System for predicting alzheimer's disease patient brain network state transition control energy
By constructing a brain structural network of Alzheimer's patients and using diffusion tensor imaging data and optimal control theory, the energy cost of brain network state transitions is predicted, solving the problem of inaccurate calculation of brain network control energy cost in existing technologies and achieving low-cost and effective brain network control.
CN116705328BActive Publication Date: 2026-06-16SHANDONG UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2023-05-31
- Publication Date
- 2026-06-16
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Figure CN116705328B_ABST
Abstract
The application discloses an Alzheimer's disease patient brain network state conversion control energy prediction system and belongs to the technical field of brain network control. The application analyzes existing public data sets of Alzheimer's disease patients, takes white matter fiber bundles between brain regions of the Alzheimer's disease patients as constraints of nodes of a dynamic system, establishes a brain network dynamic system model, determines the relationship between control nodes and state conversion control energy, and estimates and predicts energy required for controlling brain state conversion. The application can help predict energy required for controlling brain networks, thereby better measuring energy required for completing control tasks, provide theoretical support for regulation and intervention of Alzheimer's disease, and have important significance for improving health and cognitive function of Alzheimer's disease patients. The application solves the problem in the prior art that energy required for Alzheimer's disease patients to complete control tasks cannot be predicted.
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