NeuroPharm study: EEG wakefulness regulation as a biomarker in MDD.

While several electroencephalogram (EEG)-based biomarkers have been proposed as diagnostic or predictive tools in major depressive disorder (MDD), there is a clear lack of replication studies in this field. Markers that link clinical features such as disturbed wakefulness regulation in MDD with neurophysiological patterns are particularly promising candidates for e.g., EEG-informed choices of antidepressive treatment. We investigate if we in an independent MDD sample can replicate abnormal findings of EEG-vigilance regulation during rest and as a predictor for antidepressive treatment response. EEG-resting state was recorded in 91 patients and 35 healthy controls from the NeuroPharm trial. EEG-vigilance was assessed using the Vigilance Algorithm Leipzig (VIGALL). We compared the vigilance regulation during rest between patients and healthy controls and between remitters/responders and non-remitters/non-responders after eight weeks of SSRI/SNRI treatment using two different sets of response criteria (NeuroPharm and iSPOT-D). We replicated previous findings showing hyperstable EEG-wakefulness regulation in patients in comparison to healthy subjects. Responders defined by the iSPOT-D criteria showed a higher propensity toward low vigilance stages in comparison to patients with no response at pretreatment, however, this did not apply when using the NeuroPharm criteria. EEG-wakefulness regulation patterns normalized toward patterns of healthy controls after 8 weeks of treatment. This replication study supports the diagnostic value of EEG-vigilance regulation and its usefulness as a biomarker for the choice of treatment in MDD.

Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study.

Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response. Registration number: NCT02869035.

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.

Pre-intervention test-retest reliability of EEG and ERP over four recording intervals.

In this study we present the test-retest reliability of pre-intervention EEG/ERP (electroencephalogram/event-related potentials) data across four recording intervals separated by a washout period (18-22 days). POz-recording-reference EEG/ERP (28 sites, average reference) were recorded from thirty-two healthy male participants. Participants were randomly allocated into different intervention sequences, each with four intervention regimens: 10 mg vortioxetine, 20 mg vortioxetine, 15 mg escitalopram and Placebo. We report classical EEG spectra: δ (1-4 Hz), θ (4-8 Hz), α (8-12 Hz), ß (12-30 Hz), γ1 (30-45 Hz) and γ2 (45-80 Hz) of resting state and vigilance-controlled, and of auditory steady state response, as well as ERP components N100, P200 and P300 in auditory oddball task and error related negativity (ERN) and error positivity (Pe) in hybrid flanker task. Reliability was quantified using intra-class correlation coefficient (ICC). We found that θ, α and ß of continuous EEG were highly reliable (ICCs ≥ 0.84). Evoked power of other tasks demonstrated larger variability and less reliability compared to the absolute power of continuous EEG. Furthermore, reliabilities of ERP measures were lower compared to those of the EEG spectra. We saw fair to excellent reliability of the amplitude of the components such as Pe (0.60-0.82) and P300 (0.55-0.80). Moreover, blood tests confirmed that there was no measurable drug carry-over from the previous intervention. The results support that EEG/ERP is reliable across four recording intervals, thus it can be used to assess the effect of different doses and types of drugs with CNS effects.

Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.

BACKGROUND: Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases. NEW METHOD: This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinson's disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model. RESULTS: The features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients. COMPARISON WITH EXISTING METHOD: The topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration. CONCLUSIONS: This study suggests that the amount of N3 and the ability to maintain NREM and REM sleep have potential as early PD biomarkers. Data-driven analysis of sleep may contribute to the evaluation of neurodegenerative patients.

Automatic sleep classification using a data-driven topic model reveals latent sleep states.

BACKGROUND: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. NEW METHOD: To meet the criticism and reveal the latent sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group. The model was optimized using 50 subjects and validated on 76 subjects. RESULTS: The optimized sleep model used six topics, and the topic probabilities changed smoothly during transitions. According to the manual scorings, the model scored an overall subject-specific accuracy of 68.3 ± 7.44 (% µ ± σ) and group specific accuracies of 69.0 ± 4.62 (control), 70.1 ± 5.10 (PLM), 67.2 ± 8.30 (iRBD) and 67.7 ± 9.07 (PD). COMPARISON WITH EXISTING METHOD: Statistics of the latent sleep state content showed accordances to the sleep stages defined in the golden standard. However, this study indicates that sleep contains six diverse latent sleep states and that state transitions are continuous processes. CONCLUSIONS: The model is generally applicable and may contribute to the research in neurodegenerative diseases and sleep disorders.

