About
132
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Introduction
I am working on data analysis tools for human brain imaging data such as fMRI, MEG, EEG and NIRS with particular emphasis on multi-modal integration. Currently I have been involved in two research projects as follow;
1. realizing high-spatiotemporal imaging by comibining MEG/EEG and fMRI
2. realizing novel brain-circuit biomarkers of mental disorders by developing big data analysis and dynamics modeling methods of fMRI.
Current institution
Additional affiliations
November 2016 - present
Position
- Group Leader
Description
- Mental disorder is becoming one of the most serious problems in our society. We are developing big data analysis and dynamics modeling methods of human brain imaging data such as fMRI, MEG, EEG and NIRS in order to realize novel imaging-based diagnosis and therapy.
April 2013 - present
April 2010 - March 2013
Education
April 2001 - March 2004
Publications
Publications (132)
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affe...
Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms. The complexity of factors, including inter-site and developmental differences, hinders the development of a generalizable neuroimaging classifier for ASD. Here, we developed a classifier for ASD using a large-scale, multisite resting-state fMRI dataset of 730...
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affe...
Neuroimaging databases for neuro‐psychiatric disorders enable researchers to implement data‐driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi‐center, multi‐disorder databases has graduall...
Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time pre...
Major depressive disorder (MDD) is diagnosed based on symptoms and signs without relying on physical, biological, or cognitive tests. MDD patients exhibit a wide range of complex symptoms, and it is assumed that there are diverse underlying neurobiological backgrounds, possibly composed of several subtypes with relatively homogeneous biological fea...
Schizophrenia spectrum disorder (SSD) is one of the top causes of disease burden; similar to other psychiatric disorders, SSD lacks widely applicable and objective biomarkers. This study aimed to introduce a novel resting-state functional connectivity (rs-FC) magnetic resonance imaging (MRI) biomarker for diagnosing SSD. It was developed using cust...
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 p...
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 developin...
Schizophrenia spectrum disorder (SSD) is one of the top causes of disease burden; similar to other psychiatric disorders, SSD lacks widely applicable and objective biomarkers. This study aimed to introduce a novel resting-state functional connectivity (rs-FC) magnetic resonance imaging (MRI) biomarker for diagnosing SSD. It was developed using cust...
Background
The World Health Organization has reported that approximately 300 million individuals suffer from the mood disorder known as MDD. Non-invasive measurement techniques have been utilized to reveal the mechanism of MDD, with rsfMRI being the predominant method. The previous functional connectivity and energy landscape studies have shown the...
An optically pumped magnetometer (OPM) is a new generation of magnetoencephalography (MEG) devices that is small, light, and works at room temperature. Due to these characteristics, OPMs enable flexible and wearable MEG systems. On the other hand, if we have a limited number of OPM sensors, we need to carefully design their sensor arrays depending...
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 Ja...
The recent success of sequential learning models, such as deep recurrent neural networks, is largely due to their superior representation-learning capability for learning the informative representation of a targeted time series. The learning of these representations is generally goal-directed, resulting in their task-specific nature, giving rise to...
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 Ja...
Aim:
Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common a...
Background and hypothesis:
Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this p...
Background:
Recently, we developed a generalizable brain network marker for the diagnosis of major depressive disorder (MDD) across multiple imaging sites using resting-state functional magnetic resonance imaging. Here, we applied this brain network marker to newly acquired data to verify its test-retest reliability and anterograde generalization...
Phantom limb pain is attributed to abnormal sensorimotor cortical representations, although the causal relationship between phantom limb pain and sensorimotor cortical representations suffers from the potentially confounding effects of phantom hand movements. We developed neurofeedback training to change sensorimotor cortical representations withou...
Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about indi...
Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. “Whole-brain” propagating activities may also reflect a process that integrates i...
Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, mul...
Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagno...
Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases—caused by site differences in scanner/image-acquisition protocols—negatively influence the reliability and reproducibility of image-analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite data...
Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinic...
Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about indi...
In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clusterin...
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,...
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....
A multiple-view clustering method is a powerful analytical tool for high-dimensional data, such as functional magnetic resonance imaging (fMRI). It can identify clustering patterns of subjects depending on their functional connectivity in specific brain areas. However, when one applies an existing method to fMRI data, there is a need to simplify th...
Objective
To determine whether training with a brain–computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.
Methods
Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avu...
Objective. Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals have been developed to restore the communication function of severely paralyzed patients. However, the limited amount of information derived from ECoG signals hinders their clinical applications. We aimed to develop a method to decode ECoG signals using spatiotem...
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....
Fifty years ago, David Marr and James Albus proposed a computational model of cerebellar cortical function based on the pioneering circuit models described by John Eccles, Masao Ito and Janos Szentagothai. The Marr–Albus model remains one of the most enduring and influential models in computational neuroscience, despite apparent falsification of so...
