Kazuyuki Aihara

This paper identifies dynamical biomarkers of creative cognition using EEG data from problem-solving tasks, showing that creative success is linked to specific patterns of neural state traversal.

This paper introduces a modular echo state network with neuromodulation to control integration and segregation, showing improved performance on context-dependent tasks.

This study stratifies SARS-CoV-2 viral shedding patterns in saliva into three groups with distinct durations using a mathematical model and clustering, but finds no clear clinical or miRNA biomarkers explaining the differences.

BrainBeacon is a cross-species foundation model for whole-brain spatial transcriptomics that enables cross-species alignment and uncovers spatial regulatory mechanisms of aging.

The paper analyzes longitudinal antibody titers post-vaccination to identify individuals at risk for breakthrough infections and identifies three population groups with distinct immune response patterns.

CauFinder identifies and steers cell-state and phenotype transitions by causal disentanglement learning, outperforming existing methods in simulated and real-world scenarios.

STAMarker identifies spatial domain-specific variable genes using saliency maps in deep learning, outperforming existing methods on sparse spatial transcriptomic data.

This paper develops a mathematical model using early clinical data to predict deep molecular response in CML patients treated with nilotinib.

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 shows that task-dependent attractor dynamics in visual cortex enhance task-relevant information for perceptual decisions.

This paper introduces a sample-specific network method to construct individual-specific disease networks from single-sample expression data, identifying disease modules and driver genes without sequencing.