Can pharmaco-electroencephalography help improve survival of central nervous system drugs in early clinical development?

Pharmaco-electroencephalography has significant yet unrealised promise as a translatable intermediate biomarker of central pharmacodynamic activity that could help reduce Phase 2 attrition in the development of central nervous system drugs. In an effort to understand its true potential, a framework for decision-making was proposed and the utility of pharmaco-electroencephalography was assessed through several case studies. A key finding was that lack of standardisation reduces the value of data pooling and meta-analyses and renders assessment of translatability difficult, limiting utility in all but simple cases. Pre-competitive collaboration is essential both to improving understanding of translation and developing modern signal processing techniques.

Decreased sleep spindle density in patients with idiopathic REM sleep behavior disorder and patients with Parkinson's disease.

OBJECTIVE: To determine whether sleep spindles (SS) are potentially a biomarker for Parkinson's disease (PD). METHODS: Fifteen PD patients with REM sleep behavior disorder (PD+RBD), 15 PD patients without RBD (PD-RBD), 15 idiopathic RBD (iRBD) patients and 15 age-matched controls underwent polysomnography (PSG). SS were scored in an extract of data from control subjects. An automatic SS detector using a Matching Pursuit (MP) algorithm and a Support Vector Machine (SVM) was developed and applied to the PSG recordings. The SS densities in N1, N2, N3, all NREM combined and REM sleep were obtained and evaluated across the groups. RESULTS: The SS detector achieved a sensitivity of 84.7% and a specificity of 84.5%. At a significance level of α=1%, the iRBD and PD+RBD patients had a significantly lower SS density than the control group in N2, N3 and all NREM stages combined. At a significance level of α=5%, PD-RBD had a significantly lower SS density in N2 and all NREM stages combined. CONCLUSIONS: The lower SS density suggests involvement in pre-thalamic fibers involved in SS generation. SS density is a potential early PD biomarker. SIGNIFICANCE: It is likely that an automatic SS detector could be a supportive diagnostic tool in the evaluation of iRBD and PD patients.

Classification of iRBD and Parkinson's patients using a general data-driven sleep staging model built on EEG.

Sleep analysis is an important diagnostic tool for sleep disorders. However, the current manual sleep scoring is time-consuming as it is a crude discretization in time and stages. This study changes Esbroeck and Westover's [1] latent sleep staging model into a global model. The proposed data-driven method trained a topic mixture model on 10 control subjects and was applied on 10 other control subjects, 10 iRBD patients and 10 Parkinson's patients. In that way 30 topic mixture diagrams were obtained from which features reflecting distinct sleep architectures between control subjects and patients were extracted. Two features calculated on basis of two latent sleep states classified subjects as "control" or "patient" by a simple clustering algorithm. The mean sleep staging accuracy compared to classical AASM scoring was 72.4% for control subjects and a clustering of the derived features resulted in a sensitivity of 95% and a specificity of 80 %. This study demonstrates that frequency analysis of sleep EEG can be used for data-driven global sleep classification and that topic features separates iRBD and Parkinson's patients from control subjects.

Classification of iRBD and Parkinson's disease patients based on eye movements during sleep.

Patients suffering from the sleep disorder idiopathic rapid-eye-movement sleep behavior disorder (iRBD) have been observed to be in high risk of developing Parkinson's disease (PD). This makes it essential to analyze them in the search for PD biomarkers. This study aims at classifying patients suffering from iRBD or PD based on features reflecting eye movements (EMs) during sleep. A Latent Dirichlet Allocation (LDA) topic model was developed based on features extracted from two electrooculographic (EOG) signals measured as parts in full night polysomnographic (PSG) recordings from ten control subjects. The trained model was tested on ten other control subjects, ten iRBD patients and ten PD patients, obtaining a EM topic mixture diagram for each subject in the test dataset. Three features were extracted from the topic mixture diagrams, reflecting "certainty", "fragmentation" and "stability" in the timely distribution of the EM topics. Using a Naive Bayes (NB) classifier and the features "certainty" and "stability" yielded the best classification result and the subjects were classified with a sensitivity of 95 %, a specificity of 80% and an accuracy of 90 %. This study demonstrates in a data-driven approach, that iRBD and PD patients may exhibit abnorm form and/or timely distribution of EMs during sleep.

