Search results for: theta
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#34130186 2021/05/29 To Up
Reward mechanism of depressive episodes in bipolar disorder: Enhanced theta power in feedback-related negativity.This study aimed to explore the reward-related neural mechanism in patients with depressive mood in bipolar disorder (BD) using event-related potentials. It remains unknown whether or not different neurobiological markers underlying depression symptoms in BD depression and major depression disorder (MDD).
Xinyu Wang, Haiyan Wu, Jia Huang, Chenyang Gao, Ying Yin, Xiaochen Tang, Daihui Peng
1392 related Products with: Reward mechanism of depressive episodes in bipolar disorder: Enhanced theta power in feedback-related negativity.100mg5mg5mg5mg5mg2 Pieces/Box10mg5mg10mg5mg
#34129259 2021/06/15 To Up
Dissecting the Flash Chemistry of Electrogenerated Reactive Intermediates by Microdroplet Fusion Mass Spectrometry.A novel mass spectrometric method for probing the flash chemistry of electrogenerated reactive intermediates was developed based on rapid collision mixing of electrosprayed microdroplets by using a theta-glass capillary. The two individual microchannels of the theta-glass capillary are asymmetrically or symmetrically fabricated with a carbon bipolar electrode to produce intermediates in situ . Microdroplets containing the newly formed intermediates collide with those of the invoked reactants at sub-10 microsecond level, making it a powerful tool for exploring their ultrafast initial transformations. As a proof-of-concept, we present the identification of the key radical cation intermediate in the oxidative dimerization of 8-methyl-1,2,3,4-tetrahydroquinoline and also the first revealment of previously hidden nitrenium ion involved reaction pathway in the C-H/N-H cross-coupling between N,N'-dimethylaniline and phenothiazine.
Jun Hu, Ting Wang, Han Hao, Wen-Jun Zhang, Qiao Yu, Hang Gao, Nan Zhang, Yun Chen, Xing-Hua Xia, Hong-Yuan Chen, Jing-Juan Xu
1068 related Products with: Dissecting the Flash Chemistry of Electrogenerated Reactive Intermediates by Microdroplet Fusion Mass Spectrometry.50 1 kit(s) 1 mg96 wells (1 kit)5 reactions1000500 Units1 mg100 ug100ul
#34127231 2021/05/01 To Up
Interpreting deep learning models for epileptic seizure detection on EEG signals.While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-ligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have approached this issue in the context of online detection of epileptic seizures by developing a DL model from EEG signals, and associating certain properties of the model behavior with the expert medical knowledge. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: (1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; (2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and (3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures.
Valentin Gabeff, Tomas Teijeiro, Marina Zapater, Leila Cammoun, Sylvain Rheims, Philippe Ryvlin, David Atienza
2095 related Products with: Interpreting deep learning models for epileptic seizure detection on EEG signals.2x96 well plate1 g50 ml96 wells1 kit 1 G96 tests 2x5L 6 ml
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#34122071 2021/05/28 To Up
Case Report of Novel Genetic Variant in KCNT1 Channel and Pharmacological Treatment With Quinidine. Precision Medicine in Refractory Epilepsy.In this work we present a female infant patient with epilepsy of infancy with migrating focal seizures (EIMFS). Although many pharmacological schemes were attempted, she developed an encephalopathy with poor response to antiepileptic drugs and progressive cerebral dysfunction. To present the pharmacological response and therapeutic drug monitoring of a paediatric patient with a severe encephalopathy carrying a genetic variant in KCNT1 gene, whose identification led to include quinidine (QND) in the treatment regimen as an antiepileptic drug. Patient showed slow rhythmic activity (theta range) over left occipital areas with temporal propagation and oculo-clonic focal seizures and without tonic spasms three months after birth. At the age of 18 months showed severe impairments of motor and intellectual function with poor eye contact. When the patient was 4 years old, a genetic variant in the exon 24 of the KCNT1 gene was found. This led to the diagnosis of EIMFS. Due to antiepileptic treatment failed to control seizures, QND a KCNT1 blocker, was introduced as a therapeutic alternative besides topiramate (200Â mg/day) and nitrazepam (2Â mg/day). Therapeutic drug monitoring (TDM) of QND plasma levels needed to be implemented to establish individual therapeutic range and avoid toxicity. TDM for dose adjustment was performed to establish the individual therapeutic range of the patient. Seizures were under control with QND levels above 1.5 mcg/ml (65-70Â mg/kg q. i.d). In addition, QND levels higher than 4.0 mcg/ml, were related to higher risk of suffering arrhythmia due to prolongation of QT segment. Despite initial intention to withdrawal topiramate completely, QND was no longer effective by itself and failed to maintain seizures control. Due to this necessary interaction between quinidine and topiramate, topiramate was stablished in a maintenance dose of 40Â mg/day. The implementation of Precision Medicine by using tools such as Next Generation Sequencing and TDM led to diagnose and select a targeted therapy for the treatment of a KCNT1-related epilepsy in a patient presented with EIMFS in early infancy and poor response to antiepileptic drugs. QND an old antiarrhythmic drug, due to its activity as KCNT1 channel blocker, associated to topiramate resulted in seizures control. Due to high variability observed in QND levels, TDM and pharmacokinetic characterization allowed to optimize drug regimen to maintain QND concentration between the individual therapeutic range and diminish toxicity.
