scholarly journals Effect of levodopa on human brain connectome in Parkinsons disease

2021 ◽  
Author(s):  
Sajjad Farashi ◽  
Mojtaba Khazaei
Keyword(s):  
Brain Connectivity ◽  
Gamma Band ◽  
Beta Band ◽  
Positive Effects ◽  
Eyes Closed ◽  
Brain Connectome ◽  
Eeg Data ◽  
Eyes Open ◽  
Gamma Band Activity ◽  
The Brain

Levodopa-based drugs are widely used for mitigating the complications induced by PD. Despite the positive effects, several issues regarding the way that levodopa changes brain activities have remained unclear. Methods-A combined strategy using EEG data and graph theory was used for investigating how levodopa changed connectome and processing hubs of the brain during resting-state. Obtained results were subjected to ANOVA test and multiple-comparison post-hoc correction procedure. Results: Results showed that graph topology of PD patients was not significantly different with the healthy group during eyes-closed condition while in eyes-open condition statistical significant differences were found. The main effect of levodopa medication was observed for gamma-band activity of the brain in which levodopa changed the brain connectome toward a star-like topology. Considering the beta subband of EEG data, graph leaf number increased following levodopa medication in PD patients. Enhanced brain connectivity in gamma band and reduced beta band connections in basal ganglia were also observed after levodopa medication. Furthermore, source localization using dipole fitting showed that levodopa prescription suppressed the activity of collateral trigone. Conclusion: Our combined EEG and graph analysis showed that levodopa medication changed the brain connectome, especially in the high-frequency range of EEG (beta and gamma).

Olfactory Recognition Based on EEG Gamma-Band Activity

2017 ◽  
Vol 29 (6) ◽  
pp. 1667-1680 ◽  
Author(s):  
Onder Aydemir
Keyword(s):  
Gamma Band ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Average Classification Accuracy ◽  
Eyes Closed ◽  
Lotus Flower ◽  
Eyes Open ◽  
Gamma Band Activity ◽  
The Brain ◽  
Band Activity

There are various kinds of brain monitoring techniques, including local field potential, near-infrared spectroscopy, magnetic resonance imaging (MRI), positron emission tomography, functional MRI, electroencephalography (EEG), and magnetoencephalography. Among those techniques, EEG is the most widely used one due to its portability, low setup cost, and noninvasiveness. Apart from other advantages, EEG signals also help to evaluate the ability of the smelling organ. In such studies, EEG signals, which are recorded during smelling, are analyzed to determine the subject lacks any smelling ability or to measure the response of the brain. The main idea of this study is to show the emotional difference in EEG signals during perception of valerian, lotus flower, cheese, and rosewater odors by the EEG gamma wave. The proposed method was applied to the EEG signals, which were taken from five healthy subjects in the conditions of eyes open and eyes closed at the Swiss Federal Institute of Technology. In order to represent the signals, we extracted features from the gamma band of the EEG trials by continuous wavelet transform with the selection of Morlet as a wavelet function. Then the [Formula: see text]-nearest neighbor algorithm was implemented as the classifier for recognizing the EEG trials as valerian, lotus flower, cheese, and rosewater. We achieved an average classification accuracy rate of 87.50% with the 4.3 standard deviation value for the subjects in eyes-open condition and an average classification accuracy rate of 94.12% with the 2.9 standard deviation value for the subjects in eyes-closed condition. The results prove that the proposed continuous wavelet transform–based feature extraction method has great potential to classify the EEG signals recorded during smelling of the present odors. It has been also established that gamma-band activity of the brain is highly associated with olfaction.

scholarly journals Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG)

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ahmed M. A. Mohamed ◽  
Osman N. Uçan ◽  
Oğuz Bayat ◽  
Adil Deniz Duru
Keyword(s):  
Resting State ◽  
Sample Entropy ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Frequency Bands ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
The Brain

An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

scholarly journals Reduced interhemispheric and increased intrahemispheric connectivity in schizophrenia brain networks

