Mitsuo Kawato

This paper introduces hTSMNet, a method for zero-shot EEG decoding across datasets and paradigms using geometry-conditioned hyperalignment and Riemannian SPD manifold learning.

The paper develops a method to profile resting-state fMRI connectivity variations to improve the reliability and generalizability of psychiatric disorder biomarkers across multiple centers.

This study compares unsupervised feature selection with supervised methods to identify robust resting-state functional connectivity patterns associated with psychiatric disorders.

The paper shows that climbing fiber inputs in the cerebellum generate reward-prediction errors that guide modular reinforcement learning in mice, integrating with supervised learning mechanisms.

The paper explains interindividual differences in motor learning by proposing a computational model with generalist and specialist memory modules that adapt based on training schedules.

This paper identifies a thalamo-somatomotor functional connectivity biomarker that stratifies MDD patients into subtypes with distinct antidepressant treatment responses, demonstrating generalizability across independent datasets.

The paper evaluates rs-fMRI analysis pipelines for MDD biomarkers using multi-site datasets, finding optimal combinations of parcellation, connectivity estimation, harmonization, and machine learning methods.

This study uses real-time fMRI decoding to modulate pain-related brain patterns and shows that participants can down-regulate pain signatures, correlating with changes in perceived pain intensity.

This study investigates how fear is processed in the brain by analyzing hemodynamic activity patterns in the ventral visual stream and prefrontal cortex in response to animal images.

The paper analyzes two-photon imaging data from mice during reinforcement learning to show cerebellar climbing fiber responses are organized into four functional components that reduce learning space dimensions.

This study develops a generalizable neuromarker for ASD across multiple sites and developmental stages, showing significant performance in adults, children, and adolescents.

This study shows that leader-follower roles in coordination emerge through implicit haptic interactions, not explicit communication.

This study uses DecNef to show that unconscious exposure-based visual perceptual learning occurs in V1/V2 without conscious perception of motion.

This study shows that valuation of sensory features drives abstraction during learning, with value signals in the ventromedial prefrontal cortex guiding behavior and decision-making.

The paper identifies common brain networks between major depressive disorder and depression symptoms that generalize across independent imaging sites.

This study investigates how humans adapt to force fields during reaching tasks, showing a failure-driven adaptation process that enables quick task success before full internal model adaptation.

This paper outlines a multi-site MRI study aiming to establish harmonized imaging biomarkers for brain disorders across the lifespan using standardized protocols and data from healthy and patient subjects.

This paper introduces HA_time, a hyperacuity algorithm for precise spike timing estimation from calcium imaging, achieving up to 20-fold improvement in spike time precision.

The paper investigates reciprocal inhibition between the amygdala and vmPFC in PTSD patients, linking attentional biases to symptom clusters and amygdala reactivity.

This study uses neural reinforcement to alter resting-state functional connectivity in the threat regulation network, showing increased prefrontal connectivity with amygdala and ventral temporal cortex, but no Hebbian-like learning.

The paper uses brain connectivity data to show overlapping but asymmetrical relationships between schizophrenia and autism.

The paper uses fMRI and machine learning to show that subjective fear and physiological responses dissociate in key brain regions.

The paper links perceptual confidence to false color detection using decoded neurofeedback and fMRI.

The paper develops a harmonization method for resting-state fMRI data across sites by separating site differences into sampling and measurement biases, reducing measurement bias by 29% and improving signal-to-noise ratios by 40%.

This study develops a computational model to estimate 3D shapes of specular objects from a single image, showing high accuracy and similarity to human perception errors.

The paper shows that humans can use implicit metacognition to make decisions based on unconscious brain states, outperforming current AI in high-dimensional tasks.

This study models how electrical coupling between inferior olive neurons affects firing dimensionality and chaos, supporting the hypothesis that coupling controls learning efficiency in the cerebellum.

The paper builds a working memory prediction model from whole-brain functional connectivity in healthy individuals and applies it to psychiatric disorders, finding correlations between connectivity patterns and working memory performance.

This study demonstrates how endogenous brain processes influence the effectiveness of closed-loop brain-machine interfaces for pain management.

This paper introduces a method to reprogram unconscious fear responses in the human brain using fMRI decoding and monetary reward, showing reduced physiological fear responses in a double-blind study.