Brain Gene Co-Expression Network Analysis Identifies 22q13 Region Genes Associated with Autism, Intellectual Disability, Seizures, Language Impairment, and Hypotonia

Phelan-McDermid syndrome (PMS) is a rare genetic neurodevelopmental disorder caused by 22q13 region deletions or SHANK3 gene variants. Deletions vary in size and can affect other genes in addition to SHANK3. PMS is characterized by autism spectrum disorder (ASD), intellectual disability (ID), developmental delays, seizures, speech delay, hypotonia, and minor dysmorphic features. It is challenging to determine individual gene contributions due to variability in deletion sizes and clinical features. We implemented a genomic data mining approach for identifying and prioritizing the candidate genes in the 22q13 region for five phenotypes: ASD, ID, seizures, language impairment, and hypotonia. Weighted gene co-expression networks were constructed using the BrainSpan transcriptome dataset of a human brain. Bioinformatic analyses of the co-expression modules allowed us to select specific candidate genes, including EP300, TCF20, RBX1, XPNPEP3, PMM1, SCO2, BRD1, and SHANK3, for the common neurological phenotypes of PMS. The findings help understand the disease mechanisms and may provide novel therapeutic targets for the precise treatment of PMS.

Interictal Epileptiform Discharge Detection in EEG in Different Practice Settings

OBJECTIVE

The goal of the study was to measure the performance of academic and private practice (PP) neurologists in detecting interictal epileptiform discharges in routine scalp EEG recordings.

METHODS

Thirty-five EEG scorers (EEGers) participated (19 academic and 16 PP) and marked the location of ETs in 200 30-second EEG segments using a web-based EEG annotation system. All participants provided board certification status, years of Epilepsy Fellowship Training (EFT), and years in practice. The Persyst P13 automated IED detection algorithm was also run on the EEG segments for comparison.

RESULTS

Academic EEGers had an average of 1.66 years of EFT versus 0.50 years of EFT for PP EEGers (P < 0.0001) and had higher rates of board certification. Inter-rater agreement for the 35 EEGers was fair. There was higher performance for EEGers in academics, with at least 1.5 years of EFT, and with American Board of Clinical Neurophysiology and American Board of Psychiatry and Neurology-E specialty board certification. The Persyst P13 algorithm at its default setting (perception value = 0.4) did not perform as well at the EEGers, but at substantially higher perception value settings, the algorithm performed almost as well human experts.

CONCLUSIONS

Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.

Interictal epileptiform discharge characteristics underlying expert interrater agreement

OBJECTIVE

The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED.

METHODS

IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization.

RESULTS

Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11.

CONCLUSIONS

The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10).

SIGNIFICANCE

By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.

A study of morphology-based wavelet features and multiple-wavelet strategy for EEG signal classification: results and selected statistical analysis

Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy. The results indicate that both the derived features and the multiple-mother-wavelet strategy improved classifier performance, using a variety of performance measures. We assess the statistical significance of the performance improvement of the new feature sets/strategy. In most cases, the performance improvement is either significant or highly significant.

Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings

OBJECTIVE

This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings.

METHODS

Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-h EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC).

RESULTS

Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k=0.58 vs. 0.38; p<0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test.

CONCLUSIONS

IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA.

SIGNIFICANCE

This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models

This paper presents a novel computer vision algorithm to analyze 3D stacks of confocal images of fluorescently stained single cells. The goal of the algorithm is to create representative in silico model structures that can be imported into finite element analysis software for mechanical characterization. Segmentation of cell and nucleus boundaries is accomplished via standard thresholding methods. Using novel linear programming methods, a representative actin stress fiber network is generated by computing a linear superposition of fibers having minimum discrepancy compared with an experimental 3D confocal image. Qualitative validation is performed through analysis of seven 3D confocal image stacks of adherent vascular smooth muscle cells (VSMCs) grown in 2D culture. The presented method is able to automatically generate 3D geometries of the cell's boundary, nucleus, and representative F-actin network based on standard cell microscopy data. These geometries can be used for direct importation and implementation in structural finite element models for analysis of the mechanics of a single cell to potentially speed discoveries in the fields of regenerative medicine, mechanobiology, and drug discovery.

