Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Qiheng He, Yuhan Shang, Yijun Dong et al.

ObjectiveThis study aimed to enhance the Coma Recovery Scale-Revised (CRS-R) for disorders of consciousness (DoC) by developing a two-dimensional model differentiating cognition and motor function.MethodsWe analyzed 124 DoC patients retrospectively and validated findings using five multicenter datasets (n = 420). CRS-R subscores were decomposed into Consciousness_x (awareness) and Consciousness_y (arousal/motor function) using Projective Non-negative Matrix Factorization. Logistic regression established diagnostic thresholds, evaluated by accuracy, precision, recall, and F1-score.ResultsThe model achieved high accuracy (0.94), precision (0.92), and recall (0.99). Patients with minimally conscious state (MCS) or emerged MCS showed significantly higher scores than vegetative state (VS) patients (p < 0.05). The four-quadrant framework revealed distinct clinical profiles: Quadrant I (high awareness/arousal) identified patients for cognitive rehabilitation; Quadrant II (low awareness/high arousal) suggested arousal-enhancing therapies; Quadrant III (low awareness/arousal) indicated VS requiring basic support; Quadrant IV (high awareness/low arousal) highlighted needs for sensorimotor integration.ConclusionsThe two-dimensionally reduced representation of CRS-R scores maintains diagnostic accuracy while improving DoC classification. The four-quadrant model enables personalized interventions.Trial registrationOur study has been verified by the Chinese Clinical Trial Registry with the registration number: ChiCTR2400085855, and the registration date is June 19, 2024.

Sara Venturini, Saniya Mediratta, Tobias J. Adams et al.

Objective: For patients undergoing decompressive craniectomy for TBI, cranioplasty facilitates rehabilitation and reintegration into society. Cranioplasty practice is poorly documented globally. Challenges including lack of materials and technical skills disproportionately affect LMICs. This study evaluates global cranioplasty practice and the extent to which barriers preclude patients from accessing cranial reconstruction. Methods: An international survey was disseminated to centres performing cranioplasty for TBI. Survey questions addressed baseline hospital information, cranioplasty indications, barriers and techniques, follow-up. Centres in HICs and LMICs were compared. Results: 101 responses were received (86 individual institutions, 39 countries). Variation in practice was seen globally, and between HICs and LMICs. Autologous bone was the most common material used. Titanium and polymethylmethacrylate were the most used artificial implants. LMIC sites were more likely to store bone flaps in the patient's abdomen, while HICs had more access to 3D printing. Lack of infection and good neurological recovery were the commonest eligibility criteria. Cost of materials/operation and unavailable materials were the commonest barriers. Responders suggested easier access to cheaper materials would significantly improve access. Cranioplasty associated costs were higher than the country's GNI per capita in 7 cases. Less than 50 % of patients without cranioplasty had access to protective equipment. Less than a quarter of respondents stated patients had access to brain injury charities, and over 50 % believed stigma affects their patients. Conclusions: Variation in cranioplasty practice was confirmed. Barriers limiting access were identified, specifically, availability of materials and operation-related costs. These findings can inform context-specific interventions to overcome current challenges.

Deng-Pan Wu, Yan-Su Wei, Li-Xiang Hou et al.

