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In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial instrument for capturing neural activity. However, this signal is polluted by different artifacts like muscle activity, eye blinks, environmental interference, etc., which makes it more difficult to retrieve important information from the signal. Deep learning methods have demonstrated the potential to lower these artifacts and enhance the EEG's quality in recent years. In this work, a novel deep learning method,"AnEEG" is presented for eliminating artifacts from EEG signal. The quantitative matrices NMSE, RMSE, CC, SNR and SAR are calculated to confirm the effectiveness of the proposed model. Through this process, it was found that the suggested model outperformed wavelet decomposition techniques. The model achieves lower NMSE and RMSE values, which indicates better agreement with the original signal. Achieving higher CC values means stronger linear agreement with the ground truth signals. Additionally, the model shows improvements in both SNR and SAR values. Overall, this suggested approach showcases promising results in improving the quality of EEG data by utilizing deep learning. [ABSTRACT FROM AUTHOR]

Real‐time measurement of neurochemical and electrophysiological activities in the living brain is vital for advancing neuroscience and understanding brain disorders. Carbon fiber microelectrodes (CFEs) have been widely utilized for in vivo electrochemical detection due to their subcellular size, biocompatibility, and desirable electrochemical properties, and CFE arrays have demonstrated excellent multi‐site chronic sensing of dopamine (DA) and neural activity recording capabilities. However, manual fabrication of CFE arrays lacks reproducibility and batch production capacity. Alternatively, photolithography enables batch fabrication of glassy carbon (GC) multielectrode arrays (GC‐MEAs) with high resolution and reproducibility, but the insertion of planar flexible MEAs poses challenges. In this study, GC fiber‐like (GCF) MEAs are presented and fabricated using photolithography. The GCF MEAs feature fiber‐like GC electrodes with small cross‐sections, facilitating self‐insertion into brain tissue without additional aids. Batch fabrication of GCF MEAs allows for customizable designs with minimal manual intervention. Comparative analysis with CFEs demonstrates improved electrochemical properties of GCF. In vivo experiments in mouse brains confirm the GCF MEAs' ability to measure neurotransmitter concentrations and record neural activities. This novel fabrication approach is promising for multimodal electrical and chemical measurements with minimal footprint, offering significant advancements in neural interface technology for neuroscience research and clinical applications. [ABSTRACT FROM AUTHOR]

Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications. [ABSTRACT FROM AUTHOR]

In this selective review, we describe the current neuroscientific literature on disturbances of the self-other boundary in schizophrenia as they relate to structural and experiential aspects of the self. Within these two broad categories, the structural self includes body ownership and agency, and the experiential self includes self-reflection, source monitoring, and self-referential and autobiographical memory. Further, we consider how disturbances in these domains link to the phenomenology of schizophrenia. We identify faulty internal predictive coding as a potential mechanism of disturbance in body ownership and agency, which results in susceptibility to bias (over- or under-attributing outcomes to one's own actions or intentions). This is reflected in reduced activity in the temporoparietal junction (TPJ), a heteromodal association area implicated in several aspects of self-other processing, as well as reduced fronto-parietal functional connectivity. Deficits of the experiential self in schizophrenia may stem from a lack of salience of self-related information, whereby the mental representation of self is not as rich as in healthy controls and therefore does not result in the same level of privileged processing. As a result, memory for self-referential material and autobiographical memory processes is impaired, which hinders creation of a cohesive life narrative. Impairments of the experiential self implicate abnormal activation patterns along the cortical midline, including medial prefrontal cortex and posterior cingulate/precuneus, as well as TPJ. In fact, TPJ appears to be involved in all the reviewed aspects of the self-other disturbance. We conclude with suggestions for future work, including implications for interventions with critical timing considerations. [ABSTRACT FROM AUTHOR]