2019 ◽  
Author(s):  
Jorne Laton ◽  
Jeroen Van Schependom ◽  
Jeroen Decoster ◽  
Tim Moons ◽  
Marc De Hert ◽  
...  
Keyword(s):  
Corpus Callosum ◽  
Resting State ◽  
Right Hemisphere ◽  
Brain Connectivity ◽  
Similarity Measures ◽  
Beta Band ◽  
Auditory Oddball ◽  
Eyes Closed ◽  
Eyes Open ◽  
The Right

AbstractIntroductionBrain connectivity is disturbed in schizophrenia, both during resting state and during active tasks. Schizophrenia is characterised by a corpus callosum pathology and an inability to suppress overstimulation, both of which relate to this disturbed connectivity. We wanted to verify whether network analysis on EEG sensor level can reveal the corpus callosum pathology in schizophrenia.MethodsWe measured 62-channel EEG on 46 schizophrenia patients and 43 healthy controls during eyes-closed and eyes-open resting-state, mismatch negativity and visual and auditory oddball. We assessed connectivity through correlation, coherence and directed transfer function (DTF) in the delta, theta, alpha, low- and high beta bands.ResultsThe coherence and the DTF picked up a consistent pattern of reduced interhemispheric and enhanced intrahemispheric connectivity strength in schizophrenia in the alpha and beta band. This disturbance pattern appeared across all paradigms in the parietal and the occipital region and was generally more pronounced in the right hemisphere.ConclusionsThis is the first study to use multiple similarity measures and different tasks to confirm disturbed brain connectivity on EEG sensor level. We hypothesise that the interhemispheric reductions reflect transcallosal disconnection, while the intrahemispheric increases indicate the inability to suppress the response to stimuli.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Unique Contributions of Brain Stimulation to the Study of Consciousness: Where Neuroscience Meets Philosophy

CNS Spectrums ◽  
2010 ◽  
Vol 15 (3) ◽  
pp. 154-156
Author(s):  
Stefano Pallanti
Keyword(s):  
Ancient Greek ◽  
Eyes Closed ◽  
Technical Procedure ◽  
Eyes Open ◽  
Definition Of ◽  
The Individual ◽  
The Brain ◽  
Integrated Information Theory ◽  
Field Of Inquiry ◽  
Integrated Information

True progress in understanding how experience arises from the brain has been relatively slow when viewed from a historical perspective. Recently, several technologies to study and stimulate the brain have been applied to this field of inquiry. Such progress was made only 2,500 years after the ancient Greek philosopher Parmenides first adopted a technical procedure involving the application of formal logic instruments to explore the perception of experiences.At the phenomenological level, consciousness has been referred to as “what vanishes every night when we fall into dreamless sleep and reappears when we wake up or when we dream. It is also all we are and all we have: lose consciousness and, as far as you are concerned, your own self, and the entire world dissolves into nothingness”. According to the integrated information theory, consciousness is integrated information.The term “consciousness” therefore has two key senses: wakefulness and awareness. Wakefulness is a state of consciousness distinguished from coma or sleep. Having one's eyes open is generally an indication of wakefulness and we usually assume that anyone who is awake will also be aware. Awareness implies not merely being conscious but also being conscious of something. The broad definition of consciousness includes a large range of processes that we normally regard as unconscious (eg, blindsight or priming by neglected or masked stimuli).Both sleep and anesthesia are reversible states of eyes-closed unresponsiveness to environmental stimuli in which the individual lacks both wakefulness and awareness. In contrast to sleep, where sufficient stimulation will return the individual to wakefulness, even the most vigorous exogenous stimulation cannot produce awakening in a patient under an adequate level of general anesthesia.

scholarly journals Positive Effects of Blue Light on Postural Control and Motor Coordination in Older Adults: A Pilot Study

2021 ◽  
Author(s):  
C. Martyn Beaven ◽  
Liis Uiga ◽  
Kim Hébert-Losier
Keyword(s):  
Older Adults ◽  
Cognitive Function ◽  
Postural Control ◽  
Motor Coordination ◽  
Light Exposure ◽  
Clinical Approach ◽  
Positive Effects ◽  
Eyes Closed ◽  
Eyes Open ◽  
Human Costs

