Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography.

Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits. Thirty-seven antipsychotic-naïve, first-episode patients with schizophrenia (mean age 24.4 (5.4); 59.5% males) and 97 matched healthy controls (mean age 24.0 (5.1); 52.6% males) underwent assessments of rsEEG, psychopathology, and cognition. Source-localized, frequency-dependent functional connectivity was estimated using Phase Lag Index (PLI). The DMN-PLI was factorized for each frequency band using principal component analysis. Clusters of patients were identified using a Gaussian mixture model and neurocognitive and psychopathological profiles of identified clusters were explored. We identified two clusters of patients based on the theta band (4-8 Hz), and two clusters based on the beta band (12-30 Hz). Baseline psychopathology could predict theta clusters with an accuracy of 69.4% (p = 0.003), primarily driven by negative symptoms. Five a priori selected cognitive functions conjointly predicted the beta clusters with an accuracy of 63.6% (p = 0.034). The two beta clusters displayed higher and lower DMN connectivity, respectively, compared to healthy controls. In conclusion, the functional connectivity within the DMN provides a novel, data-driven means to stratify patients into clinically relevant clusters. The results support the notion of biological subgroups in schizophrenia and endorse the application of data-driven methods to recognize pathophysiological patterns at earliest stage of this syndrome.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.

Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy.

BACKGROUND: The interplay between sleep structure and seizure probability has previously been studied using electroencephalography (EEG). Combining sleep assessment and detection of epileptic activity in ultralong-term EEG could potentially optimize seizure treatment and sleep quality of patients with epilepsy. However, the current gold standard polysomnography (PSG) limits sleep recording to a few nights. A novel subcutaneous device was developed to record ultralong-term EEG, and has been shown to measure events of clinical relevance for patients with epilepsy. We investigated whether subcutaneous EEG recordings can also be used to automatically assess the sleep architecture of epilepsy patients. METHOD: Four adult inpatients with probable or definite temporal lobe epilepsy were monitored simultaneously with long-term video scalp EEG (LTV EEG) and subcutaneous EEG. In total, 11 nights with concurrent recordings were obtained. The sleep EEG in the two modalities was scored independently by a trained expert according to the American Academy of Sleep Medicine (AASM) rules. By using the sleep stage labels from the LTV EEG as ground truth, an automatic sleep stage classifier based on 30 descriptive features computed from the subcutaneous EEG was trained and tested. RESULTS: An average Cohen's kappa of [Formula: see text] was achieved using patient specific leave-one-night-out cross validation. When merging all sleep stages into a single class and thereby evaluating an awake-sleep classifier, we achieved a sensitivity of 94.8% and a specificity of 96.6%. Compared to manually labeled video-EEG, the model underestimated total sleep time and sleep efficiency by 8.6 and 1.8 min, respectively, and overestimated wakefulness after sleep onset by 13.6 min. CONCLUSION: This proof-of-concept study shows that it is possible to automatically sleep score patients with epilepsy based on two-channel subcutaneous EEG. The results are comparable with the methods currently used in clinical practice. In contrast to comparable studies with wearable EEG devices, several nights were recorded per patient, allowing for the training of patient specific algorithms that can account for the individual brain dynamics of each patient. Clinical trial registered at ClinicalTrial.gov on 19 October 2016 (ID:NCT02946151).

Gait analysis for individually tailored interdisciplinary interventions in children with cerebral palsy: a randomized controlled trial.

