Yuichi Yamashita
Biographic Data
| ID | 7954497 |
|---|---|
| NAME | Yuichi Yamashita |
| GIVEN NAMES | Yuichi |
| FAMILY NAME | Yamashita |
| SIGNATURE | YAMASHITA Y |
| AFFILIATIONS | National Center of Neurology and Psychiatry |
| ORCID | 0000-0002-2779-8222 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1997 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Reliability and Validity of the DSM-5 Level 1 Cross-Cutting Symptom Measure in Japan: Insights into General and Specific Psychiatric Symptom Assessment
The dimensional approach to psychiatric symptoms, including the general psychopathology factor (p-factor), has gained increasing attention for its potential to elucidate pathophysiology and inform treatment. However, comprehensive assessment of transdiagnostic psychiatric symptoms remains particularly challenging, especially in Japan. The DSM-5 Level 1 Cross-Cutting Symptom Measure (DSM-XC) was developed to capture a broad spectrum of symptoms ac…
Generative artificial intelligence model for simulating structural brain changes in schizophrenia
The results suggest that our generative AI model can capture subtle changes in brain structures associated with SZ, providing a novel tool for visualizing brain changes in different diseases. The potential of this model extends beyond clinical diagnosis to advances in the simulation of disease mechanisms, which may ultimately contribute to the refinement of therapeutic strategies
Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy
These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment
Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers
The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinica…
Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder
Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. In particular, developmental learning aspects of symptoms and interactions with the physical environment remain largely unexplored in computational modeling studies, although a leading computational theory has posited associations between psychiatric…
Análisis crítico del cine argumental
dDLR reduces image noise while preserving image quality on brain MR images
No prominent works on this page.
Análisis crítico del cine argumental
dDLR reduces image noise while preserving image quality on brain MR images
Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers
The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinica…
Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder
Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. In particular, developmental learning aspects of symptoms and interactions with the physical environment remain largely unexplored in computational modeling studies, although a leading computational theory has posited associations between psychiatric…
Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy
These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment
Generative artificial intelligence model for simulating structural brain changes in schizophrenia
The results suggest that our generative AI model can capture subtle changes in brain structures associated with SZ, providing a novel tool for visualizing brain changes in different diseases. The potential of this model extends beyond clinical diagnosis to advances in the simulation of disease mechanisms, which may ultimately contribute to the refinement of therapeutic strategies
Reliability and Validity of the DSM-5 Level 1 Cross-Cutting Symptom Measure in Japan: Insights into General and Specific Psychiatric Symptom Assessment
The dimensional approach to psychiatric symptoms, including the general psychopathology factor (p-factor), has gained increasing attention for its potential to elucidate pathophysiology and inform treatment. However, comprehensive assessment of transdiagnostic psychiatric symptoms remains particularly challenging, especially in Japan. The DSM-5 Level 1 Cross-Cutting Symptom Measure (DSM-XC) was developed to capture a broad spectrum of symptoms ac…
Psychology (5 works) · Computer Science (4 works) · Artificial Intelligence (3 works) · Functional Brain Connectivity Studies (3 works) · Neuroscience (3 works) · Psychiatry (3 works) · Cognitive psychology (2 works) · Computational model (2 works) · Computational neuroscience (2 works) · Machine Learning in Healthcare (2 works)