Abstract Purpose: Falls are a risk factor for mortality in older adults. Light interventions can improve cognitive function and performance in motor tasks, but the potential impact on postural control with relevance to falling is unknown. This study aimed to examine the effect of light on postural control, motor coordination, and cognitive functioning. Methods: Sixteen older adults participated in an intervention study that involved four counter-balanced sessions with blue-enriched light delivered visually and/or transcranially for 12 minutes. Postural control in three conditions (60 s eyes open, dual-task, and eyes closed), lower extremity motor coordination, and cognitive function were assessed. Area of sway (AoS), coordination, and cognitive function were compared between the groups via repeated-measured ANOVA. Results: Relative to placebo, visual blue-enriched light exposure clearly decreased AoS (d = 0.68 ±0.73; p =0.166) and improved reaction time in the motor coordination task (d = 1.44 ±0.75; p =0.004); however, no significant effect was seen on cognitive function. Conclusion Blue-enriched light demonstrates a novel clinical approach to positively impact on postural control and lower-limb motor coordination in older adults. By impacting on metrics associated with fall risk, blue-enriched light may provide a clinically meaningful countermeasure to decrease the human costs of falls.

scholarly journals A covariate-constraint method to map brain feature space into lower dimensional manifolds

2020 ◽  
pp. 1-30
Author(s):  
Félix Renard ◽  
Christian Heinrich ◽  
Marine Bouthillon ◽  
Maleka Schenck ◽  
Francis Schneider ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Feature Space ◽  
Graph Metrics ◽  
Brain Connectome ◽  
Biological Phenomena ◽  
Number Of Patients ◽  
Population Comparison ◽  
Constraint Method ◽  
The Brain ◽  
Insight Into

Human brain connectome studies aim at both exploring healthy brains, and extracting and analyzing relevant features associated to pathologies of interest. Usually this consists in modeling the brain connectome as a graph and in using graph metrics as features. A fine brain description requires graph metrics computation at the node level. Given the relatively reduced number of patients in standard cohorts, such data analysis problems fall in the high-dimension low sample size framework. In this context, our goal is to provide a machine learning technique that exhibits flexibility, gives the investigator grip on the features and covariates, allows visualization and exploration, and yields insight into the data and the biological phenomena at stake. The retained approach is dimension reduction in a manifold1 learning methodology, the originality lying in that one (or several) reduced variables be chosen by the investigator. The proposed method is illustrated on two studies, the first one addressing comatose patients, the second one addressing young versus elderly population comparison. The method sheds light on the differences between brain connectivity graphs using graph metrics and potential clinical interpretations of theses differences.

scholarly journals Fronto-Temporal Analysis of EEG Signals of Patients with Depression: Characterisation, Nonlinear Dynamics and Surrogate Analysis

2020 ◽  
Author(s):  
Subha D. Puthankattil
Keyword(s):  
Time Series ◽  
Unipolar Depression ◽  
Temporal Analysis ◽  
Mental States ◽  
Eeg Signals ◽  
Biosignal Analysis ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
The Brain

The recent advances in signal processing techniques have enabled the analysis of biosignals from brain so as to enhance the predictive capability of mental states. Biosignal analysis has been successfully used to characterise EEG signals of unipolar depression patients. Methods of characterisation of EEG signals and the use of nonlinear parameters are the major highlights of this chapter. Bipolar frontopolar-temporal EEG recordings obtained under eyes open and eyes closed conditions are used for the analysis. A discussion on the reliability of the use of energy distribution and Relative Wavelet Energy calculations for distinguishing unipolar depression patients from healthy controls is presented. The potential of the application of Wavelet Entropy to differentiate states of the brain under normal and pathologic condition is introduced. Details are given on the suitability of ascertaining certain nonlinear indices on the feature extraction, assuming the time series to be highly nonlinear. The assumption of nonlinearity of the measured EEG time series is further verified using surrogate analysis. The studies discussed in this chapter indicate lower values of nonlinear measures for patients. The higher values of signal energy associated with the delta bands of depression patients in the lower frequency range are regarded as a major characteristic indicative of a state of depression. The chapter concludes by presenting the important results in this direction that may lead to better insight on the brain activity and cognitive processes. These measures are hence posited to be potential biomarkers for the detection of depression.

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