EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder

Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from baseline of >33% on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 h after infusion. EEG recordings were obtained pre-, start-, end- and 24 h post- infusion, and the resting EEG was automatically scored using the Vigilance Algorithm Leipzig (VIGALL). Relative to placebo (sodium chloride 0.9%), ketamine increased the amount of low-vigilance stage B1 at end-infusion. This increase in B1 was positively related to serum concentrations of ketamine, but not to norketamine, and was independent of clinical response. In contrast, treatment responders showed a distinct EEG pattern characterized by a decrease in high-vigilance stage A1 and an increase in low-vigilance B2/3, regardless of whether placebo or ketamine had been given. Furthermore, pretreatment EEG differed between responders and non-responders with responders showing a higher percentage of stage A1 (53% vs. 21%). The logistic regression fitted on the percent of A1 stages was able to predict treatment outcomes in the testing dataset with an area under the ROC curve of 0.7. Ketamine affects EEG vigilance in a distinct pattern observed only in responders. Consequently, the percentage of pretreatment stage A1 shows significant potential as a predictive biomarker of treatment response.Clinical Trials Registration: https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-000952-17/CZ Registration number: EudraCT Number: 2013-000952-17.

Controversies regarding lithium-associated weight gain: case-control study of real-world drug safety data

BACKGROUND

The impact of long-term lithium treatment on weight gain has been a controversial topic with conflicting evidence. We aim to assess reporting of weight gain associated with lithium and other mood stabilizers compared to lamotrigine which is considered free of metabolic adverse drug reactions (ADRs).

METHODS

We conducted a case/non-case pharmacovigilance study using data from the AMSP project (German: "Arzneimittelsicherheit in der Psychiatrie"; i.e., Drug Safety in Psychiatry), which collects data on ADRs from patients treated in psychiatric hospitals in Germany, Austria, and Switzerland. We performed a disproportionality analysis of reports of weight gain (> 10% of baseline body weight) calculating reporting odds ratio (ROR). We compared aripiprazole, carbamazepine, lithium, olanzapine, quetiapine, risperidone, and valproate to lamotrigine. Additional analyses related to different mood stabilizers as reference medication were performed. We also assessed sex and age distributions of weight-gain reports.

RESULTS

We identified a total of 527 cases of severe drug-induced weight gain representing 7.4% of all severe ADRs. The ROR for lithium was 2.1 (95%CI 0.9-5.1, p > 0.05), which did not reach statistical significance. Statistically significant disproportionate reporting of weight gain was reported for olanzapine (ROR: 11.5, 95%CI 4.7-28.3, p < 0.001), quetiapine (ROR: 3.4, 95%CI 1.3-8.4, p < 0.01), and valproate (ROR: 2.4, 95%CI 1.1-5.0, p = 0.03) compared to lamotrigine. Severe weight gain was more prevalent in non-elderly (< 65 years) than in elderly patients, with an ROR of 7.6 (p < 0.01) in those treated with lithium, and an ROR of 14.7 (p < 0.01) in those not treated with lithium.

CONCLUSIONS

Our findings suggest that lithium is associated with more reports of severe weight gain than lamotrigine, although this difference did not reach statistical significance. However, lithium use led to fewer reports of severe weight gain than some alternative drugs for long-term medication (olanzapine, quetiapine, and valproate), which is consistent with recent studies. Monitoring of weight gain and metabolic parameters remains essential with lithium and its alternatives.

Deep Learning in the Identification of Electroencephalogram Sources Associated with Sexual Orientation

INTRODUCTION

It is unclear if sexual orientation is a biological trait that has neurofunctional footprints. With deep learning, the power to classify biological datasets without an a priori selection of features has increased by magnitudes. The aim of this study was to correctly classify resting-state electroencephalogram (EEG) data from males with different sexual orientation using deep learning and to explore techniques to identify the learned distinguishing features.

METHODS

Three cohorts (homosexual men, heterosexual men, and a mixed sex cohort), one pretrained network on sex classification, and one newly trained network for sexual orientation classification were used to classify sex. Further, Grad-CAM methodology and source localization were used to identify the spatiotemporal patterns that were used for differentiation by the networks.

