bluuebunny/crossref_metadata_2025_split huggingface dataset:
The DREAM implant: A Lightweight, Modular and Cost-Effective Implant System for Chronic Electrophysiology in Head-fixed and Freely Behaving Mice
Authors: Schröder Tim , Taylor Robert , Abd El Hay Muad , Nemri Abdellatif , França Arthur , Battaglia Francesco , Tiesinga Paul , Schölvinck Marieke L. , Havenith Martha N. 
Published: February 2024

ABSTRACTChronic electrophysiological recordings in rodents have significantly improved our understanding of neuronal dynamics and their behavioral relevance. However, current methods for chronically implanting probes present steep trade-offs between cost, ease of use, size, adaptability and long-term stability.SUMMARYIntroducing a lightweight, cost-effective probe implant system for chronic electrophysiology in rodents, optimized for ease of use, probe recovery, experimental versatility and compatibility with behavior.This protocol introduces a novel chronic probe implant system for mice called the DREAM (Dynamic, Recoverable, Economical, Adaptable and Modular), designed to overcome the trade-offs associated with currently available options. The system provides a lightweight, modular and cost-effective solution with standardized hardware elements that can be combined and implanted in straightforward steps and explanted safely for recovery and multiple re-use of probes, significantly reducing experimental costs.The DREAM implant system integrates three hardware modules: (1) a microdrive that can carry all standard silicon probes, allowing experimenters to adjust recording depth across a travel distance of up to 7mm; (2) a 3D-printable, open-source design for a wearable Faraday cage covered in copper mesh for electrical shielding, impact protection and connector placement, and (3) a miniaturized head-fixation system for improved animal welfare and ease of use. The corresponding surgery protocol was optimized for speed (total duration: 2 hours), probe safety and animal welfare.The resulting implants had minimal impact on animals’ behavioral repertoire, were easily applicable in freely moving and head-fixed contexts and delivered clearly identifiable spike waveforms and healthy neuronal responses for weeks of data collection post-implant. Infections and other surgery complications were extremely rare.As such, the DREAM implant system is a versatile, cost-effective solution for chronic electrophysiology in mice, enhancing animal well-being, and enabling more ethologically sound experiments. Its design simplifies experimental procedures across various research needs, increasing accessibility of chronic electrophysiology in rodents to a wide range of research labs.
Intracellular Lactate Dynamics inDrosophilaGlutamatergic Neurons
Authors: Price Matthew S. , Rastegari Elham , Gupta Richa , Vo Katie , Moore Travis I. , Venkatachalam Kartik 
Published: February 2024

SUMMARYRates of lactate production and consumption reflect the metabolic state of many cell types, including neurons. Here, we investigate the effects of nutrient deprivation on lactate dynamics inDrosophilaglutamatergic neurons by leveraging the limiting effects of the diffusion barrier surrounding cells in culture. We found that neurons constitutively consume lactate when availability of trehalose, the glucose disaccharide preferred by insects, is limited by the diffusion barrier. Acute mechanical disruption of the barrier reduced this reliance on lactate. Through kinetic modeling and experimental validation, we demonstrate that neuronal lactate consumption rates correlate inversely with their mitochondrial density. Further, we found that lactate levels in neurons exhibited temporal correlations that allowed prediction of cytosolic lactate dynamics after the disruption of the diffusion barrier from pre-perturbation lactate fluctuations. Collectively, our findings reveal the influence of diffusion barriers on neuronal metabolic preferences, and demonstrate the existence of temporal correlations between lactate dynamics under conditions of nutrient deprivation and those evoked by the subsequent restoration of nutrient availability.
Volume electron microscopy reveals 3D synaptic nanoarchitecture in postmortem human prefrontal cortex
Authors: Glausier Jill R. , Bouchet-Marquis Cedric , Maier Matthew , Banks-Tibbs Tabitha , Wu Ken , Ning Jiying , Melchitzky Darlene , Lewis David A. , Freyberg Zachary 
Published: February 2024

SUMMARYSynaptic function is directly reflected in quantifiable ultrastructural features using electron microscopy (EM) approaches. This coupling of synaptic function and ultrastructure suggests thatin vivosynaptic function can be inferred from EM analysis ofex vivohuman brain tissue. To investigate this, we employed focused ion beam-scanning electron microscopy (FIB-SEM), a volume EM (VEM) approach, to generate ultrafine-resolution, three-dimensional (3D) micrographic datasets of postmortem human dorsolateral prefrontal cortex (DLPFC), a region with cytoarchitectonic characteristics distinct to human brain. Synaptic, sub-synaptic, and organelle measures were highly consistent with findings from experimental models that are free from antemortem or postmortem effects. Further, 3D neuropil reconstruction revealed a unique, ultrastructurally-complex, spiny dendritic shaft that exhibited features characteristic of heightened synaptic communication, integration, and plasticity. Altogether, our findings provide critical proof-of-concept data demonstrating thatex vivoVEM analysis is an effective approach to inferin vivosynaptic functioning in human brain.
Discovery of the First-in-class G9a/GLP PROTAC Degrader
Authors: Velez Julia , Han Yulin , Yim Hyerin , Yang Peiyi , Deng Zhijie , Park Kwang-su , Kabir Md , Kaniskan H. Ümit , Xiong Yan , Jin Jian 
Published: February 2024

