Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Minho Lee, Jinwon Park, Ji-Hyun Kim et al.

Biological water is an ionic solution containing both monovalent and divalent ions. However, the effects of divalent ions on the dynamics of biological water remain largely unknown. Here, we investigate how the transport dynamics of water molecules nanoconfined between lipid membranes depends on the concentration of calcium (Ca2+) and magnesium (Mg2+) ions by using molecular dynamics simulations and the generalized transport equation for biological water. We find that the diffusion coefficient of biological water monotonically increases with Ca2+ ion concentration but exhibits a largely opposite, non-monotonic dependence on Mg2+ concentration. The deviation of the water molecules' displacement distribution from the Gaussian also shows distinct dependence on the concentrations of Mg2+ and Ca2+. These contrasting behaviors originate from the different hydration radii of these divalent ions and their distinct effects on the interfacial structure and dynamics of biological water. The relaxation of the lateral displacement distribution of water molecules toward a Gaussian is determined by the time-correlation function of diffusion coefficient fluctuations, whose relaxation time increases with salt concentrations. The primary source of the lateral diffusion coefficient fluctuation is thermal motion of water molecules in the longitudinal direction, along which microscopic environments surrounding a water molecule, including the functional groups of lipid membrane and ion concentrations, drastically change.

Renzhi He, Haowen Zhou, Yubei Chen et al.

Volumetric reconstruction of label-free living cells from non-destructive optical microscopic images reveals cellular metabolism in native environments. However, current optical tomography techniques require hundreds of 2D images to reconstruct a 3D volume, hindering them from intravital imaging of biological samples undergoing rapid dynamics. This poses the challenge of reconstructing the entire volume of semi-transparent biological samples from sparse views due to the restricted viewing angles of microscopes and the limited number of measurements. In this work, we develop Neural Volumetric Prior (NVP) for high-fidelity volumetric reconstruction of semi-transparent biological samples from sparse-view microscopic images. NVP integrates explicit and implicit neural representations and incorporates the physical prior of diffractive optics. We validate NVP on both simulated data and experimentally captured microscopic images. Compared to previous methods, NVP significantly reduces the required number of images by nearly 50-fold and processing time by 3-fold while maintaining state-of-the-art performance. NVP is the first technique to enable volumetric reconstruction of label-free biological samples from sparse-view microscopic images, paving the way for real-time 3D imaging of dynamically changing biological samples. \href{https://xue-lab-cobi.github.io/Sparse-View-FDT/}{Project Page}

Gabriel Asher, Devesh Shah, Amy A. Caudy et al.

A vast majority of mass spectrometry data remains uncharacterized, leaving much of its biological and chemical information untapped. Recent advances in machine learning have begun to address this gap, particularly for tasks such as spectral identification in tandem mass spectrometry data. Here, we present the latest generation of LSM-MS2, a large-scale deep learning foundation model trained on millions of spectra to learn a semantic chemical space. LSM-MS2 achieves state-of-the-art performance in spectral identification, improving on existing methods by 30% in accuracy of identifying challenging isomeric compounds, yielding 42% more correct identifications in complex biological samples, and maintaining robustness under low-concentration conditions. Furthermore, LSM-MS2 produces rich spectral embeddings that enable direct biological interpretation from minimal downstream data, successfully differentiating disease states and predicting clinical outcomes across diverse translational applications.

Si-Qing Cai, Yue Tian, Zi‑Xian Zhang et al.

