Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo.

Objective: Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroencephalography (EEG)-based method to predict response to antidepressant treatment.

Methods: Pre-treatment EEG data were collected from study participants treated with either sertraline (N = 105), placebo (N = 119), or bupropion (N = 35).

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers.

Background: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment.

Competencies for Repetitive Transcranial Magnetic Stimulation in Postgraduate Medical Education: Expert Consensus Using a Modified Delphi Process.

Background: Repetitive transcranial magnetic stimulation (rTMS) is recommended in Canadian guidelines as a first-line treatment for major depressive disorder. With the shift towards competency-based medical education, it remains unclear how to determine when a resident is considered competent in applying knowledge of rTMS to patient care. Given inconsistencies between postgraduate training programmes with regards to training requirements, defining competencies will improve the standard of care in rTMS delivery.

EEG Biomarkers to Predict Response to Sertraline and Placebo Treatment in Major Depressive Disorder.

Objective: Major depressive disorder (MDD) is a persistent psychiatric condition, and the leading cause of disability, affecting up to 5% of the population worldwide. Antidepressant medications (ADMs) are often the first-line treatment for MDD, but it may take the clinician months of "trial and error" to find an effective ADM for a particular patient. Therefore, identification of predictive biomarkers that can be used to accurately determine the effectiveness of a specific treatment for an individual patient is extremely valuable.

Naturalistic Study on the Effects of Electroconvulsive Therapy (ECT) on Depressive Symptoms.

Objective: The effectiveness of ECT under naturalistic conditions has not been well-studied. The current study aimed to 1) characterize a naturalistic sample of ECT patients; and 2) examine the long-term outcomes of ECT on depressive symptoms (Beck Depression Inventory-II; BDI-II) and functional disability symptoms (WHO Disability Assessment Schedule 2.0) in this sample.

Wavelet-Based Muscle Artefact Noise Reduction for Short Latency rTMS Evoked Potentials.

This paper presents a new method of reducing the noise in the EEG response signal recorded during repetitive transcranial magnetic stimulation (rTMS). This noise is principally composed of the residual stimulus artefact and millivolt amplitude compound muscle action potentials (CMAP) recorded from the scalp muscles and precludes analysis of the cortical evoked potentials, especially during the first 20-ms post stimulus. The proposed method uses the wavelet transform with a fourth-order Daubechies mother wavelet and a novel coefficient reduction algorithm based on cortical amplitude thresholds.

Effects of electroconvulsive therapy on cognitive functioning in patients with depression: protocol for a systematic review and meta-analysis.

Introduction: Depression is the leading cause of disability worldwide, affecting approximately 350 million people. Evidence indicates that only 60-70% of persons with major depressive disorder who tolerate antidepressants respond to first-line drug treatment; the remainder become treatment resistant. Electroconvulsive therapy (ECT) is considered an effective therapy in persons with treatment-resistant depression.

A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy.

Objective: To develop a machine learning (ML) methodology based on features extracted from odd-ball auditory evoked potentials to identify neurophysiologic changes induced by Clozapine (CLZ) treatment in responding schizophrenic (SCZ) subjects. This objective is of particular interest because CLZ, though a potentially dangerous drug, can be uniquely effective for otherwise medication-resistant SCZ subjects. We wish to determine whether ML methods can be used to identify a set of EEG-based discriminating features that can simultaneously (1) distinguish all the SCZ subjects before treatment (BT) from healthy volunteer (HV) subjects, (2) distinguish EEGs collected before CLZ treatment (BT) vs.

Minimum variance brain source localization for short data sequences.

In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second-order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. Due to the correlated background EEG/MEG signal, an adaptive approach to localization is desirable.

A review of recent literature employing electroencephalographic techniques to study the pathophysiology, phenomenology, and treatment response of schizophrenia.

Clinical experience and research findings suggest that schizophrenia is a disorder comprised of multiple genetic and neurophysiological subtypes with differential response to treatment. Electroencephalography (EEG) is a non-invasive, inexpensive and useful tool for investigating the neurobiology of schizophrenia and its subtypes. EEG studies elucidate the neurophysiological mechanisms potentially underlying clinical symptomatology.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

Objective: The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).

