Topic: Hyperalignment

Information is encoded in fine-scale functional topographies that vary from brain to brain. Hyperalignment models information that is shared across brain in a high-dimensional common information space. Hyperalignment transformations project idiosyncratic individual topographies into the common model information space. These transformations contain topographic basis functions, affording estimates of how shared information in the common model space is instantiated in the idiosyncratic functional topographies of individual brains. This new model of the functional organization of cortex – as m

Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment jointly models shared information and idiosyncratic topographies. Pattern vectors for neural responses and connectivities are projected into a common, high-dimensional information space, rather than being aligned in a canonical anatomical space. Hyperalignment calculates individual transformation matrices that preserve the geometry of pairwise dissimilarities between pattern vectors. Individual cortical topographies are modeled as mixtures of overlapping, individual-specific

Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naïve and commonly used solution is to assume that participants' recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to functional-anatomical organization differences between individual brains. In this work, multiple methods were compared to find a common representation of magnetoencephalographic (MEG) activations recorded from different participants performing a grasping task with three d