Band 2, Heft 3 - Special Issue on Neuroscience, Psychology and Brain Science

At least 78 million people worldwide are affected by autism, a neurodevelopmental disorder characterized by deficits in social interactions, and repetitive behaviors and/or interests. Autism typically manifests in early childhood, and affects social communications and behaviors throughout the lifespan of the individual. Under the umbrella of autism spectrum disorder, it is a highly heterogeneous disorder, with some individuals profoundly affected and needing care every day, while others can live highly independent lives, with some adjustments. The past 60 years has seen a major influx of interest in autism, and significant advances in many areas. However, a large gap remains between current scientific knowledge and the help and support that people with autism and their families need. To address these concerns, the Lancet commissioned a report on the “future of care and clinical research in autism”. The Commission calls for government coordination between health-care, education and social sectors, as well as active participation from people with autism and their families. The Commission proposes personalized, evidence-based assessments and intervention, that is accessible and affordable to all, and call for increased appreciation of neurodiversity and prioritization of research that can improve the lives of people with autism and their families. How to support each and every autistic individual and their families is highly challenging. The 64-page Lancet Commission Report, published on December 2021, was written jointly by 32 authors from 6 continents and 13 disciplines, including clinicians, other health-care providers, researchers, advocates, autistic individuals and their parents.

Post-traumatic stress disorder (PTSD) is a severe and heterogenous psychiatric disorder that was first defined as a mental disorder in 1980. Currently, the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) and the International Classification of Diseases 11th Edition (ICD-11) offer the most widely accepted diagnostic guidelines for PTSD. In both diagnostic categories, experiencing a traumatic event (TE) is the necessary criterion for diagnosing PTSD. The TEs described in the DSM-5 include actual or threatened death, serious injury, sexual violence, and other extreme stressors, either directly or indirectly. More than 70% of adults worldwide are exposed to a TE at least once in their lifetime, and approximately 10% of individuals develop PTSD after experiencing a TE. The important features of PTSD are intrusion or re-experiencing fear memories, pervasive sense of threat, active avoidance, hyperarousal symptoms, and negative alterations of cognition and mood. Individuals with PTSD have high comorbidities with other psychiatric diseases, including major depressive disorder, generalized anxiety disorder, and substance use disorder. Multiple lines of evidence suggest that the pathophysiology of PTSD is complex, involving abnormal neural circuits, molecular mechanisms, and genetic mechanisms. A combination of both psychotherapy and pharmacotherapy is used to treat PTSD, but has limited efficacy in patients with refractory PTSD. Because of the high prevalence, heavy burden, and limited treatments, PTSD is a psychiatric disorder that requires urgent attention. In this review, we summarize and discuss the diagnosis, prevalence, TEs, pathophysiology, and treatments of PTSD and draw attention to its prevention.

Neural networks are groups of interconnected neurons, which collectively give rise to emergent neural activities and functions that cannot be explained by the activity of single neurons. How neural networks are assembled is poorly understood. While all aspects of neuronal development are essential for the assembly of a functional neural network, we know little about high-level principles that govern neural network assembly beyond the basic steps of neuronal development. In this review, I use vertebrate spinal motor columns, Drosophila larval motor circuit, and the lamination in the vertebrate inner retina to highlight the spatial codes, temporal codes, and cell adhesion codes for neural network assembly. Nevertheless, these examples only show preliminary connections between neural network development and their functions. Much needs to be done to understand the molecular mechanisms that underlie the assembly of functional neural networks.

Acutely, pain serves to protect us from potentially harmful stimuli, however damage to the somatosensory system can cause maladaptive changes in neurons leading to chronic pain. Although acute pain is fairly well controlled, chronic pain remains difficult to treat. Chronic pain is primarily a neuropathic condition, but studies examining the mechanisms underlying chronic pain are now looking beyond afferent nerve lesions and exploring new receptor targets, immune cells, and the role of the autonomic nervous system in contributing chronic pain conditions. The studies outlined in this review reveal how chronic pain is not only confined to alterations in the nervous system and presents findings on new treatment targets and for this debilitating disease.

The visual system remains highly malleable even after its maturity or impairment. Our visual function can be enhanced through many ways, such as transcranial electrical stimulation (tES) and visual perceptual learning (VPL). TES can change visual function rapidly, but its modulation effect is short-lived and unstable. By contrast, VPL can lead to a substantial and long-lasting improvement in visual function, but extensive training is typically required. Theoretically, visual function could be further improved in a shorter time frame by combining tES and VPL than by solely using tES or VPL. Vision enhancement by combining these two methods concurrently is both theoretically and practically significant. In this review, we firstly introduced the basic concept and possible mechanisms of VPL and tES; then we reviewed the current research progress of visual enhancement using the combination of two methods in both general and clinical population; finally, we discussed the limitations and future directions in this field. Our review provides a guide for future research and application of vision enhancement and restoration by combining VPL and tES.

Cancer is a fetal and complex disease. Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments. Pathological differences are reflected in tissues, cells and gene levels etc. The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis. It is a huge challenge to understand all of these mechanistically and quantitatively. Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications. With the rapidly growing and available computing powers, researchers begin to integrate huge data sets, multi-dimensional data types and information. The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators. For example, the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle. Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks, which are essential for cancer study when the information and gene regulations are clear and available. In this review, we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks (ANNs), Decision Trees (DTs), Support Vector Machine (SVM) and naive Bayes. Then we describe a few typical models for building up gene regulatory networks such as Correlation, Regression and Bayes methods based on available data. These methods can help on cancer diagnosis such as susceptibility, recurrence, survival etc. At last, we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks. These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.

Spontaneous pain occurring without apparent external stimuli, is a significant complaint of individuals with chronic pain whose mechanisms, somewhat surprisingly, remain poorly understood. Over the past decades, neuroimaging studies start to reveal brain activities accompanying spontaneous pain. Meanwhile, a variety of animal models and behavioral tests have been established, including non-reflexive tests and free-choice tests, which have been shown to be effective in assessing spontaneous pain. For the spontaneous pain mechanisms, multiple lines of research mainly focus on three aspects: (1) sensitization of peripheral nociceptor receptors and ion channels, (2) spontaneous neuronal firing and abnormal activity patterns at the dorsal root ganglion and spinal cord level, (3) functional and structural alterations in the brain, particularly the limbic system and the medial pain pathway. Despite accumulating evidence revealing distinct neuronal mechanisms from evoked pain, we are still far from full understanding of spontaneous pain, leaving a big gap between bench and bedside for chronic pain treatment. A better understanding of the neural processes in chronic pain, with specific linkage as to which anatomical structures and molecules related to spontaneous pain perception and comorbidities, will greatly improve our ability to develop novel therapeutics.