Translational psychiatry-light at the end of the tunnel

Click train elicited local gamma synchrony: differing performance and pharmacological responsivity of primary auditory and prefrontal cortices

Auditory neural networks in the brain naturally entrain to rhythmic stimuli. Such synchronization is an accessible index of local network performance as captured by EEG. Across species, click trains delivered ∼ 40 Hz show strong entrainment with primary auditory cortex (Actx) being a principal source. Imaging studies have revealed additional cortical sources, but it is unclear if they are functionally distinct. Since auditory processing evolves hierarchically, we hypothesized that local synchrony would differ between between primary and association cortices. In female SD rats (N = 12), we recorded 40 Hz click train-elicited gamma oscillations using epidural electrodes situated at two distinct sites; one above the prefrontal cortex (PFC) and another above the Actx, after dosing with saline (1 ml/kg, sc) or the NMDA antagonist, MK801 (0.025, 0.05 or 0.1 mpk), in a blocked crossover design. Post-saline, both regions showed a strong 40 Hz auditory steady state response (ASSR). The latencies for the N1 response were ∼ 16 ms (Actx) and ∼ 34 ms (PFC). Narrow band (38-42 Hz) gamma oscillations appeared rapidly (<40 ms from stim onset at Actx but in a more delayed fashion (∼200 ms) at PFC. MK801 augmented gamma synchrony at Actx while dose-dependently disrupting at the PFC. Event-related gamma (but not beta) coherence, an index of long-distance connectivity, was disrupted by MK801. In conclusion, local network gamma synchrony in a higher order association cortex performs differently from that of the primary auditory cortex. We discuss these findings in the context of evolving sound processing across the cortical hierarchy.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Amygdala response predicts clinical symptom reduction in patients with borderline personality disorder: A pilot fMRI study

Borderline personality disorder (BPD) is a prevalent, devastating, and heterogeneous psychiatric disorder. Treatment success is highly variable within this patient group. A cognitive neuroscientific approach to BPD might contribute to precision psychiatry by identifying neurocognitive factors that predict who will benefit from a specific treatment. Here, we build on observations that BPD is accompanied by the enhanced impact of the aversive effect on behavior and abnormal neural signaling in the amygdala. We assessed whether BPD is accompanied by abnormal aversive regulation of instrumental behavior and associated neural signaling, in a manner that is predictive of symptom reduction after therapy. We tested a clinical sample of 15 female patients with BPD, awaiting dialectical behavior therapy (DBT), and 16 matched healthy controls using fMRI and an aversive Pavlovian-to-instrumental transfer (PIT) task that assesses how instrumental behaviors are influenced by aversive Pavlovian stimuli. Patients were assessed 1 year after the start of DBT to quantify changes in BPD symptom severity. At baseline, behavioral aversive PIT and associated neural signaling did not differ between groups. However, the BOLD signal in the amygdala measured during aversive PIT was associated with symptom reduction at 1-year follow-up: higher PIT-related aversive amygdala signaling before treatment was associated with reduced clinical improvement at follow-up. Thus, within the evaluated group of BPD patients, the BOLD signal in the amygdala before treatment was related to clinical symptom reduction 1 year after the start of treatment. The results suggest that less PIT-related responsiveness of the amygdala increases the chances of treatment success. We note that the relatively small sample size is a limitation of this study and that replication is warranted.

Electroencephalographic Parameters Differentiating Melancholic Depression, Non-melancholic Depression, and Healthy Controls. A Systematic Review

Introduction: The objective of this systematic review was to investigate whether electroencephalographic parameters can serve as a tool to distinguish between melancholic depression, non-melancholic depression, and healthy controls in adults. Methods: A systematic review comprising an extensive literature search conducted in PubMed, Embase, Google Scholar, and PsycINFO in August 2020 with monthly updates until November 1st, 2020. In addition, we performed a citation search and scanned reference lists. Clinical trials that performed an EEG-based examination on an adult patient group diagnosed with melancholic unipolar depression and compared with a control group of non-melancholic unipolar depression and/or healthy controls were eligible. Risk of bias was assessed by the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results: A total of 24 studies, all case-control design, met the inclusion criteria and could be divided into three subgroups: Resting state studies (n = 5), sleep EEG studies (n = 10), and event-related potentials (ERP) studies (n = 9). Within each subgroup, studies were characterized by marked variability on almost all levels, preventing pooling of data, and many studies were subject to weighty methodological problems. However, the main part of the studies identified one or several EEG parameters that differentiated the groups. Conclusions: Multiple EEG modalities showed an ability to distinguish melancholic patients from non-melancholic patients and/or healthy controls. The considerable heterogeneity across studies and the frequent methodological difficulties at the individual study level were the main limitations to this work. Also, the underlying premise of shifting diagnostic paradigms may have resulted in an inhomogeneous patient population. Systematic Review Registration: Registered in the PROSPERO registry on August 8th, 2020, registration number CRD42020197472.