AIM: To test the hypothesis that improvements in gait and function following individualized interdisciplinary interventions consisting of physical therapy, orthotics, spasticity management, and orthopaedic surgery using instrumented gait analysis are superior to 'usual care' in children with cerebral palsy (CP). METHOD: This was a prospective, single-blind, parallel-group, randomized controlled trial investigating the effectiveness of interventions based on the use of gait analysis. Primary outcome was gait (Gait Deviation Index) and secondary outcomes were walking and patient-reported outcome measures of function, disability, and health-related quality of life. Follow-ups were done at 26 weeks (questionnaires) and at the primary end point of 52 weeks (all outcomes). RESULTS: Sixty participants with CP (39 males, 21 females, mean age 6y 10mo, standard deviation 1y 3mo, range 5y-9y 1mo) in Gross Motor Function Classification System levels I or II, were randomized to interventions with or without gait analysis. No significant or clinically relevant between-group differences in change scores of the primary or secondary outcomes were found. The recommended categories of interventions were dominated by non-surgical interventions and were applied in 36% to 86% of the participants. INTERPRETATION: Interventions using gait analysis were not superior to 'usual care' on gait, walking, or patient-reported outcomes in a sample of relatively young and independently walking children with CP not expected to need surgery. WHAT THIS PAPER ADDS: Gait analysis in children with cerebral palsy in Gross Motor Function Classification System levels I or II recommends interdisciplinary interventions. Compliance to interventions recommended after gait analysis was low. No statistically significant advantages were identified for the intervention group versus the control group.

ANÁLISIS DE MARCHA PARA LA INTERVENCIÓN INTERDISCIPLINARIA ADAPTADA INDIVIDUALMENTE EN NIÑOS CON PARÁLISIS CEREBRAL: ENSAYO CONTROLADO RANDOMIZADO: OBJETIVO: Comprobar la hipótesis que las mejoras en la marcha y la función luego de las intervenciones interdisciplinarias individualizadas de terapia física, ortésis, tratamiento antiespástico, y cirugía ortopédica son superiores que "tratamiento convencional" en parálisis cerebral (PC) utilizando un análisis de marcha instrumentada METODO: Este fue un ensayo randomizado controlado, prospectivo, ciego, con grupo paralelo que investigó la efectividad de intervenciones basada en el uso de análisis de marcha. El resultado primario fue la marcha (Índice de Desviación de la Marcha) y los resultados secundarios fueron el paso y los resultados reportados por los pacientes de función, discapacidad y calidad de vida relacionada a la salud. Los seguimientos se realizaron a las 26 semanas (cuestionarios) y el punto de fin primario de 52 semanas (todos los resultados). RESULTADOS: Sesenta participantes con PC (39 masculinos, 21 femeninos, edad media de 6 años 10 meses, desviación estándar de 1 años y 3 meses, rango 5 años 0 meses- 9 años y 1 mes) con niveles de GMFCS I o II, fueron asignados al azar intervenciones con y sin análisis de marcha. No se encontraron diferencias significativas o clínicamente relevantes entre los grupos en cuanto a los cambios de los resultados primarios y secundarios. INTERPRETACION: Las intervenciones que usaron análisis de marcha no fueron superiores al tratamiento convencional sobre el paso, la marcha o resultados reportados por los pacientes en una muestra de niños con PC relativamente jóvenes y de marcha independiente que no se espera que necesiten cirugía.

