Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

V. Bowden, C. Bowden

Rebecca Nalloor, Rebecca Nalloor, Khadijah Shanazz et al.

IntroductionOnly a subset of people who experience a traumatic event develop Post-Traumatic Stress Disorder (PTSD) suggesting that there are susceptibility factors influencing PTSD pathophysiology. While post trauma sequelae factors are extensively studied, susceptibility factors are difficult to study and therefore poorly understood. To address this gap, we previously developed an animal model - Revealing Individual Susceptibility to PTSD-like phenotype (RISP). RISP allows studying susceptibility factors by identifying, before trauma, male rats that are likely to develop a PTSD-like phenotype after trauma. Hypofunctioning prefrontal cortex (PFC) has been reported in people with PTSD, however, it is unclear if it is a susceptibility factor, sequalae factor, or both. Here we tested the hypothesis that male rats classified as Susceptible with RISP will have altered medial prefrontal cortical (mPFC) function prior to a PTSD-inducing trauma.MethodsExperiment 1: Susceptible and Resilient male rats classified with RISP performed spatial exploration and were sacrificed immediately to assess neuronal expression of plasticity-related immediate early genes (Arc and Homer1a) in the medial PFC (mPFC). Experiment 2: Cognitive performance of Susceptible and Resilient rats was evaluated on an attentional set shifting task. Experiment 3: We also analyzed pre-trauma cognitive performance scores of a small group of male military personnel some of whom developed PTSD post-trauma.ResultsExperiment 1: Susceptible rats showed altered expression of plasticity-related immediate early genes in the Prelimbic and Infralimbic subregions of the mPFC following spatial exploration. Experiment 2: Susceptible rats showed deficits in attentional set shifting task only when task demands increased. Experiment 3: Male military personnel who developed PTSD post-trauma showed pre-trauma cognitive deficits in a task involving the PFC.DiscussionSusceptible rats showed mPFC deficits both at the cellular and behavioral level before PTSD-inducing trauma. Combined with the findings from the human data, these results support the hypothesis that mPFC deficits in males exist before trauma and thus are a putative susceptibility factor for PTSD. Whether these deficits are a bona fide susceptibility factor will be determined in future studies by testing if enhancing mPFC function in susceptible individuals before trauma will confer resilience to developing PTSD. Building resilience is crucial for minimizing the number of people suffering from PTSD, given that it is difficult to treat and treatments are resource intensive and benefit only a subpopulation of people suffering from PTSD.