This minireview explores the burgeoning field of targeted protein degradation (TPD) and its promising applications in neuroscience and clinical development. TPD offers innovative strategies for modulating protein levels, presenting a paradigm shift in small-molecule drug discovery and therapeutic interventions. Importantly, small-molecule protein degraders specifically target and remove pathogenic proteins from central nervous system cells without the drug delivery challenges of genomic and antibody-based modalities. Here, we review recent advancements in TPD technologies, highlight proteolysis targeting chimera (PROTAC) protein degrader molecules with proximity-induced degradation event-driven and iterative pharmacology, provide applications in neuroscience research, and discuss the high potential for translation of TPD into clinical settings. Targeted protein degradation is a revolutionary small-molecule approach for neurologic diseases. Gregory et al. highlight cutting-edge advances in PROTAC degrader molecules, showcasing their ability to cross the blood-brain barrier and selectively degrade disease-causing proteins by design. Targeted protein degradation is compared and contrasted to other modalities aimed at treating neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Neuroimaging technologies such as brain–computer interfaces and neurofeedback have evolved rapidly as new tools for cognitive neuroscience and as potential clinical interventions. However, along with these developments, concern has grown based on the fear of the potential misuse of neurotechnology. In October 2021, Chile became the first country to include neurorights in its Constitution. The present article is divided into two parts. In the first section, we describe the path followed by neurorights that led to its inclusion in the Chilean Constitution, and the neurotechnologies usually involved in neurorights discussions. In the second part, we discuss two potential problems of neurorights. We begin by pointing out some epistemological concerns regarding neurorights, mainly referring to the ambiguity of the concepts used in neurolegislations, the difficult relationship between neuroscience and politics and the weak reasons for urgency in legislating. We then describe the dangers of overprotective laws in medical research, based on the detrimental effect of recent legislation in Chile and the potential risk posed by neurorights to the benefits of neuroscience development. This article aims to engage with the scientific community interested in neurotechnology and neurorights in an interdisciplinary reflection of the potential consequences of neurorights. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is currently the most common psychiatric disorder in the world. It characterized by a high incidence of disease with the symptoms like depressed mood, slowed thinking, and reduced cognitive function. Without timely intervention, there is a 20–30% risk of conversion to treatment-resistant depression (TRD) and a high burden for the patient, family and society. Numerous studies have shown that physical activity (PA) is a nonpharmacological treatment that can significantly improve the mental status of patients with MDD and has positive effects on cognitive function, sleep status, and brain plasticity. However, the physiological and psychological effects of different types of PA on individuals vary, and the dosage profile of PA in improving symptoms in patients with MDD has not been elucidated. In most current studies of MDD, PA can be categorized as continuous endurance training (ECT), explosive interval training (EIT), resistance strength training (RST), and mind–body training (MBT), and the effects on patients’ depressive symptoms, cognitive function, and sleep varied. Therefore, the present study was based on a narrative review and included a large number of existing studies to investigate the characteristics and differences in the effects of different PA interventions on MDD. The study also investigated the characteristics and differences of different PA interventions in MDD, and explained the neural mechanisms through the results of multimodal brain function monitoring, including the intracranial environment and brain structure. It aims to provide exercise prescription and theoretical reference for future research in neuroscience and clinical intervention in MDD. [ABSTRACT FROM AUTHOR]

Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications. [ABSTRACT FROM AUTHOR]

Clinical neurosciences, and psychiatry specifically, have been challenged by the lack of a comprehensive and practical framework that explains the core mechanistic processes of variable psychiatric presentations. Current conceptualization and classification of psychiatric presentations are primarily centered on a non-biologically based clinical descriptive approach. Despite various attempts, advances in neuroscience research have not led to an improved conceptualization or mechanistic classification of psychiatric disorders. This perspective article proposes a new--work-in-progress--framework for conceptualizing psychiatric presentations based on neural network components (NNC). This framework could guide the development of mechanistic disease classification, improve understanding of underpinning pathology, and provide specific intervention targets. This model also has the potential to dissolve artificial barriers between the fields of psychiatry and neurology. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice. [ABSTRACT FROM AUTHOR]

