Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders

1. 1Extract amplitude and phase information from fMRI signals using techniques such as sliding window correlation
2. 2Integrate amplitude and phase features into a single model using fusion learning approaches
3. 3Train machine learning models to identify patterns and correlations between brain regions and disorders
4. 4Evaluate the performance of the model using metrics such as accuracy, sensitivity, and specificity

Neuroscientists, data scientists, and AI engineers on a team can benefit from this approach as it provides a more comprehensive understanding of brain function and disorders, enabling them to develop more accurate diagnostic tools

💡 Combining amplitude and phase information from fMRI signals can provide a more comprehensive understanding of brain function and disorders

🧠 Fusion learning approach combines amplitude & phase of fMRI signals to identify brain disorders #neuroscience #AI