Resting-state functional connectivity (RSFC) has been generally assessed with functional magnetic resonance imaging (fMRI) thanks to its high spatial resolution. However, fMRI has several disadvantages such as high cost and low portability. In addition, fMRI may not be appropriate for people with metal or electronic implants in their bodies, with c...
Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass...
Recently, we proposed a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data (SpatioTemporal Pattern estimation, STeP) (Takeda et al., 2016). From such resting-state data as functional MRI (fMRI), STeP can estimate several spatiotemporal patterns and their onsets even if they are overlapping. Nowadays, a grow...
When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, mul...
Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) (https://vbmeg.atr.jp/). VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a dif...
Multi-directional measurement using multi-directional light sources and multi-directional photodetectors drastically increases the amount of observation data without increasing the number of optical probes. In this study, we developed a novel multi-directional functional near-infrared spectroscopy (fNIRS) system for human neuroimaging studies. We t...
When collecting large neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent the greatest barrier when acquiring multi-site neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multi-site, m...
In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provid...
Brain information flow for Control 1.
Brain information flow for Control 2.
Brain information flow for Stroke 2.
Brain information flow for Stroke 1.
To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and d...
Correlation coefficient between patterns of original and estimated cortical current.
Results for V1 at 50 ms and IT at 225 ms for artificial experimental condition 2 are shown (averaged across participants; error bars, s.d.; gray dashed lines, significance level [uncorrected P < 0.05]). 0* indicates γ0 = 0.
(PDF)
Individual data points from correlation analysis (Fig 4 and S3 Fig).
(XLSX)
Visual stimuli used for the MEG experiment.
(a) Pairs of wedges rotating clockwise in 30° steps. Black frames around the background are drawn for visibility (not shown in the actual experiment). (b) Correspondence between experimental conditions and labels. Three pairs of wedges presented in the upper right and lower left areas were labeled as cond...
Time-resolved decoding in each ROI.
(a–d) Time courses of the prediction accuracy for MNE, MCE, VBMEG, and LCMV, respectively. Solid lines indicate the mean prediction accuracy across participants. Shading indicates the 1st–99th percentiles of prediction accuracy across participants. Red dashed lines indicate the mean significance levels across par...
Individual data points from real data analysis (Fig 7 and S9 Fig).
(XLSX)
Accuracy of source localization.
APRs for V1 at 50 ms and IT at 225 ms in experimental condition 2 (averaged across participants; error bars, s.d.; gray dashed lines, baseline value of APR) are shown. 0* indicates γ0 = 0.
(PDF)
Comparisons of difference in definition of statistical significance levels.
The number of time points that showed significant prediction accuracy via time-resolved decoding in each ROI (averaged across participants; error bars, s.d.). Results for the hyperparameters that achieved (a) the highest and (b) second-highest correlation coefficients in V1...
Test of signal leakage reduction.
(a) Change of correlation by orthogonalization in simulation data (averaged across participants; error bars, s.d.). (b) Change of prediction accuracy by orthogonalization in simulation data (averaged across participants; error bars, s.d.;). (c) Change of correlation by orthogonalization in real data (averaged acros...
Individual data points from APR analysis (Fig 3 and S2 Fig).
(XLSX)
Individual data points from time-resolved decoding in each ROI (Fig 5 and S4 Fig).
(XLSX)
Time-resolved decoding in each ROI with random noise source in PR.
(a–d) Time courses of prediction accuracy for MNE, MCE, VBMEG, and LCMV, respectively. Solid lines indicate the mean prediction accuracy across participants. Shading indicates the 1st–99th percentiles of the prediction accuracy across participants. Results with random source in PR a...
Spatial extent of information spreading.
(a, b) Results for V1 left and V1 right at 50 ms. (c, d) Results for IT left and IT right at 225 ms. The horizontal axes represent distance from the center of mass of each ROI. Solid lines indicate the mean prediction accuracy across participants. For an illustration purpose, prediction accuracy was averaged...
Spatial extent of t-value.
(a, b) Results for V1 left and V1 right at 50 ms. (c, d) Results for IT left and IT right at 225 ms. The horizontal axes represent distance from the center of mass of each ROI. Solid lines indicate the mean t-value across participants. For an illustration purpose, t-value was averaged for each 5-mm distance bin. Shading i...
Spatial extent of information spreading (univariate).
(a, b) Results for V1 left and V1 right at 50 ms. (c, d) Results for IT left and IT right at 225 ms. The horizontal axes represent distance from the center of mass of each ROI. Solid lines indicate the mean prediction accuracy across participants. For an illustration purpose, prediction accuracy...
