Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Jiachen Chen, Jiachen Chen, Jiachen Chen et al.

IntroductionThere are notable sex differences in the gray matter of Alzheimer’s disease(AD) patients’ brains, but current evidence is insufficient to prove these differences aid diagnosis effectively.MethodsMultivariate analysis of variance was performed on the preprocessed gray matter of healthy female and healthy male groups to identify the gray matter clusters with significant intergroup differences. Subsequently, multiple machine learning models were employed to develop sex-specific diagnostic models for AD.ResultsWe identified 11 brain regions showing sex differences, of which 8 were sex-specific in both female and male AD patients, exhibiting significant atrophy. Graph theory analysis demonstrated that the sex-specific gray matter structural brain networks in female and male AD patients exhibited distinct network alterations. We subsequently employed five advanced machine learning algorithms to develop diagnostic models for AD based on these sex-specific gray matter clusters, resulting in a notable improvement in performance.DiscussionSex-specific gray matter characteristics can facilitate more accurate diagnosis of AD.

Eric R. Cohn, José R. Zubizarreta

ABSTRACT Objective Inverse probability weighting is a fundamental and general methodology for estimating the causal effects of exposures and interventions, but standard approaches to constructing such weights are often suboptimal. Methods In this paper, we describe a recent approach for constructing such weights that directly balances covariates while optimizing the stability of the resulting weighting estimator. Results To illustrate the use of this approach in mental health research, we present an exploratory study of the effects of exposure to violence on the risk of suicide attempt. Conclusions The direct balancing approach to weighting should be given strong consideration in empirical research due to its robustness and transparency in building weighting estimators.

Dhruvik Patel, Md Sayed Tanveer, Jesus Gonzalez-Ferrer et al.

NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.

Jan Cammann, Hannah Laeverenz-Schlogelhofer, Kirsty Y. Wan et al.

Nature uses elongated shapes and filaments to build stable structures, generate motion, and allow complex geometric interactions. In this Review, we examine the role of biological filaments across different length scales. From the molecular scale, where cytoskeletal filaments provides a robust but dynamic cellular scaffolding, over the scale of cellular appendages like cilia and flagella, to the scale of filamentous microorganisms like cyanobacteria, among the most successful genera on Earth, and even to the scale of elongated animals like worms and snakes, whose motility modes inspire robotic analogues. We highlight the general mechanisms that couple form and function. We discuss physical principles and models, such as classical elasticity and the non-reciprocity of active matter, that can be used to trace unifying themes linking these systems across about nine orders of magnitude in length.

Lauren N. Miterko-Myers

Dystonia is a neurodevelopmental disorder characterized by severe involuntary twisting movements, hypothesized to arise from a dysfunctional motor network involving the cortex, basal ganglia, and cerebellum. Within this network, striatal cholinergic interneurons have been identified as possible contributors to dystonia pathophysiology. However, little is known about striatal cholinergic interneuron development in the mammalian brain, limiting our understanding of its role in dystonia and therapeutic potential. Here, I review striatal cholinergic interneuron development in the context of early-onset DYT1 (or “DYT-TOR1A”) dystonia. I discuss clinical and laboratory research findings that support cholinergic dysfunction in DYT1 dystonia and the implications of abnormal cholinergic cell development on disease penetrance and striatal connectivity.

D.E. Ogolo, E.C. Ajare, O. Okwuoma et al.

Background and objectives: While various pathologies affecting the foramen magnum region can have severe consequences, little research has been conducted on the unique morphological patterns in the West African subregion. The study aimed to assess these patterns and their implications for surgeries, comparing them with global standards. Methods: A descriptive study was conducted on 315 patients over a two-year period, excluding those with specific abnormalities. Measurements obtained from cranial 1.5T MRI scans included anteroposterior and transverse diameters of the foramen magnum. From these, the foramen magnum area and index were calculated. The data was analyzed by inferential, comparative and descriptive statistics, and a p value < 0.05 was regarded as statistically significant. Results: On average, the transverse and anteroposterior diameters were 28.51 mm and 33.02 mm for males and 28.39 mm and 33.47 mm for females, with a slightly smaller foramen magnum area in males (7.42 cm²) compared to females (7.47 cm²). Despite these differences, the variations were not statistically significant. However, the foramen magnum indices indicated medium size configuration for females and large size configuration for males, aligning with global trends. Conclusion: The study concluded that West Africans exhibited lower foramen magnum area and indices compared to other regions, with minor differences between sexes. Females tended to have a medium size configuration, while males tended to have a larger size configuration. These findings provide valuable insights for clinicians, highlighting the importance of considering ethno-regional variations in surgical approaches and interventions related to the craniocervical junction.