AbstractAberrantly expressed lysine methyltransferases G9a and GLP, which catalyze mono- and di-methylation of histone H3 lysine 9 (H3K9), have been implicated in numerous cancers. Recent studies have uncovered both catalytic and non-catalytic oncogenic functions of G9a/GLP. As such, G9a/GLP catalytic inhibitors have displayed limited anticancer activity. Here, we report the discovery of the first-in-class G9a/GLP proteolysis targeting chimera (PROTAC) degrader,10(MS8709), as a potential anticancer therapeutic.10induces G9a/GLP degradation in a concentration-, time, and ubiquitin-proteasome system (UPS)-dependent manner, does not alter the mRNA expression of G9a/GLP and is selective for G9a/GLP over other methyltransferases. Moreover,10displays superior cell growth inhibition to the parent G9a/GLP inhibitor UNC0642 in prostate, leukemia, and lung cancer cells and has suitable mouse pharmacokinetic properties forin vivoefficacy studies. Overall,10is a valuable chemical biology tool to further investigate the functions of G9a/GLP and a potential therapeutic for treating G9a/GLP-dependent cancers.Graphical Abstract
Cerebrovascular reactivity dispersion as a new biomarker of recent stroke symptomatology in moyamoya
Authors: Han Caleb , Richerson Wesley T. , Garza Maria , Rodeghier Mark , Mishra Murli , Davis L. Taylor , Fusco Matthew , Chitale Rohan , Shiino Shuhei , Jordan Lori C. , Donahue Manus J. 
Published: February 2024

AbstractBackgroundMoyamoya disease (MMD) is a non-atherosclerotic intracranial steno-occlusive condition placing patients at high risk for ischemic stroke. Direct and indirect surgical revascularization can improve blood flow in MMD; however, randomized trials demonstrating efficacy have not been performed and biomarkers of parenchymal hemodynamic impairment are needed to triage patients for interventions and evaluate post-surgical efficacy. We test the hypothesis that hypercapnia-induced maximum cerebrovascular reactivity (CVRMAX) and the more novel indicator cerebrovascular reactivity (CVR) response time (CVRDELAY), both assessed from time-regression analyses of non-invasive hypercapnic imaging, correlate with recent focal ischemic symptoms.MethodsHypercapnic reactivity medical resonance imaging (blood oxygenation level-dependent; echo time=35ms; spatial resolution=3.5×3.5×3.5mm) and catheter angiography assessments of cortical reserve capacity and vascular patency, respectively, in MMD participants (n=73) were performed in sequence. Time regression analyses were applied to quantify CVRMAXand CVRDELAY. Symptomatology information for each hemisphere (n=109) was categorized into symptomatic (ischemic symptoms within six months) or asymptomatic (no history of ischemic symptoms) and logistic regression analysis assessed the association of CVR metrics with ischemic symptoms after controlling for age and sex.ResultsSymptomatic hemispheres displayed lengthened CVRDELAY(p<0.001), which was more discriminatory between hemispheres than CVRMAX(p=0.037). CVRDELAY(p<0.001), but not CVRMAX(p=0.127), was found to be sensitively related to age in asymptomatic tissue (0.33-unit increase/year); age-dependent normative ranges are presented to enable quantitative assessment of patient-specific impairment. Furthermore, the area under the receiver operating characteristic curves shows that CVRDELAYpredicts ischemic symptoms (p<0.001), whereas CVRMAXdoes not (p=0.056).ConclusionFindings support that CVR metrics are uniquely altered in hemispheres with recent ischemic symptoms, motivating the investigation of CVR as a surrogate of ischemic symptomatology and treatment efficacy.
Psychological status of medical students and interns in the wake of the armed conflict of April 2023 in Sudan. A cross-sectional study
Authors: Abdulhamid Salaheddin M. , Almokdad Faris Kh. , Ahmed Razan J. Q. , Elbashir Rawah M. A. M. , Suleiman Yousif Ali Ahmed ,   SudaMedReCollab group
Published: February 2024

