Biology subjects

Misaki, M.

Publications and source records attributed to Misaki, M..

14 recordsLinked to original sources

bioRxiv2026

Dose-Dependent Effects of Low-Intensity Focused Ultrasound on Human Deep White Matter Tracts

Background: Low-intensity focused ultrasound (LIFU) is emerging as a method for anatomically specific, noninvasive, and reversible neuromodulation of deep brain structures relevant to psychopathology. A key requirement for clinical translation is evidence of graded target engagement. We examined whether LIFU applied to deep white matter tracts produces dose-dependent functional connectivity changes in healthy adults. Methods: In this preregistered, double-blind, randomized study, 35 healthy adults underwent LIFU targeting (80 s, 10% duty cycle, estimated ISPPA 2.26 W/cm2, 0.5 MHz, in a {theta}-burst pattern) of individually modeled right thalamo-prefrontal white matter tracts under two dose conditions: one and three consecutive sonications. Dose-dependent changes in resting-state functional connectivity were examined during a 30-minute poststimulation period in 31 participants. Results: One sonication acutely increased right thalamic functional connectivity with the left thalamus (28 voxels; Z = 5.48, Cohen's d = 0.61), whereas three sonications decreased connectivity with the right dorsolateral prefrontal cortex (Z = -4.05, Cohen's d = -0.45). Poststimulation time-course analysis revealed a 3-LIFU-specific linear recovery trend in the left cerebellum (Z = 4.14, d = 0.48), reflecting initial suppression followed by progressive recovery across 30 minutes. Stronger thalamo-prefrontal structural connectivity was associated with condition-specific functional connectivity changes (left frontal pole; F = 14.73, {rho}2 = 0.41). Conclusions: These findings provide initial human evidence that LIFU is well tolerated and produces dose-dependent changes in functional connectivity associated with deep white matter tract stimulation, informing parameter selection for future circuit-based mechanistic and clinical studies.

neuroscience↗

bioRxiv2026

Closing the loop between brain and electrical stimulation: A proof-of-concept randomized trial of real-time fMRI-guided tACS optimization

Closed-loop transcranial alternating current stimulation (tACS) combined with real-time functional MRI (tACS-fMRI) enables adaptive optimization of noninvasive brain stimulation based on ongoing neural dynamics. We implemented one of the first randomized, double-arm, closed-loop tACS-fMRI protocols to iteratively adjust stimulation frequency and phase alignment between the right dorsolateral prefrontal cortex (F4) and right inferior parietal cortex (P4) to modulate frontoparietal functional connectivity (FFC) during a 2-back working memory task. Twenty healthy adults were randomized to an up-regulation group (n = 10), receiving parameters optimized to maintain or enhance FFC, or a down-regulation group (n = 10), receiving parameters optimized to reduce FFC using a simplex-based adaptive algorithm across two training runs. Individualized optimized parameters were applied during a subsequent test run, with resting-state fMRI acquired before and after stimulation. During the test phase, connectivity trajectories diverged across groups: FFC was preserved in the up-regulation group but declined in the down-regulation group (signed-rank p = 0.019), with a significant between-group difference confirmed by permutation testing (20,000 iterations; p = 0.043). Behavioral effects were primarily observed in the accuracy learning dynamics. Although overall mean performance was comparable, the up-regulation group demonstrated a more positive accuracy trajectory, greater within-test accuracy gain (p = 0.036), and steeper trial-wise improvement (p = 0.035). Reaction time decreased across runs in both groups, consistent with practice effects, with no significant group differences in gain; however, the test-run RT slope showed a trend toward a steeper reduction in reaction time in the up-regulation group relative to the down-regulation group (p = 0.065). Resting-state analyses revealed significant time-by-group interaction clusters in seed-based connectivity (voxel-wise p < 0.001; cluster-level FDR p < 0.05), with connectivity increases observed in the up-regulation group relative to the down-regulation group, indicating persistent network modulation following stimulation. These findings demonstrate the feasibility of closed-loop tACS-fMRI for individualized network-targeted neuromodulation and suggest that real-time connectivity optimization selectively stabilizes task-relevant connectivity and enhances accuracy learning while producing sustained effects on intrinsic brain networks.