Abstract Background Abnormal microglial polarization phenotypes contribute to the pathogenesis of Alzheimer’s disease (AD). Circular RNAs (circRNAs) have garnered increasing attention due to their significant roles in human diseases. Although research has demonstrated differential expression of circRNAs in AD, their specific functions in AD pathogenesis remain largely unexplored. Methods CircRNA microarray was performed to identify differentially expressed circRNAs in the hippocampus of APP/PS1 and WT mice. The stability of circAPP was assessed via RNase R treatment assay. CircAPP downstream targets miR-1906 and chloride intracellular channel 1 (CLIC1) were identified using bioinformatics and proteomics, respectively. RT-PCR assay was conducted to detect the expression of circAPP, miR-1906 and CLIC1. Morris water maze (MWM) test, passive avoidance test and novel object recognition task were used to detect cognitive function of APP/PS1 mice. Microglial M1/M2 polarization and AD pathology were assessed using Western blot, flow cytometry and Golgi staining assays. CLIC1 expression and channel activity were evaluated using Western blot and functional chloride channel assays, respectively. The subcellular location of circAPP was assessed via FISH and RT-PCR assays. RNA pull-down assay was performed to detect the interaction of miR-1906 with circAPP and 3’ untranslated region (3’UTR) of CLIC1 mRNA. Results In this study, we identified a novel circRNA, named circAPP, that is encoded by amyloid precursor protein (APP) and is implicated in AD. CircAPP is a stable circRNA that was upregulated in Aβ-treated microglial cells and the hippocampus of APP/PS1 mice. Downregulation of circAPP or CLIC1, or overexpression of miR-1906 in microglia modulated microglial M1/M2 polarization in Aβ-treated microglial cells and the hippocampus of APP/PS1 mice, and improved AD pathology and the cognitive function of APP/PS1 mice. Further results revealed that circAPP was mainly distributed in the cytoplasm, and circAPP could regulate CLIC1 expression and channel activity by interacting with miR-1906 and affecting miR-1906 expression, thereby regulating microglial polarization in AD. Conclusions Taken together, our study elucidates the regulatory role of circAPP in AD microglial polarization via miR-1906/CLIC1 axis, and suggests that circAPP may act as a critical player in AD pathogenesis and represent a promising therapeutic target for AD.

Stephan Pohl, Edgar Y. Walker, David L. Barack et al.

Despite the centrality of the notion of representation in neuroscience, the field lacks a unified framework for the concepts used to characterize representation, leading to disparate use of both terminology and measures associated with it. To offer clarification, we propose a core set of conceptual dimensions that characterize representations in neuroscience. These dimensions describe relations between a neural response, features that may be represented, and downstream effects of the neural response. A neural response may be shown to be sensitive or specific to a feature, invariant to other features, or functional (it is used downstream in the brain). We use information-theoretic measures to illustrate these conceptual dimensions and explain how they relate to data analysis methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence or adaptation. We consider several canonical examples, including models of the representation of orientation, numerosity, and spatial location, which illustrate how the evidence put forth in support or criticism of these models is systematized by our framework. By offering a unified conceptual framework to characterize representation in neuroscience, we hope to aid the comparison and integration of results across studies and research groups and to help determine when evidence for a neural representation is strong.

Hyeongjoo Row, Joshua B. Fernandes, Kranthi K. Mandadapu et al.

Electrical signals in excitable cells involve spatially localized ionic fluxes through ion channels and pumps on cellular lipid membranes. Common approaches to understand how these localized fluxes spread assume that the membrane and the surrounding electrolyte comprise an equivalent circuit of capacitors and resistors, which ignores the localized nature of transmembrane ion transport, the resulting ionic gradients and electric fields, and their spatiotemporal relaxation. Here, we consider a model of localized ion pumping across a lipid membrane, and use theory and simulation to investigate how the electrochemical signal propagates spatiotemporally in- and out-of-plane along the membrane. The localized pumping generates long-ranged electric fields with three distinct scaling regimes along the membrane: a constant potential near-field region, an intermediate "monopolar" region, and a far-field "dipolar" region. Upon sustained pumping, the monopolar region expands radially in-plane with a steady speed that is enhanced by the dielectric mismatch and the finite thickness of the lipid membrane. For unmyelinated lipid membranes in physiological settings, we find remarkably fast propagation speeds of $\sim\!40 \, \mathrm{m/s}$, allowing faster ionic reorganization compared to bare diffusion. Together, our work shows that transmembrane ionic fluxes induce transient long-ranged electric fields in electrolyte solutions, which may play hitherto unappreciated roles in biological signaling.

Vinicius Hernandes, Eliska Greplova

Understanding of how biological neural networks process information is one of the biggest open scientific questions of our time. Advances in machine learning and artificial neural networks have enabled the modeling of neuronal behavior, but classical models often require a large number of parameters, complicating interpretability. Quantum computing offers an alternative approach through quantum machine learning, which can achieve efficient training with fewer parameters. In this work, we introduce a quantum generative model framework for generating synthetic data that captures the spatial and temporal correlations of biological neuronal activity. Our model demonstrates the ability to achieve reliable outcomes with fewer trainable parameters compared to classical methods. These findings highlight the potential of quantum generative models to provide new tools for modeling and understanding neuronal behavior, offering a promising avenue for future research in neuroscience.