RESULTS

Using a pretrained network for classification of males and females, no differences existed between classification of homosexual and heterosexual males. The newly trained network was able, however, to correctly classify the cohorts with a total accuracy of 83%. The retrograde activation using Grad-CAM technology yielded distinctive functional EEG patterns in the Brodmann area 40 and 1 when combined with Fourier analysis and a source localization.

DISCUSSION

This study shows that electrophysiological trait markers of male sexual orientation can be identified using deep learning. These patterns are different from the differentiating signatures of males and females in a resting-state EEG.

Dear Doctor Letters regarding citalopram and escitalopram: guidelines vs real-world data

Dear Doctor Letters (DDLs, Direct Healthcare Professional Communications) from 2011 provided guidance regarding QTc-prolonging effects with risk of torsade de pointes during treatment with citalopram and escitalopram. This study examines the DDLs' effects on prescription behavior. Data from 8842 inpatients treated with citalopram or escitalopram with a primary diagnosis of major depressive disorder (MDD) were derived from a European pharmacovigilance study (Arzneimittelsicherheit in der Psychiatrie, AMSP) from 2001 to 2017. It was examined to what extent new maximum doses were adhered to and newly contraindicated combinations with QTc-prolonging drugs were avoided. In addition, the prescriptions of psychotropic drugs before and after DDLs were compared in all 43,480 inpatients with MDD in the data set. The proportion of patients dosed above the new limit decreased from 8 to 1% in patients ≤ 65 years and from 46 to 23% in patients > 65 years old for citalopram versus 14-5% and 47-31% for escitalopram. Combinations of es-/citalopram with other QTc-prolonging psychotropic drugs reduced only insignificantly (from 35.9 to 30.9%). However, the proportion of patients with doses of quetiapine > 150 mg/day substantially decreased within the combinations of quetiapine and es-/citalopram (from 53 to 35%). After the DDLs, prescription of citalopram decreased and of sertraline increased. The DDLs' recommendations were not entirely adhered to, particularly in the elderly and concerning combination treatments. This might partly be due to therapeutic requirements of the included population. Official warnings should consider clinical needs.

Treatment of depression: Are psychotropic drugs appropriately dosed in women and in the elderly? Dosages of psychotropic drugs by sex and age in routine clinical practice

BACKGROUND

Several researchers have shown higher concentration-dose ratios of psychotropic drugs in women and the elderly. Therefore, lower dosages of psychotropic drugs may be recommended in women and the elderly. This study describes sex- and age-related dosage of psychotropic drugs prescribed to patients with major depressive disorder (MDD) in routine clinical practice.

METHOD

Influence of sex and age on dosages are analysed for the 10 most commonly prescribed drugs in our dataset consisting of 32,082 inpatients with MDD. Data stems from the European drug safety program "Arzneimittelsicherheit in der Psychiatrie". The observed sex and age differences in prescriptions are compared to differences described in literature on age- and gender-related pharmacokinetics.

RESULTS

Among patients over 65 years, a statistically significant decrease in dosages with increasing age (between 0.65% and 2.83% for each increasing year of age) was observed, except for zopiclone. However, only slight or no influence of sex-related adjustment of dosage in prescriptions was found.

CONCLUSION

Age appears to influence adjustment of dosage in most psychotropic drugs, but to a lower extent than data on age-related pharmacokinetics suggests. Although literature also suggests that lower dosages of psychotropic drugs may be appropriate for females, this study found women are usually prescribed the same dosage as men.

Deep learning applied to electroencephalogram data in mental disorders: A systematic review

In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictive purposes in conducting research on mental disorders. EEG-studies on psychiatric diseases based on the ICD-10 or DSM-V classification that used either convolutional neural networks (CNNs) or long -short-term-memory (LSTMs) networks for classification were searched and examined for the quality of the information they contained in three domains: clinical, EEG-data processing, and deep learning. Although we found that the description of EEG acquisition and pre-processing was sufficient in most of the studies, we found, that many of them lacked a systematic characterization of clinical features. Furthermore, many studies used misguided model selection procedures or flawed testing. It is recommended that the study of psychiatric disorders using DL in the future must improve the quality of clinical data and follow state of the art model selection and testing procedures so as to achieve a higher research standard and head toward a clinical significance.