Morphology-based wavelet features and multiple mother wavelet strategy for spike classification in EEG signals

New wavelet-derived features and strategies that can improve autonomous EEG classifier performance are presented. Various feature sets based on the morphological structure of wavelet subband coefficients are derived and evaluated. The performance of these new feature sets is superior to Guler's classic features in both sensitivity and specificity. In addition, the use of (scalp electrode) spatial information is also shown to improve EEG classification. Finally, a new strategy based upon concurrent use of several mother wavelets is shown to result in increased sensitivity and specificity. Various attempts at reducing feature vector dimension are shown. A non-parametric method, k-NNR, is implemented for classification and 10-fold cross-validation is used for assessment.

A computational approach to understand phenotypic structure and constitutive mechanics relationships of single cells

The goal of this study is to construct a representative 3D finite element model (FEM) of individual cells based on their sub-cellular structures that predicts cell mechanical behavior. The FEM simulations replicate atomic force microscopy (AFM) nanoindentation experiments on live vascular smooth muscle cells. Individual cells are characterized mechanically with AFM and then imaged in 3D using a spinning disc confocal microscope. Using these images, geometries for the FEM are automatically generated via image segmentation and linear programming algorithms. The geometries consist of independent structures representing the nucleus, actin stress fiber network, and cytoplasm. These are imported into commercial software for mesh refinement and analysis. The FEM presented here is capable of predicting AFM results well for 500 nm indentations. The FEM results are relatively insensitive to both the exact number and diameter of fibers used. Despite the localized nature of AFM nanoindentation, the model predicts that stresses are distributed in an anisotropic manner throughout the cell body via the actin stress fibers. This pattern of stress distribution is likely a result of the geometric arrangement of the actin network.

Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis

The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made. The development of reliable computerized detection of ETs in the EEG could assist physicians in interpreting rsEEGs. We report progress in developing a standardized database for testing and training ET detection algorithms. We describe a new version of our EEGnet software system for collecting expert opinion on EEG datasets, a completely web-browser based system. We report results of EEG scoring from a group of 11 board-certified academic clinical neurophysiologists who annotated 30-s excepts from rsEEG recordings from 100 different patients. The scorers had moderate inter-scorer reliability and low to moderate intra-scorer reliability. In order to measure the optimal size of this standardized rsEEG database, we used machine learning models to classify paroxysmal EEG activity in our database into ET and non-ET classes. Based on our results, it appears that our database will need to be larger than its current size. Also, our non-parametric classifier, an artificial neural network, performed better than our parametric Bayesian classifier. Of our feature sets, the wavelet feature set proved most useful for classification.

Controlled trial of pyridoxine in the premenstrual syndrome

A total of 617 patients diagnosed by their general practitioner, according to set criteria, as having premenstrual symptoms were treated in general practice for three menstrual cycles with either pyridoxine or placebo. Treatment was randomized and administered blind. In the 434 patients analyzed, an improvement was found in 7 of the 9 symptoms assessed for both treatments, but the differences between treatments did not reach conventional significance levels. However, improvement as measured by global assessment after three cycles was significantly greater in the patients treated with pyridoxine (p less than 0.02).

Comparison of limbitrol (chlordiazepoxide/amitriptyline) and amitriptyline alone as a single night-time dose for the treatment of depression with anxiety

Four hundred and thirty-four patients suffering from mild to moderate depression were recruited into a double-blind multicentre general practice study and randomly received either Limbitrol (25 mg amitriptyline and 10 mg chlordiazepoxide) or amitriptyline 25 mg in matching white capsules given as a single night-time dose. The dose was titrated to a maximum of four capsules per night. Patients were assessed on entry and at 7-day intervals for depressive state (Hamilton Rating Scale for Depression), cardiovascular effect and side-effects for 28 days. Self-assessments of depression and anxiety using the Leeds Scale were made in the form of a patient questionnaire. Both groups showed significant improvement at each visit and significantly more patients improved after 7 days in the Limbitrol group. There was no significant difference in treatment over all between groups, in the incidence of side-effects, or in the effects on the heart.

A multi-centre, double-blind parallel trial of bromazepam ('Lexotan') and lorazepam to compare the acute benefit-risk ratio in the treatment of patients with anxiety

A double-blind, multi-centre study was carried out in general practice to compare the efficacy and tolerance of treatment with bromazepam and lorazepam in 671 patients with anxiety. Patients were treated at random with either bromazepam (3 to 9 mg per day) or lorazepam (1 to 3 mg per day) for periods up to 2 weeks. In the doctors' global assessment of response, significantly more patients improved on bromazepam (84%) compared with lorazepam (77%). Thirty-three percent of the bromazepam patients reported at least one unwanted event compared with 37% in the lorazepam group. The results are discussed in the context of improving the benefit-risk ratio.

