dcho-higher-order-brain-connectivity

Higher-order brain connectivity models interactions across three or more regions simultaneously, capturing complex neural network dynamics beyond standard pairwise connectivity in neuroimaging data.

Yes, you can use PyTorch for dynamic trajectory forecasting of brain network activity by employing a sequence predictor alongside dual-branch encoders to model evolving fMRI signals.

Higher-order brain connectivity infers simultaneous interactions across three or more regions from fMRI data, capturing complex multi-region neural network dynamics that standard pairwise connectivity cannot model.

Yes, the dual-encoder architecture supports brain state classification by processing multi-region neuroimaging data through local and global branches to infer higher-order connections and predict dynamic activity trajectories.

Yes, you need NumPy and PyTorch to run higher-order brain network predictions, as the implementation relies on these Python frameworks for tensor operations and sequence prediction modeling.

Using decomposition-composition for neurological disease analysis is limited to predicting higher-order brain interactions from fMRI signals, requiring multi-region neuroimaging data suitable for trajectory forecasting and state classification.