonic lethality, but our Nfyav1-deficient mice have no apparent developmental abnormalities. Taken together, our results elucidate NFYAv1-lipogenesis axis has significant tumor-promoting effects and the potential for NFYAv1 to be a safer therapeutic target for TNBC.

cancer biology↗

bioRxiv2020

A unique neurogenomic state emerges after aggressive confrontations in males of the fish Betta splendens

Territorial defense involves frequent aggressive confrontations with competitors, but little is known about how brain-transcriptomic profiles change between individuals competing for territory establishment. Our previous study elucidated that brain-transcriptomic synchronization occurs in a pair-specific manner between two males of the fish Betta splendens during fighting, reflecting a mutual assessment process between them at the level of gene expression. Here we evaluated how the brain-transcriptomic profiles of opponents change immediately after shifting their social status (i.e., the winner/loser has emerged) and 30 min after this shift. We showed that unique and carryover hypotheses can be adapted to this system, in which changes in the expression of certain genes are unique to different fighting stages and in which the expression patterns of certain genes are transiently or persistently changed across all fighting stages. Interestingly, the specificity of the brain-transcriptomic synchronization of a pair during fighting was gradually lost after fighting ceased, because of the decrease in the variance in gene expression across all individuals, leading to the emergence of a basal neurogenomic state. Strikingly, this unique state was more basal than the state that existed in the before-fighting group and resulted in the reduced and consistent expression of genes across all individuals. In spite of the consistent and basal overall gene expression in each individual in this state, expression changes for genes related to metabolism, learning and memory, and autism still differentiated losers from winners. The fighting system using male B. splendens thus provides a promising platform for investigating neurogenomic states of aggression in vertebrates. Author summaryCompetitive interactions involve complex decision-making tasks that are shaped by mutual feedback between participants. When two animals interact, transcriptomes across their brains synchronize in a way that reflects how they assess and predict the others fighting ability and react to each others decisions. Here, we elucidated the gradual loss of brain-transcriptomic synchrony between interacting opponents after their interaction ceased, leading to the emergence of a basal neurogenomic state, in which the variations in gene expression were reduced to a minimum among all individuals. This basal neurogenomic state shares common characteristics with the hibernation state, which animals adopt to minimize their metabolic rates to cope with harsh environmental conditions. We demonstrated that this unique neurogenomic state, which is newly characterized in the present study, is composed of the expression of a unique set of genes, each of which was presumably minimally required for survival, providing a hypothesis that this state represents the smallest unit of neurogenomic activity for sustaining an active life.

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↗