Remifentanil is an ultrashort-acting opioid receptor agonist that has the greatest advantage in clinical analgesia but often induces hyperalgesia. The underlying mechanisms of remifentanil-induced hyperalgesia (RIH) remain unclear. Here, we report that exposure of remifentanil to rats induces an elevation of spontaneous neuronal activity, innoxious and noxious stimuli-evoked neuronal hyperactivities, and enhanced theta band local field potential (LFP) oscillations across the ventral posterolateral nucleus of the thalamus (VPL), primary somatosensory cortex (S1), insular cortex (IC), and anterior cingulate cortex (ACC) brain regions. Either chemogenetic inhibition of glutamatergic neurons in the S1 cortex, or blockade of the S1→ACC projection by chemogenetic and optogenetic approaches, attenuates the remifentanil-induced mechanical and thermal hyperalgesia in RIH model rats. Besides, electroacupuncture (EA) intervention at 2-Hz frequency alleviates the animals’ mechanical and thermal hyperalgesia in remifentanil-treated rats. Also, 2 Hz-EA treatment inhibits the remifentanil-induced enhancement of spontaneous neuronal activity, the innoxious and noxious stimuli-evoked hyperactivities, and the increase of theta band LFP oscillations in the VPL, S1, IC, and ACC cortexes. These results indicate that the sensitized neuronal activity and the heightened theta band brain rhythmical oscillations in the VPL, S1, IC, and ACC cortexes contributes to the development of RIH, while 2 Hz-EA treatment exerts its analgesic effects on the RIH through inhibiting the aberrant neuronal activities of pain-related thalamo-cortico-limbic circuits including VPL, S1, IC, and ACC brain regions. Our findings identify a cortical circuit mechanism underlying the pathogenesis of RIH, and present potential therapeutic targets for the treatment of RIH by EA. Summary: Sensitized neuronal activity and elevated theta rhythms in pain-related thalamo-cortico-limbic circuits contributes to RIH and the analgesic effects of 2 Hz-EA treatment on RIH.

Ayesha Vermani, Matthew Dowling, Hyungju Jeon et al.

Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap restricts the scope of experiments vital for advancing both fundamental and clinical neuroscience. Recent advances in real-time machine learning technologies, particularly in analyzing neural time series as nonlinear stochastic dynamical systems, are beginning to bridge this gap. These technologies enable immediate interpretation of and interaction with neural systems, offering new insights into neural computation. However, several significant challenges remain. Issues such as slow convergence rates, high-dimensional data complexities, structured noise, non-identifiability, and a general lack of inductive biases tailored for neural dynamics are key hurdles. Overcoming these challenges is crucial for the full realization of real-time neural data analysis for the causal investigation of neural computation and advanced perturbation based brain machine interfaces. In this paper, we provide a comprehensive perspective on the current state of the field, focusing on these persistent issues and outlining potential paths forward. We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges. These approaches represent promising research directions that could redefine the landscape of neuroscience experiments and brain-machine interfaces, facilitating breakthroughs in understanding brain function, and treatment of neurological disorders.

Menghua Wu, Umesh Padia, Sean H. Murphy et al.

Identifying variables responsible for changes to a biological system enables applications in drug target discovery and cell engineering. Given a pair of observational and interventional datasets, the goal is to isolate the subset of observed variables that were the targets of the intervention. Directly applying causal discovery algorithms is challenging: the data may contain thousands of variables with as few as tens of samples per intervention, and biological systems do not adhere to classical causality assumptions. We propose a causality-inspired approach to address this practical setting. First, we infer noisy causal graphs from the observational and interventional data. Then, we learn to map the differences between these graphs, along with additional statistical features, to sets of variables that were intervened upon. Both modules are jointly trained in a supervised framework, on simulated and real data that reflect the nature of biological interventions. This approach consistently outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets. We also demonstrate significant improvements over current causal discovery methods for predicting soft and hard intervention targets across a variety of synthetic data.

Rachael J. Murphy, Subin Paul, Ralph Primelo

Primary hyperparathyroidism (PHPT) and subsequent hypercalcemia have been reported to be associated with psychosis. Here we report the case of a 28-year-old cannabis using male with his first contact with psychiatric care because of mood instability, bizarre behavior, and poor ability to carry out activities of daily living. Hypercalcemia was identified, and a subsequent endocrine workup confirmed PHPT. After parathyroidectomy, there was no longer any need for antipsychotic or other psychotropic medications; the report emphasizes the importance of considering organic causes, such as hyperparathyroidism, in patients presenting with psychotic-like symptoms, including in the setting of substance use disorder. Prompt recognition and appropriate management of the underlying condition are crucial for optimizing patient outcomes.

Kang-Min Choi, Kang-Min Choi, Taegyeong Lee et al.