Neural imaging plays a crucial role in understanding the structure and function of the living body. While near‐infrared‐II (NIR‐II) imaging techniques have emerged as potential tools for non‐invasive and high‐resolution imaging of neural activity, there have been rare relevant reports on in vivo neural imaging. This study presents the development of biodegradable NIR‐II rare‐earth nanoprobes as a novel contrast agent for enhanced neural imaging. The biodegradable NIR‐II rare‐earth nanoprobes exhibit excellent long wavelength emission, high quantum yield, and tunable biodegradation time in vivo. This work demonstrates the effectiveness of these nanoprobes in enhancing the imaging depth and resolution in a mouse model of neural activity, thereby facilitating a better understanding of the structure and function of the nervous system. Furthermore, their biodegradability ensures minimal long‐term toxicity and allows for repeatedly monitoring of damaged nerves during the recovery or deterioration process. The results highlight the potential of biodegradable NIR‐II rare‐earth nanoprobes for advancing neural imaging techniques and facilitating deeper insights into neuroscience research and clinical diagnostics. [ABSTRACT FROM AUTHOR]

Neurosciences clinical trials continue to have notoriously high failure rates. Appropriate outcomes selection in early clinical trials is key to maximizing the likelihood of identifying new treatments in psychiatry and neurology. The field lacks good standards for designing outcome strategies, therefore The Outcomes Research Group was formed to develop and promote good practices in outcome selection. This article describes the first published guidance on the standardization of the process for clinical outcomes in neuroscience. A minimal step process is defined starting as early as possible, covering key activities for evidence generation in support of content validity, patient-centricity, validity requirements and considerations for regulatory acceptance. Feedback from expert members is provided, regarding the risks of shortening the process and examples supporting the recommended process are summarized. This methodology is now available to researchers in industry, academia or clinics aiming to implement consensus-based standard practices for clinical outcome selection, contributing to maximizing the efficiency of clinical research. [ABSTRACT FROM AUTHOR]

The advent of neurotechnologies including advanced functional magnetic resonance imaging and electroencephalography to detect states of awareness not detectable by traditional bedside neurobehavioral techniques (i.e., covert consciousness) promises to transform neuroscience research and clinical practice for patients with brain injury. As these interventions progress from research tools into actionable, guideline-endorsed clinical tests, ethical guidance for clinicians on how to responsibly communicate the sensitive results they yield is crucial yet remains underdeveloped. Drawing on insights from empirical and theoretical neuroethics research and our clinical experience with advanced neurotechnologies to detect consciousness in behaviorally unresponsive patients, we critically evaluate ethical promises and perils associated with disclosing the results of clinical covert consciousness assessments and describe a semistructured approach to responsible data sharing to mitigate potential risks. [ABSTRACT FROM AUTHOR]

Attention, working memory, and executive control are commonly considered distinct cognitive functions with important reciprocal interactions. Yet, longstanding evidence from lesion studies has demonstrated both overlap and dissociation in their behavioural expression and anatomical underpinnings, suggesting that a lower dimensional framework could be employed to further identify processes supporting goal-directed behaviour. Here, we describe the anatomical and functional correspondence between attention, working memory, and executive control by providing an overview of cognitive models, as well as recent data from lesion studies, invasive and non-invasive multimodal neuroimaging and brain stimulation. We emphasize the benefits of considering converging evidence from multiple methodologies centred on the identification of brain mechanisms supporting goal-driven behaviour. We propose that expanding on this approach should enable the construction of a comprehensive anatomo-functional framework with testable new hypotheses, and aid clinical neuroscience to intervene on impairments of executive functions. [ABSTRACT FROM AUTHOR]