AbstractThe ongoing civil war in Sudan of April 2023 has erupted as two belligerent groups vie for the position of ruler in the country, resulting in severe detrimental effects throughout the population, from deaths to property losses to severe deterioration of the already strained infrastructures of the country, to the sheer psychological trauma from observing and suffering the ongoing dramatic events. A subgroup is the medical students and interns in Sudan, who suffered direct losses in every regard, from sheer psychological distress to deaths, with material losses and career disruptions. We believe this is a group that should be paid due attention. This study aims to determine whether certain elements can be viewed as correlates of the psychological disorders we expect to see prevalent and their prevalences.An online cross-sectional study was undertaken using several sampling methods to reach as far as we possibly could under the circumstances, with a structured questionnaire containing items divided between demographics, what we perceive to be factors, and scales (PHQ-9, GAD-7, AIS-8, and ITQ). The Survey was conducted from September 11thto 30th, 2023, resulting in 7306 responses, of which 665 were excluded. A sample of 6641 were included, of which 2370 (35.7%) were males and 4271 (64.3%) were females. We found prevalences of 33.7% for depression, 22.4% for anxiety, 13.6% for PTSD, and 59.5% for Insomnia, correlating with several factors, noted to impact the psychological health of our sample. Our findings suggest the need for purposeful, directed interventions to mitigate the impact of this conflict, and all that may follow.
Development and Application of Natural Language Processing on Unstructured Data in Hypertension: A Scoping Review
Authors: Ye Jiancheng , He Lu , Hai Jiarui , Xu Chengqi , Ding Sirui , Beestrum Molly 
Published: February 2024

ABSTRACTBackgroundHypertension is a global health concern with a vast body of unstructured data, such as clinical notes, diagnosis reports, and discharge summaries, that can provide valuable insights. Natural Language Processing (NLP) has emerged as a powerful tool for extracting knowledge from unstructured data. This scoping review aims to explore the development and application of NLP on unstructured clinical data in hypertension, synthesizing existing research to identify trends, gaps, and underexplored areas for future investigation.MethodsWe conducted a systematic search of electronic databases, including PubMed/MEDLINE, Embase, Cochrane Library, Scopus, Web of Science, ACM Digital Library, and IEEE Xplore Digital Library, to identify relevant studies published until the end of 2022. The search strategy included keywords related to hypertension, NLP, and unstructured data. Data extraction included study characteristics, NLP methods, types of unstructured data sources, and key findings and limitations.ResultsThe initial search yielded 951 articles, of which 45 met the inclusion criteria. The selected studies spanned various aspects of hypertension, including diagnosis, treatment, epidemiology, and clinical decision support. NLP was primarily used for extracting clinical information from unstructured electronic health records (EHRs) documents and text classification. Clinical notes were the most common sources of unstructured data. Key findings included improved diagnostic accuracy and the ability to comprehensively identify hypertensive patients with a combination of structured and unstructured data. However, the review revealed a lack of more advanced NLP techniques used in hypertension, generalization of NLP outside of benchmark datasets, and a limited focus on the integration of NLP tools into clinical practice.DiscussionThis scoping review highlights the diverse applications of NLP in hypertension research, emphasizing its potential to transform the field by harnessing valuable insights from unstructured data sources. There is a need to adopt and customize more advanced NLP for hypertension research. Future research should prioritize the development of NLP tools that can be seamlessly integrated into clinical settings to enhance hypertension management.ConclusionNLP demonstrates considerable promise in gleaning meaningful insights from the vast expanse of unstructured data within the field of hypertension, shedding light on diagnosis, treatment, and the identification of patient cohorts. As the field advances, there is a critical need to promote the use and development of advanced NLP methodologies that are tailored to hypertension and validated on real-world unstructured data.
Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators
Authors: Aktay Sinan , Sander Leonard M. , Zochowski Michal 
Published: February 2024

AbstractNeuromodulatory processes in the brain can critically change signal processing on a cellular level leading to dramatic changes in network level reorganization. Here, we use coupled non-identical Kuramoto oscillators to investigate how changes in the shape of phase response curves from Type 1 to Type 2, mediated by varying ACh levels, coupled with activity dependent plasticity may alter network reorganization. We first show that when plasticity is absent, the Type 1 networks, as expected, exhibit asynchronous dynamics with oscillators of the highest natural frequency robustly evolving faster in terms of their phase dynamics. At the same time, the Type 2 networks synchronize, with oscillators locked so that the ones with higher natural frequency have a constant phase lead as compared to the ones with lower natural frequency. This relationship establishes a robust mapping between the frequency and oscillators’ phases in the network, leading to structure/frequency mapping when plasticity is present. Further we show that while connection plasticity can produce stable synchrony (so called splay states) in Type 1 networks, the structure/frequency reorganization observed in Type 2 networks is not present.
Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
Authors: Gür Burak , Ramirez Luisa , Cornean Jacqueline , Thurn Freya , Molina-Obando Sebastian , Ramos-Traslosheros Giordano , Silies Marion 
Published: February 2024

