scholarly journals EEG data might help identify children at risk for social anxiety

2021 ◽  

Electroencephalography (EEG) is a non-invasive method to monitor the electrical activity of the brain. There are five main broad frequency bands in the EEG power spectrum: alpha, beta, gamma, delta and theta. Data suggest that EEG-derived delta–beta coupling — indicating related activity in the delta and beta frequency bands — might serve as a marker of emotion regulation.

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Matthew L Flaherty ◽  
Joseph Korfhagen ◽  
George J Shaw ◽  
Opeolu Adeoye ◽  
William Knight

Introduction: Intracerebral hemorrhage (ICH) is a devastating form of stroke. Hemorrhage expansion after ICH occurs in ~40% of patients and leads to worse outcomes. Currently, ICH patients are monitored for hemorrhage expansion by neurologic exam and head CT. CT studies are a source of radiation exposure and can require transporting the patient out of the ICU. There is a clinical need for a non-invasive bedside monitor of ICH. Methods: A radiofrequency based monitor (RFM) was developed as a non-invasive method to monitor ICH at the bedside. The RFM consists of a 9-antenna array mounted around the head, cables, and driving electronics. A 913 MHz signal is transmitted from a given antenna, crosses the brain, and is received by the remaining 8 antennae. A complete measurement consists of one cycle with all antenna serving as the transmitting antenna. As the signal traverses the brain, it is partially scattered and absorbed by the ICH, thus changing the signal at the receiving antennae. The altered signal can be compared to signals at earlier times to detect changes induced by ICH expansion. Based upon pre-clinical work it was hypothesized that ICH expansion of ≥3 ml would be detected by the RFM. The RFM device was approved for human study under an IDE from the FDA. The device was tested on 10 ICH subjects admitted within 24 hours of stroke onset. All subjects received a baseline head CT and a repeat head CT at 12 (+/- 6) hours. ICH volumes were determined by a blinded neuroradiologist. Subjects were scanned with the device every 10 minutes. Results: Data from one subject was lost due to user error. Among the remaining nine, two experienced hemorrhage expansion of ≥ 3ml (3 and 8.2 ml respectively). The RFM readings were 100% concordant with CT scans in identifying presence and absence of hemorrhage expansion. The figure shows monitor readings from a subject with expansion. Conclusion: The RFM may be useful in detection of real-time hemorrhage expansion in ICH patients. A pivotal clinical study is planned.


2015 ◽  
Vol 58 (3) ◽  
pp. 71-78 ◽  
Author(s):  
Photios Anninos ◽  
Adam Adamopoulos ◽  
Athanasia Kotini

Magnetoencephalography (MEG) is the recording of the magnetic field produced by the flowing of ions in the brain. This article reports our experience in the application of MEG in patients and healthy volunteers in the Greek population. We provide a brief description of our research work. The MEG data were recorded in a magnetically shielded room with a whole-head 122 channel or an one-channel biomagnetometer. Our results lead us to believe that the MEG is an important research field which is evolving quickly with a number of interesting findings with respect to normal and abnormal functions of the human brain. It could provide clinical practice with an easy to perform non invasive method, which could be adjunct to conventional methods for the evaluation of brain disorders.


2020 ◽  
Author(s):  
Alexander D Shaw ◽  
Hannah L Chandler ◽  
Khalid Hamandi ◽  
Suresh D Muthukumaraswamy ◽  
Alexander Hammers ◽  
...  

AbstractThe non-invasive study of cortical oscillations provides a window onto neuronal processing. Temporal correlation of these oscillations between distinct anatomical regions is considered a marker of functional connectedness. As the most abundant inhibitory neurotransmitter in the mammalian brain, γ-aminobutyric acid (GABA) is thought to play a crucial role in shaping the frequency and amplitude of oscillations, which thereby suggests a role for GABA in shaping the topography of functional activity and connectivity. This study explored the effects of pharmacologically blocking the reuptake of GABA (increasing local concentrations) through oral administration of the GABA transporter 1 (GAT1) blocker tiagabine (15 mg). We show that the spatial distribution of tiagabine-induced activity changes, across the brain, corresponds to group-average flumazenil PET maps of GABAA receptor distribution.In a placebo-controlled crossover design, we collected resting magnetoencephalography (MEG) recordings from 15 healthy male individuals prior to, and at 1-, 3- and 5- hours post, administration of tiagabine and placebo pill. Using leakage-corrected amplitude envelope correlations (AECs), we quantified the functional connectivity in discrete frequency bands across the whole brain, using the 90-region Automatic Anatomical Labelling atlas (AAL90), as well as quantifying the average oscillatory activity across the brain.Analysis of variance in connectivity using a drug-by-session (2×4) design revealed interaction effects, accompanied by main effects of drug and session. Post-hoc permutation testing of each post-drug recording against the respective pre-drug baseline revealed consistent reductions of a bilateral occipital network spanning theta, alpha and beta frequencies, and across 1- 3- and 5- hour recordings following tiagabine, but not placebo.The same analysis applied to activity, across the brain, also revealed a significant interaction, with post-hoc permutation testing demonstrating significant increases in activity across frontal regions, coupled with reductions in activity in posterior regions, across the delta, theta, alpha and beta frequency bands.Crucially, we show that the spatial distribution of tiagabine-induced changes in oscillatory activity overlap significantly with group-averaged maps of the estimated distribution of GABAA receptors, derived from scaled flumazenil volume-of-distribution (FMZ-VT) PET, hence demonstrating a possible mechanistic link between GABA availability, GABAA receptor distribution, and low-frequency network oscillations. We therefore propose that electrophysiologically-derived maps of oscillatory connectivity and activity can be used as sensitive, time-resolved, and targeted receptor-mapping tools for pharmacological imaging at the group level, providing direct measures of target engagement and pharmacodynamics.