Methods: A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection.

A machine learning approach using P300 responses to investigate effect of clozapine therapy.

Clozapine (CLZ) is uniquely effective as a treatment for medication resistant schizophrenia. Information regarding its mechanism of action may offer clues to the pathophysiology of the disease and to improved treatment. In this study we employ a machine learning (ML) analysis of P300 evoked potentials obtained from quantitative electroencephalography (QEEG) data to identify changes in the brain induced by CLZ treatment.

Application of wavelet based denoising techniques to rTMS evoked potentials.

This paper presents a new method of removing noise from the EEG response signal recorded during repetitive transcranial magnetic stimulation (rTMS). This noise is principally composed of the residual stimulus artifact and mV amplitude compound muscle action potentials recorded from the scalp muscles and precludes analysis of the cortical evoked potentials, especially during the first 15 ms post stimulus. The method uses the wavelet transform with a fourth order Daubechies mother wavelet and a novel coefficient reduction algorithm based on cortical amplitude thresholds.

Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression.

We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes.

Dorsolateral prefrontal cortex sensitivity to rTMS.

This paper presents the preliminary results of a study to determine dorsolateral prefrontal cortex sensitivity to rTMS stimulation presented at clinically accepted amplitudes, frequencies and locations. A specially developed EEG system with 10-20 electrode locations was used to record the short latency magnetically evoked potentials. Sixteen normal subjects were stimulated using 10 Hz for the left hemisphere and 1 Hz for the right.

Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model.

An automated diagnosis procedure based on a statistical machine learning methodology using electroencephalograph (EEG) data is proposed for diagnosis of psychiatric illness. First, a large collection of candidate features, mostly consisting of various statistical quantities, are calculated from the subject's EEG. This large set of candidate features is then reduced into a much smaller set of most relevant features using a feature selection procedure.

Using pre-treatment EEG data to predict response to SSRI treatment for MDD.

The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure.

A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy.

Objective: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia.

Methods: Pre-treatment EEG data are collected in 23+14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results.

Repetitive transcranial magnetic stimulation for treatment of medication-resistant depression in older adults: a case series.

Unlabelled: The antidepressant effects of repetitive transcranial magnetic stimulation (rTMS) are well documented, but studies to date have produced heterogeneous results in late-life depression.

Objective: To address this matter, we evaluated the efficacy of both high- and low-frequency rTMS delivered to the prefrontal cortex of older adults with treatment-resistant major depression.

Methods: Forty-nine older adults (69 +/- 6.

The long-term impact of treatment with electroconvulsive therapy on discrete memory systems in patients with bipolar disorder.

Objective: Electroconvulsive therapy (ECT) has been controversially associated with long-lasting memory problems. Verbal learning and memory deficits are commonly reported in studies of people with bipolar disorder (BD). Whether memory deficits can be exacerbated in patients with BD who receive ECT has, to our knowledge, not been systematically examined.

Predictors of antidepressant response in clinical trials of transcranial magnetic stimulation.

Although previous clinical trials have suggested that repetitive transcranial magnetic stimulation (rTMS) has a significant antidepressant effect, the results of these trials are heterogeneous. We hypothesized that individual patients' characteristics might contribute to such heterogeneity. Our aim was to identify predictors of antidepressant response to rTMS.

The effects of pulse configuration on magnetic stimulation.

A study is presented in which the authors have examined the effects of pulse configuration, stimulation intensity, and coil current direction during magnetic stimulation. Using figure-8 and circular coils, the median nerve was stimulated at the cubital fossa and at the wrist of 10 healthy volunteers, and the response amplitude and site of stimulation were determined. The key findings of this study are in agreement with other researchers' findings and confirm that biphasic stimulating pulses produce significantly higher M-wave amplitudes than monophasic stimulating pulses for the same stimulus intensity.