neuroscience↗

bioRxiv2025

Neuroticism Heterogeneity Through Item-Level Associations in Resting-State Functional Connectivity

Neuroticism is characterized by emotional instability and increased susceptibility to stress-related disorders. While traditionally treated as a unitary construct, growing evidence suggests that neuroticism is heterogeneous in both its genetic basis and its effects on social and health outcomes. To quantify this heterogeneity at the neurofunctional level, we analyzed resting-state functional connectivity (RSFC) in a large sample (n = 33,180) from the UK Biobank dataset. Using machine learning regression analysis, we identified RSFC patterns associated with item-level responses from the neuroticism scale of the Eysenck Personality Questionnaire. The pattern of RSFC associations across questionnaire items reflected genetically defined clusters (Worry and Depressed Affect), showing a significant correlation (r = 0.767, p < 0.001). It also aligned with psycholometrically derived factors (Anxiety/Tension and Worry/Vulnerability), reflecting a factor structure consistent with prior psychological studies. These findings were replicated in a separate MRI scan session from the UK biobank dataset. Associated connectivity was primarily observed in cognitive control, sensory integration, and self-referential processing networks. These neurofunctional signatures position RSFC as a robust intermediate phenotype, bridging genetic predisposition for neuroticism and psychological states. This framework could enhance precision in predicting psychiatric vulnerability and inform tailored therapeutic interventions.

neuroscience↗

bioRxiv2024

Aging increases the distinctiveness of emotional brain states across rumination, worry, and positive thinking

Emotional well-being improves with age, but how neural activity related to emotional brain states changes with aging remains unclear. This study examined individualized brain activation patterns for rumination, worry, and positive thinking to explore age-related variations in emotional state representation. Thirty-five participants (aged 18-64) recalled autobiographical events tied to these emotional states during fMRI scanning. Brain activity was analyzed using an individualized machine learning classifier. Results showed increased discriminability of rumination and worry with age, with older adults exhibiting heightened activation in cognitive control regions during rumination and reduced activation in the cingulate and temporoparietal junction during worry. No significant age-related changes were found for positive thinking, although increased discriminability between positive and negative states correlated with well- being (FDR < 0.05). These findings suggest that aging enhances cognitive control during rumination and reduces anxiety responses during worry, potentially contributing to improved emotional well-being in older adults.

neuroscience↗

bioRxiv2024

High-precision neurofeedback-guided meditation training optimises real-world self-guided meditation practice for well-being

Meditation can benefit well-being and mental health, but novices often struggle to effectively recognize and disengage from mental processes during meditation due to limited awareness, potentially diminishing meditations benefits. We investigated whether personalised high-precision neurofeedback (NF) can improve disengagement from mental activity during meditation and enhance meditations outcomes. In a single-blind, controlled, longitudinal paradigm, 40 novice meditators underwent two consecutive days of meditation training with intermittent visual feedback from either their own (N=20) or a matched participants (N=20; control group) posterior cingulate cortex (PCC) activity measured using 7 Tesla functional magnetic resonance imaging. During training, the experimental group showed stronger functional decoupling of PCC from dorsolateral prefrontal cortex, indicating better control over disengagement from mental processes during meditation. This led to greater improvements in emotional well-being and mindful awareness of mental processes during a week of real-world self-guided meditation. We provide compelling evidence supporting the utility of high-precision NF-guided meditation training to optimise real-world meditation practice for well-being.

neuroscience↗

bioRxiv2024

Clinical Response to Neurofeedback in Major Depression Relates to Subtypes of Whole-Brain Activation Patterns During Training