Manasvi Lingam, Amedeo Balbi, Madhur Tiwari

The striking recent putative detection of "dark oxygen" (dark O$_2$) sources on the abyssal ocean floor in the Pacific at $\sim 4$ km depth raises the intriguing scenario that complex (i.e., animal-like) life could exist in underwater environments sans oxygenic photosynthesis. In this work, we thus explore the possible (astro)biological implications of this discovery. From the available data, we roughly estimate the concentration of dissolved O$_2$ and the corresponding O$_2$ partial pressure, as well as the flux of O$_2$ production, associated with dark oxygen sources. Based on these values, we infer that organisms limited by internal diffusion may reach maximal sizes of $\sim 0.1-1$ mm in habitats with dark O$_2$, while those with circulatory systems might achieve sizes of $\sim 0.1-10$ cm. Optimistically, the estimated dark oxygen flux can potentially support biomass densities up to $\sim 3-30$ g m$^{-2}$, perhaps surpassing typical reported densities at similar depths in global deep-sea surveys. Finally, we outline how oceanic settings with dark O$_2$ may facilitate the origin(s) of life via the emergence of electrotrophy. Our findings indicate that complex life fueled by dark oxygen is plausibly capable of inhabiting submarine environments devoid of photosynthesis on Earth, conceivably extending likewise to extraterrestrial locations such as icy worlds with subsurface oceans (e.g., Enceladus and Europa), which are likely common throughout the Universe.

Heather Marriott, Renata Kabiljo, Guy P Hunt et al.

Abstract Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80–90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .

Yiwen Tang, Siyuan Chen, Mark J. Bowick et al.

Biological tissues transform between solid-like and liquid-like states in many fundamental physiological events. Recent experimental observations further suggest that in two-dimensional epithelial tissues these solid-liquid transformations can happen via intermediate states akin to the intermediate hexatic phases observed in equilibrium two-dimensional melting. The hexatic phase is characterized by quasi-long-range (power-law) orientational order but no translational order, thus endowing some structure to an otherwise structureless fluid. While it has been shown that hexatic order in tissue models can be induced by motility and thermal fluctuations, the role of cell division and apoptosis (birth and death) has remained poorly understood, despite its fundamental biological role. Here we study the effect of cell division and apoptosis on global hexatic order within the framework of the self-propelled Voronoi model of tissue. Although cell division naively destroys order and active motility facilitates deformations, we show that their combined action drives a liquid-hexatic-liquid transformation as the motility increases. The hexatic phase is accessed by the delicate balance of dislocation defect generation from cell division and the active binding of disclination-antidisclination pairs from motility. We formulate a mean-field model to elucidate this competition between cell division and motility and the consequent development of hexatic order.

Nicolas Lenner, Matthias Häring, Stephan Eule et al.

The dynamics of living systems often serves the purpose of reaching functionally important target states. We previously proposed a theory to analyze stochastic biological dynamics evolving towards target states in reverse time. However, a large class of systems in biology can only be adequately described using state-dependent noise, which had not been discussed. For example, in gene regulatory networks, biochemical signaling networks or neuronal circuits, count fluctuations are the dominant noise component. We characterize such dynamics as an ensemble of target state aligned (TSA) trajectories and characterize its temporal evolution in reverse-time by generalized Fokker-Planck and stochastic differential equations with multiplicative noise. We establish the classification of boundary conditions for target state modeling for a wide range of power law dynamics, and derive a universal low-noise approximation of the final phase of target state convergence. Our work expands the range of theoretically tractable systems in biology and enables novel experimental design strategies for systems that involve target states.

Grant D. Searchfield, Grant D. Searchfield, Philip J. Sanders et al.