Separation of Parkinson's patients in early and mature stages from control subjects using one EOG channel.

In this study, polysomnographic left side EOG signals from ten control subjects, ten iRBD patients and ten Parkinson's patients were decomposed in time and frequency using wavelet transformation. A total of 28 features were computed as the means and standard deviations in energy measures from different reconstructed detail subbands across all sleep epochs during a whole night of sleep. A subset of features was chosen based on a cross validated Shrunken Centroids Regularized Discriminant Analysis, where the controls were treated as one group and the patients as another. Classification of the subjects was done by a leave-one-out validation approach using same method, and reached a sensitivity of 95%, a specificity of 70% and an accuracy of 86.7%. It was found that in the optimal subset of features, two hold lower frequencies reflecting the rapid eye movements and two hold higher frequencies reflecting EMG activity. This study demonstrates that both analysis of eye movements during sleep as well as EMG activity measured at the EOG channel hold potential of being biomarkers for Parkinson's disease.

Broadband criticality of human brain network synchronization.

Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal "avalanches" previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05-0.11 to 62.5-125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.

Local and global effects of I(h) distribution in dendrites of mammalian neurons.

The hyperpolarization-activated cation current I(h) exhibits a steep gradient of channel density in dendrites of pyramidal neurons, which is associated with location independence of temporal summation of EPSPs at the soma. In striking contrast, here we show by using dendritic patch-clamp recordings that in cerebellar Purkinje cells, the principal neurons of the cerebellar cortex, I(h) exhibits a uniform dendritic density, while location independence of EPSP summation is observed. Using compartmental modeling in realistic and simplified dendritic geometries, we demonstrate that the dendritic distribution of I(h) only weakly affects the degree of temporal summation at the soma, while having an impact at the dendritic input location. We further analyze the effect of I(h) on temporal summation using cable theory and derive bounds for temporal summation for any spatial distribution of I(h). We show that the total number of I(h) channels, not their distribution, governs the degree of temporal summation of EPSPs. Our findings explain the effect of I(h) on EPSP shape and temporal summation, and suggest that neurons are provided with two independent degrees of freedom for different functions: the total amount of I(h) (controlling the degree of temporal summation of dendritic inputs at the soma) and the dendritic spatial distribution of I(h) (regulating local dendritic processing).

Membrane potential bistability is controlled by the hyperpolarization-activated current I(H) in rat cerebellar Purkinje neurons in vitro.

We investigated the role of the hyperpolarization-activated mixed cation current, I(H), in the control of spontaneous action potential firing of rat cerebellar Purkinje neurons in brain slices. Extracellular recordings revealed that the continual action potential firing of Purkinje neurons was disrupted by the pharmacological blockade of I(H). Blockade of I(H) revealed spontaneous transitions between periods of tonic action potential firing and quiescence, without effects on the frequency or variance of action potential generation. Whole-cell recordings revealed that blockade of I(H) unmasked a form of membrane potential bistability, where transitions between tonic firing and quiescent states (separated by approximately 20 mV) were evoked by excitatory and inhibitory postsynaptic potentials, or by the delivery of brief (20 ms) somatic or dendritic positive and negative current pulses. The stable upper state of tonic action potential firing was maintained by the recruitment of axo-somatic voltage-activated sodium, but not calcium, channels. Negative modulation of I(H) by serotonin unmasked bistability, indicating that bistability of Purkinje neurons is likely to occur under physiological conditions. These data indicate that I(H) acts as a 'safety net', maintaining the membrane potential of Purkinje neurons within the range necessary for the generation of tonic action potential firing. Following the downregulation of I(H), synaptic inhibition can generate long periods (seconds) of quiescence, the duration of which can be controlled by climbing fibre activation and by the underlying 'tone' of parallel fibre activity.