On the Complexity of Brain Disorders: A Symptom-Based Approach

Common and divergent neuroimaging features in major depression, posttraumatic stress disorder, and their comorbidity

Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) are common stress-related psychiatric disorders. Genetic and neurobiology research has supported the viewpoint that PTSD and MDD may possess common and disorder-specific underlying mechanisms. In this systematic review, we summarize evidence for the similarities and differences in brain functional and structural features of MDD, PTSD, and their comorbidity, as well as the effects of extensively used therapies in patients with comorbid PTSD and MDD (PTSD + MDD). These functional magnetic resonance imaging (MRI) studies highlight the (i) shared hypoactivation in the prefrontal cortex during cognitive and emotional processing in MDD and PTSD; (ii) higher activation in fear processing regions including amygdala, hippocampus, and insula in PTSD compared to MDD; and (iii) distinct functional deficits in brain regions involved in fear and reward processing in patients with PTSD + MDD relative to those with PTSD alone. These structural MRI studies suggested that PTSD and MDD share features of reduced volume in focal frontal areas. The treatment effects in patients with PTSD + MDD may correlate with the normalization trend of structural alterations. Neuroimaging predictors of repetitive transcranial magnetic stimulation response in patients with PTSD + MDD may differ from the mono-diagnostic groups. In summary, neuroimaging studies to date have provided limited information about the shared and disorder-specific features in MDD and PTSD. Further research is essential to pave the way for developing improved diagnostic markers and eventually targeted treatment approaches for the shared and distinct brain alterations presented in patients with MDD and PTSD.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

Complexity emerges in measures of the marking dynamics in football games

In this article, we study the dynamics of marking in football matches. To do this, we survey and analyze a database containing the trajectories of players from both teams on the field of play during three professional games. We describe the dynamics through the construction of temporal bipartite networks of proximity. Based on the introduced concept of proximity, the nodes are the players, and the links are defined between opponents that are close enough to each other at a given moment. By studying the evolution of the heterogeneity parameter of the networks during the game, we characterize a scaling law for the average shape of the fluctuations, unveiling the emergence of complexity in the system. Moreover, we propose a simple model to simulate the players' motion in the field from where we obtained the evolution of a synthetic proximity network. We show that the model captures with a remarkable agreement the complexity of the empirical case, hence it proves to be helpful to elucidate the underlying mechanisms responsible for the observed phenomena.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.

Beneficial effects of whole-body vibration exercise for brain disorders in experimental studies with animal models: a systematic review

Brain disorders have been a health challenge and is increasing over the years. Early diagnosis and interventions are considered essential strategies to treat patients at risk of brain disease. Physical exercise has shown to be beneficial for patients with brain diseases. A type of exercise intervention known as whole-body vibration (WBV) exercise gained increasing interest. During WBV, mechanical vibrations, produced by a vibrating platform are transmitted, to the body. The purpose of the current review was to summarize the effects of WBV exercise on brain function and behavior in experimental studies with animal models. Searches were performed in EMBASE, PubMed, Scopus and Web of Science including publications from 1960 to July 2021, using the keywords "whole body vibration" AND (animal or mice or mouse or rat or rodent). From 1284 hits, 20 papers were selected. Rats were the main animal model used (75%) followed by mice (20%) and porcine model (5%), 16 studies used males species and 4 females. The risk of bias, accessed with the SYRCLE Risk of Bias tool, indicated that none of the studies fulfilled all methodological criteria, resulting in possible bias. Despite heterogeneity, the results suggest beneficial effects of WBV exercise on brain functioning, mainly related to motor performance, coordination, behavioral control, neuronal plasticity and synapse function. In conclusion, the findings observed in animal studies justifies continued clinical research regarding the effectiveness and potential of WBV for the treatment of various types of brain disorders such as trauma, developmental disorders, neurogenetic diseases and other neurological diseases.

Comprehensive analyses of RNA-seq and genome-wide data point to enrichment of neuronal cell type subsets in neuropsychiatric disorders

Neurological and psychiatric disorders, including substance use disorders, share a range of symptoms, which could be the result of shared genetic background. Many genetic loci have been identified for these disorders using genome-wide association studies, but conclusive evidence about cell types wherein these loci are active is lacking. We aimed to uncover implicated brain cell types in neuropsychiatric traits and to assess consistency in results across RNA datasets and methods. We therefore comprehensively employed cell type enrichment methods by integrating single-cell transcriptomic data from mouse brain regions with an unprecedented dataset of 42 human genome-wide association study results of neuropsychiatric, substance use and behavioral/quantitative brain-related traits (n = 12,544,007 individuals). Single-cell transcriptomic datasets from the Karolinska Institute and 10x Genomics were used. Cell type enrichment was determined using Linkage Disequilibrium Score Regression, Multi-marker Analysis of GenoMic Annotation, and Data-driven Expression Prioritized Integration for Complex Traits. We found the largest degree of consistency across methods for implication of pyramidal cells in schizophrenia and cognitive performance. For other phenotypes, such as bipolar disorder, two methods implicated the same cell types, i.e., medium spiny neurons and pyramidal cells. For autism spectrum disorders and anorexia nervosa, no consistency in implicated cell types was observed across methods. We found no evidence for astrocytes being consistently implicated in neuropsychiatric traits. In conclusion, we provide comprehensive evidence for a subset of neuronal cell types being consistently implicated in several, but not all psychiatric disorders, while non-neuronal cell types seem less implicated.

