Clinical EEG and Neuroscience最新文献

Attention was influenced by yoga breathing in previously published research. Each yoga breathing practice uniquely modifies specific breath characteristics. Differences in the study designs, assessment methods and interventions resulted in difficulty in comparing effects between yoga breathing practices. This study aimed (i) to compare four yoga breathing practices on attention using an auditory oddball task and (ii) to determine cardiac autonomic activity associated with attention using heart rate variability. P300 event related potential was recorded simultaneously with heart rate variability before and after 18-minute periods each of (i) high frequency yoga breathing (with increased breath frequency), (ii) bellows yoga breathing (with increased depth of respiration), (iii) alternate nostril yoga breathing (with alternate nostril patency), (iv) bumblebee yoga breathing (with prolonged exhale), (v) breath awareness (with attention to the breath) and (vi) quiet seated rest as control in 38 yoga experienced males (average age ± SD; 24.08 ± 4.01 years). The six sessions were on separate, randomly allocated days. The P300 peak amplitude recorded at Pz was significantly increased after four yoga breathing practices (Bonferroni adjusted post-hoc tests, repeated measures ANOVA). No significant changes were noted in heart rate variability following yoga breathing or control sessions. These findings suggest that the four yoga breathing practices increase the attentional neural resources engaged for this auditory oddball task, irrespective of the characteristic of breath uniquely regulated in the four yoga breathing practices.

Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.

Skin picking disorder (SPD) characterized by repetitive compulsive scratching in the absence of a primary skin disease is strongly associated with psychiatric comorbidities, including obsessive-compulsive disorder (OCD) and depression (MDD). Selective serotonin reuptake inhibitors (SSRIs) have been used in the treatment of SPD with variable success. Nevertheless, the optimum treatment choice for SPD is an issue for clinicians. This case report presents a 32-year-old female SPD patient treated with four-week paroxetine monotherapy. Based upon the clinical interview and standardized questionnaires, the patient was diagnosed with OCD with depressive features and Skin Picking Disorder. In addition to symptom severity scales, quantitative electroencephalography (qEEG) was also applied. Paroxetine treatment was started (titrated from 5 to 40 mg/day) and doubled each week. After four-week paroxetine monotherapy, OCD symptoms were diminished, and skin lesions were completely regressed leaving solely post inflammatory hyperpigmentation. Post-treatment qEEG assessment also showed a normalization of frontal alpha power and amplitude asymmetry. It can be concluded that if OCD includes SPD with abnormal EEG patterns; then the treatment success using paroxetine will be very high.

Sporadic Creutzfeldt-Jakob disease (sCJD) is a rare transmissible disease. According to molecular classification, six clinical phenotypes of sCJD have been described: MM1, MM2, MV1, MV2, VV1 and VV2. MV2 subtype comprises 9% of sCJD cases. Atypical clinical course has been reported to be the main caveat for the diagnosis of the MV2 subtype. We hereby present a rare case of MV2 subtype of sCJD, highlighting the evolution of clinical, EEG and imaging attributes over a two-year period, thus underlining the atypical course of the disease.

Objective: Previous reports on the simultaneous recording of electroencephalography (EEG) and electrocorticography (ECoG) have demonstrated that, in patients with temporal lobe epilepsy (TLE), ictal ECoG discharges with an amplitude as high as 1000 μV originating from the medial temporal lobe could not be recorded on EEG. In contrast, ictal EEG discharges were recorded after ictal ECoG discharges propagated to the lateral temporal lobe. Here, we report a case of TLE in which the ictal EEG discharges, corresponding to ictal ECoG discharges confined to the medial temporal lobe, were recorded. Case report: In the present case, ictal EEG discharges were hardly recognized when the amplitude of the ECoG discharges was less than 1500 μV. During the evolution and burst suppression phase, corresponding to highly synchronized ECoG discharges with amplitudes greater than 1500 to 2000 μV, rhythmic negative waves with the same frequency were clearly recorded both on the lateral temporal lobe and scalp. The amplitude of the lateral temporal ECoG was approximately one-tenth of that of the medial temporal ECoG. The amplitude of the scalp EEG was approximately one-tenth of that of the lateral temporal ECoG. Conclusions: Highly synchronized ictal ECoG discharges with high amplitude of greater than 1500 to 2000 μV in the medial temporal lobe could be recorded on the scalp as ictal EEG discharges via volume conduction.

In 2014, a 26-year-old male was involved in a motor vehicle accident resulting in a severe traumatic brain injury (TBI). The patient sustained a closed-head left temporal injury with coup contrecoup impact to the frontal region. The patient underwent a left side craniotomy and was comatose for 26 days. After gaining consciousness, he was discharged to a brain injury treatment center that worked with physical, speech, and occupational issues. He was discharged after eight months with significant speech, ambulation, spasticity, and cognitive issues as well as the onset of posttraumatic epilepsy. His parents sought hyperbaric oxygen treatment (HBOT) from a doctor in Louisiana. After 165 dives, the HBOT doctor recommended an addition of neurofeedback (NFB) therapy. In March 2019 the patient started NFB therapy intermixed with HBOT. The combination of NFB and HBOT improved plasticity and functionality in the areas of injury and the correlated symptoms including short-term memory, personality, language, and executive function, as well as significantly reducing the incidence of seizures. Severe brain injuries often leave lasting deficits with little hope for major recovery and there is a need for further research into long-term, effective neurological treatments for severe brain injuries. These results suggest that HBOT combined with NFB may be a viable option in treating severe brain injuries and should be investigated.

Background: Electroconvulsive therapy (ECT) is a clinically effective treatment for schizophrenia (SZD). However, studies have shown that only about 50 to 80% of patients show response to ECT. To identify the most suitable patients for ECT, developing biomarkers predicting ECT response remains an important goal. This study aimed to explore the quantitative electroencephalography (QEEG) biomarkers to predict ECT efficacy. Methods: Thirty patients who met DSM-5 criteria for SZD and had been assigned to ECT were recruited. 32-lead Resting-EEG recordings were collected one hour before the initial ECT treatment. Positive and negative symptoms scale (PANSS) was assessed at baseline and after the eighth ECT session. EEG data were analyzed using mutual information. Results: In the brain network density threshold range of 0.05 to 0.2, the assortativity of the right temporal, right parietal, and right occipital cortex in the response group was significantly higher than that in the non-response group (p < .05) in the beta band. In the theta band, the left frontal, parietal, right occipital cortex, and central area assortativity were higher in the response group than in the non-response group (p < .05). Conclusions: QEEG might be a useful approach to identify the candidate biomarker for ECT in clinical practice.