Major Depressive Disorder (MDD) poses a significant public health challenge due to its high prevalence and the substantial burden it places on individuals and healthcare systems. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) shows promise as a treatment for this disorder, although its mechanisms of action remain unclear. This study investigated whole-brain response patterns during rtfMRI-NF training to explain interindividual variability in clinical efficacy in MDD. We analyzed data from 95 participants (67 active, 28 control) with MDD from previous rtfMRI-NF studies designed to increase left amygdala activation through positive autobiographical memory recall. Significant symptom reduction was observed in the active group (t=-4.404, d=-0.704, p<0.001) but not in the control group (t=-1.609, d=-0.430, p=0.111). However, left amygdala activation did not account for the variability in clinical efficacy. To elucidate the brain training process underlying the clinical effect, we examined whole-brain activation patterns during two critical phases of the neurofeedback procedure: activation during the self-regulation period, and transient responses to feedback signal presentations. Using a systematic process involving feature selection, manifold extraction, and clustering with cross-validation, we identified two subtypes of regulation activation and three subtypes of brain responses to feedback signals. These subtypes were significantly associated with the clinical effect (regulation subtype: F=8.735, p=0.005; feedback response subtype: F=5.326, p=0.008; subtypes interaction: F=3.471, p=0.039). Subtypes associated with significant symptom reduction were characterized by selective increases in control regions, including lateral prefrontal areas, and decreases in regions associated with self-referential thinking, such as default mode areas. These findings suggest that large-scale brain activity during training is more critical for clinical efficacy than the level of activation in the neurofeedback target region itself. Tailoring neurofeedback training to incorporate these patterns could significantly enhance its therapeutic efficacy.

neuroscience↗

bioRxiv2023

Whole-brain Mechanism of Neurofeedback Therapy: Predictive Modeling of Neurofeedback Outcomes on Repetitive Negative Thinking in Depression

Real-time fMRI neurofeedback (rtfMRI-NF) has emerged as a promising intervention for psychiatric disorders, yet its clinical efficacy remains underexplored due to limited controls and an incomplete mechanistic understanding. This study aimed to elucidate the whole-brain mechanisms underpinning the effects of rtfMRI-NF on repetitive negative thinking in depression. In a double-blind randomized controlled trial, forty-three depressed individuals underwent NF training targeting the functional connectivity (FC) between the posterior cingulate cortex and the right temporoparietal junction, linked to rumination severity. Participants were randomly assigned to active or sham groups, with the sham group receiving synthesized feedback mimicking real NF signal patterns. The active group demonstrated a significant reduction in brooding rumination scores (p<0.001, d=-1.52), whereas the sham group did not (p=0.503, d=- 0.23). While the target FC did not show discernible training effects or group differences, we found that the interaction between brain activities during regulation and the response to the feedback signal was the critical factor in explaining treatment outcomes. Connectome-based predictive modeling (CPM) analysis, incorporating this interaction, successfully predicted rumination changes across both groups. The FCs significantly contributing to the prediction were distributed across broad brain regions, notably the frontal control, salience network, and subcortical reward processing areas. These results underscore the importance of considering the interplay between brain regulation activities and brain response to the feedback signal in understanding the therapeutic mechanisms of rtfMRI-NF. The study not only affirms the potential of rtfMRI-NF as a therapeutic intervention for repetitive negative thinking in depression but also highlights the need for a more nuanced understanding of the whole-brain mechanisms contributing to its efficacy.

neuroscience↗

bioRxiv2023

Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state, not resting state

Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression. However, identifying the specific functional process associated with RSFC alterations is challenging, and it remains unclear whether alterations in RSFC for depressed individuals are directly related to the RNT process or to individual characteristics distinct from the negative thinking process per se. To investigate the relationship between RSFC alterations and the RNT process in individuals with major depressive disorder (MDD), we compared RSFC with functional connectivity during an induced negative-thinking state (NTFC) in terms of their predictability of RNT traits and associated whole-brain connectivity patterns using connectome-based predictive modeling (CPM) and connectome-wide association (CWA) analyses. Thirty-six MDD participants and twenty-six healthy control participants underwent both resting state and induced negative thinking state fMRI scans. Both RSFC and NTFC distinguished between healthy and depressed individuals with CPM. However, trait RNT in depressed individuals, as measured by the Ruminative Responses Scale-Brooding subscale, was only predictable from NTFC, not from RSFC. CWA analysis revealed that negative thinking in depression was associated with higher functional connectivity between the default mode and executive control regions, which was not observed in RSFC. These findings suggest that RNT in depression involves an active mental process encompassing multiple brain regions across functional networks, which is not represented in the resting state. Although RSFC indicates brain functional alterations in MDD, they may not directly reflect the negative thinking process.