IntroductionRecent resting-state electroencephalogram (EEG) studies have consistently reported an association between aberrant functional brain networks (FBNs) and treatment-resistant traits in patients with major depressive disorder (MDD). However, little is known about the changes in FBNs in response to external stimuli in these patients. This study investigates whether changes in the salience network (SN) could predict responsiveness to pharmacological treatment in resting-state and external stimuli conditions.MethodsThirty-one drug-naïve patients with MDD (aged 46.61 ± 10.05, female 28) and twenty-one healthy controls (aged 43.86 ± 14.14, female 19) participated in the study. After 8 weeks of pharmacological treatment, the patients were divided into non-remitted MDD (nrMDD, n = 14) and remitted-MDD (rMDD, n = 17) groups. EEG data under three conditions (resting-state, standard, and deviant) were analyzed. The SN was constructed with three cortical regions as nodes and weighted phase-lag index as edges, across alpha, low-beta, high-beta, and gamma bands. A repeated measures analysis of the variance model was used to examine the group-by-condition interaction. Machine learning-based classification analyses were also conducted between the nrMDD and rMDD groups.ResultsA notable group-by-condition interaction was observed in the high-beta band between nrMDD and rMDD. Specifically, patients with nrMDD exhibited hypoconnectivity between the dorsal anterior cingulate cortex and right insula (p = 0.030). The classification analysis yielded a maximum classification accuracy of 80.65%.ConclusionOur study suggests that abnormal condition-dependent changes in the SN could serve as potential predictors of pharmacological treatment efficacy in patients with MDD.

Shasha Li, Shasha Li, Yuting Gao et al.

Patients with neuromyelitis optica spectrum disorder (NMOSD) coexisting with both Sjögren’s syndrome (SS) and pancytopenia are exceptionally rare. There is no study on the treatment of such patients. We presented a case of AQP4-IgG seropositive refractory NMOSD patient combined with SS and pancytopenia with significant response to inebilizumab. In 2017 the 49-year-old female patient was diagnosed with SS and pancytopenia without any treatment. In August 2022, she had a sudden onset of lower limbs weakness, manifested as inability to walk, accompanied by urinary incontinence. After receiving methylprednisolone and cyclophosphamide, she regained the ability to walk. In February 2023, she suffered from weakness of both lower limbs again and paralyzed in bed, accompanied by retention of urine and stool, and loss of vision in both eyes. After receiving methylprednisolone and three plasmapheresis, the condition did not further worsen, but there was no remission. In March 2023, the patient was admitted to our hospital and was formally diagnosed with AQP4-IgG seropositive NMOSD combined with SS and pancytopenia. After receiving two 300 mg injections of inebilizumab, not only the symptoms of NMOSD improved significantly, but also the symptoms of concurrent SS and pancytopenia. In the cases of AQP4-IgG seropositive NMOSD who have recurrent episodes and are comorbid with other autoimmune disorders, inebilizumab may be a good choice.

Alex K Miller, MD, Matthew R Cederman, BS, Daniel K Park, MD

ABSTRACT: Background: There is growing interest in transitioning various surgical procedures to the outpatient care setting. However, for Medicare patients, the site of service for surgical procedures is influenced by regulations within the Inpatient and Outpatient Prospective Payment Systems. The purpose of this study is to quantify changes in utilization of outpatient spine surgery within the Medicare population, as well as to determine changes in outpatient volume after removal of a procedure from the “inpatient-only” list. Methods: This is a cross-sectional study of Medicare billing database information for selected spine procedures included in the Medicare Physician/Supplier Procedure Summary (PSPS) public use files from 2010–2021. These files include aggregated data from Medicare Part B fee-for-service claims, published yearly. Procedures from Healthcare Common Procedural Coding System (HCPCS) code ranges 22010–22899 and 62380–63103 were selected for analysis, limited to surgical services delivered in the inpatient, hospital outpatient department (HOPD), and ambulatory surgical center (ASC) settings. For each HCPCS code included, estimates of the total number of services and corresponding changes in volume were calculated. Results: Within the range of codes included in the study, the total number of outpatient spine procedures rose approximately 193% from 2010 to 2021, with compound annual growth rate (CAGR) for outpatient procedures per year of 9.9% for HOPDs and 15.7% for ASCs (-2.2% for inpatient procedures). Within this period, the ASC list grew from 12 procedures to 58 procedures. In 2021, the highest volume ASC procedure was HCPCS 63047, at approximately 4970 procedures. Conclusions: This study demonstrates a trend of increasing utilization of HOPDs and ASCs for spine procedures among Medicare beneficiaries from 2010 to 2021. Though HOPDs are currently more widely utilized, the ongoing additions of spine procedures to the ASC covered procedures list may shift this balance.