ANÁLISE DE MARCHA PARA INTERVENÇÕES INDIVIDUALMENTE PLANEJADAS EM CRIANÇAS COM PARALISIA CEREBRAL: UM ENSAIO CONTROLADO RANDOMIZADO: OBJETIVO: Testar a hipótese de que melhoras na marcha e função após intervenções interdisciplinares individualizadas consistindo de fisioterapia, órteses, manejo da espasticidade, e cirurgia ortopédica usando análise de marcha instrumentalizada são superiors comparadas ao "cuidado usual" em crianças com paralisia cerebral (PC). MÉTODO: Este foi um estudo randomizado controlado prospectivo, único cego, com grupos paralelos, investigando a efetividade de intervenções baseadas no uso da análise de marcha. O desfecho primário foi a marcha (Índice de Desvio da Marcha) e os desfechos secundários foram o caminhar e medidas relatadas pelo paciente da função, incapacidade, e qualidade de vida relacionada à saúde. Acompanhamentos foram feitos com 26 semanas (questionários) e o encerramento primário foi 52 semanas (todos os resultados). RESULTADOS: Sessenta participantes com PC (39 do sexo masculino, 21 do sexo feminino, média de idade 6a 10 m, desvio padrão 1a 3 m, variação 5a 0 m- 9a 1 m) nos níveis do Sistema de Classificação da Função Motora Grossa (GMFCS) I ou II foram fandomizados para intervenções com ou sem análise de marcha. Nenhuma diferença significativa ou clinicamente relevante entre grupos nos escores de mudança dos desfechos primários e secundários foram encontradas. As categorias de intervenção recomendadas foram dominadas pelas intervenções não-cirúrgicas e foram aplicadas em 36% a 86% dos participantes. INTERPRETAÇÃO: Intervenções usando análise de marcha não foram superiores ao "cuidado usual" para a marcha, o caminhar, ou resultados reportados por pacientes em ma amostra de crianças com PC relativamente jovens e com deambulação indepente, para a qual não se espera a necessidade de cirurgia.

Mosaic MECP2 variants in males with classical Rett syndrome features, including stereotypical hand movements.

Rett syndrome is rarely suspected in males because of the X-linked dominant inheritance. In the literature, only six male patients have been reported with methyl-CpG-binding protein 2 (MECP2) mosaicism. Next-generation sequencing (NGS) methods have enabled better detection of somatic mosaicism compared to conventional Sanger sequencing; however, mosaics can still be difficult to detect. We present clinical and molecular findings in two males mosaic for a pathogenic MECP2 variant. Both have been reexamined using deep sequencing of DNA isolated from four different cell tissues (blood, muscle, fibroblasts and oral mucosa). Deep sequencing of the different tissues revealed that the variants were present in all tissues. In one patient, the molecular diagnosis could only be established by reexamination after a normal whole exome sequencing, and the other case is an example of reverse genetic diagnostics. Rett syndrome should be considered in males with neurodevelopmental delay and stereotypical hand movements. Subsequent to clinical diagnosis males should be investigated with NGS-based technologies of MECP2 with high read depth and a low threshold for variant calls. If the initial analysis on full blood derived DNA fails to confirm the suspicion, we recommend repeating the analysis on another tissue, preferentially fibroblasts to increase the diagnostic yield.

Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients.

BACKGROUND: A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation. METHODS: Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission. RESULTS: Cognitive data significantly classified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission. CONCLUSIONS: In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.

On the Keyhole Hypothesis: High Mutual Information between Ear and Scalp EEG.

We propose and test the keyhole hypothesis-that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10 subjects exposed to a battery of stimuli, including alpha-attenuation, auditory onset, and mismatch-negativity responses and a new medium-long EEG experiment involving data acquisition during 13 h. Linear models were estimated to lower bound the scalp-to-ear capacity, i.e., predicting ear-EEG data from simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG "keyhole," furthermore we show that the view-represented as a linear mapping-is stable across both time and mental states. Specifically, we find that ear-EEG data can be predicted reliably from scalp EEG. We also address the reverse view, and demonstrate that large portions of the scalp EEG can be predicted from ear-EEG, with the highest predictability achieved in the temporal regions and when using ear-EEG electrodes with a common reference electrode.

Data Driven Estimation of Imputation Error-A Strategy for Imputation with a Reject Option.

Missing data is a common problem in many research fields and is a challenge that always needs careful considerations. One approach is to impute the missing values, i.e., replace missing values with estimates. When imputation is applied, it is typically applied to all records with missing values indiscriminately. We note that the effects of imputation can be strongly dependent on what is missing. To help make decisions about which records should be imputed, we propose to use a machine learning approach to estimate the imputation error for each case with missing data. The method is thought to be a practical approach to help users using imputation after the informed choice to impute the missing data has been made. To do this all patterns of missing values are simulated in all complete cases, enabling calculation of the "true error" in each of these new cases. The error is then estimated for each case with missing values by weighing the "true errors" by similarity. The method can also be used to test the performance of different imputation methods. A universal numerical threshold of acceptable error cannot be set since this will differ according to the data, research question, and analysis method. The effect of threshold can be estimated using the complete cases. The user can set an a priori relevant threshold for what is acceptable or use cross validation with the final analysis to choose the threshold. The choice can be presented along with argumentation for the choice rather than holding to conventions that might not be warranted in the specific dataset.