Information spreading in real data analysis.
(a) Time courses of prediction accuracy for each participant. Solid lines indicate the prediction accuracy for V1 (red) and HVC (blue), respectively. Gray dashed lines indicate significance levels. (b) Time courses of F-statistics for each participant, normalized between 0 and 1 for visibility. Vertical...
Individual data points from searchlight decoding (Fig 6 and S7 Fig).
(XLSX)
Creative insight occurs with an "Aha!" experience when solving a difficult problem. Here, we investigated large-scale networks associated with insight problem solving. We recruited 232 healthy participants aged 21-69 years old. Participants completed a magnetic resonance imaging study (MRI; structural imaging and a 10 min resting-state functional M...
Humans often utilize past experience to solve difficult problems. However, if past experience is insufficient to solve a problem, solvers may reach an impasse. Insight can be valuable for breaking an impasse, enabling the reinterpretation or re-representation of a problem. Previous studies using between-subjects designs have revealed a causal relat...
Supplementary analyses and results.
(DOCX)
Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectiv...
Relation to Nonnegative Tensor Factorization.
(PDF)
Proofs and derivations.
(PDF)
Additional results by stepwise analysis.
(PDF)
Diffuse optical tomography (DOT) is an advanced imaging method used to visualize the internal state of biological tissues as 3D images. However, current continuous-wave DOT requires high-density probe arrays for measurement (less than 15-mm interval) to gather enough information for 3D image reconstruction, which makes the experiment time-consuming...
Functional near-infrared spectroscopy (fNIRS) is used to measure cerebral activity because it is simple and portable. However, scalp-hemodynamics often contaminates fNIRS signals, leading to detection of cortical activity in regions that are actually inactive. Methods for removing these artifacts using standard source–detector distance channels (Lo...
Diffuse optical tomography (DOT) is an emerging technology for improving the spatial resolution and spatial specificity of conventional multi-channel near-infrared spectroscopy (NIRS) by the use of high-density measurements and an image reconstruction algorithm. We recently proposed a hierarchical Bayesian DOT algorithm that allows for accurate sim...
Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in nonhuman studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain...
This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric...
The inverse problem for estimating model parameters from brain spike data is an ill-posed problem because of a huge mismatch in the system complexity between the model and the brain as well as its non-stationary dynamics, and needs a stochastic approach that finds the most likely solution among many possible solutions. In the present study, we deve...
Diffuse optical tomography (DOT) is an emerging technology for improving the spatial resolution of conventional multi-channel near infrared spectroscopy (NIRS). The hemodynamics changes in two distinct anatomical layers, the scalp and the cortex, are known as the main contributor of NIRS measurement. Although any DOT algorithm has the ability to re...
A brain information output apparatus includes an intention determination information storage unit in which two or more pieces of intention determination information can be stored, with each intention determination information including a pair of an intention identifier, and a learning feature amount group including one or more feature amounts extra...
We present an MEG source reconstruction method that simultaneously reconstructs source amplitudes and identifies source interactions across the whole brain. In the proposed method, a full multivariate autoregressive (MAR) model formulates directed interactions (i.e., effective connectivity) between sources. The MAR coefficients (the entries of the...
Functional near-infrared spectroscopy (fNIRS) can non-invasively measure hemodynamic responses in the cerebral cortex with a portable apparatus. However, the observation signal in fNIRS measurements is contaminated by the artifact signal from the hemodynamic response in the scalp. In this paper, we propose a method to separate the signals from the...
Previous studies have shown that MEG source reconstruction is improved by temporal constraints from local current source dynamics. Extending these constraints, we have developed a source reconstruction method that is spatiotemporally constrained by a whole brain dynamical model. The source dynamics are represented by a multivariate autoregressive (...
Diffuse optical tomography (DOT) is emerging technology to improve spatial resolution of conventional multichannel near infrared spectroscopy (NIRS). Although the scalp blood flow heavily contaminates the cerebral blood flow, all of previously proposed DOT algorithms fail to provide a way to segregate these two components. Here we propose a hierarc...
High-γ amplitude (80-150 Hz) represents motor information, such as movement types, on the sensorimotor cortex. In several cortical areas, high-γ amplitudes are coupled with low-frequency phases, e.g., α and θ (phase-amplitude coupling, PAC). However, such coupling has not been studied in the sensorimotor cortex; thus, its potential functional role...
High-density diffuse optical tomography (HD-DOT) is an emerging technique for visualizing the internal state of biological tissues. The large number of overlapping measurement channels due to the use of high-density probe arrays permits the reconstruction of the internal optical properties, even with a reflectance-only measurement. However, accurat...
State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling...