ObjectiveThis randomized single-blind controlled trial tested the hypothesis that a prototype digital therapeutic developed to provide goal-based counseling with personalized passive and active game-based sound therapy would provide superior tinnitus outcomes, and similar usability, to a popular passive sound therapy app over a 12 week trial period.MethodsThe digital therapeutic consisted of an app for iPhone or Android smartphone, Bluetooth bone conduction headphones, neck pillow speaker, and a cloud-based clinician dashboard to enable messaging and app personalization. The control app was a popular self-help passive sound therapy app called White Noise Lite (WN). The primary outcome measure was clinically meaningful change in Tinnitus Functional Index (TFI) between baseline and 12 weeks of therapy. Secondary tinnitus measures were the TFI total score and subscales across sessions, rating scales and the Client Oriented Scale of Improvement in Tinnitus (COSIT). Usability of the US and WN interventions were assessed using the System Usability Scale (SUS) and the mHealth App Usability Questionnaire (MAUQ). Ninety-eight participants who were smartphone app users and had chronic moderate-severe tinnitus (&gt;6 months, TFI score &gt; 40) were enrolled and were randomly allocated to one of the intervention groups. Thirty-one participants in the USL group and 30 in the WN group completed 12 weeks of trial.ResultsMean changes in TFI for the USL group at 6 (16.36, SD 17.96) and 12 weeks (17.83 points, SD 19.87) were clinically meaningful (&gt;13 points reduction), the mean change in WN scores were not clinically meaningful (6 weeks 10.77, SD 18.53; 12 weeks 10.12 points, SD 21.36). A statistically higher proportion of USL participants achieved meaningful TFI change at 6 weeks (55%) and 12 weeks (65%) than the WN group at 6 weeks (33%) and 12 weeks (43%). Mean TFI, rating and COSIT scores favored the US group but were not statistically different from WN. Usability measures were similar for both groups.ConclusionsThe USL group demonstrated a higher proportion of responders than the WN group. The usability of the USL therapeutic was similar to the established WN app. The digital polytherapeutic demonstrated significant benefit for tinnitus reduction supporting further development.

Claus Kadelka, Reinhard Laubenbacher, David Murrugarra et al.

This paper presents the foundation for a decomposition theory for Boolean networks, a type of discrete dynamical system that has found a wide range of applications in the life sciences, engineering, and physics. Given a Boolean network satisfying certain conditions, there is a unique collection of subnetworks so that the network can be reconstructed from these subnetworks by an extension operation. The main result of the paper is that this structural decomposition induces a corresponding decomposition of the network dynamics. The theory is motivated by the search for a mathematical framework to formalize the hypothesis that biological systems are modular, widely accepted in the life sciences, but not well-defined and well-characterized. As an example of how dynamic modularity could be used for the efficient identification of phenotype control, the control strategies for the network can be found by identifying controls in its modules, one at a time.

M. Wagenmakers, K. Vansteelandt, E. Exel et al.

Alexander Nahmad-Rohen, Augusto García-Valenzuela

The van de Hulst formula provides an expression for the effective refractive index or effective propagation constant of a suspension of particles of arbitrary shape, size and refractive index in an optically homogeneous medium. However, its validity for biological matter, which often consists of very dense suspensions of cells, is unclear because existing derivations of the formula or similar results rely on far-field scattering and/or on the suspension in question being dilute. We present a derivation of the van de Hulst formula valid for suspensions of large, tenuous scatterers -- the type biological suspensions are typically made of -- which does not rely on these conditions, showing that they are not strictly necessary for the formula to be valid. We apply these results specifically to blood and epithelial tissue. Furthermore, we determine the true condition for the formula to be valid for these types of tissues. We finally provide a simple way to estimate -- and, more importantly, correct -- the error incurred by the van de Hulst formula when this condition is not met.

Matteo Paoluzzi, Luca Angelani, Giorgio Gosti et al.

Experimental evidence shows that there is a feedback between cell shape and cell motion. How this feedback impacts the collective behavior of dense cell monolayers remains an open question. We investigate the effect of a feedback that tends to align the cell crawling direction with cell elongation in a biological tissue model. We find that the alignment interaction promotes nematic patterns in the fluid phase that eventually undergo a non-equilibrium phase transition into a quasi-hexagonal solid. Meanwhile, highly asymmetric cells do not undergo the liquid-to-solid transition for any value of the alignment coupling. In this regime, the dynamics of cell centers and shape fluctuation show features typical of glassy systems.

