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Exploring intricate connectivity patterns for cognitive functioning and neurological disorders: incorporating
1College of Media Engineering, Communication University of Zhejiang, 998 Xue Yuan Street, Qiantang District, Hangzhou, Zhejiang 310018, China.
Cerebral Cortex (New York, N.Y. : 1991)
|May 14, 2024
Summary
A new causality method accurately identifies brain connectivity patterns in cognitive impairment. This approach shows promise for detecting Alzheimer's disease biomarkers using functional MRI data.
Area of Science:
- Neuroscience
- Medical Imaging
- Computational Biology
Background:
- Cognitive impairment, including Alzheimer's disease, is associated with altered brain connectivity.
- Existing methods for analyzing functional magnetic resonance imaging (fMRI) data may not fully capture complex causal relationships within brain networks.
Purpose of the Study:
- To apply and validate the frequency-domain new causality method for analyzing directed efficient connectivity in fMRI data.
- To investigate topological variations in brain networks related to cognitive impairment.
- To develop a deep learning model using these network characteristics as features for identifying cognitive impairment.
Main Methods:
- Simulated varying degrees of causal associations among multivariate fMRI blood-oxygen-level-dependent signals using different causality types.
- Applied the frequency-domain new causality method to construct directed efficient brain connectivity networks.
- Utilized topological statistical characteristics of these networks as features for a deep learning model trained on data from 1,252 individuals with varying cognitive impairment.
Main Results:
- The frequency-domain new causality method accurately detected simulated causal associations.
- The deep learning model demonstrated superior performance (accuracy, precision, recall) compared to three other methods.
- Significant differences in brain efficiency networks were observed, highlighting fine-grained cortical subregions associated with cognitive impairment.
Conclusions:
- The frequency-domain new causality method is effective for analyzing causal relationships in fMRI data.
- The identified topological features of brain efficiency networks serve as potential biomarkers for cognitive impairment.
- These findings suggest fine-grained cortical subregions are crucial for cognitive function and may aid in Alzheimer's disease biomarker discovery.