Noelia Martínez-Molina, Yonatan Sanz-Perl, Anira Escrichs et al.

Traumatic Brain Injury (TBI) is a prevalent disorder mostly characterized by persistent impairments in cognitive function that poses a substantial burden on caregivers and the healthcare system worldwide. Crucially, severity classification is primarily based on clinical evaluations, which are non-specific and poorly predictive of long-term disability. In this Mini Review, we first provide a description of our model-free and model-based approaches within the turbulent dynamics framework as well as our vision on how they can potentially contribute to provide new neuroimaging biomarkers for TBI. In addition, we report the main findings of our recent study examining longitudinal changes in moderate-severe TBI (msTBI) patients during a one year spontaneous recovery by applying the turbulent dynamics framework (model-free approach) and the Hopf whole-brain computational model (model-based approach) combined with in silico perturbations. Given the neuroinflammatory response and heightened risk for neurodegeneration after TBI, we also offer future directions to explore the association with genomic information. Moreover, we discuss how whole-brain computational modeling may advance our understanding of the impact of structural disconnection on whole-brain dynamics after msTBI in light of our recent findings. Lastly, we suggest future avenues whereby whole-brain computational modeling may assist the identification of optimal brain targets for deep brain stimulation to promote TBI recovery.

Tatyana Bogatenko, Konstantin Sergeev, Andrei Slepnev et al.

In this paper we show the possibility of creating and identifying the features of an artificial neural network (ANN) which consists of mathematical models of biological neurons. The FitzHugh--Nagumo (FHN) system is used as an example of model demonstrating simplified neuron activity. First, in order to reveal how biological neurons can be embedded within an ANN, we train the ANN with nonlinear neurons to solve a a basic image recognition problem with MNIST database; and next, we describe how FHN systems can be introduced into this trained ANN. After all, we show that an ANN with FHN systems inside can be successfully trained and its accuracy becomes larger. What has been done above opens up great opportunities in terms of the direction of analog neural networks, in which artificial neurons can be replaced by biological ones. \end{abstract}

James Greene, Eitan Tadmor, Ming Zhong

The emergence of large scale structures in biological systems, and in particular the formation of lines of hierarchy, is observed in many scales, from collections of cells to groups of insects to herds of animals. Motivated by phenomena in chemotaxis and phototaxis, we present a new class of alignment models which exhibit alignment into lines. The spontaneous formation of such ``fingers" can be interpreted as the emergence of leaders and followers in a system of identically interacting agents. Various numerical examples are provided, which demonstrate emergent behaviors similar to the ``fingering'' phenomenon observed in some phototaxis and chemotaxis experiments; this phenomenon is generally known as a challenging pattern to capture for existing models. The novel pairwise interactions provides a fundamental mechanism by which agents may form social hierarchy across a wide range of biological systems.

Qing-Sheng Li, Yan-Jie Jia

Ferroptosis, a new non-necrotizing programmed cell death (PCD), is driven by iron-dependent phospholipid peroxidation. Ferroptosis plays a key role in secondary traumatic brain injury and secondary spinal cord injury and is closely related to inflammation, immunity, and chronic injuries. The inhibitors against ferroptosis effectively improve iron homeostasis, lipid metabolism, redox stabilization, neuronal remodeling, and functional recovery after trauma. In this review, we elaborate on the latest molecular mechanisms of ferroptosis, emphasize its role in secondary central nervous trauma, and update the medicines used to suppress ferroptosis following injuries.

Chenhao Li, Wei Dai, Shuai Miao et al.

It has yet to be determined whether medication overuse headache (MOH) is an independent disorder or a combination of primary headache and substance addiction. To further explore the causes of MOH, we compared MOH with substance use disorder (SUD) in terms of the brain regions involved to draw more targeted conclusions. In this review, we selected alcohol use disorder (AUD) as a representative SUD and compared MOH and AUD from two aspects of neuroimaging and basic research. We found that in neuroimaging studies, there were many overlaps between AUD and MOH in the reward circuit, but the extensive cerebral cortex damage in AUD was more serious than that in MOH. This difference was considered to reflect the sensitivity of the cortex structure to alcohol damage. In future research, we will focus on the central amygdala (CeA), prefrontal cortex (PFC), orbital-frontal cortex (OFC), hippocampus, and other brain regions for interventions, which may have unexpected benefits for addiction and headache symptoms in MOH patients.