neuroscience↗

bioRxiv2021

A library for fMRI real-time processing systems in python (RTPSpy) with comprehensive online noise reduction, fast and accurate anatomical image processing, and online processing simulation

Real-time fMRI (rtfMRI)has enormous potential for both mechanistic brain imaging studies or treatment-oriented neuromodulation. However, the adaption of rtfMRI has been limited due to technical difficulties implement an efficient computational framework. Here, we introduce a python library for real-time fMRI (rtfMRI) data processing systems, Real-Time Processing System in python (RTPSpy), to provide building blocks for a custom rtfMRI application with extensive and advanced functionalities. RTPSpy is a library package including 1) a fast, comprehensive, and flexible online fMRI image processing modules comparable to offline denoising, 2) utilities for fast and accurate anatomical image processing to define an anatomical target region, 3) a simulation system of online fMRI processing to optimize a pipeline and target signal calculation, 4) simple interface to an external application for feedback presentation, and 5) a boilerplate graphical user interface (GUI) integrating operations with RTPSpy library. The fast and accurate anatomical image processing utility wraps external tools, including FastSurfer, ANTs, and AFNI, to make tissue segmentation and region of interest masks. We confirmed that the quality of the output masks was comparable with FreeSurfer, and the anatomical image processing could complete in a few minutes. The modular nature of RTPSpy provides the ability to use it for a simulation analysis to optimize a processing pipeline and target signal calculation. We present a sample script for building a real-time processing pipeline and running a simulation using RTPSpy. The library also offers a simple signal exchange mechanism with an external application using a TCP/IP socket. While the main components of the RTPSpy are the library modules, we also provide a GUI class for easy access to the RTPSpy functions. The boilerplate GUI application provided with the package allows users to develop a customized rtfMRI application with minimum scripting labor. The limitations of the package as it relates to environment-specific implementations are discussed. These library components can be customized and can be used in parts. Taken together, RTPSpy is an efficient and adaptable option for developing rtfMRI applications. The package is available from GitHub (https://github.com/mamisaki/RTPSpy) with GPL3 license.

neuroscience↗

bioRxiv2021

Online Closed-Loop Real-Time tES-fMRI for Brain Modulation: Feasibility, Noise/Safety and Pilot Study

Recent studies suggest that transcranial electrical stimulation (tES) can be performed during functional magnetic resonance imaging (fMRI). The novel approach of using concurrent tES-fMRI to modulate and measure targeted brain activity/connectivity may provide unique insights into the causal interactions between the brain neural responses and psychiatric/neurologic signs and symptoms, and importantly, guide the development of new treatments. However, tES stimulation parameters to optimally influence the underlying brain activity in health and disorder may vary with respect to phase, frequency, intensity and electrodes montage. Here, we delineate how a closed-loop tES-fMRI study of frontoparietal network modulation can be designed and performed. We also discuss the challenges of running a concurrent tES-fMRI, describing how we can distinguish clinically meaningful physiological changes caused by tES from tES-related artifacts. There is a large methodological parameter space including electrode types, electrolytes, electrode montages, concurrent tES-fMRI hardware, online fMRI processing pipelines and closed-loop optimization algorithms that should be carefully selected for closed-loop tES-fMRI brain modulation. We also provide technical details on how safety and quality of tES-fMRI settings can be tested, and how these settings can be monitored during the study to ensure they do not exceed safety standards. The initial results of feasibility and applicability of closed-loop tES-fMRI are reported and potential hypotheses for the outcomes are discussed. Highlight pointsO_LIMethodological details of a closed-loop tES-fMRI study protocol are provided. C_LIO_LIThe protocol is performed successfully on a frontoparietal network without side-effects. C_LIO_LIThe temperature of electrodes in concurrent tES-fMRI remains in the safe range. C_LIO_LIProperly setup concurrent tES does not introduce MRI artifacts and noise. C_LIO_LISimplex optimizer could be used to find an optimal tES stimulation parameter. C_LI

neuroscience↗

bioRxiv2021

The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity

ObjectiveComprehensive denoising is imperative in fMRI analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity. ApproachWe performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT). Main resultsAll the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETORICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time. SignificanceThe results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.

