Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings.

Sparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain-computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms.

Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.

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 preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability.

End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.

Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models that can explain neural decoding for intended behaviors are crucial to identifying essential features of deep learning decoders in brain activity. In this study, we examine the performance of deep learning to classify mouse behavioral states from mesoscopic cortex-wide calcium imaging data.

Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data.

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 difference in the coactivation patterns between MDD and HCs.

Sensor array design of optically pumped magnetometers for accurately estimating source currents.

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 on our purposes and regions of interests (ROIs).

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.

Virtual reality-based real-time imaging reveals abnormal cortical dynamics during behavioral transitions in a mouse model of autism.

Functional connectivity (FC) can provide insight into cortical circuit dysfunction in neuropsychiatric disorders. However, dynamic changes in FC related to locomotion with sensory feedback remain to be elucidated. To investigate FC dynamics in locomoting mice, we develop mesoscopic Ca imaging with a virtual reality (VR) environment.

Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets.

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 paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders.

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 and disease-specific features related to mesocorticolimbic circuits.

Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data.

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 performance for new patients.

Methods: We tested the sensitivity and specificity of our brain network marker of MDD using data acquired from 43 new patients with MDD as well as new data from 33 healthy controls (HCs) who participated in our previous study.

Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial.

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 without explicit phantom hand movements or hand-like visual feedback. We tested the feasibility of neurofeedback training in fourteen patients with phantom limb pain.

Diffuse Optical Tomography Using fNIRS Signals Measured from the Skull Surface of the Macaque Monkey.

Diffuse optical tomography (DOT), as a functional near-infrared spectroscopy (fNIRS) technique, can estimate three-dimensional (3D) images of the functional hemodynamic response in brain volume from measured optical signals. In this study, we applied DOT algorithms to the fNIRS data recorded from the surface of macaque monkeys' skulls when the animals performed food retrieval tasks using either the left- or right-hand under head-free conditions. The hemodynamic response images, reconstructed by DOT with a high sampling rate and fine voxel size, demonstrated significant activations at the upper limb regions of the primary motor area in the central sulcus and premotor, and parietal areas contralateral to the hands used in the tasks.

Predicting behavior through dynamic modes in resting-state fMRI data.

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 individual behavioral differences.

Whole-brain propagating patterns in human resting-state brain activities.

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 information distributed over the entire brain, such as visual and motor information.

Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork.

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 diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly.

Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics.

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 datasets.

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

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 clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities.

Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization.

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, clustering of subjects based on FC recently draws much attention, which can potentially reveal important heterogeneity in subjects such as subtypes of psychiatric disorders.

MEG current source reconstruction using a meta-analysis fMRI prior.

Magnetoencephalography (MEG) offers a unique way to noninvasively investigate millisecond-order cortical activities by mapping sensor signals (magnetic fields outside the head) to cortical current sources using current source reconstruction methods. Current source reconstruction is defined as an ill-posed inverse problem, since the number of sensors is less than the number of current sources. One powerful approach to solving this problem is to use functional MRI (fMRI) data as a spatial constraint, although it boosts the cost of measurement and the burden on subjects.

Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across 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 (BMB-HBM, 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.