Jay Sanguinetti

Michele Gentili, Leonardo Martini, Marialuisa Sponziello et al.

Motivation: Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. Results: In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. Availability and implementation: All code is publicly available and can be downloaded at \url{https://github.com/LeoM93/BiologicalRandomWalks}. We used publicly available datasets, details on their retrieval and preprocessing are provided in the supplementary material.

R. Kleiser, T. Raffelsberger, J. Trenkler et al.

In the past years, no event has affected people around the globe more than the SARS-COVID-2 pandemic. Besides the health system and the economy, it has affected social life. A grave sequela is the social distancing due to the ubiquitous use of medical face masks. Since these face masks cover approximately two thirds of the face including the mouth and nose, we hypothesized that they may impair affect reading of emotional face expressions. We used functional magnetic resonance imaging in 16 healthy volunteers to investigate brain activity changes related to the recognition of evolving emotional face expressions in short video-clips. We found that the face masks delayed emotion recognition, but at a normal nearly 100% success rate. This effect was related to a decreased activation in the cortical network mediating face recognition. Our data support the notion that face masks can have an adverse impact of social interactions.

Edgar De Jesus Ramos Muñoz, Veronica Ann Swanson, Christopher Johnson et al.

Persevering with home rehabilitation exercise is a struggle for millions of people in the US each year. A key factor that may influence motivation to engage with rehabilitation exercise is the challenge level of the assigned exercises, but this hypothesis is currently supported only by subjective, self-report. Here, we studied the relationship between challenge level and perseverance using long-term, self-determined exercise patterns of a large number of individuals (N = 2,581) engaging in home rehabilitation with a sensor-based exercise system without formal supervision. FitMi is comprised of two puck-like sensors and a library of 40 gamified exercises for the hands, arms, trunk, and legs that are designed for people recovering from a stroke. We found that individuals showed the greatest perseverance with the system over a 2-month period if they had (1) a moderate level of motor impairment and (2) high but not perfect success during the 1st week at completing the exercise game. Further, a steady usage pattern (vs. accelerating or decelerating use) was associated with more overall exercise, and declines in exercise amount over time were associated with exponentially declining session initiation probability rather than decreasing amounts of exercise once a session was initiated. These findings confirm that an optimized challenge level and regular initiation of exercise sessions predict achievement of a greater amount of overall rehabilitation exercise in a group of users of commercial home rehabilitation technology and suggest how home rehabilitation programs and exercise technologies can be optimized to promote perseverance.

Dan Zhu, Zhenzhen Fan, Fujun Cheng et al.

Objectives: To investigate the efficacy and safety of an improved ultrasound-guided pulsed radiofrequency (PRF) and nerve block (NB) for patients with pudendal neuralgia (PN). Methods: This retrospective analysis included 88 adults with PN treated in the Pain Department of Southwest Hospital from November 2011 to June 2021, with treatment including NB (<i>n</i> = 40) and PRF (<i>n</i> = 48). The primary outcome variable was pain severity, measured by a standardized visual analog scale (VAS). VAS values were collected at 1, 3, 7, and 14 days and 1 and 3 months after patients were treated with NB or PRF. Results: Compared with patients treated with NB (<i>n</i> = 40) and those treated with PRF (<i>n</i> = 48), no significant difference in pain reduction was observed in the short term (<i>p</i> = 0.739 and 0.981, at 1 and 3 days, respectively); however, in the medium and long term (1 to 3 months), there were statistically significant improvements in the PRF group over the NB group (<i>p</i> < 0.001). Moreover, it was noted that the average pain severity of primary PN and PN due to sacral perineurial cyst was significantly reduced with PRF therapy in the medium and long term when compared to other secondary PNs, including surgery, trauma, and diabetes. Discussion: The ultrasound-guided, improved, and innovative PRF/NB puncture path technique allows for gentler stimulation and faster identification of the pudendal nerve. The PRF technique may provide better treatments for primary PN and sacral perineurial cyst causing secondary PN in the medium and long term.