Aki Tsuchiyagaito

This study examines dose-dependent functional connectivity changes in healthy adults after low-intensity focused ultrasound (LIFU) targeting deep white matter tracts.

The paper identifies distinct structural and functional brain correlates of depression in middle-aged adults with and without a family history of Alzheimer's disease.

This study demonstrates that real-time fMRI-guided tACS can optimize brain stimulation to modulate frontoparietal connectivity and improve working memory accuracy.

This study investigates structural connectivity in limbic networks and its correlation with response to electroconvulsive therapy (ECT) in treatment-resistant depression (TRD).

This study finds that aging increases the distinctiveness of rumination and worry brain states while reducing anxiety responses, potentially improving emotional well-being.

This study shows that high-precision neurofeedback-guided meditation training improves disengagement from mental activity and enhances emotional well-being in novice meditators.

This study uses predictive modeling to show that the interaction between brain regulation and feedback response predicts neurofeedback outcomes for repetitive negative thinking in depression.

The paper investigates how curiosity and monetary incentives affect memory encoding and neural activation during incidental learning of dynamic stimuli using fMRI and behavioral experiments.

The paper investigates whether resting-state functional connectivity (RSFC) or induced negative-thinking state functional connectivity (NTFC) better predicts repetitive negative thinking (RNT) traits in depression, finding that NTFC is more predictive.

This paper describes a closed-loop tES-fMRI protocol for brain modulation, demonstrating feasibility, safety, and the ability to distinguish physiological changes from artifacts.

This study identifies white matter microstructural alterations in adult OCD patients and links them to clinical features, while finding no such abnormalities in pediatric patients.

This paper predicts sex from resting-state fMRI using machine learning across multiple datasets, achieving high accuracy with low-frequency BOLD signal features.

This study examines the relationship between EEG microstate dynamics and fMRI signals, finding that activity regressors are mostly anticorrelated with fMRI in specific brain networks.

The paper identifies a functional connectivity locus associated with rumination symptoms and optimizes a neurofeedback signal for real-time fMRI intervention.