Electroencephalographic (EEG) rhythms are linked to any kind of learning and cognitive performance including motor tasks. The brain is a complex network consisting of spatially distributed networks dedicated to different functions including cognitive domains where dynamic interactions of several brain areas play a pivotal role. Brain connectome could be a useful approach not only to mechanisms underlying brain cognitive functions, but also to those supporting different mental states. This goal was approached via a learning task providing the possibility to predict performance and learning along physiological and pathological brain aging. Eighty-six subjects (22 healthy, 47 amnesic mild cognitive impairment, 17 Alzheimer's disease) were recruited reflecting the whole spectrum of normal and abnormal brain connectivity scenarios. EEG recordings were performed at rest, with closed eyes, both before and after the task (Sensory Motor Learning task consisting of a visual rotation paradigm). Brain network properties were described by Small World index (SW), representing a combination of segregation and integration properties. Correlation analyses showed that alpha 2 SW in pre-task significantly predict learning (r = -0.2592, p < 0.0342): lower alpha 2 SW (higher possibility to increase during task and better the learning of this task), higher the learning as measured by the number of reached targets. These results suggest that, by means of an innovative analysis applied to a low-cost and widely available techniques (SW applied to EEG), the functional connectome approach as well as conventional biomarkers would be effective methods for monitoring learning progress during training both in normal and abnormal conditions.
Keywords: Alpha band; Alzheimer’s disease; EEG; eLORETA; functional brain connectivity; graph theory; learning; mild cognitive impairment; precision medicine.