Recovery of consciousness after coma remains one of the most challenging areas for accurate diagnosis and effective therapeutic engagement in the clinical neurosciences. Recovery depends on preservation of neuronal integrity and evolving changes in network function that re-establish environmental responsiveness. It typically occurs in defined steps: it begins with eye opening and unresponsiveness in a vegetative state, then limited recovery of responsiveness characterizes the minimally conscious state, and this is followed by recovery of reliable communication. This review considers several points for novel interventions, for example, in persons with cognitive motor dissociation in whom a hidden cognitive reserve is revealed. Circuit mechanisms underlying restoration of behavioral responsiveness and communication are discussed. An emerging theme is the possibility to rescue latent capacities in partially damaged human networks across time. These opportunities should be exploited for therapeutic engagement to achieve individualized solutions for restoration of communication and environmental interaction across varying levels of recovery. Here, Nicholas Schiff reviews several entry points for innovations aimed at recovery of consciousness after coma. Opportunities for rescuing latent capacities in partially damaged human neurons are considered across a range of recoveries from coma to chronic cognitive impairment. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the repeatability of a new swept source optical coherence tomography (SS-OCT)-based biometer to measure anterior segment parameters and to assess the agreement with 3 other imaging devices based on different measurement principles. Setting: Unit of Eye and Vision, Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden. Design: Prospective, comparative case series. Methods: 3 consecutive measurements were obtained in unoperated eyes with the Eyestar900 (SS-OCT), Lenstar 900, MS-39, and Sirius. The following anterior segment parameters were evaluated: central corneal thickness (CCT), corneal diameter (CD), aqueous depth (AQD), and corneal power metrics. The repeatability limit (Rlim), coefficient of variation (CoV), and a repeated measures Bland-Altman analysis were performed. Results: 74 eyes of 74 participants were measured. The Rlims for CCT, CD, and AQD were lower than 10 µm, 0.3 mm, and 0.10 mm for all devices, respectively. The corresponding CoVs for these parameters never exceeded 1.2%. The Rlim for the corneal power metrics never exceeded 0.60 diopter (D) for any of the instruments. Lenstar showed the best agreement with the MS-39 to measure CCT, CD, and AQD (limit of agreement interval, LoA: 15.54 µm, 0.55 mm, and 0.16 mm, respectively). The mean difference for keratometry parameters was lower than 0.3 D for all device comparisons, and the LoA interval ranged between 0.52 D and 1.21 D. Conclusions: The repeatability for measuring anterior segment parameters was good, and the agreement among all the instruments was good for CD and AQD measurements. However, for CCT and keratometer parameters, the instruments cannot be used interchangeably due to large LoA interval. [ABSTRACT FROM AUTHOR]

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions. [ABSTRACT FROM AUTHOR]

Non-invasive brain stimulation (NIBS) approaches have seen a rise in utilization in both clinical and basic neuroscience in recent years. Here, we concentrate on the two methods that have received the greatest research: transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS). Both approaches have yielded pertinent data regarding the cortical excitability in subjects in good health as well as pertinent advancements in the management of various clinical disorders. NIBS is a helpful method for comprehending the cortical control of the ANS. Previous research has shown that there are notable changes in muscular sympathetic nerve activity when the motor cortex is modulated. Furthermore, in NIBS investigations, the ANS has been employed more frequently as an outcome measure to comprehend the overall impacts of these methods, including their safety profile. Though there is ample proof that brain stimulation has autonomic effects on animals, new research on the connection between NIBS and the ANS has produced contradictory findings. In order to better understand NIBS processes and ANS function, it is crucial to take into account the reciprocal relationship that exists between central modulation and ANS function. [ABSTRACT FROM AUTHOR]