Akepogu Venkateshwarlu, Akshayveer, Sundeep Singh et al.

Living tissues experience various external forces on cells, influencing their behaviour, physiology, shape, gene expression, and destiny through interactions with their environment. Despite much research done in this area, challenges remain in our better understanding of the behaviour of the cell in response to external stimuli, including the arrangement, quantity, and shape of organelles within the cell. This study explores the electromechanical behaviour of biological cells, including organelles like microtubules, mitochondria, nuclei, and cell membranes. Two distinct cell structures have been developed to explore the cell responses to mechanical displacement, resembling actual cell shapes. The finite element method has been utilized to integrate the linear piezoelectric and non-local flexoelectric effects accurately. It is found that the longitudinal stress is absent and only the transverse stress plays a crucial role when the mechanical load is imposed on the top side of the cell through compressive displacement. The impact of flexoelectricity is elucidated by introducing a new parameter called the maximum electric potential ratio ($V_{\text{R,max}}$). It has been found that $V_{\text{R,max}}$ depends upon the orientation angle and shape of the microtubules. Further, the study reveals that the number of microtubules significantly impacts effective elastic and piezoelectric coefficients, affecting cell behaviour based on structure, microtubule orientation, and mechanical stress direction. The insight obtained from the current study can assist in advancements in medical therapies such as tissue engineering and regenerative medicine.

Jonathan Kadmon

This essay, derived from a lecture at "The Physics Modeling of Thought" workshop in Berlin in winter 2023, explores the mutually beneficial relationship between theoretical neuroscience and statistical physics through the lens of efficient coding and computation in cortical circuits. It highlights how the study of neural networks has enhanced our understanding of complex, nonequilibrium, and disordered systems, while also demonstrating how neuroscientific challenges have spurred novel developments in physics. The paper traces the evolution of ideas from seminal work on chaos in random neural networks to recent developments in efficient coding and the partial suppression of chaotic fluctuations. It emphasizes how concepts from statistical physics, such as phase transitions and critical phenomena, have been instrumental in elucidating the computational capabilities of neural networks. By examining the interplay between order and disorder in neural computation, the essay illustrates the deep connection between theoretical neuroscience and the statistical physics of nonequilibrium systems. This synthesis underscores the ongoing importance of interdisciplinary approaches in advancing both fields, offering fresh perspectives on the fundamental principles governing information processing in biological and artificial systems. This multidisciplinary approach not only advances our understanding of neural computation and complex systems but also points toward future challenges at the intersection of neuroscience and physics.

Weixuan Liu, Xinyue Zhang, Yuhan Helena Liu

Understanding how the brain learns can be advanced by investigating biologically plausible learning rules -- those that obey known biological constraints, such as locality, to serve as valid brain learning models. Yet, many studies overlook the role of architecture and initial synaptic connectivity in such models. Building on insights from deep learning, where initialization profoundly affects learning dynamics, we ask a key but underexplored neuroscience question: how does initial synaptic connectivity shape learning in neural circuits? To investigate this, we train recurrent neural networks (RNNs), which are widely used for brain modeling, with biologically plausible learning rules. Our findings reveal that initial weight magnitude significantly influences the learning performance of such rules, mirroring effects previously observed in training with backpropagation through time (BPTT). By examining the maximum Lyapunov exponent before and after training, we uncovered the greater demands that certain initialization schemes place on training to achieve desired information propagation properties. Consequently, we extended the recently proposed gradient flossing method, which regularizes the Lyapunov exponents, to biologically plausible learning and observed an improvement in learning performance. To our knowledge, we are the first to examine the impact of initialization on biologically plausible learning rules for RNNs and to subsequently propose a biologically plausible remedy. Such an investigation can lead to neuroscientific predictions about the influence of initial connectivity on learning dynamics and performance, as well as guide neuromorphic design.