*Jakub Filip Możaryn, A. Szczegielniak

Abstract Background Current classifications of neuropsychiatric disorders are primarily based on qualitative groupings of well-defined symptoms, whereas a change in the diagnostic framework is needed. The goal of precision psychiatry is to provide a personalized and tailored approach to prevention, diagnosis, and treatment for better individual outcomes. It is based on multiple data domains such as unique symptom expression, genetics, cognition, neuroimaging, and psychosocial factors to identify different clinical phenotypes and individual biotypes among patients. It also requires a translational approach to the underlying neurobiological mechanisms and the identification of reliable biomarkers. Computational psychiatry seems to be an essential tool to connect these two fields. Aims & Objectives The study aims to highlight the place of computational psychiatry in modern mental health care and the challenges associated with its implementation. Research objectives are as follows: 1) summarize types of computational approaches to multi-level complex data used in computational psychiatry; 2) discuss utilization areas of computational modelling in psychiatry; 3) present general limitations and challenges in implementation. Method Focusing on the basic theoretical assumptions of computational psychiatry and its applications in mental health care, a narrative review of the literature published in English in the PubMed and EMBASE databases until January 10, 2024 was conducted. Results The leading areas of medicine currently exploiting the opportunities offered by new technologies to achieve contextualized precision diagnosis and treatment are radiology, oncology, neurology, and cardiology. While computational modelling of behavior has been used in neuroscience, direct translation of the results into the context of both diagnosis and psychiatric treatment appears to be much more difficult due to the interaction of genetic, physiological, comorbidity and environmental factors on mental status. Several key methods from computational psychiatry can improve precision psychiatry. First, biophysically realistic neural network (BRNN) models allow the simulation of brain functions to understand cognitive patterns in mental disorders. Second, algorithmic reinforcement learning (ARL) models are proposed for psychiatric analysis. Finally, probabilistic approaches, such as Bayesian models (BM), can be used to predict mental states and behaviors, taking into account individual variability. These techniques facilitate a personalized approach to psychiatry, enabling tailored insights and treatments for individual patients. In addition to the methods known in other leading areas of medicine, there is an increasing interest in natural language processing (NLP) to search for the traits of the changes in mental status. In this area especially, combination of the probabilistic methods and large language models (LLM’ s) based on transformer architecture are the prospective solutions for the psychiatric treatment. Discussion & Conclusions Precision psychiatry can enhance its approach by integrating big data and machine learning techniques from computational psychiatry. However, addressing the challenges requires a multi-faceted approach, including more ecologically valid models, better integration of computational methods into clinical practice, and further research into the reliability and validity of these techniques. References [1]Fernandes, B. S., Williams, L. M., Steiner, J., Leboyer, M., Carvalho, A. F., &Berk, M. (2017). The new field of ‘precision psychiatry’. BMC medicine, 15(1), 1-7. [2]Friston, K. J. (2017). Precision psychiatry. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(8), 640-643. [3]Zanardi, R., Prestifilippo, D., Fabbri, C., Colombo, C., Maron, E., &Serretti, A. (2021). Precision psychiatry in clinical practice. International Journal of Psychiatry in Clinical Practice, 25(1), 19-27 [4]Anticevic, A., &Murray, J. D. (Eds.). (2017). Computational psychiatry: Mathematical modeling of mental illness. Academic Press. [5]Koutsouleris, N., Hauser, T. U., Skvortsova, V., &De Choudhury, M. (2022). From promise to practice: towards the realisation of AI-informed mental health care. The Lancet Digital Health, 4(11), e829-e840. [6]Hauser, T. U., Skvortsova, V., De Choudhury, M., &Koutsouleris, N. (2022). The promise of a model- based psychiatry: building computational models of mental ill health. The Lancet Digital Health, 4(11), e816-e828. [5]Castro Martí nez, J. C., &Santamarí a-Garcí a, H. (2023). Understanding mental health through computers: An introduction to computational psychiatry. Frontiers in Psychiatry, 14, 1092471. [6]Mujica-Parodi, L. R., &Strey, H. H. (2020). Making sense of computational psychiatry. International Journal of Neuropsychopharmacology, 23(5), 339-347. [7]Bzdok, D., &Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 223-230. [8]Jeon, E., Yoon, N., &Sohn, S. Y. (2023). Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa. Technological Forecasting and Social Change, 186, 122130. [9]Ray, A., Bhardwaj, A., Malik, Y. K., Singh, S., &Gupta, R. (2022). Artificial intelligence and Psychiatry: An overview. Asian Journal of Psychiatry, 70, 103021. [10]Sun, J., Dong, Q. X., Wang, S. W., Zheng, Y. B., Liu, X. X., Lu, T. S.,... &Han, Y. (2023). Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian Journal of Psychiatry, 103705. [11]Rumshisky, A., Ghassemi, M., Naumann, T., Szolovits, P., Castro, V. M., McCoy, T. H., &Perlis, R. H. (2016). Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Translational psychiatry, 6(10), e921-e921. [12]Ló pez-Ojeda, W., &Hurley, R. A. (2023). Medical Metaverse, Part 2: Artificial Intelligence Algorithms and Large Language Models in Psychiatry and Clinical Neurosciences. The Journal of Neuropsychiatry and Clinical Neurosciences, 35(4), 316-320.

Hengjin Ke, Cang Cai, Jia Wu et al.

Jane Kondev, Marc Kirschner, Hernan G. Garcia et al.

Many biological processes can be thought of as the result of an underlying dynamics in which the system repeatedly undergoes distinct and abortive trajectories with the dynamical process only ending when some specific process, purpose, structure or function is achieved. A classic example is the way in which microtubules attach to kinetochores as a prerequisite for chromosome segregation and cell division. In this example, the dynamics is characterized by apparently futile time histories in which microtubules repeatedly grow and shrink without chromosomal attachment. We hypothesize that for biological processes for which it is not the initial conditions that matter, but rather the final state, this kind of exploratory dynamics is biology's unique and necessary solution to achieving these functions with high fidelity. This kind of cause and effect relationship can be contrasted to examples from physics and chemistry where the initial conditions determine the outcome. In this paper, we examine the similarities of many biological processes that depend upon random trajectories starting from the initial state and the selection of subsets of these trajectories to achieve some desired functional final state. We begin by reviewing the long history of the principles of dynamics, first in the context of physics, and then in the context of the study of life. These ideas are then stacked against the broad categories of biological phenomenology that exhibit exploratory dynamics. We then build on earlier work by making a quantitative examination of a succession of increasingly sophisticated models for exploratory dynamics, all of which share the common feature of being a series of repeated trials that ultimately end in a "winning" trajectory. We also explore the ways in which microscopic parameters can be tuned to alter exploratory dynamics as well as the energetic burden of performing such processes.

