1,720,987 research outputs found
The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment condition
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Effect of combined Cognitive Training and Neuronavigated Magnetic Stimulation in Alzheimer’s Disease Treatment: a Graph Analysis study
No full textThe impact of virtual reality and distractors on attentional processes: insights from EEG
No full textVirtual reality (VR) allows to create controlled scenarios in which the quantity of stimuli can be modulated, as happen in real-life, where humans are subjected to various multisensory—often overlapping—stimuli. The present research aimed to study changes in attentional processes within an auditory oddball paradigm during a virtual exploration, while varying the amount of distractors. Twenty healthy volunteers underwent electroencephalography (EEG) during three different experimental conditions: an auditory oddball without VR (No-VR condition), an auditory oddball during VR exploration without distractors (VR-Empty condition), and an auditory oddball during VR exploration with a high level of distractors (VR-Full condition). Event-related potentials (ERPs) were computed averaging epochs of EEGs and analyzing peaks at 100 ms (N100) and 300 ms (P300) latencies. Results showed modulation of N100 amplitude in Fz and of P300 amplitude in Pz. Statistically significant differences in latency were observed only for P300 where the latency results delayed from the No-VR to VR-Full. The scalp topography revealed for P100 no significant differences between frequent and rare stimuli in either the No-VR and VR-Empty conditions. However, significant results were found in N100 in VR-Full condition. For P300, results showed differences between frequent and rare stimuli, in every condition. However, this difference is gradually less widespread from No-VR condition to the VR-Full. The emerging integration of VR with EEG may have important implications for studying brain attentional processing
The Potential of Functional Connectivity and Graph Theory Measures in Identifying Alterations in Brain Plasticity
No full textEEG brain networks modulation during sleep onset: the effects of aging
No full textThe aim of the present study is to investigate differences in brain networks modulation during the pre- and post-sleep onset period, both within and between two groups of young and older individuals. Thirty-six healthy elderly and 40 young subjects participated. EEG signals were recorded during pre- and post-sleep onset periods and functional connectivity analysis, specifically focusing on the small world (SW) index, applied to EEG data (i.e., frequency bands) was examined. Significant differences in SW values were found between the pre-sleep and post-sleep onset phases in both young and older groups, with a reduction in the SW index in the theta band common to both groups. Additionally, an increase in the SW index in the beta band was exclusive to the elderly group during the post-sleep onset period, while an increase in the sigma band was exclusive to the young group. Furthermore, differences between the young and elderly groups were found during both phases, including a decrease in the SW index within the delta band, an increment in the sigma and beta bands in the elderly compared to the young group during the pre-sleep onset period, and a notable absence of sigma band modulation in the elderly group during the post-sleep onset condition. These findings provide insights into age-related changes in sleep-related brain network dynamics and their potential impact on sleep quality and cognitive functions, prompting interventions aimed at supporting healthy aging and addressing age-related cognitive decline
Heart Health and Cognitive Decline: Exploring Brain Rhythms, Connectivity, and Complexity in Congestive Heart Failure and Alzheimer's Disease
No full textUn approccio diagnostico efficace per la valutazione del rischio di conversione in soggetti con declino cognitivo lieve: connettività e teoria dei grafi combinate con l’apolipoproteina E
No full textAnalysis of complexity in the resting-state EEG activity of Parkinson’s disease patients by means of Approximate Entropy
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