2021 ◽  
Author(s):  
Parul Verma ◽  
Srikantan Nagarajan ◽  
Ashish Raj

Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso- (regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.


2016 ◽  
Vol 8 (3-4) ◽  
pp. 461-466
Author(s):  
T. Herrmann ◽  
E. Gremillet ◽  
J. Juge ◽  
A. Champailler ◽  
P. Rusch ◽  
...  

1999 ◽  
Vol 354 (1387) ◽  
pp. 1229-1238 ◽  
Author(s):  
Alvaro Pascual-Leone

Transcranial magnetic stimulation (TMS) provides a non-invasive method of induction of a focal current in the brain and transient modulation of the function of the targeted cortex. Despite limited understanding about focality and mechanisms of action, TMS provides a unique opportunity of studying brain-behaviour relations in normal humans. TMS can enhance the results of other neuroimaging techniques by establishing the causal link between brain activity and task performance, and by exploring functional brain connectivity.


Author(s):  
Muthulakshmi P ◽  
Gopika R

The project entitled “A Robust Emotion Extraction System from EEG signal Dataset using Machine Learning” has been developed using MATLAB. The brain activity produces the different kinds of signals like electrical and magnetic signals. This activity can be recorded using different kind of approaches, which are normally classified as invasive and non-invasive. In invasive methods surgical intervention are made to implant certain device in the brain whereas in non-invasive methods no such intervention is made. Among the different non-invasive methods, Electroencephalography is one of the most commonly used methods to record the brain signals. EEG is regarded as direct and simple non-invasive method to record the brain electrical activity. Current flow in the neurons of the brain is represented as voltage fluctuation (EEG). EEG waves which can be represented as the signal over time are recorded by the electrodes places on scalp over the brain. EEG Asymmetry and Spectral Centroids techniques in extracting unique features for human stress. In our proposed work we have to classify the EEG signal whether that is stress or not. In our proposed work we will extract the features and optimizing Using Genetic Algorithm then we finally classify the EEG signal.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ahmed M. A. Mohamed ◽  
Osman N. Uçan ◽  
Oğuz Bayat ◽  
Adil Deniz Duru

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.


2012 ◽  
Vol 31 (04) ◽  
pp. 224-230 ◽  
Author(s):  
Leonardo Christian Welling ◽  
Eberval Gadelha Figueiredo ◽  
Fábio Santana Machado ◽  
Almir Ferreira Andrade ◽  
Vinicius Monteiro Guirado ◽  
...  

AbstractComputed tomography is essential in head injuried patients for the detection of structural damage to the brain. However, the ability of CT scanning to predict the presence or absence of intracranial hypertension has been debated in the literature. Since the optic nerve is part of the central nervous system and in case of raised pressure in the cerebrospinal fluid its sheath inflates. Based in this hypothesis the authors reviewed the role of the optic nerve sheat diameter in diagnosis intracranial hypertension after traumatic brain injury. This non-invasive method is useful to predict the risk of intracranial hypertension and select patients to ICP monitoring, especially in those with normal CT scans.


Pharmaceutics ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 15
Author(s):  
Sheng-Kai Wu ◽  
Chia-Lin Tsai ◽  
Yuexi Huang ◽  
Kullervo Hynynen

The presence of blood–brain barrier (BBB) and/or blood–brain–tumor barriers (BBTB) is one of the main obstacles to effectively deliver therapeutics to our central nervous system (CNS); hence, the outcomes following treatment of malignant brain tumors remain unsatisfactory. Although some approaches regarding BBB disruption or drug modifications have been explored, none of them reach the criteria of success. Convention-enhanced delivery (CED) directly infuses drugs to the brain tumor and surrounding tumor infiltrating area over a long period of time using special catheters. Focused ultrasound (FUS) now provides a non-invasive method to achieve this goal via combining with systemically circulating microbubbles to locally enhance the vascular permeability. In this review, different approaches of delivering therapeutic agents to the brain tumors will be discussed as well as the characterization of BBB and BBTB. We also highlight the mechanism of FUS-induced BBB modulation and the current progress of this technology in both pre-clinical and clinical studies.


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