Ryota Kanai C

This paper predicts table tennis player intentions using EEG and pose data, showing that a multi-modal ensemble improves prediction accuracy compared to single-modal approaches.

OptiNiSt provides a framework for creating scalable, reproducible, and extendable pipelines for analyzing optical neural data.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

The paper uses deep learning to predict electrode locations in macaque brains from ECoG signals, finding unique temporal patterns associated with different cortical regions.

This paper introduces an algorithm to efficiently identify essential sub-networks (complexes) in brain networks using an information-theoretic approach, demonstrating its application to electrocorticogram data.

This study analyzes subcortical volume changes across the lifespan using MRI data from 18,605 individuals, finding age-related declines in some structures and increases in others.

The paper identifies a duration-selective cortico-cerebellar network and its topographic connectivity structure, showing the cerebellum as a hub and the medial premotor cortex as the final stage of duration recognition.

The paper identifies genetic loci associated with human cortical structure and finds genetic correlations with neuropsychiatric disorders.

The paper identifies duration-sensitive neural populations in human medial premotor cortex that form topographical maps of time, termed chronomaps.