Differential effects of traxoprodil and S-ketamine on quantitative EEG and auditory event-related potentials as translational biomarkers in preclinical trials in rats and mice

Quantitative Electroencephalography (qEEG) and event-related potential (ERP) assessment have emerged as powerful tools to unravel translational biomarkers in preclinical and clinical psychiatric drug discovery trials. The aim of the present study was to compare the GluN2B negative allosteric modulator (NAM) traxoprodil (CP-101,606) with the unselective NMDA receptor channel blocker S-ketamine to give insight into central target engagement and differentiation on multiple EEG readouts. For qEEG recordings telemetric transmitters were implanted in male Wistar rats. Recorded EEG data were analyzed using fast Fourier transformation to determine power spectra and vigilance states. Additionally, body temperature and locomotor activity were assessed via telemetry. For recordings of auditory event-related potentials (AERP) male C57Bl/6J mice were chronically implanted with deep electrodes using a tethered system. Power spectral analysis revealed a significant increase in gamma power following ketamine treatment, whereas traxoprodil (6&18 mg/kg) induced an overall decrease primarily within alpha and beta bands. Additionally, ketamine disrupted sleep and enhanced time spent in wake vigilance states, whereas traxoprodil did not alter sleep-wake architecture. AERP and mismatch negativity (MMN) revealed that ketamine (10 mg/kg) selectively disrupts auditory deviance detection, whereas traxoprodil (6 mg/kg) did not alter MMN at clinically relevant doses. In contrast to ketamine treatment, traxoprodil did not produce hyperactivity and hypothermia. In conclusion, ketamine and traxoprodil showed very different effects on diverse EEG readouts differentiating selective GluN2B antagonism from non-selective pan-NMDA-R antagonists like ketamine. These readouts are thus perfectly suited to support drug discovery efforts on NMDA-R and understanding the different functions of NMDA-R subtypes.

Pharmacotherapy of schizophrenia: toward a metabolomic-based approach

Approximately 20%-30% of schizophrenia patients are resistant to current standard pharmacotherapies. Recent schizophrenia research aims to identify specific pathophysiological abnormalities and novel targets in the disease, with the goals of identifying at-risk individuals, facilitating diagnosis, prompting early and personalized interventions, and helping predict response to treatment. Metabolomics involves the systematic study of the profile of biochemical alterations early in the course of a given disorder. Major aspects of the schizophrenia metabolome have been characterized, uncovering potential selective biomarkers for the disease that may change how the disorder is diagnosed, and how patients are stratified and treated. This review focuses on the most common metabolomic fingerprints of the different pathways involved in the pathophysiology of schizophrenia, and the potential development of novel metabolomic-related pharmacotherapies for improved treatment of schizophrenia and other related idiopathic psychotic disorders.

NMDA Receptors in the Central Nervous System

NMDA-type glutamate receptors are ligand-gated ion channels that mediate a major component of excitatory neurotransmission in the central nervous system (CNS). They are widely distributed at all stages of development and are critically involved in normal brain functions, including neuronal development and synaptic plasticity. NMDA receptors are also implicated in the pathophysiology of numerous neurological and psychiatric disorders, such as ischemic stroke, traumatic brain injury, Alzheimer's disease, epilepsy, mood disorders, and schizophrenia. For these reasons, NMDA receptors have been intensively studied in the past several decades to elucidate their physiological roles and to advance them as therapeutic targets. Seven NMDA receptor subunits exist that assemble into a diverse array of tetrameric receptor complexes, which are differently regulated, have distinct regional and developmental expression, and possess a wide range of functional and pharmacological properties. The diversity in subunit composition creates NMDA receptor subtypes with distinct physiological roles across neuronal cell types and brain regions, and enables precise tuning of synaptic transmission. Here, we will review the relationship between NMDA receptor structure and function, the diversity and significance of NMDA receptor subtypes in the CNS, as well as principles and rules by which NMDA receptors operate in the CNS under normal and pathological conditions.

Decoding Advances in Psychiatric Genetics: A Focus on Neural Circuits in Rodent Models

Appropriately powered genome-wide association studies combined with deep-sequencing technologies offer the prospect of real progress in revealing the complex biological underpinnings of schizophrenia and other psychiatric disorders. Meanwhile, recent developments in genome engineering, including CRISPR, constitute better tools to move forward with investigating these genetic leads. This review aims to assess how these advances can inform the development of animal models for psychiatric disease, with a focus on schizophrenia and in vivo electrophysiological circuit-level measures with high potential as disease biomarkers.