1,721,112 research outputs found
Searching for signs of aging and dementia in EEG through network analysis
No full textGraph theory applications had spread widely in understanding how human cognitive functions are linked to dynamics of neuronal network structure, providing a conceptual frame that can reduce the analytical brain complexity. This review summarizes methodological advances in this field. Electroencephalographic functional network studies in pathophysiological aging will be presented, focusing on neurodegenerative disease −such Alzheimer's disease-aiming to discuss whether network science is changing the traditional concept of brain disease and how network topology knowledge could help in modeling resilience and vulnerability of diseases. Aim of this work is to open discussion on how network model could better describe brain architecture
Effects of transcranial direct current stimulation on the functional coupling of the sensorimotor cortical network
No full textTranscranial direct current stimulation (tDCS) is well established—among the non-invasive brain stimulation techniques—as a method to modulate brain excitability. Polarity-dependent modulations of membrane potentials are detected after the application of anodal and cathodal stimulation, leading to changes in the electrical activity of the neurons. The main aim of the present study was to test the hypothesis that tDCS can affect—in a polarity-specific manner—the functional coupling of the sensorimotor areas during the eyes-open resting condition as revealed by total EEG coherence (i.e., coherence across the average of all combinations of the electrode pairs placed around the stimulation electrode). The changes in the total EEG coherence were evaluated pre-, during, and post-anodal and cathodal tDCS. While no differences were observed in the connectivity characteristics of the two pre-stimulation periods, a connectivity increase was observed in the alpha 2 band in the post-anodal tDCS with respect to pre-anodal and post-cathodal tDCS. The present study suggests that a specific approach based on the analyses of the functional coupling of EEG rhythms might enhance understanding of tDCS-induced effects on cortical connectivity. Moreover, this result suggests that anodal tDCS could possibly modify cortical connectivity more effectively with respect to cathodal tDCS
Brain network modulation in response to directional and Non-Directional Cues: Insights from EEG connectivity and graph theory
No full textObjective: Directional cues have a profound impact on cognitive processes and behavior, and studying the involved brain networks can provide insights into their processing. This research aimed to investigate the neural network modulation associated with cognitive processing after the administration of directional cues using connectivity and graph theory. Methods: Twenty healthy volunteers were enrolled and underwent EEG recording while they were asked to perform a visuomotor task, such as directional (DS) and non-directional (nDS). From EEG data, network parameters such as Small-World (SW) and Lagged linear connectivity across different EEG frequency bands were evaluated, analyzing the response to DS and nDS. Results: The results revealed significant differences in the SW index, particularly in the Alpha 1 band, where participants exhibited a higher SW index when presented with DS compared to nDS. Moreover, the analysis of Alpha 1 band Lagged linear connectivity revealed close to statistically significant differences predominantly in the frontal and central regions. Conclusions: This research contributes to our understanding of the neural mechanisms underlying the processing of directional cues. Significance: It has potential implications for rehabilitation settings, for example in the rehabilitation of visual dysfunction and motor impairment following a stroke, by optimizing cognitive processing to enhance functional outcomes
Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals
No full text: Understanding the neural responses to indoor characteristics like temperature and light is crucial for comprehending how the physical environment influences the human brain. Our study introduces an innovative approach using entropy analysis, specifically, approximate entropy (ApEn), applied to electroencephalographic (EEG) signals to investigate neural responses to temperature and light variations in indoor environments. By strategically placing electrodes over specific brain regions linked to temperature and light processing, we show how ApEn can be influenced by indoor factors. We also integrate heart indices from a multi-sensor bracelet to create a machine learning classifier for temperature conditions. Results showed that in anterior frontal and temporoparietal areas, neutral temperature conditions yield higher ApEn values. The anterior frontal area showed a trend of gradually decreasing ApEn values from neutral to warm conditions, with cold being in an intermediate position. There was a significant interaction between light and site factors, only evident in the temporoparietal region. Here, the neutral light condition had higher ApEn values compared to blue and red light conditions. Positive correlations between anterior frontal ApEn and thermal comfort scores suggest a link between entropy and perceived thermal comfort. Our quadratic SVM classifier, incorporating entropy and heart features, demonstrates strong performance (until 90% in terms of AUC, accuracy, sensitivity, and specificity) in classifying temperature sensations. This study offers insights into neural responses to indoor factors and presents a novel approach for temperature classification using EEG entropy and heart features
Non-Ceruloplasmin Copper Distinguishes A Distinct Subtype of Alzheimer's Disease: A Study of EEG-Derived Brain Activity
No full textMeta-analyses show that percentages of non-Cp-Cu-copper that is not bound to ceruloplasmin (also known as 'free' copper)-in serum are higher in Alzheimer's disease (AD) patients. Genetic heterogeneity in AD patients stratified on the basis of non-Cp-Cu cut-off sustains the existence of a copper AD metabolic subtype. Non-Cp-Cu abnormalities correlated with alterations of electroencephalographic rhythms (EEG)
Electroencephalography-Derived Sensory and Motor Network Topology in Multiple Sclerosis Fatigue
No full textPeople with multiple sclerosis (MS) frequently complain of excessive fatigue, which is the most disabling symptom for half of them. While the few drugs used to treat MS fatigue are of limited utility, we recently observed the efficacy of a personalized neuromodulation treatment. Here, we aim at strengthening knowledge of the brain network changes that occur when MS fatigue increases, using graph theory. We collected electroencephalographic (EEG; 23 or 64 channels) data in resting state with eyes open in 27 relapsing-remitting (RR) patients with mild MS (EDSS ≤2), suffering a wide range of fatigue as scored by the modified Fatigue Impact Scale (mFIS) (2-69, within a total range 0-84). To estimate graph theory small-world index (SW), we calculated the lagged linear coherence between EEG cortical eLORETA sources, in the standard frequency bands delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-45 Hz). We calculated the SW of these undirected and weighted networks separately in the four left and right frontal (motor) and parieto-occipito-temporal (sensory) brain networks. A correlative analysis demonstrated increased fatigue symptoms along with the SW specifically in the Sensory network of the left dominant hemisphere in the beta1 band (Pearson's r = 0.404, P =.020). Our study indicates a specific involvement of the dominant-hemisphere sensory network in MS fatigue. It suggests that compensatory neuromodulation interventions could enhance efficacy in relieving this debilitating symptom by targeting this area
Small world brain network characteristics during EEG Holter recording of a stroke event
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment condition
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