Rhythm recognition is accountable for the majority of hands-off time during cardiopulmonary resuscitation - a simulation study.

Cardiopulmonary resuscitation guidelines are constantly optimized to increase survival. Keeping hands-off time brief is vital. Our hypothesis is that rhythm recognition is time-consuming during cardiopulmonary resuscitation. A Laerdal Sim-Man simulated three shockable and four nonshockable rhythms. Rhythms were presented to physicians who identified whether they were shockable and whether they would defibrillate. We measured time to stated decision. Thirty-five doctors participated, 32 had completed advanced life support training. The mean time to make a decision on whether to defibrillate or not was 3.4 s [95% confidence interval (CI): 2.8-3.9] for shockable and 4.4 s (95% CI: 3.6-5.3) for nonshockable rhythms (P<0.05). For all rhythms, the mean time was 4.0 s (95% CI: 3.5-4.5). Of all shockable rhythms, 95.2 % were correctly diagnosed as shockable, compared with 88.6 % of nonshockable rhythms being correctly diagnosed. Our simulation study indicates that doctors are able to correctly identify shockable rhythms within 4 s.

Design of a highly selective and potent class of non-planar estrogen receptorâ€ ß agonists.

Selective activation of the estrogen receptorâ€…ß (ERß) could be a safe approach to hormone replacement therapy for both women and men, in contrast to the estrogens currently used for women which activate both ERß and ERα, occasionally causing severe side effects. The selective ERß agonist AC-131 has shown efficacy in animal models of Parkinson's disease and neuropathic pain. With the use of AC-131 as template, herein we report the discovery, synthesis, and structure-activity relationship (SAR) study of a new class of dihydrobenzofurans as potent and selective ERß agonists. The SAR was established by enantioselective synthesis, molecular modeling, and whole-cell-based functional assays. The most potent diastereomer, cis-10-SR, was shown to have an EC50 value of <1 nM, potency 100-fold higher than that of AC-131. Even more interestingly, compound trans-10-SS exhibited 1000-fold ERß/ERα selectivity while still maintaining good potency (∼10 nM). In addition, trans-10-SS showed only partial agonist activity (30-60 % Eff.) toward ERα at 10 µM. This unprecedented selectivity could be rationalized by molecular modeling. Compound trans-10-SS appears to be the first molecule to take advantage of both conservative amino acid differences found in the α- and ß-faces of the binding cavities of ERα and ERß.

Movement disorders in patients with schizophrenia and a history of substance abuse.

OBJECTIVE: The movement disorders acute dystonia, akathisia, Parkinsonian symptoms and tardive dyskinesia [extrapyramidal side effects (EPSs)] are recognized adverse effects of antipsychotic medication. Previous studies have indicated that substance abuse in patients with schizophrenia can worsen EPS. This study therefore investigated the relationship between drug and alcohol use and EPS in a group of patients with schizophrenia. METHODS: Seventy patients with schizophrenia assessed for drug and alcohol use, global functioning, EPS and suicidality. Chlorpromazine equivalents were correlated to levels of EPS and substance abuse. RESULTS: Current EPS were found in 65% of the sample despite three-quarters of the patients receiving second-generation antipsychotics. An even higher level of patients, 87%, was found to have a history of EPS. A long history of schizophrenia independently predicted presence of any EPS, particularly akathisia, controlling for history of substance abuse which was a non-significant predictor. CONCLUSIONS: History or current use of alcohol or drug abuse did not predict EPS, except for alcohol abuse at the time of diagnosis which was associated with current akathisia. Length of illness was correlated with EPS, whereas suicidality was not linked to akathisia. Neither chlorpromazine equivalent antipsychotic dose nor whether the patient received first-generation or second-generation antipsychotic medication was significantly associated with EPS or substance abuse.