SummaryNatural scenes are highly dynamic, challenging the reliability of visual processing. Yet, humans and many animals perform accurate visual behaviors, whereas computer vision devices struggle with changing environments. How does animal vision achieve this? Here, we reveal the algorithms and mechanisms of rapid luminance gain control inDrosophila,resulting in stable visual processing. We identify the dendrites of specific third order neurons, Tm1 and Tm9, as the site of luminance gain control. The circuitry further involves neurons with wide-field properties, matching computational predictions that local spatial pooling can drive optimal contrast processing in natural scenes where light conditions change rapidly. Experiments and theory argue that a spatially pooled luminance signal achieves luminance gain control via divisive normalization. This process relies on shunting inhibition using the glutamate-gated chloride channel GluClα. Our work describes computationally, algorithmically, and mechanistically, how visual circuits robustly process visual information in dynamically changing, natural scenes.
Decoding Musical Valence And Arousal: Exploring The Neural Correlates Of Music Evoked Emotions And The Role Of Expressivity Features
Authors: Sayal Alexandre , Guedes Ana Gabriela , Almeida Inês , Pereira Daniela Jardim , Lima César , Panda Renato , Paiva Rui Pedro , Sousa Teresa , Castelo-Branco Miguel , Bernardino Inês , Direito Bruno 
Published: February 2024

AbstractMusic can convey basic emotions, such as joy and sadness, and more complex ones, such as tenderness or nostalgia. Its effects on emotion regulation and reward have attracted much attention in cognitive and affective neuroscience. Understanding the neural correlates of music-evoked emotions may guide the development of neurorehabilitation interventions based on music. Here, we used fMRI to examine the relationship between the classification of music excerpts regarding perceived valence and arousal and their neural correlates. Twenty participants were scanned while listening to 96 musical excerpts, which were classified beforehand into four categories as a function of valence (positive vs. negative) and arousal (high vs. low). Differences in valence and arousal modulated activity in cortical regions, most noticeably the music-specific subregions of the auditory cortex, but also in the thalamus and regions of the reward network such as the amygdala. Using multivoxel pattern analysis, we created a computational model able to decode the valence and arousal of the music excerpts significantly above chance. We further explored how a set of musical features relate to brain activity in valence-, arousal-, reward-, and auditory-related ROIs. The results emphasize the differential involvement of musical features in the brain, notably expressive features such as Vibrato and Tonal and Spectral dissonance in valence, arousal, and reward brain networks, while a broader set of features modulate sensory auditory networks. Using ecologically valid music stimuli, we contribute to the definition of the neural substrates of music listening and evoked emotions. Moreover, the definition of the musical features that modulate specific brain networks paves the way to developing novel music-based neurorehabilitation strategies.
MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics
Authors: Tremblay Stefanie A , Alasmar Zaki , Pirhadi Amir , Carbonell Felix , Iturria-Medina Yasser , Gauthier Claudine J , Steele Christopher J 
Published: February 2024

AbstractMultivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group-or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.
A mediating framework in resting-state connectivity between the medial prefrontal cortex and anterior cingulate in mild cognitive impairment
Authors: Huang Yiyuan Teresa , Yan Sui-Hing , Chuang Yi-Fang , Shih Yao-Chia , Huang Yan-Siang , Liu Yi-Chien , Kao Scott Shyh-Chang , Chiu Yen-Ling , Fan Yang-Teng 
Published: February 2024

AbstractMild cognitive impairment (MCI) is recognized as the prodromal phase of dementia, a condition that can be either maintained or reversed through timely medical interventions to prevent cognitive decline. Considerable studies using functional magnetic resonance imaging (fMRI) have indicated that altered activity in the medial prefrontal cortex (mPFC) serves as an indicator of various cognitive stages of aging. However, the impacts of intrinsic functional connectivity in the mPFC as a mediator on cognitive performance in individuals with and without MCI have not been fully understood. In this study, we recruited 42 MCI patients and 57 healthy controls, assessing their cognitive abilities and functional brain connectivity patterns through neuropsychological evaluations and resting-state fMRI, respectively. The MCI patients exhibited poorer performance on multiple neuropsychological tests compared to the healthy controls. At the neural level, functional connectivity between the mPFC and the anterior cingulate cortex (ACC) was significantly weaker in the MCI group and correlated with multiple neuropsychological test scores. The result of the mediation analysis further demonstrated that functional connectivity between the mPFC and ACC notably mediated the relationship between the MCI and semantic working memory. These findings suggest that altered mPFC-ACC connectivity may have a plausible causal influence on cognitive decline and provide implications for early identifications of neurodegenerative diseases and precise monitoring of disease progression.