Michael Konopik, Till Korten, Heiner Linke et al.

We consider the solution of the subset sum problem based on a parallel computer consisting of self-propelled biological agents moving in a nanostructured network that encodes the NP-complete task in its geometry. We develop an approximate analytical method to analyze the effects of small errors in the nonideal junctions composing the computing network by using a Gaussian confidence interval approximation of the multinomial distribution. We concretely evaluate the probability distribution for error-induced paths and determine the minimal number of agents required to obtain a proper solution. We finally validate our theoretical results with exact numerical simulations of the subset sum problem for different set sizes and error probabilities.

C. Montejo, A. Rodríguez, M. Pascual-Vicente et al.

Olga Pollatos, Eleana Georgiou, Susanne Kobel et al.

BackgroundBody image dissatisfaction (BID) is related to an increased risk for various health issues including descreased health-related quality of life (HRQoL), the development of problematic eating behaviors and obesity. Previous research indicates that emotional intelligence is one important factor related to BID in adults. Whether this is the case in children, remains yet unknown. Taking this into consideration, the aim of this study was to explore the relationship between BID and trait-based emotion intelligence (TEI) as well as HRQoL in female and male primary school children.Materials and methodsTEI and BID were assessed via self-reports as well as HRQoL via parental reports in a large sample of 991 primary school children (429 girls) within the “Baden Württemberg Study”, which evaluated the effectiveness of the health prevention programm “Join the Healthy Boat” in Southwestern Germany.ResultsOur findings demonstrated the interrelation between higher levels of TEI and lower levels of BID among girls and boys. Positive associations were found between better HRQoL, better intrapersonal and stress management abilites (subscales of TEI) and lower BID, as reflected by parental and self-reports.ConclusionsOur results reveal an interconnectivity between TEI, BID, and better HRQoL in female and male primary school children. Although the observed correlations were rather small, they nervertheless support the idea that TEI consists a key-factor for the self-regulation of health-related behavior. Prevention programs could benefit from including processes, that sough to improve aspects of emotional intelligence such as intrapersonal, interpersonal abilities, and adaptability, as an effort of preventing problematic habits or lifestyles that could lead to disordered eating behaviors as well as to obesity in middle childhood.

Zi Wang, Di Guo, Zhangren Tu et al.

The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled exponentials is highly expected in general signal processing and many applications. Deep learning has shown astonishing potential in this field but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining merits of the sparse model-based optimization method and data-driven deep learning, we propose a deep learning architecture for spectra reconstruction from undersampled data, called MoDern. It follows the iterative reconstruction in solving a sparse model to build the neural network and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that MoDern enables more robust, high-fidelity, and ultra-fast reconstruction than the state-of-the-art methods. Remarkably, MoDern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-MoDern), contributing a promising strategy for further development of biological applications.

K. Demin, D. Meshalkina, E. Kysil et al.

&NA; The endocannabinoid and opioid systems are two interplaying neurotransmitter systems that modulate drug abuse, anxiety, pain, cognition, neurogenesis and immune activity. Although they are involved in such critical functions, our understanding of endocannabinoid and opioid physiology remains limited, necessitating further studies, novel models and new model organisms in this field. Zebrafish (Danio rerio) is rapidly emerging as one of the most effective translational models in neuroscience and biological psychiatry. Due to their high physiological and genetic homology to humans, zebrafish may be effectively used to study the endocannabinoid and opioid systems. Here, we discuss current models used to target the endocannabinoid and opioid systems in zebrafish, and their potential use in future translational research and high‐throughput drug screening. Emphasizing the high degree of conservation of the endocannabinoid and opioid systems in zebrafish and mammals, we suggest zebrafish as an excellent model organism to study these systems and to search for the new drugs and therapies targeting their evolutionarily conserved mechanisms. HighlightsThe endocannabinoid and opioid systems potently modulate brain functionsZebrafish (Danio rerio) is a novel translational model in neuroscience research.Here, we discuss models to target the endocannabinoid and opioid systems in zebrafishWe emphasize the high degree of conservation of these systems in zebrafish and mammalsZebrafish are an excellent model to study endocannabinoid and opioid mechanisms and drugs in‐vivo