Structured relaxation in the treatment of akathisia: case series.

PURPOSE: Akathisia remains a common side effect especially from antipsychotic medication. If the condition is diagnosed the management options are limited. SUBJECTS/METHODOLOGY: We tested a structured relaxation program on nine patients with a diagnosis of schizophrenia suffering from akathisia. All patients were rated on Barnes Akathisia Scale (BAS) before the relaxation program, immediately after and again one week later. RESULTS: The mean BAS score was before the relaxation 3.3 which reduced to 1.4 immediately after to finally 1.0 a week later. A Wilcoxon signed ranks test revealed a significant reduction in BAS score from baseline to endpoint (P = 0.026; Z = -2.232) and a highly significant reduction from baseline to follow-up (P = 0.008; Z = -2.636). DISCUSSION: Although the study has a number of limitations the relaxation program appears to be a promising alternative to traditional treatment of akathisia. The patients appreciated the relaxation session but none of them managed to carry it out on their own without professional encouragement. The findings in this case series warrant further investigation with larger numbers of patients.

Infantile spasms and pigmentary mosaicism.

We present a 3-year-old boy with pigmentary mosaicism and persistent intractable infantile spasms due to mosaicism of chromosome 7. Getting the diagnosis of pigmentary mosaicism in a child with infantile spasms may not be easy, as most diagnostic work-up is done in infancy, at a time when skin manifestations can be subtle. We stress the need for a meticulous search for an etiology in cases of infantile spasms. Diagnostic work-up should include a dermatologic evaluation with skin biopsies for fibroblast culture (and karyotyping) from abnormal pigmented skin areas.

Novel and potent small-molecule urotensin II receptor agonists.

A series of analogs of the non-peptidic urotensin II receptor agonist N-[1-(4-chlorophenyl)-3-(dimethylamino)propyl]-4-phenylbenzamide (FL104) has been synthesized and evaluated pharmacologically. The enantiomers of the two most potent racemic analogues were obtained from the corresponding diastereomeric mandelic amides. In agreement with previously observed SAR, most of the agonist potency resided in the (S) enantiomers. The most potent UII receptor agonist in the new series was (S)-N-[3-dimethylamino-1-(2-naphthyl)propyl]-4-(4-chlorophenyl)benzamide (EC(50)=23 nM at the urotensin II receptor).

Gilbert's syndrome in a patient with predominantly negative symptoms of schizophrenia.

Idiopathic unconjugated hyperbilirubinemia (Gilbert's syndrome) is a common genetic enzyme deficiency found in 3-10% of the general population. It occurs with greater frequency in patients with schizophrenia. We report the case of a young man with mainly negative symptoms of schizophrenia in whom there has been little improvement in mental state with prolonged treatment despite an improvement in total bilirubin, contrary to other published cases. We examine the literature related to Gilbert's syndrome and symptom severity and we discuss the research into the pathophysiology of schizophrenia in Gilbert's syndrome and negative symptom schizophrenia.

Cluster analysis of activity-time series in motor learning.

Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing.

Two octanordammarane triterpenes from Commiphora kua.

The resin of Commiphora kwo yielded two new octanordammarane triterpenes namely 15 alpha-hydroxymansumbinone and 28-acetoxy-15 alpha-hydroxymansumbinone, along with the four known compounds, mansumbinone, mansumbinol, (16S, 20R)-dihydroxydammar-24-en-3-one and T-cadinol. These structures were elucidated by spectroscopic techniques, including 1D and 2D NMR spectroscopy, and X-ray analysis.