Bromazepam: acute benefit-risk assessment in general practice

A study was carried out in general practice to assess the benefit-risk ratio of a single new drug, bromazepam, prior to marketing. Analysis of data supplied by 393 participating doctors on 3101 patients showed that bromazepam, in a dose range of 3 mg to 9 mg daily in divided doses, was effective as an anxiolytic in 79% of the patients and that the acute risk of treatment was predictable and low. It is concluded that the acute benefit-risk ratio is acceptable with respect to the class of drug and indication for which bromazepam is prescribed.

A double-blind comparison of two benzodiazepine hypnotics, flunitrazepam and triazolam, in general practice

A double-blind study was carried out in 312 patients seen by their general practitioner for a sleep disorder requiring hypnotic treatment to compare the efficacy and event profile of flunitrazepam and triazolam. Patients were allocated at random to receive a night-time dose of 1 mg flunitrazepam or 0.25 mg triazolam for a minimum of 7 and a maximum of 14 nights. Sleep disturbances, classified as difficulty in falling asleep, troublesome awakenings in the first or the second half of the night, and early morning awakening, were assessed on the initial and final visits and details of any events recorded. The results showed no significant difference between the two treatments with respect to efficacy and event profile and the findings are discussed in the context of the differing pharmacokinetics of the two drugs.

Flunitrazepam: acute benefit-risk assessment in general practice

A methodology is described which is practical in assessing the benefit-risk ratio of a single new drug prior to marketing. Flunitrazepam at doses of 0.5 mg, 1.0 mg (and 2 mg) nocte in 2,435 patients is shown to be effective in 78% of patients and the acute risk, predictable and low, irrespective of age or dose. One may conclude that the acute benefit-risk ratio is acceptable with respect to the class of drug and indication for which flunitrazepam is prescribed.

Improved control of brittle Parkinsonism by separate administration of levodopa and benserazide

A 59-year-old woman who had had Parkinsonism for 12 years was treated with orphenadrine and levodopa combined with a dopa carboxylase inhibitor. The initial response was good, but after several years' treatment her condition alternated between severe bradykinesia and incapacitating, violent chorea, interspersed with short periods of mobility. A new regimen was devised, using levodopa in capsules of 40 mg and benserazide in separate capsules of 10 or 25 mg. Levodopa 40 mg was taken at intervals of half to two and a half hours, usually with benserazide 10 mg but alone in the late morning and evening. Additional benserazide was required one hour after lunch. With this regimen her condition was greatly improved, though she still had an abnormal gait and spells of bradykinesia and chorea. Separate, frequent small doses of levodopa and benserazide may give better control of brittle Parkinsonism.

A comparison of a single night-time and a divided daily dosage regimen of a chlordiazepoxide/amitriptyline combination

A study was carried out in 80 mild to moderately depressed patients seen in general practice to assess the relative effectiveness and tolerance of a single night-time dose compared with the usual divided daily dosage regimen of a chlordiazepoxide (5 mg)/amitriptyline (12.5 mg) combination. Patients were assigned at random to receive either 1 capsule 3-times daily or 3 capsules as a single dose at night over a period of 3 weeks. Self-assessments of anxiety and depression using the Leeds scale were made at the initial visit and at weekly intervals, and an assessment of overall response to treatment was made by the doctor at the end of the trial period. Both groups showed significant improvement at each visit but the results suggest that the single night-time dose was more effective than the same dose given in a divided regimen. The good response obtained on relatively low doses of the tricyclic would support the view that chlordiazepoxide potentiates the antidepressant effects of amitriptyline.

Clorazepate: double blind crossover comparison of a single nightly dose with diazepam thrice daily in anxiety

In a double blind cross-over study in 40 patients with mild to moderate anxiety, clorazepate 15 mg at bedtime was as effective as diazepam on global rating and slightly superior on target symptom assessment. There was a significantly higher incidence and frequency of side effects during diazepam treatment. Occurrence of side effect related to plasma diazepam, and anxiolytic effect related to plasma nordiazepam. These results are discussed in relation to convenience of the single dose regimen and psychomotor performance.