Renato de Filippis, Maiko Fukasawa, Mohammadreza Shalbafan

Alan Nawzad Amin, Eli Nathan Weinstein, Debora Susan Marks

Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological understanding. However, many existing machine learning methods are ineffective or unreliable in this problem domain. We study these challenges theoretically, through the lens of kernels. Methods based on kernels are ubiquitous: they are used to predict molecular phenotypes, design novel proteins, compare sequence distributions, and more. Many methods that do not use kernels explicitly still rely on them implicitly, including a wide variety of both deep learning and physics-based techniques. While kernels for other types of data are well-studied theoretically, the structure of biological sequence space (discrete, variable length sequences), as well as biological notions of sequence similarity, present unique mathematical challenges. We formally analyze how well kernels for biological sequences can approximate arbitrary functions on sequence space and how well they can distinguish different sequence distributions. In particular, we establish conditions under which biological sequence kernels are universal, characteristic and metrize the space of distributions. We show that a large number of existing kernel-based machine learning methods for biological sequences fail to meet our conditions and can as a consequence fail severely. We develop straightforward and computationally tractable ways of modifying existing kernels to satisfy our conditions, imbuing them with strong guarantees on accuracy and reliability. Our proof techniques build on and extend the theory of kernels with discrete masses. We illustrate our theoretical results in simulation and on real biological data sets.

Masoumeh Najafi, Amin Jahanbakhshi, Sebastiano Finocchi Ghersi et al.

The most prevalent and deadly primary malignant glioma in adults is glioblastoma (GBM), which has a median survival time of about 15 months. Despite the standard of care for glioblastoma, which includes gross total resection, high-dose radiation, and temozolomide chemotherapy, this tumor is still one of the most aggressive and difficult to treat. So, it is critical to find more potent therapies that can help glioblastoma patients have better clinical outcomes. Additionally, the prognosis for recurring malignant gliomas is poor, necessitating the need for innovative therapeutics. Immunotherapy is a rather new treatment for glioblastoma and its effects are not well studied when it is combined with standard chemoradiation therapy. We conducted this study to evaluate different glioblastoma immunotherapy approaches in terms of feasibility, efficacy, and safety. We conducted a computer-assisted literature search of electronic databases for essays that are unique, involve either prospective or retrospective research, and are entirely written and published in English. We examined both observational data and randomized clinical trials. Eighteen studies met the criteria for inclusion. In conclusion, combining immunotherapy with radiochemotherapy and tumor removal is generally possible and safe, and rather effective in the prolongation of survival measures.

N. Braidy, Bilal Çiğ, Lin-Hua Jiang et al.

Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia, Wolfson Centre for Age-Related Diseases, School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, Department of Physiology, School of Medicine, Kirsehir Ahi Evran University, Kirsehir, Türkiye, Sino-UK Joint Laboratory of Brain Function and Injury of Henan Province, Department of Physiology and Pathophysiology, Xinxiang Medical University, Xinxiang, China, A4245-Transplantation, Immunology and Inflammation, Faculty of Medicine, University of Tours, Tours, France, Faculty of Biological Sciences, School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom, Department of Biological and Biomedical Sciences, Glasgow Caledonian University, Glasgow, United Kingdom, School of Basic Medical Sciences, Shaoyang University, Shaoyang, China

Ayelet M. Rosenberg, M. Saggar, Peter Rogu et al.

M. Royer, Balagopal Pai, R. Menon et al.

Irina Higgins, Sébastien Racanière, Danilo Rezende

Biological intelligence is remarkable in its ability to produce complex behaviour in many diverse situations through data efficient, generalisable and transferable skill acquisition. It is believed that learning "good" sensory representations is important for enabling this, however there is little agreement as to what a good representation should look like. In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation. The idea that there exist transformations (symmetries) that affect some aspects of the system but not others, and their relationship to conserved quantities has become central in modern physics, resulting in a more unified theoretical framework and even ability to predict the existence of new particles. Recently, symmetries have started to gain prominence in machine learning too, resulting in more data efficient and generalisable algorithms that can mimic some of the complex behaviours produced by biological intelligence. Finally, first demonstrations of the importance of symmetry transformations for representation learning in the brain are starting to arise in neuroscience. Taken together, the overwhelming positive effect that symmetries bring to these disciplines suggest that they may be an important general framework that determines the structure of the universe, constrains the nature of natural tasks and consequently shapes both biological and artificial intelligence.

