EEG alpha activity as predictor for TBS-rTMS treatment outcome in depression.

Repetitive transcranial magnetic stimulation (rTMS) is an established psychiatric procedure for patients suffering from treatment-resistant depression (TRD). Biomarker identification to predict rTMS outcomes may assist the clinician in optimizing treatment selection. In recent years, different electrophysiological markers, in particular electroencephalographic (EEG) markers, were shown to yield discriminative power between responders and non-responders to various TRD treatments.

Identifying putative allergens from Cenostigma pluviosum pollen using proteomic bioinformatics.

Background: Routinely, signs and symptoms of pollinosis worsen during the flowering period of the Cenostigma pluviosum var. peltophoroides (Sibipiruna) trees. This study aimed to determine whether Sibipiruna pollen contains allergenic proteins.

Proof of concept: Portable ultra-low-field magnetic resonance imaging for the diagnosis of epileptogenic brain pathologies.

Objective: High-field magnetic resonance imaging (MRI) is a standard in the diagnosis of epilepsy. However, high costs and technical barriers have limited adoption in low- and middle-income countries. Even in high-income nations, many individuals with epilepsy face delays in undergoing MRI.

The boon and bane of nitrous oxide.

Nitrous oxide (N2O) has been known since the end of the eighteenth century. Today, N2O plays a huge role as a greenhouse gas and an ozone-depleting stratospheric molecule. The main sources of anthropogenic N2O emissions are agriculture, fuel combustion, wastewater treatment, and various industrial processes.

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).

Outcomes of electroconvulsive therapy in patients with depressive symptoms with versus without comorbid personality disorders/traits: A systematic review and meta-analysis.

Aims: To assess electroconvulsive therapy (ECT) outcomes in patients affected by depressive symptoms with versus without additional comorbid personality disorders/traits.

Methods: We identified observational studies investigating ECT clinical outcomes in patients affected by depressive symptoms with versus without comorbid personality disorders/traits in Embase/Medline in 11/2022. Our protocol was registered with PROSPERO (CRD42023390833).

Autism spectrum disorders in adults and the autonomic nervous system: Heart rate variability markers in the diagnostic procedure.

The diagnostic assessment of autism spectrum disorders (ASD) in adults is a challenging and time-consuming procedure. In order to address the lack of specialised health-care professionals and improve the waiting time, we aimed to identify specific electrocardiogram (ECG) derived Heart Rate Variability (HRV) parameters that could be used for diagnostic purposes. 152 patients were diagnosed based on a standardised clinical procedure and assigned to one of three groups: ASD (n = 56), any other psychiatric disorder (OD) (n = 72), and patients with no diagnosis (ND) (n = 24).

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.

Postpartum hemorrhage and postpartum depression: A systematic review and meta-analysis of observational studies.

Objective: To assess the postpartum depression (PPD) risk in women with postpartum hemorrhage (PPH) and moderators.

Methods: We identified observational studies of PPD rates in women with versus without PPH in Embase/Medline/PsychInfo/Cinhail in 09/2022. Study quality was evaluated using the Newcastle-Ottawa-Scale.

Attitudes, hesitancy, concerns, and inconsistencies regarding vaccines reported by parents of preschool children.

Objective: This study aimed to assess attitudes, concerns, information and knowledge about vaccines among parents of preschool children attending kindergartens in a city in the interior of São Paulo, Brazil, using a self-administered questionnaire.

Methods: Cross-sectional, questionnaire-based study of knowledge and attitudes regarding vaccination among parents of children aged up to 72 months from public and private schools, between 2018 and 2019.

Results: Among the 2,528 questionnaires, 1,261 were answered and grouped by respondents' educational level.

Association between the loudness dependence of auditory evoked potential, serotonergic neurotransmission and treatment outcome in patients with depression.

Previous studies have suggested that the loudness dependence of auditory evoked potential (LDAEP) is associated with the effectiveness of antidepressant treatment in patients with major depressive disorders (MDD). Furthermore, both LDAEP and the cerebral serotonin 4 receptor (5-HTR) density is inversely related to brain serotonin levels. We included 84 patients with MDD and 22 healthy controls to examined the association between LDAEP and treatment response and its association with cerebral 5-HTR density.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD.

The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database.

In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database.

Fit for Work and Life-an eight-week program for improvement of functionality and quality of life : A two-stage study.

Background: The current two-stage study focused on work integration and quality of life of patients in an acute psychiatric day care unit. There is evidence that a longer absence from work due to illness negatively affects job retention, life satisfaction and clinical prognosis. Furthermore, there are individual supportive methods that proved to be effective in work integration.

Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD).

Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry.

Stratified psychiatry: Tomorrow's precision psychiatry?

Here we review the paradigm-change from one-size-fits-all psychiatry to more personalized-psychiatry, where we distinguish between 'precision psychiatry' and 'stratified psychiatry'. Using examples in Depression and ADHD we argue that stratified psychiatry, using biomarkers to facilitate patients to best 'on-label' treatments, is a more realistic future for implementing biomarkers in clinical practice.