2024-2028 AMED-PRIME
This research aims to develop objective diagnostic indicators (biomarkers) for mental disorders by proposing new analysis methods for resting-state fMRI data. To overcome the limitations of conventional functional connectivity analysis between brain regions, it employs deep learning techniques (multilayer perceptron and Transformer models) to achieve high-precision analysis while preserving local spatial pattern information.
Specifically, the research has three main objectives: (1) improving the accuracy of functional connectivity, (2) developing deep learning models for estimating individual characteristics, and (3) creating diagnostic algorithms for mental and neurological disorders. It utilizes data from the Human Connectome Project and the AMED International Brain Project to construct ensemble models that combine conventional and novel methods.
To address the issue of limited sample sizes, a two-stage approach is adopted: creating group models followed by individual optimization. This methodology is applicable to other biological data analyses, and the research team aims to share insights through code sharing and training courses.
Ultimately, the goal is to provide new solutions for precise evaluation of individual brain function characteristics and early diagnosis and treatment of mental and neurological disorders. This research is expected to break through the limitations of conventional analysis methods and bring innovative advancements in the fields of brain science and medicine.
Specifically, the research has three main objectives: (1) improving the accuracy of functional connectivity, (2) developing deep learning models for estimating individual characteristics, and (3) creating diagnostic algorithms for mental and neurological disorders. It utilizes data from the Human Connectome Project and the AMED International Brain Project to construct ensemble models that combine conventional and novel methods.
To address the issue of limited sample sizes, a two-stage approach is adopted: creating group models followed by individual optimization. This methodology is applicable to other biological data analyses, and the research team aims to share insights through code sharing and training courses.
Ultimately, the goal is to provide new solutions for precise evaluation of individual brain function characteristics and early diagnosis and treatment of mental and neurological disorders. This research is expected to break through the limitations of conventional analysis methods and bring innovative advancements in the fields of brain science and medicine.
https://www.amed.go.jp/koubo/16/02/1602C_00026.html (in Japanese only)