Bosco Garcia, Eugene Y. S. Chua, Harman Singh Brah

Large language models (LLMs) are increasingly proposed as scalable solutions to the global mental health crisis. But their deployment in psychiatric contexts raises a distinctive ethical concern: the problem of atypicality. Because LLMs generate outputs based on population-level statistical regularities, their responses -- while typically appropriate for general users -- may be dangerously inappropriate when interpreted by psychiatric patients, who often exhibit atypical cognitive or interpretive patterns. We argue that standard mitigation strategies, such as prompt engineering or fine-tuning, are insufficient to resolve this structural risk. Instead, we propose dynamic contextual certification (DCC): a staged, reversible and context-sensitive framework for deploying LLMs in psychiatry, inspired by clinical translation and dynamic safety models from artificial intelligence governance. DCC reframes chatbot deployment as an ongoing epistemic and ethical process that prioritises interpretive safety over static performance benchmarks. Atypicality, we argue, cannot be eliminated -- but it can, and must, be proactively managed.

Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely

At its core, the physics paradigm adopts a reductionist approach, aiming to understand fundamental phenomena by decomposing them into simpler, elementary processes. While this strategy has been tremendously successful in physics, it has often fallen short in addressing fundamental questions in the biological sciences. This arises from the inherent complexity of biological systems, characterised by heterogeneity, polyfunctionality and interactions across spatiotemporal scales. Nevertheless, the traditional framework of complex systems modelling falls short, as its emphasis on broad theoretical principles has often failed to produce predictive, empirically-grounded insights. To advance towards actionable mathematical models in biology, we argue, using neuroscience as a case study, that it is necessary to move beyond reductionist approaches and instead embrace the complexity of biological systems - leveraging the growing availability of high-resolution data and advances in high-performance computing. We advocate for a holistic mathematical modelling paradigm that harnesses rich representational structures such as annotated and multilayer networks, employs agent-based models and simulation-based approaches, and focuses on the inverse problem of inferring system dynamics from observations. We emphasise that this approach is fully compatible with the search for fundamental biophysical principles, and highlight the potential it holds to drive progress in mathematical biology over the next two decades.

David Benrimoh, Ryan Smith, Andreea O. Diaconescu et al.

Studying psychiatric illness has often been limited by difficulties in connecting symptoms and behavior to neurobiology. Computational psychiatry approaches promise to bridge this gap by providing formal accounts of the latent information processing changes that underlie the development and maintenance of psychiatric phenomena. Models based on these theories generate individual-level parameter estimates which can then be tested for relationships to neurobiology. In this review, we explore computational modelling approaches to one key aspect of health and illness: affect. We discuss strengths and limitations of key approaches to modelling affect, with a focus on reinforcement learning, active inference, the hierarchical gaussian filter, and drift-diffusion models. We find that, in this literature, affect is an important source of modulation in decision making, and has a bidirectional influence on how individuals infer both internal and external states. Highlighting the potential role of affect in information processing changes underlying symptom development, we extend an existing model of psychosis, where affective changes are influenced by increasing cortical noise and consequent increases in either perceived environmental instability or expected noise in sensory input, becoming part of a self-reinforcing process generating negatively valenced, over-weighted priors underlying positive symptom development. We then provide testable predictions from this model at computational, neurobiological, and phenomenological levels of description.

Inés Abalo-Rodríguez, C. Blithikioti

Despite predictions that neuroscientific discoveries would revolutionize psychiatry, decades of research have not yet led to clinically significant advances in psychiatric care. For this reason, an increasing number of researchers are recognizing the limitations of a purely biomedical approach in psychiatric research. These researchers call for reevaluating the conceptualization of mental disorders and argue for a non‐reductionist approach to mental health. The aim of this paper is to discuss philosophical assumptions that underly neuroscientific research in psychiatry and offer practical tools to researchers for overcoming potential conceptual problems that are derived from those assumptions. Specifically, we will discuss: the analogy problem, questioning whether mental health problems are equivalent to brain disorders, the normativity problem, addressing the value‐laden nature of psychiatric categories and the priority problem, which describes the level of analysis (e.g., biological, psychological, social, etc.) that should be prioritized when studying psychiatric conditions. In addition, we will explore potential strategies to mitigate practical problems that might arise due to these implicit assumptions. Overall, the aim of this paper is to suggest philosophical tools of practical use for neuroscientists, demonstrating the benefits of a closer collaboration between neuroscience and philosophy.

