Anisleidy González Mitjans

How anatomy constrains large-scale cortical dynamics is contested. Two lines of evidence about what shapes large-scale cortical activity appear to conflict. One holds that geometric eigenmodes of the cortical surface explain brain function more parsimoniously than connectome-derived modes; another holds that long-range connections shape dynamics over and above geometry. We show that neither is categorically right, and that the disagreement dissolves once spatial scale is resolved: connectivity accounts for the broadest cortical patterns and histological geometry for the finer ones. Using cortico-hippocampal neural field models built on ultrahigh-resolution histological geometry and tested against human intracranial recordings and task fMRI, we identify a second constraint. Anatomical pathways are commonly treated as fixed contributions that operate similarly across brain states. Instead, the same hippocampal–entorhinal relay degrades cortical theta organisation during sleep yet supplies it during spatial navigation. Brain state changes the effective spatial scale at which cortical geometry is read out, so anatomy constrains cortical function only jointly with state.

The reconstruction of electrophysiological sources within the brain is sensitive to the constructed head model, which depends on the positioning accuracy of anatomical landmarks known as fiducials. In this work, we propose an algorithm for the automatic detection of fiducial landmarks of EEG electrodes on the 3D human head model. Our proposal combines a dimensional reduction approach with a perspective projection from 3D to 2D object space; the eye and ear automatic detection in a 2D face image by two cascades of classifiers and geometric transformations to obtain 3D spatial coordinates of the landmarks and to generate the head coordinate system, This is accomplished by considering the characteristics of the scanner information. Capturing the 3D model of the head is done with Occipital Inc. ST01 structure sensor and the implementation of our algorithm was carried out on MATLAB R2018b using the Computer Vision Toolbox and the FieldTrip Toolbox. The experimental results were aimed at recursively exploring the efficacy of the facial feature detectors as a function of the projection angle; they show that robust results are obtained in terms of false acceptance rate. Our proposal is an initial step of an approach for the automatic digitization of electrode locations. The experimental results demonstrate that the proposed method detects anatomical facial landmarks automatically, accurately, and rapidly.