neuroscience↗

bioRxiv2020

Predicting Sex from Resting-State fMRI Across Multiple Independent Acquired Datasets

Sex is an important biological variable often used in analyzing and describing the functional organization of the brain during cognitive and behavioral tasks. Several prior studies have shown that blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) functional connectivity (FC) can be used to differentiate sex among individuals. Herein, we demonstrate that sex can be further classified with high accuracy using the intrinsic BOLD signal fluctuations from resting-state fMRI (rs-fMRI). We adopted the amplitude of low-frequency fluctuation (ALFF), and the fraction of ALFF (fALFF) features from the automated anatomical atlas (AAL) and Powers functional atlas as an input to different machine learning (ML) methods. Using datasets from five independently acquired subject cohorts and with eight fMRI scanning sessions, we comprehensively assessed unbiased performance using nested-cross validation for within-sample and across sample accuracies. The results demonstrated high prediction accuracies for the Human Connectome Project (HCP) dataset (area under cure (AUC) > 0.89). The yielded accuracies suggest that sex difference is embodied and well-pronounced in the low-frequency BOLD signal fluctuation. The performance degrades with the heterogeneity of the cohort and suggests that other factors.e.g. psychiatric disorders and demographics influences the BOLD signal and may interact with the classification of sex. In addition, the results revealed high learning generalizability with the HCP scan, but not across different datasets. The intraclass correlation coefficient (ICC) across HCP scans showed moderate-to-good reliability based on atlas selection (ICC = 0.65 [0.63-0.67] and ICC= 0.78 [0.76-0.80].). We also assessed the effect of scan duration on the predictability of sex and showed that sex differences could be detected even with a short rs-fMRI scan (e.g., 2 minutes). Moreover, we provided statistical maps of the brain regions differentially recruited by or predicting sex using Shapely values and determined an overlap with previous reports of brain response due to sex differences. Altogether, our analysis suggests that sex differences are well-pronounced in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation.

neuroscience↗

bioRxiv2020

Canonical EEG Microstate Dynamic Properties and Their Associations with fMRI Signals at Resting Brain

Electroencephalography microstates (EEG-ms) capture and reflect the spatio-temporal neural dynamics of the brain. A growing literature is employing EEG-ms-based analyses to study various mental illnesses and to evaluate brain mechanisms implicated in cognitive and emotional processing. The spatial and functional interpretation of the EEG-ms is still being investigated. Previous works studied the association of EEG-ms time courses with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal and suggested an association between EEG-ms and resting-state networks (RSNs). However, the distinctive association between EEG-ms temporal dynamics and brain neuronal activities is still not clear, despite the assumption that EEG-ms are an electrophysiological representation of RSNs activity. Recent works suggest a role for brain spontaneous EEG rhythms in contributing to and modulating canonical EEG-ms topographies and determining their classes (coined A through D) and metrics. This work simultaneously utilized EEG and fMRI to understand the EEG-ms and their properties further. We adopted the canonical EEG-ms analysis to extract three types of regressors for EEG-informed fMRI analyses: EEG-ms direct time courses, temporal activity per microstate, and pairwise temporal transitions among microstates (the latter two coined activity regressors). After convolving EEG-ms regressors with a hemodynamic response function, a generalized linear model whole-brain voxel-wise analysis was conducted to associate EEG-ms regressors with fMRI signals. The direct time course regressors replicated prior findings of the association between the fMRI signal and EEG-ms time courses but to a smaller extent. Notably, EEG-ms activity regressors were mostly anticorrelated with fMRI, including brain regions in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with no significant overlap for default mode, limbic or frontoparietal networks. A similar pattern emerged in using the transition regressors among microstates but not in self-transitions. The relatively short duration of each EEG-ms and the significant association of EEG-ms activity regressors with fMRI signals suggest that EEG-ms manifests successive transition from one brain functional state to another rather than being associated with specific brain functional state or RSN networks.

neuroscience↗

bioRxiv2020

Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention

Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N=223) and healthy controls (N=45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (-6, -54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, -49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.

neuroscience↗