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Ota, H.

Publications and source records attributed to Ota, H..

3 recordsLinked to original sources

Preserved Intrinsic Neural Timescale Organization with Hierarchical Variation in Autism Spectrum Disorder

Intrinsic neural timescales (INTs) index the temporal decay of neural activity and form a cortical hierarchy from fast sensorimotor to slow transmodal regions. Altered INTs have been reported in autism spectrum disorder (ASD), but it remains unclear whether the hierarchical organization is preserved and how individual variability along this hierarchy relates to sensory traits. Using resting-state fMRI from 182 participants (67 ASD, 115 typically developed controls (TDC)), we estimated INT at each cortical vertex from the autocorrelation half-life and averaged these values across four five-minute runs per participant. Vertex-wise INTs were then averaged within predefined cortical parcels and large-scale functional networks for subsequent analyses. The cortical INT hierarchy was preserved in ASD, showing comparable sensorimotor-to-transmodal hierarchy in both groups. However, regions operating at longer timescales showed prolonged INTs in ASD, and such tendency increased systematically along the hierarchy. No vertex, parcel, or network survived correction of multiple comparisons, indicating that observed alterations followed a distributed hierarchical trend rather than a focal pattern. To disentangle group-level differences from inter-individual variability, we next modeled each participants parcel-wise INT profile relative to a TDC-derived group-averaged template. At the individual level, decomposition of INT profiles revealed that global shifts and hierarchical scaling primarily reflected demographic variation (plimarily sex) rather than diagnostic group membership. After accounting for these components, residual deviations from theTDC-derived cortical INT hierarchy showed a modest association with sensory traits characterized by reduced sensory registration. Together, these findings indicate that while the large-scale hierarchical organization of cortical temporal dynamics is largely preserved in ASD, individual-specific deviations from this hierarchy may contribute to variability in sensory experience beyond group-level differences. HighlightsO_LIAcross both autism spectrum disorder (ASD) and typically developed controls (TDC), intrinsic neural timescales (INTs) followed the established sensory cortical hierarchy and showed a negative association with cortical microstructural markers (myelin content and neurite density index), with no group difference in hierarchical slope. C_LIO_LIAt the whole-brain level, the hierarchical organization of INTs was preserved across ASD and TDC, although regions operating at longer timescales exhibited relatively greater extension in ASD. C_LIO_LIGlobal shifts and hierarchical scaling, describing individual positioning within the INT-based cortical hierarchy, were more strongly associated with demographic variation (primarily sex) than with diagnosis. C_LIO_LIAfter accounting for these global and hierarchical components, residual deviations from the INT-based cortical hierarchy were modestly associated with sensory traits characterized by reduced sensory registration. C_LI

neuroscience↗

OptoChaperone - A biohybrid tool for regulating protein condensates in cells and in vitro

Protein condensates formed via liquid-liquid phase separation (LLPS) are increasingly recognized as key players in diverse cellular processes, including those associated with disease. Despite extensive efforts to characterize their formation and function, tools that enable precise, reversible, and spatiotemporal control of LLPS remain limited. Here, we report OptoChaperone, a light-activatable molecular system designed to manipulate protein condensates both in vitro and in living cells. This biohybrid system leverages photoresponsive switching to control chaperone activity: blue light triggers the suppressive function, leading to the dissolution of protein condensates, whereas UV light deactivates the system, allowing condensate formation. We demonstrate the efficacy of OptoChaperone in regulating several disease-related protein condensates, such as fused in sarcoma, TAR DNA-binding protein 43, and heat shock factor 1. Importantly, the system exhibits reversible and robust control over droplet dynamics without requiring chemical additives or genetic modifications of the client proteins. Given the reversibility and efficiency of OptoChaperone in the manipulation of protein condensates, this tool offers a powerful platform for dissecting the roles of protein condensation in cellular physiology and pathology. This strategy also holds potential for broader applications in synthetic biology, biomolecular engineering, and therapeutic modulation of aberrant phase separation.

biochemistry↗

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↗