Objective: (1) This trial will compare the clinical and psychosocial effectiveness of in-group and individually pain neuroscience education (PNE) in patients with chronic low back pain (CLBP). In addition, (2) the influence of social determinants of health on post-treatment results will be analyzed. Methods: A three-arm randomized controlled trial will be conducted. Sixty-nine participants with CLBP will be recruited in a 1:1:1 ratio. Participants, assessor, and statistician will be blinded to group assignment. The PNE intervention will be adapted to the context of the participants. An experimental group (n = 33) will receive PNE in an in-group modality, the other experimental group (n = 33) will receive PNE in an individually modality and the control group (n = 33) will continue with usual care. Additionally, participants will be encouraged to stay active by walking for 20–30 min 3–5 times per week and will be taught an exercise to improve transversus abdominis activation (bracing or abdominal following). The outcome measures will be fear avoidance and beliefs, pressure pain threshold, pain self-efficacy, catastrophizing, pain intensity, and treatment expectation. Outcome measures will be collected at one-week before intervention, immediately post-intervention, and four-weeks post-intervention. Conclusion: The innovative approach of PNE oriented to fear beliefs proposed in this study could broaden the application strategies of this educational therapeutic modality. Impact. Contextualized PNE delivered by physical therapist could be essential to achieve a good cost-effectiveness ratio of this intervention to improve the clinical condition of people with CLBP. [ABSTRACT FROM AUTHOR]

Cognitive neuroscience of diseases faces challenges dealing with complex structure–function associations, disease phenotype heterogeneity, lack of transdiagnostic models, and oversimplified cognitive approaches. Synergetics offers a unified framework to progressively tackle these challenges, emphasizing the complex dynamics of brain, body, and environmental interactions. We review recent advances in neuroscience, focusing on low-dimensional spatiotemporal hierarchies, whole-brain modeling and perturbation, interlevel integration, and naturalistic frameworks. The future of synergetics depends on handling variability, incorporating environmental factors, integrating more levels of analysis, and accessing large multimodal data sets, setting the stage for a transformative approach in clinical neuroscience. Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure–function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches. [ABSTRACT FROM AUTHOR]

Our daily experiences unfold continuously, but we remember them as a series of discrete events through a process called event segmentation. Prominent theories of event segmentation suggest that event boundaries in memory are triggered by significant shifts in the external environment, such as a change in one's physical surroundings. In this review, we argue for a fundamental extension of this research field to also encompass internal state changes as playing a key role in structuring event memory. Accordingly, we propose an expanded taxonomy of event boundary-triggering processes, and review behavioral and neuroscience research on internal state changes in three core domains: affective states, goal states, and motivational states. Finally, we evaluate how well current theoretical frameworks can accommodate the unique and interactive contributions of internal states to event memory. We conclude that a theoretical perspective on event memory that integrates both external environment and internal state changes allows for a more complete understanding of how the brain structures experiences, with important implications for future research in cognitive and clinical neuroscience. [ABSTRACT FROM AUTHOR]

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with singlecell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience. [ABSTRACT FROM AUTHOR]

Recent advancements in neural probe technology have become pivotal in both neuroscience research and the clinical management of neurological disorders. State-of-the-art developments have led to the advent of multichannel, high-density bidirectional neural interfaces that are adept at both recording and modulating neuronal activity within the central nervous system. Despite this progress, extant bidirectional probes designed for simultaneous recording and stimulation are beset with limitations, including elicitation of inflammatory responses and insufficient charge injection capacity. In this paper, we delineate the design and application of an innovative ultraflexible bidirectional neural probe engineered from polyimide. This probe is distinguished by its ability to facilitate high-resolution recordings and precise stimulation control in deep brain regions. Electrodes enhanced with a PEDOT:PSS/IrOx composite exhibit a substantial increase in charge storage capacity, escalating from 0.14 ± 0.01 mC/cm2 to an impressive 24.75 ± 0.18 mC/cm2. This augmentation significantly bolsters the electrodes' charge transfer efficacy. In tandem, we observed a notable reduction in electrode impedance, from 3.47 ± 1.77 MΩ to a mere 41.88 ± 4.04 kΩ, while the phase angle exhibited a positive shift from −72.61 ± 1.84° to −34.17 ± 0.42°. To substantiate the electrodes' functional prowess, we conducted in vivo experiments, where the probes were surgically implanted into the bilateral motor cortex of mice. These experiments involved the synchronous recording and meticulous analysis of neural signal fluctuations during stimulation and an assessment of the probes' proficiency in modulating directional turning behaviors in the subjects. The empirical evidence corroborates that targeted stimulation within the bilateral motor cortex of mice can modulate the intensity of neural signals in the stimulated locale, enabling the directional control of the mice's turning behavior to the contralateral side of the stimulation site. [ABSTRACT FROM AUTHOR]

Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models that can explain neural decoding for intended behaviors are crucial to identifying essential features of deep learning decoders in brain activity. In this study, we examine the performance of deep learning to classify mouse behavioral states from mesoscopic cortex-wide calcium imaging data. Our convolutional neural network (CNN)-based end-to-end decoder combined with recurrent neural network (RNN) classifies the behavioral states with high accuracy and robustness to individual differences on temporal scales of sub-seconds. Using the CNN-RNN decoder, we identify that the forelimb and hindlimb areas in the somatosensory cortex significantly contribute to behavioral classification. Our findings imply that the end-to-end approach has the potential to be an interpretable deep learning method with unbiased visualization of critical brain regions. Author summary: Deep learning is used in neuroscience, and it has become possible to classify and predict behavior from massive data of neural signals from animals, including humans. However, little is known about how deep learning discriminates the features of neural signals. In this study, we perform behavioral classification from calcium imaging data of the mouse cortex and investigate brain regions important for the classification. By the end-to-end approach, an unbiased method without selecting regions of interest, we clarify that information on the somatosensory areas in the cortex is important for distinguishing between resting and moving states in mice. This study will contribute to the development of interpretable deep-learning technology. [ABSTRACT FROM AUTHOR]

We outline how a predictive processing framework based on active inference can help overcome the limitations of fragmented feed-forward processing theories and more comprehensively explain the role of the amygdala in anxiety, fear, and danger detection. This framework integrates theoretical predictions with empirical findings, suggesting that the central amygdala subnucleus acts as a Bayesian regulator of an interoceptive self-model, sending top-down predictions to the basolateral amygdala for efficient perception. Fear conditioning is discussed as an example of proactive homeostatic regulation, where exteroceptive cues are used to anticipate negative interoceptive experiences. By extension, other amygdala functions (e.g., its role in approach/avoidance behavior or attention) can be explained using the same computational principles within a hierarchical active inference model. The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands. [ABSTRACT FROM AUTHOR]

Biophysical diffusion‐weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (fAW), which in turn is key for noninvasive estimation of the axonal volume fraction (fA). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation of fAW. We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM‐ and DWI‐based metrics of fA. To this end, we (a) used six DWI models based on the so‐called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmental T2 and can substantially enhance the comparability between EM‐ and DWI‐based metrics of fA. We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM‐based fA. Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one‐to‐one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI‐based models in clinical research and neuroscience. [ABSTRACT FROM AUTHOR]

Recent research suggests evidence for biases in latent-state and model-based learning in post-traumatic stress disorder (PTSD). Biases in latent-state and model-based learning potentially explain a larger set of behavioral and neurocircuitry findings in PTSD. Biases in latent-state and model-based learning potentially operate as a common target of both exposure therapy and cognitive processing therapy for PTSD. More research is needed to inform how to drive learning towards specific model updates during treatment. More research is needed integrating biases in latent-state and model-based learning within existing neurocircuitry and behavior models of PTSD. Post-traumatic stress disorder (PTSD) is characterized by altered emotional and behavioral responding following a traumatic event. In this article, we review the concepts of latent-state and model-based learning (i.e., learning and inferring abstract task representations) and discuss their relevance for clinical and neuroscience models of PTSD. Recent data demonstrate evidence for brain and behavioral biases in these learning processes in PTSD. These new data potentially recast excessive fear towards trauma cues as a problem in learning and updating abstract task representations, as opposed to traditional conceptualizations focused on stimulus-specific learning. Biases in latent-state and model-based learning may also be a common mechanism targeted in common therapies for PTSD. We highlight key knowledge gaps that need to be addressed to further elaborate how latent-state learning and its associated neurocircuitry mechanisms function in PTSD and how to optimize treatments to target these processes. [ABSTRACT FROM AUTHOR]

