Ayumu Yamashita

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Research

My research centers on objectively describing and inferring internal mental states that are difficult to observe directly, using brain activity, physiological measures, and behavioral data. By combining mathematical models and machine learning to clarify the neural substrates of those states, I aim to understand conditions in which attention and mood fluctuate, and to provide insights that help everyone perform at their best.

Traditional approach

Phenotype-based definitions (self-report and behavioral measures)

Assessment is based on external observation (phenotype), such as physician interviews, structured interviews, rating scales, and cognitive task performance.

Our approach

Biosensing-based definitions (brain activity and physiological signals)

Using biosensing data including brain activity, we reframe understanding on biological grounds.

Research topic 1: Neural basis of psychiatric disorders

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1. Integrative analysis of large-scale brain imaging data

I work on statistical harmonization of site effects in multisite, multi-disorder resting-state fMRI and on shared-protocol databases to support cross-cutting analyses.

Representative publications

1. Yamashita, A., et al. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias PLOS Biology, 2019.
2. Tanaka, S. C., Yamashita, A., et al. A multi-site, multi-disorder resting-state magnetic resonance image database Scientific Data, 2021.

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2. Brain circuit markers for psychiatric disorders

Using resting-state functional connectivity, I study network markers and classifiers for disorders such as depression, schizophrenia, and autism, emphasizing generalization and confound robustness.

Representative publications

1. Yamashita, A., et al. Generalizable brain network markers of major depressive disorder across multiple imaging sites PLOS Biology, 2020.
2. Yamashita, A., et al. Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism Molecular Psychiatry, 2025.

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Research topic 2: Neural basis of attention and concentration

3. Estimating attentional focus from brain imaging

During sustained-attention tasks, I infer brain states from fMRI time series and relate them to fluctuations in attention and reaction times in a data-driven manner.

Representative publications

1. Yamashita, A., et al. Brain state-based detection of attentional fluctuations and their modulation NeuroImage, 2021.
2. Yamashita, A., et al. Variable rather than extreme slow reaction times distinguish brain states during sustained attention Scientific Reports, 2021.

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4. Real-world experiments with portable EEG

I combine wearable, easy-to-use EEG with autonomic physiology to estimate internal states in everyday and social contexts and to test how context modulates those estimates.

Representative publications

1. Yamashita, A., et al. Internal State Estimation via Physiological Data and Its Modulation by Environmental Context during Social Activity. Journal of Cognitive Neuroscience, 2025.

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