1. Learning Processes and Brain Connectivity in A Cognitive-Motor Task in Neurodegeneration: Evidence from EEG Network Analysis
- Author
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Camillo Marra, Fabrizio Vecchio, Francesca Miraglia, Paolo Maria Rossini, Davide Quaranta, and Giordano Lacidogna
- Subjects
Male ,Electroencephalography ,Functional Laterality ,050105 experimental psychology ,Task (project management) ,03 medical and health sciences ,0302 clinical medicine ,Alzheimer Disease ,Neural Pathways ,medicine ,Humans ,Cognitive Dysfunction ,0501 psychology and cognitive sciences ,Effects of sleep deprivation on cognitive performance ,Aged ,Mini–Mental State Examination ,medicine.diagnostic_test ,Learning Disabilities ,General Neuroscience ,05 social sciences ,Cognition ,General Medicine ,medicine.disease ,Brain Waves ,Magnetic Resonance Imaging ,Psychiatry and Mental health ,Clinical Psychology ,Connectome ,Female ,Geriatrics and Gerontology ,Alzheimer's disease ,Mental Status Schedule ,Motor learning ,Psychology ,Neuroscience ,Psychomotor Performance ,030217 neurology & neurosurgery - Abstract
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
- Published
- 2018