M C Kravetz, M S Viola, J Prenz, M Curi, G F Bramuglia, S Tenembaum
2513 related Products with: Case Report of Novel Genetic Variant in KCNT1 Channel and Pharmacological Treatment With Quinidine. Precision Medicine in Refractory Epilepsy.
#34122021 2021/05/28 To Up
An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals.In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
Manuel A VÃ¡zquez, Arash Maghsoudi, InÃ©s P MariÃ±o
2290 related Products with: An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals.0.25 mg100tests100 assays 100ul400 assays96 tests1 ml100.00 ul96 Tests
#34120899 2021/06/08 To Up
TREM2 Deficiency Disrupts Network Oscillations Leading to Epileptic Activity and Aggravates Amyloid-Î²-Related Hippocampal Pathophysiology in Mice.Genetic mutations in triggering receptor expressed on myeloid cells-2 (TREM2) have been strongly associated with increased risk of developing Alzheimer's disease (AD) and other progressive dementias. In the brain, TREM2 protein is specifically expressed on microglia suggesting their active involvement in driving disease pathology. Using various transgenic AD models to interfere with microglial function through TREM2, several recent studies provided important data indicating a causal link between TREM2 and underlying amyloid-Î² (AÎ²) and tau pathology. However, mechanisms by which TREM2 contributes to increased predisposition to clinical AD and influences its progression still remain largely unknown.
Milan Stoiljkovic, Karel Otero Gutierrez, Craig Kelley, Tamas L Horvath, MihÃ¡ly HajÃ³s
2520 related Products with: TREM2 Deficiency Disrupts Network Oscillations Leading to Epileptic Activity and Aggravates Amyloid-Î²-Related Hippocampal Pathophysiology in Mice.1 kit1 kit100 μg96T2 Pieces/Box100 mg5096 tests48 assays 500 gm.96 tests
#34119908 2021/06/02 To Up
Investigation of electrophysiological precursors of attentional errors in schizophrenia: Toward a better understanding of abnormal proactive control engagement.Impaired cognitive control has been associated with the occurrence of attentional errors in those with schizophrenia. However, the extent of altered proactive or reactive control underlying such errors is still unknown. Twenty-two patients with schizophrenia and 21 healthy matched controls performed a detection task (i.e., the continuous temporal expectancy task). Electrophysiological measures of proactive and reactive control were based on two periods of interest: during the target presentation (the critical window) and four trials before the critical window. Regarding the proactive mode, patients with schizophrenia exhibited a specific decrease in frontal midline theta power during the critical window before a miss compared to a correct detection. In contrast, the contingent negative variation amplitude was altered regardless of the response type, four trials before the critical window. Regarding the reactive mode, a reduced P3 amplitude was revealed later before a miss than a correct detection with differences apparent only two trials before the critical window in patients with schizophrenia, whereas it was observable up to four trials prior in healthy controls. Moreover, only the P3 amplitude reduction in patients with schizophrenia predicted the miss rate and was anti-correlated with the clinical symptoms. Thus, our results revealed a specific impairment of the proactive goal-updating process before an error and an altered implementation of the endogenous proactive mode engagement regardless of the response type. The results also highlighted the strong relationship between the disrupted reactive mode and the increased rate of attentional errors and severity of the clinical symptoms of schizophrenia.
Matthieu Chidharom, Julien Krieg, Eduardo Marques-Carneiro, Bich-Thuy Pham, Anne Bonnefond
2741 related Products with: Investigation of electrophysiological precursors of attentional errors in schizophrenia: Toward a better understanding of abnormal proactive control engagement.
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