Background: Dynamic technological development and its enormous impact on modern societies are posing new challenges for 21st-century neuroscience. A special place is occupied by technologies based on virtual reality (VR). VR tools have already played a significant role in both basic and clinical neuroscience due to their high accuracy, sensitivity and specificity and, above all, high ecological value. Objective: Being in a digital world affects the functioning of the body as a whole and its individual systems. The data obtained so far, both from experimental and modeling studies, as well as (clinical) observations, indicate their great and promising potential, but apart from the benefits, there are also losses and negative consequences for users. Methods: This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across electronic databases (such as Web of Science Core Collection; PubMed; and Scopus, Taylor & Francis Online and Wiley Online Library) to identify beneficial effects and applications, as well as adverse impacts, especially on brain health in human neuroscience. Results: More than half of these articles were published within the last five years and represent state-of-the-art approaches and results (e.g., 54.7% in Web of Sciences and 63.4% in PubMed), with review papers accounting for approximately 16%. The results show that in addition to proposed novel devices and systems, various methods or procedures for testing, validation and standardization are presented (about 1% of articles). Also included are virtual developers and experts, (bio)(neuro)informatics specialists, neuroscientists and medical professionals. Conclusions: VR environments allow for expanding the field of research on perception and cognitive and motor imagery, both in healthy and patient populations. In this context, research on neuroplasticity phenomena, including mirror neuron networks and the effects of applied virtual (mirror) tasks and training, is of interest in virtual prevention and neurogeriatrics, especially in neurotherapy and neurorehabilitation in basic/clinical and digital neuroscience. [ABSTRACT FROM AUTHOR]

Catatonia is featured by complex symptoms combining motor, affective and behavioral phenomena as well as by its syndrome character with trans-diagnostic occurrence. It paradigmatically shows the limits of current forms of psychopathology like affective and cognitive approaches with respect to both clinical symptoms and brain mechanisms. We therefore suggest Spatiotemporal Psychopathology (STPP) which, as recently introduced, is here developed further following the latest findings in both clinical psychiatry and neuroscience. STPP is characterized by two core features: (i) an experience-based approach that accounts for symptoms primarily in terms of first-person experience of time-space as distinct from third-person observation of specific functions and related behavior; (ii) an integrated brain-mind approach where the brain's neural topography and dynamic, e.g., inner time and space, are shared by the mind's mental topography and dynamic, e.g., time-space experience, as their "common currency". We demonstrate how these two features can well account for both symptom complexity and trans-diagnostic nature of catatonia. In conclusion, catatonia can serve as paradigmatic example for the need to develop a more comprehensive psychopathological approach in psychiatry. This is provided by STPP that allows integrating subjective experience, clinical symptoms and the brain's neural activity in terms of their inner space-time, e.g., topography and dynamic. [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) is a safe, tolerable, and evidence‐based intervention for major depressive disorder (MDD). However, even after decades of research, nearly half of the patients with MDD fail to respond to conventional TMS, with responding slowly and requiring daily attendance at the treatment site for 4–6 weeks. To intensify antidepressant efficacy and shorten treatment duration, accelerated TMS protocols, which involve multiple sessions per day over a few days, have been proposed and evaluated for safety and viability. We reviewed and summarized the current knowledge in accelerated TMS, including stimulation parameters, antidepressant efficacy, anti‐suicidal efficacy, safety, and adverse effects. Limitations and suggestions for future directions are also addressed, along with a brief discussion on the application of accelerated TMS during the COVID‐19 pandemic. This article is categorized under:Neuroscience > Clinical Neuroscience [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a complex, progressive neurodegenerative disorder. The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice. In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfer model and enhanced with an attention mechanism. We aim to improve the precision of AD diagnosis and facilitate its early identification. Initially, we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation, which are commonly observed in imaging datasets. Subsequently, an attention mechanism is incorporated to selectively focus on the salient features within the imaging data. Building upon this foundation, we present the novelMed-3D transfermodel, designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results, achieving a classification accuracy of 92%. To emphasize the robustness and practicality of our approach, we introduce an adaptive 'hot-updating' auxiliary diagnostic system. This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD. [ABSTRACT FROM AUTHOR]

