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.

The therapeutic approaches currently applied in Alzheimer's disease (AD) and similar neurodegenerative diseases are essentially based on pharmacological strategies. However, despite intensive research, the effectiveness of these treatments is limited to transient symptomatic effects, and they are still far from exhibiting a true therapeutic effect capable of altering prognosis. The lack of success of such pharmacotherapy-based protocols may be derived from the cases in the majority of trials being too advanced to benefit significantly in therapeutic terms at the clinical level. For neurodegenerative diseases, mild cognitive impairment (MCI) may be an early stage of the disease continuum, including Alzheimer's. Noninvasive brain stimulation (NIBS) techniques have been developed to modulate plasticity in the human cortex in the last few decades. NIBS techniques have made it possible to obtain unique findings concerning brain functions, and design novel approaches to treat various neurological and psychiatric conditions. In addition, its synaptic and cellular neurobiological effects, NIBS is an attractive treatment option in the early phases of neurodegenerative diseases, such as MCI, with its beneficial modifying effects on cellular neuroplasticity. However, there is still insufficient evidence about the potential positive clinical effects of NIBS on MCI. Furthermore, the huge variability of the clinical effects of NIBS limits its use. In this article, we reviewed the combined approach of NIBS with various neuroimaging and electrophysiological methods. Such methodologies may provide a new horizon to the path for personalized treatment, including a more individualized pathophysiology approach which might even define new specific targets for specific symptoms of neurodegenerations.

Mild cognitive impairment (MCI) is a clinical diagnosis based on subjective cognitive decline, objective cognitive impairment, and relative preservation of activities of daily living. The diagnosis may be established via clinical interview, collateral history from an informant, and psychometric examination. Various consensus groups have proposed criteria for MCI in Alzheimer's disease (AD), Parkinson's disease, dementia with Lewy bodies, and vascular cognitive impairment. These diagnostic criteria have subtle but important differences. Criteria for subjective decline vary according to whether memory is impaired or whether impairment in any cognitive domain is sufficient. There are also differences with respect to whether the subjective decline is noted by the patient, a carer, or a clinician. The precise thresholds for classifying objective cognitive impairment also vary between various diagnostic criteria. There are also differences in the description of functional abilities. Once established, the diagnosis of MCI may be refined to 1 of 4 subtypes based on the pattern of cognitive impairment. The 4 subtypes are defined according to whether or not memory is impaired and whether 1 or more cognitive domains are impaired. Once a diagnosis of MCI has been made, the patient and the family should be counseled about social and legal implications as well as strategies for reducing the risk of progression to dementia. The main utilities of MCI as a nosology are to understand the natural history of neurodegenerative disorders such as AD, to identify those at increased risk of progressing to develop dementia, and to identifying individuals for putative treatments.

Amnestic mild cognitive impairment (aMCI), which is characterized by normal daily activity, but a significant decline in episodic memory, is now widely accepted as a risk factor for the development of Alzheimer's dementia (AD). Research suggests that many of the same neuropathological changes associated with AD also occur in patients diagnosed with aMCI. A recent review of the literature revealed that the latency of the flash visual-evoked potential-P2 (FVEP-P2) may possess pathognomonic information that may assist in the early detection of aMCI. While standards exist for the recording of FVEP-P2, individual clinics often use recording parameters that may differ, resulting in latencies that may not generalize beyond the clinic that produced them. The present article illustrates the process by which the FVEP-P2 latency can be standardized across clinics using FVEP-P2 Conversion Scores. We then demonstrate the diagnostic accuracy of the newly developed scores. Method: In the present investigation, we used the previously unpublished data containing the FVEP-P2 latencies of 45 AD and 60 controls. Result: We were able to demonstrate the process by which individual clinics may first standardize FVEP-P2 latencies and then examine patient performance using FVEP-P2 Conversion Scores, providing clinicians with a richer context from which to examine the patient performance. Conclusion: Consistent with the findings of previous research, the findings of the present investigation support the use of the FVEP-P2 Conversion Scores in the diagnosis of AD. Future directions, including the modification of recording parameters associated with the FVEP-P2, are also discussed.

Background. Alzheimer's cognitive-behavioral syndrome is the result of impaired connectivity between nerve cells, due to misfolded proteins, which accumulate and disrupt specific brain networks. Electroencephalography, because of its excellent temporal resolution, is an optimal approach for assessing the communication between functionally related brain regions. Objective. To detect and compare EEG resting-state networks (RSNs) in patients with amnesic mild cognitive impairment (aMCI), and healthy elderly (HE). Methods. We recruited 125 aMCI patients and 70 healthy elderly subjects. One hundred and twenty seconds of artifact-free EEG data were selected and compared between patients with aMCI and HE. We applied standard low-resolution brain electromagnetic tomography (sLORETA)-independent component analysis (ICA) to assess resting-state networks. Each network consisted of a set of images, one for each frequency (delta, theta, alpha1/2, beta1/2). Results. The functional ICA analysis revealed 17 networks common to groups. The statistical procedure demonstrated that aMCI used some networks differently than HE. The most relevant findings were as follows. Amnesic-MCI had: i) increased delta/beta activity in the superior frontal gyrus and decreased alpha1 activity in the paracentral lobule (ie, default mode network); ii) greater delta/theta/alpha/beta in the superior frontal gyrus (i.e, attention network); iii) lower alpha in the left superior parietal lobe, as well as a lower delta/theta and beta, respectively in post-central, and in superior frontal gyrus(ie, attention network). Conclusions. Our study confirms sLORETA-ICA method is effective in detecting functional resting-state networks, as well as between-groups connectivity differences. The findings provide support to the Alzheimer's network disconnection hypothesis.