Chuan Wang, Qian Wang, Guangming Xu et al.

Prolonged or repeated exposure to stress elevates the risk of various psychological diseases, many of which are characterized by central nervous system dysfunction. Recent studies have demonstrated that circular RNAs (circRNAs) are highly abundant in the mammalian brain. Although their precise expression and function remain unknown, they have been hypothesized to regulate transcriptional and post-transcriptional gene expression. In this investigation, we comprehensively analyzed whether restraint stress for 2 days altered the circRNA expression profile in the amygdala of male rats. The impact of restraint stress on behavior was evaluated using an elevated plus maze and open field test. Serum corticosterone levels were measured using an enzyme-linked immunosorbent assay. A total of 10,670 circRNAs were identified using RNA sequencing. Ten circRNAs were validated by reverse transcription and quantitative polymerase chain reaction analysis. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analyzes supported the notion that genes associated with differentially expressed circRNAs are primarily implicated in neuronal activity and neurotransmitter transport. Moreover, the three differentially expressed circRNAs showed high specificity in the amygdala. Overall, these findings indicate that differentially expressed circRNAs are highly enriched in the amygdala and offer a potential direction for further research on restraint stress.

Chenlong Yang, Xiaohui Lou, Xiaohui Lou et al.

ObjectiveThis study aimed to develop an arbitrary-dimensional nerve root reconstruction magnetic resonance imaging (ANRR-MRI) technique for identifying the leakage orificium of sacral meningeal cysts (SMCs) without spinal nerve root fibres (SNRFs).MethodsThis prospective study enrolled 40 consecutive patients with SMCs without SNRFs between March 2021 and March 2022. Magnetic resonance neural reconstruction sequences were performed for preoperative evaluation. The cyst and the cyst-dura intersection planes were initially identified based on the original thin-slice axial T2-weighted images. Sagittal and coronal images were then reconstructed by setting each intersecting plane as the centre. Then, three-dimensional reconstruction was performed, focusing on the suspected leakage point of the cyst. Based on the identified leakage location and size of the SMC, individual surgical plans were formulated.ResultsThis cohort included 30 females and 10 males, with an average age of 42.6 ± 12.2 years (range, 17–66 years). The leakage orificium was located at the rostral pole of the cyst in 23 patients, at the body region of the cyst in 12 patients, and at the caudal pole in 5 patients. The maximum diameter of the cysts ranged from 2 cm to 11 cm (average, 5.2 ± 1.9 cm). The leakage orificium was clearly identified in all patients and was ligated microscopically through a 4 cm minimally invasive incision. Postoperative imaging showed that the cysts had disappeared.ConclusionANRR-MRI is an accurate and efficient approach for identifying leakage orificium, facilitating the precise diagnosis and surgical treatment of SMCs without SNRFs.

Alexander Erlich, Giuseppe Zurlo

Morphogenesis, the process of growth and shape formation in biological tissues, is driven by complex interactions between mechanical, biochemical, and genetic factors. Traditional models of biological growth often rely on the concept of homeostatic Eshelby stress, which defines an ideal target state for the growing body. Any local deviation from this state triggers growth and remodelling, aimed at restoring balance between mechanical forces and biological adaptation. Despite its relevance in the biomechanical context, the nature of homeostatic stress remains elusive, with its value and spatial distribution often chosen arbitrarily, lacking a clear biological interpretation or understanding of its connection to the lower scales of the tissue. To bring clarity on the nature of homeostatic stress, we shift the focus from Eshelby stress to growth incompatibility, a measure of geometric frustration in the tissue that is the primary source of residual stresses in the developing body. Incompatibility, measured by the Ricci tensor of the growth metric at the continuous level, can be potentially regulated at the cell level through connections with the surrounding cells, making it a more meaningful concept than homeostatic stress. In this geometric perspective, achieving a homeostatic state corresponds to the establishment of a physiological level of frustration in the body, a process leading to the generation and maintenance of the mechanical stresses that are crucial to tissue functionality. In this work we present a formulation of biological growth that penalises deviations from a desired state of incompatibility, similar to the way the Einstein-Hilbert action operates in General Relativity. The proposed framework offers a clear and physically grounded approach that elucidates the regulation of size and shape, while providing a means to link cellular and tissue scales in biological systems.

