Research Topics

We conduct studies to understand the key components, including neural information dynamics, that play a role in cognitive and motor control. For instance, we have developed a model of speed-accuracy tradeoff and perceptual decision-making using neural signals obtained from fMRI and EEG. Additionally, we employ simultaneous EEG-TMS recordings to reveal causal relationships between our behavior and neural activity.

-Uehara et al. (2022) Modulation of cortical beta oscillations influences motor vigor: A rhythmic TMS-EEG study. Human Brain Mapping, 1-15

-Uehara et al. (2022) Precise motor rhythmicity relies on motor network responsivity. Cerebral Cortex. 1-16

Our studies focus on understanding the neural substrates underlying cognitive and motor learning, particularly the relationship between neural information dynamics and learning ability throughout the lifespan.

In our daily lives, people often cooperate to achieve common goals, such as carrying a large object together, participating in team sports, or performing in an orchestra. We are conducting research to understand how people process neural information beyond the individual level when they are in resonance with each other, using EEG hyperscanning.

Through the use of psychophysical measurements, brain imaging, and neurophysiological approaches, we aim to clarify why top athletes, musicians, and other skilled performers are able to produce exceptional skills. This project will further our understanding of neural plasticity.

-Uehara et al. (2019) Distinct roles of brain activity and somatotopic representation in pathophysiology of focal dystonia. Human Brain Mapping 40, 1738-1749

-Furuya*, Uehara*  et al. (2018) Aberrant cortical excitability explains the loss of hand dexterity in musician’s dystonia. The Journal of Physiology, 596, 2397-2411

We utilize non-invasive brain stimulation techniques (TMS, tDCS) and spinal reflex testing to investigate how corticospinal and interhemispheric interactions contribute to motor control and rehabilitation. This approach allows us to quantify neural information in the millisecond range.

-Uehara et al. (2015) Transcranial direct current stimulation improves ipsilateral selective muscle activation in a frequency dependent manner. PLOS ONE 10, e0122434

-Kubota, Uehara et al. (2014) Inter-individual variation in reciprocal Ia inhibition is dependent on the descending volleys delivered from corticospinal neurons to Ia interneurons. Journal of Electromyography and Kinesiology. 24, 46-51

-Uehara et al. (2014) Functional difference in short- and long-latency interhemispheric inhibitions from active to resting hemisphere during a unilateral muscle contraction. Journal of Neurophysiology 111, 17-25

We test the trustworthiness of Explainable AI (XAI) based on a causal approach via the human body. Using biological signals such as cortical neural activity and muscle activity obtained during human cognitive or motor tasks, AI (Deep Learning) identifies the most important biological features that play a crucial role in the given tasks. Using this feature extraction, we manipulate human cortical neural activity using non-invasive brain stimulation and manipulate electromyogram signals using the cyber-space during the tasks. This project will extend from laboratory research to clinical research to build a prototype system of XAI.