Universal method for the isolation of microvessels from frozen brain tissue: A proof-of-concept multiomic investigation of the neurovasculature.

The neurovascular unit, comprised of vascular cell types that collectively regulate cerebral blood flow to meet the needs of coupled neurons, is paramount for the proper function of the central nervous system. The neurovascular unit gatekeeps blood-brain barrier properties, which experiences impairment in several central nervous system diseases associated with neuroinflammation and contributes to pathogenesis. To better understand function and dysfunction at the neurovascular unit and how it may confer inflammatory processes within the brain, isolation and characterization of the neurovascular unit is needed. Here, we describe a singular, standardized protocol to enrich and isolate microvessels from archived snap-frozen human and frozen mouse cerebral cortex using mechanical homogenization and centrifugation-separation that preserves the structural integrity and multicellular composition of microvessel fragments. For the first time, microvessels are isolated from postmortem ventromedial prefrontal cortex tissue and are comprehensively investigated as a structural unit using both RNA sequencing and Liquid Chromatography with tandem mass spectrometry (LC-MS/MS). Both the transcriptome and proteome are obtained and compared, demonstrating that the isolated brain microvessel is a robust model for the NVU and can be used to generate highly informative datasets in both physiological and disease contexts.

The emerging tale of microglia in psychiatric disorders.

As the professional phagocytes of the brain, microglia orchestrate the immunological response and play an increasingly important role in maintaining homeostatic brain functions. Microglia are activated by pathological events or slight alterations in brain homeostasis. This activation is dependent on the context and type of stressor or pathology. Through secretion of cytokines, chemokines and growth factors, microglia can strongly influence the response to a stressor and can, therefore, determine the pathological outcome. Psychopathologies have repeatedly been associated with long-lasting priming and sensitization of cerebral microglia. This review focuses on the diversity of microglial phenotype and function in health and psychiatric disease. We first discuss the diverse homeostatic functions performed by microglia and then elaborate on context-specific spatial and temporal microglial heterogeneity. Subsequently, we summarize microglia involvement in psychopathologies, namely major depressive disorder, schizophrenia and bipolar disorder, with a particular focus on post-mortem studies. Finally, we postulate microglia as a promising novel therapeutic target in psychiatry through antidepressant and antipsychotic treatment.

Neuron-derived extracellular vesicles enriched from plasma show altered size and miRNA cargo as a function of antidepressant drug response.

Previous work has demonstrated that microRNAs (miRNAs) change as a function of antidepressant treatment (ADT) response. However, it is unclear how representative these peripherally detected miRNA changes are to those occurring in the brain. This study aimed to use peripherally extracted neuron-derived extracellular vesicles (NDEV) to circumvent these limitations and investigate neuronal miRNA changes associated with antidepressant response. Samples were collected at two time points (baseline and after 8 weeks of follow-up) from depressed patients who responded (N = 20) and did not respond (N = 20) to escitalopram treatment, as well as controls (N = 20). Total extracellular vesicles (EVs) were extracted from plasma, and then further enriched for NDEV by immunoprecipitation with L1CAM. EVs and NDEVs were characterized, and NDEV miRNA cargo was extracted and sequenced. Subsequently, studies in cell lines and postmortem tissue were conducted. Characterization of NDEVs revealed that they were smaller than other EVs isolated from plasma (p < 0.0001), had brain-specific neuronal markers, and contained miRNAs enriched for brain functions (p < 0.0001) Furthermore, NDEVs from depressed patients were smaller than controls (p < 0.05), and NDEV size increased with ADT response (p < 0.01). Finally, changes in NDEV cargo, specifically changes in miR-21-5p, miR-30d-5p, and miR-486-5p together (p < 0.01), were associated with ADT response. Targets of these three miRNAs were altered in brain tissue from depressed individuals (p < 0.05). Together, this study indicates that changes in peripherally isolated NDEV can act as both a clinically accessible and informative biomarker of ADT response specifically through size and cargo.

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction.

BACKGROUND: Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. AIMS: Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction. METHOD: Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. RESULTS: All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction. CONCLUSIONS: The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression.

Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, we describe methods for analyzing deep learning models of clinical and demographic psychiatric data, using our recent work on a deep learning model of STAR*D and CO-MED remission prediction. Methods: Our deep learning analysis with STAR*D and CO-MED yielded four models that predicted response to the four treatments used across the two datasets. Here, we use classical statistics and simple data representations to improve interpretability of the features output by our deep learning model and provide finer grained understanding of their clinical and etiological significance. Specifically, we use representations derived from our model to yield features predicting both treatment non-response and differential treatment response to four standard antidepressants, and use linear regression and t-tests to address questions about the contribution of trauma, education, and somatic symptoms to our models. Results: Traditional statistics were able to probe the input features of our deep learning models, reproducing results from previous research, while providing novel insights into depression causes and treatments. We found that specific features were predictive of treatment response, and were able to break these down by treatment and non-response categories; that specific trauma indices were differentially predictive of baseline depression severity; that somatic symptoms were significantly different between males and females, and that education and low income proved important psycho-social stressors associated with depression. Conclusion: Traditional statistics can augment interpretation of deep learning models. Such interpretation can lend us new hypotheses about depression and contribute to building causal models of etiology and prognosis. We discuss dataset-specific effects and ideal clinical samples for machine learning analysis aimed at improving tools to assist in optimizing treatment.

Association of a History of Child Abuse With Impaired Myelination in the Anterior Cingulate Cortex: Convergent Epigenetic, Transcriptional, and Morphological Evidence.

OBJECTIVE: Child abuse has devastating and long-lasting consequences, considerably increasing the lifetime risk of negative mental health outcomes such as depression and suicide. Yet the neurobiological processes underlying this heightened vulnerability remain poorly understood. The authors investigated the hypothesis that epigenetic, transcriptomic, and cellular adaptations may occur in the anterior cingulate cortex as a function of child abuse. METHOD: Postmortem brain samples from human subjects (N=78) and from a rodent model of the impact of early-life environment (N=24) were analyzed. The human samples were from depressed individuals who died by suicide, with (N=27) or without (N=25) a history of severe child abuse, as well as from psychiatrically healthy control subjects (N=26). Genome-wide DNA methylation and gene expression were investigated using reduced representation bisulfite sequencing and RNA sequencing, respectively. Cell type-specific validation of differentially methylated loci was performed after fluorescence-activated cell sorting of oligodendrocyte and neuronal nuclei. Differential gene expression was validated using NanoString technology. Finally, oligodendrocytes and myelinated axons were analyzed using stereology and coherent anti-Stokes Raman scattering microscopy. RESULTS: A history of child abuse was associated with cell type-specific changes in DNA methylation of oligodendrocyte genes and a global impairment of the myelin-related transcriptional program. These effects were absent in the depressed suicide completers with no history of child abuse, and they were strongly correlated with myelin gene expression changes observed in the animal model. Furthermore, a selective and significant reduction in the thickness of myelin sheaths around small-diameter axons was observed in individuals with history of child abuse. CONCLUSIONS: The results suggest that child abuse, in part through epigenetic reprogramming of oligodendrocytes, may lastingly disrupt cortical myelination, a fundamental feature of cerebral connectivity.