Wei-Chia Chen, Juannan Zhou, David M. McCandlish

Biological sequences do not come at random. Instead, they appear with particular frequencies that reflect properties of the associated system or phenomenon. Knowing how biological sequences are distributed in sequence space is thus a natural first step toward understanding the underlying mechanisms. Here we propose a new method for inferring the probability distribution from which a sample of biological sequences were drawn for the case where the sequences are composed of elements that admit a natural ordering. Our method is based on Bayesian field theory, a physics-based machine learning approach, and can be regarded as a nonparametric extension of the traditional maximum entropy estimate. As an example, we use it to analyze the aneuploidy data pertaining to gliomas from The Cancer Genome Atlas project. In addition, we demonstrate two follow-up analyses that can be performed with the resulting probability distribution. One of them is to investigate the associations among the sequence sites. This provides us a way to infer the governing biological grammar. The other is to study the global geometry of the probability landscape, which allows us to look at the problem from an evolutionary point of view. It can be seen that this methodology enables us to learn from a sample of sequences about how a biological system or phenomenon in the real world works.

J. C. Medina-Rodríguez

This editorial provides a concise and updated overview of neuropsychiatry, emphasizing its definitional challenges and profound implications for education, training, research, and the integration of phenomenology and philosophy of mind. Neuropsychiatry, situated at the crossroads of neurology and psychiatry, grapples with complex definitional issues that impede research progress. Establishing a unified conceptual framework is essential for focused research and delving into fundamental questions regarding topics like "consciousness." Nevertheless, the integration of philosophical perspectives into neuropsychiatry, while valuable, faces hurdles due to conceptual ambiguities and the fluid boundaries of the field. These obstacles disrupt research and hinder progress in effectively addressing neuropsychiatric conditions. This editorial advocates for a systematic approach to defining neuropsychiatry to mitigate these concerns. Additionally, as neuropsychiatry evolves, it necessitates an integrative approach. Recent advancements in neuroscience, propelled by technologies like artificial intelligence and advanced neuroimaging, reshape our comprehension of brain-behavior interactions, offering potential biomarkers and comprehensive treatment approaches.

Sharli Paphitis, Fatima Akilu, Natasha Chilambo et al.

Background Despite theoretical support for including mental health and psychosocial support (MHPSS) with peacebuilding, few programmes in conflict-affected regions fully integrate these approaches. Aims To describe and assess preliminary outcomes of the Counselling on Wheels programme delivered by the NEEM Foundation in the Borno State of North-East Nigeria. Method We first describe the components of the Counselling on Wheels programme, including education and advocacy for peace and social cohesion through community peacebuilding partnerships and activities, and an MHPSS intervention open to all adults, delivered in groups of eight to ten people. We then conducted secondary analysis of data from 1550 adults who took part in the MHPSS intervention, who provided data at baseline and 1–2 weeks after the final group session. Vulnerability to violent extremism was assessed with a locally developed 80-item scale. Symptoms of common mental disorders were assessed with the Depression, Anxiety and Stress Scale (DASS-21) and Post-Traumatic Stress Disorder Scale (PTSD-8). Data were analysed through a mixed-effect linear regression model, accounting for clustering by community and adjusted for age and gender. Results After taking part in group MHPSS, scores fell for depression (−5.8, 95% CI −6.7 to −5.0), stress (−5.5, 95% CI −6.3 to −4.6), post-traumatic stress disorder (−2.9, 95% CI −3.4 to −2.4) and vulnerability to violent extremism (−44.6, 95% CI −50.6 to −38.6). Conclusions The Counselling on Wheels programme shows promise as a model for integrating MHPSS with community peacebuilding activities in this conflict-affected region of Africa.

Jeanette Zanker, Daniela Hüser, Adrien Savy et al.