Using Xenopus oocytes in neurological disease drug discovery

Introduction: Neurological diseases present a difficult challenge in drug discovery. Many of the current treatments have limited efficiency or result in a variety of debilitating side effects. The search of new therapies is of a paramount importance, since the number of patients that require a better treatment is growing rapidly. As an in vitro model, Xenopus oocytes provide the drug developer with many distinct advantages, including size, durability, and efficiency in exogenous protein expression. However, there is an increasing need to refine the recent breakthroughs.Areas covered: This review covers the usage and recent advancements of Xenopus oocytes for drug discovery in neurological diseases from expression and functional measurement techniques to current applications in Alzheimer's disease, painful neuropathies, and amyotrophic lateral sclerosis (ALS). The existing limitations of Xenopus oocytes in drug discovery are also discussed.Expert opinion: With the rise of aging population and neurological disorders, Xenopus oocytes, will continue to play an important role in understanding the mechanism of the disease, identification and validation of novel molecular targets, and drug screening, providing high-quality data despite the technical limitations. With further advances in oocytes-related techniques toward an accurate modeling of the disease, the diagnostics and treatment of neuropathologies will be becoming increasing personalized.

Multitarget 1,4-Dioxane Compounds Combining Favorable D2-like and 5-HT1A Receptor Interactions with Potential for the Treatment of Parkinson's Disease or Schizophrenia

The effect of methoxy and hydroxy substitutions in different positions of the phenoxy moiety of the N-((6,6-diphenyl-1,4-dioxan-2-yl)methyl)-2-phenoxyethan-1-amine scaffold on the affinity/activity for D₂-like, 5-HT_1A, and α₁-adrenoceptor subtypes was evaluated. Multitarget compounds with suitable combinations of dopaminergic and serotoninergic profiles were discovered. In particular, the 2-methoxy derivative 3 showed a multitarget combination of 5-HT_1A/D₄ agonism and D₂/D₃/5-HT_2A antagonism, which may be a favorable profile for the treatment of schizophrenia. Interestingly, the 3-hydroxy derivative 8 behaved as a partial agonist at D₂ and as a potent full agonist at D₃ and D₄ subtypes. In addition to its potent 5-HT_1A receptor agonism, such a dopaminergic profile makes 8 a potential multitarget compound for the treatment of Parkinson's disease (PD). Indeed, the activation of 5-HT_1A receptors might be helpful in reducing dyskinetic side effects associated with dopaminergic stimulation.

Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research

The alterations of functional connectivity brain networks in major depressive disorder (MDD) have been subject of a large number of studies. Using different methodologies and focusing on diverse aspects of the disease, research shows heterogeneous results lacking integration. Disrupted network connectivity has been found in core MDD networks like the default mode network (DMN), the central executive network (CEN), and the salience network, but also in cerebellar and thalamic circuitries. Here we review literature published on resting state brain network function in MDD focusing on methodology, and clinical characteristics including symptomatology and antidepressant treatment related findings. There are relatively few investigations concerning the qualitative aspects of symptomatology of MDD, whereas most studies associate quantitative aspects with distinct resting state functional connectivity alterations. Such depression severity associated alterations are found in the DMN, frontal, cerebellar and thalamic brain regions as well as the insula and the subgenual anterior cingulate cortex. Similarly, different therapeutical options in MDD and their effects on brain function showed patchy results. Herein, pharmaceutical treatments reveal functional connectivity alterations throughout multiple brain regions notably the DMN, fronto-limbic, and parieto-temporal regions. Psychotherapeutical interventions show significant functional connectivity alterations in fronto-limbic networks, whereas electroconvulsive therapy and repetitive transcranial magnetic stimulation result in alterations of the subgenual anterior cingulate cortex, the DMN, the CEN and the dorsal lateral prefrontal cortex. While it appears clear that functional connectivity alterations are associated with the pathophysiology and treatment of MDD, future research should also generate a common strategy for data acquisition and analysis, as a least common denominator, to set the basis for comparability across studies and implementation of functional connectivity as a scientifically and clinically useful biomarker.