Asaf Marco

Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik

Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.

Gehui Li, Gehui Li, Wanxian Luo et al.

Autophagy dysfunction has been directly linked with the onset and progression of Parkinson’s disease (PD), but the underlying mechanisms are not well understood. High-mobility group A1 (HMGA1), well-known chromatin remodeling proteins, play pivotal roles in diverse biological processes and diseases. Their function in neural cell death in PD, however, have not yet been fully elucidated. Here, we report that HMGA1 is highly induced during dopaminergic cell death in vitro and mice models of PD in vivo. Functional studies using genetic knockdown of endogenous HMGA1 show that HMGA1 signaling inhibition accelerates neural cell death, at least partially through aggravating MPP+-induced autophagic flux reduction resulting from partial block in autophagic flux at the terminal stages, indicating a novel potential neuroprotective role for HMGA1 in dopaminergic neurons death. MicroRNA-103/107 (miR-103/107) family, which is highly expressed in neuron, coordinately ensures proper end-stage autophagy. We further illustrate that MPP+/1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced HMGA1 elevation counterparts the effect of miR-103/107 downregulation by directly binding to their promoters, respectively, sustaining their expression in MPP+-damaged MN9D cells and modulates autophagy through CDK5R1/CDK5 signaling pathway. We also find that HMGA1 is a direct target of miR-103/107 family. Thus, our results suggest that HMGA1 forms a negative feedback loop with miR-103/107-CDK5R1/CDK5 signaling to regulate the MPP+/MPTP-induced autophagy impairment and neural cell death. Collectively, we identify a paradigm for compensatory neuroprotective HMGA1 signaling in dopaminergic neurons that could have important therapeutic implications for PD.

Teruhiko Higuchi, Tadafumi Kato, Mari Miyajima et al.

Abstract Background The current study evaluated the long-term (52 week) safety and impact on symptom measures of lurasidone (with or without lithium or valproate) for the treatment of bipolar I disorder in Japanese patients. Methods Bipolar patients for this open-label flexibly dosed lurasidone (20–120 mg/day) study were recruited from those with a recent/current depressive episode who completed an initial 6 week, double-blind, placebo-controlled, lurasidone study (depressed group), and those with a recent/current manic, hypomanic, or mixed episode (non-depressed group) who agreed to enroll directly into the long-term study. Measures of adverse events and safety included treatment-emergent adverse events, vital signs, body weight, ECG, laboratory tests, and measures of suicidality and extrapyramidal symptoms. Symptom measures included Montgomery Åsberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS). Results The most common adverse events associated with lurasidone were akathisia (30.7%), nasopharyngitis (26.6%), nausea (12.1%), and somnolence (12.1%). Minimal changes in lipids and measures of glycemic control occurred. Mean change in body weight was + 1.0 kg in the non-depressed group and − 0.8 kg in the depressed group. MADRS total scores declined by a mean (SD) of 2.0 (14.7) points from long-term baseline to endpoint in the depressed group who had received placebo in the prior 6 week trial. The depressed group that had received lurasidone during the prior 6 week study maintained their depressive symptom improvements. For the non-depressed group, YMRS total scores decreased over time. Limitations No control group was included, treatment was open-label, and 49.7% of patients completed the 52 week study. Conclusions Long-term treatment with lurasidone 20–120 mg/day for Japanese patients with bipolar disorder maintained improvements in depressive symptoms for depressed patients who were treated in a prior 6 week trial and led to improvements in manic symptoms among a newly recruited subgroup of patients with a recent/current manic, hypomanic, or mixed episode. Few changes in weight or metabolic parameters were evident. Clinical trial registration: JapicCTI-132319, clinicaltrials.gov—NCT01986114.