Viral vectors have become important tools for basic research and clinical gene therapy over the past years. However, in vitro testing of vector-derived transgene function can be challenging when specific post-translational modifications are needed for biological activity. Similarly, neuropeptide precursors need to be processed to yield mature neuropeptides. SH-SY5Y is a human neuroblastoma cell line commonly used due to its ability to differentiate into specific neuronal subtypes. In this study, we evaluate the suitability of SH-SY5Y cells in a potency assay for neuropeptide-expressing adeno-associated virus (AAV) vectors. We looked at the impact of neuronal differentiation and compared single-stranded (ss) AAV and self-complementary (sc) AAV transduction at increasing MOIs, RNA transcription kinetics, as well as protein expression and mature neuropeptide production. SH-SY5Y cells proved highly transducible with AAV1 already at low MOIs in the undifferentiated state and even better after neuronal differentiation. Readouts were GFP or neuropeptide mRNA expression. Production of mature neuropeptides was poor in undifferentiated cells. By contrast, differentiated cells produced and sequestered mature neuropeptides into the medium in a MOI-dependent manner.

Satheesh Kumar, Guei-Sheung Liu

Matthew J Burke

Dr. Matthew Burke is a Cognitive Neurologist in the Neuropsychiatry Program and Department of Psychiatry at Sunnybrook Health Sciences Center. He is currently the Director of Sunnybrook’s Traumatic Brain Injury Clinic and he also sees patients with functional neurological disorders, headache disorders, and other neuropsychiatric conditions. Before starting at Sunnybrook, Dr. Burke completed medical school and his neurology residency training at the University of Toronto, where he was the Chief Neurology Resident in his last year of training. He then completed a two-year Sidney R. Baer, Jr. Foundation fellowship in Cognitive Neurology and Neuropsychiatry at Beth Israel Deaconess Medical Center and Harvard Medical School. Dr. Burke’s clinical research fellowship provided specialized training in non-invasive brain stimulation and brain network mapping. His research applies these novel techniques to investigate the complex and poorly understood brain disorders at the interface between neurology and psychiatry. He also has research interests in the neurobiology of placebo effects and is an active collaborator with the Harvard Program in Placebo Studies. Finally, concurrent with his fellowship, Dr. Burke completed the Harvard Catalyst Clinical Translational Research Academy. This is a NIH-funded program that provides advanced training in methods of clinical investigation. Dr. Burke’s research to date has resulted in multiple peer-reviewed publications, media attention on platforms such as CNN and BBC, and recent recognition with the American Neuropsychiatric Association’s 2019 Young Investigator Award and the American Headache Society’s 2018 Frontiers in Headache Research Award. Abstract Placebo effects are the beneficial therapeutic effects derived from the context surrounding the administration of a treatment rather than the treatment itself. Recent research has shifted our understanding of placebo effects from a mystical unempirical entity to a biologically-based phenomenon capable of meaningfully modulating brain regions and neurotransmitter systems. In this presentation, Dr. Burke will begin by summarizing the evidence underlying the principles and neurobiology of placebo effects. He will then discuss clinical factors that contribute to placebo effects and how placebo effects could be harnessed in the management of neuropsychiatric disorders. Finally, Dr. Burke will interrogate how our evolving understanding of placebo effects may impact the way we design, appraise and interpret research studies in neuropsychiatry and across medicine.

Evolutionary psychiatry attempts to explain and examine the development and prevalence of psychiatric disorders through the lens of evolutionary and adaptationist theories. In this edited volume, leading international evolutionary scholars present a variety of Darwinian perspectives that will encourage readers to consider 'why' as well as 'how' mental disorders arise. Using insights from comparative animal evolution, ethology, anthropology, culture, philosophy and other humanities, evolutionary thinking helps us to re-evaluate psychiatric epidemiology, genetics, biochemistry and psychology. It seeks explanations for persistent heritable traits shaped by selection and other evolutionary processes, and reviews traits and disorders using phylogenetic history and insights from the neurosciences as well as the effects of the modern environment. By bridging the gap between social and biological approaches to psychiatry, and encouraging bringing the evolutionary perspective into mainstream psychiatry, this book will help to inspire new avenues of research into the causation and treatment of mental disorders.

Coral Elizondo Carmona

El diseño universal para el aprendizaje es un marco educativo que guía el diseño de métodos, materiales y entornos flexibles que minimizan las barreras al aprendizaje. Está formado por pautas y puntos de verificación que ofrecen propuestas para un diseño universal que logre el aprendizaje experto para todos. Estas pautas y puntos de verificación se organizan en torno a la neurociencia y la psicología cognitiva, lo cual permite aportaciones a la educación desde un estudio transdisciplinar.