dSPM

dSPM stands for dynamic Source Phương pháp Model. It is a method used in computational neuroscience for estimating the distribution of neural activity on the surface of the brain, typically from magnetoencephalography (MEG) or electroencephalography (EEG) data. dSPM is a type of distributed source localization technique, meaning it aims to find the locations and strengths of all active neural sources across the entire cortical surface, rather than assuming a small number of focal sources.

The core idea behind dSPM is to solve an inverse problem. Given the measured magnetic fields or electric potentials on the scalp, the goal is to infer the underlying neural currents within the brain. dSPM achieves this by minimizing a cost function that balances the goodness of fit to the measured data with a regularization term. This regularization encourages smooth and distributed solutions, preventing overly sharp or unrealistic activity patterns. A key feature of dSPM is its use of a noise normalization procedure that accounts for the signal-to-noise ratio of the data at each cortical location. This helps to produce more reliable estimates of source activity, especially in regions with weaker signals.

Compared to other distributed methods like MNE (Minimum Norm Estimate), dSPM provides estimates that are more comparable across different cortical locations, even if the underlying sensitivity of the MEG/EEG sensors varies. The output of dSPM is typically represented as a map of estimated neural activity distributed over the cortical surface, often visualized on a flattened or 3D representation of the brain. This allows researchers to investigate the spatio-temporal dynamics of brain activity during cognitive tasks or in various neurological conditions.