Drawing on perspectives from West and Southern Africa, this Comment critically examines the current state of neuroscience progress in Africa, describing the unique landscape and ongoing challenges as embedded within wider socio-political realities. Distinct research opportunities in the African context are explored to include genetic and bio-diversity, multilingual and multicultural populations, life-course development, clinical neuroscience and neuropsychology, with applications to machine learning models, in light of complex post-colonial legacies that often impede research progress. Key determinants needed to accelerate African neuroscience are then discussed, as well as cautionary underpinnings that together create an equitable neuroscience framework. [ABSTRACT FROM AUTHOR]

Background and purpose: Transparent reporting and appropriate interpretation of statistical methods and results are important to facilitate scientific evaluation and enable future replication. The goal of this study was to describe statistical reporting guidance provided to authors by clinical neurology and neuroscience journals. Methods: For first‐quartile journals in each discipline (per Clarivate InCites), information collected from Instructions to Authors website sections included whether journals required presentation of sample size justification, estimates of precision, and method of checking assumptions; and guidance for interpretation of p‐values and appropriate presentation of descriptive statistics and graphs. Journal endorsement of common but statistically nonspecific published transparent reporting guidelines for human and animal research was also collected, to capture the select statistical reporting items included in each guideline. Results: Journals (n = 85) frequently did not require/recommend sample size justifications (15% not required; 62% only required per external transparent reporting guideline), estimates of precision (15% not required; 41% only required per external guidelines), or disclosure of method of checking assumptions (46%); nor provide guidance for reporting/interpretation of p‐values (71%), reporting of descriptive statistics (75%), or use of appropriate graphs (92%). Endorsement of statistically nonspecific standalone reporting guidelines ranged between 52% and 68%, depending on the guideline. Conclusions: There is opportunity for journals to facilitate improvement in transparency of statistical methods and results for clinical neurology and neuroscience studies by providing guidelines and advice to authors at manuscript submission. [ABSTRACT FROM AUTHOR]

Multiphoton microscopy (MPM) has emerged as a vital tool in neuroscience, enabling deeper imaging with a broader field of view, as well as faster and sub-cellular resolution. Recent innovations in ultrafast fiber laser technology have revolutionized MPM applications in living brains, offering advantages like cost-effectiveness and user-friendliness. In this review, we explore the progress in ultrafast fiber laser technology, focusing on its integration into MPM for neuroscience research. We also examine the utility of femtosecond fiber lasers in fluorescence and label-free two- and three-photon microscopy applications within the field. Furthermore, we delve into future possibilities, including next-generation fiber laser designs, novel laser characteristics, and their potential for achieving high spatial and temporal resolution imaging. We also discuss the integration of fiber lasers with implanted microscopes, opening doors for clinical and fundamental neuroscience investigations. [ABSTRACT FROM AUTHOR]

The article focuses on the crucial role of first-order thalamic nuclei in relaying sensory information to the primary sensory cortices. Topics include the distinction between "driver" and "modulatory" thalamic projections, the influence of fast-spiking interneurons on sensory processing, and recent research on the synaptic organization and functional properties of gustatory thalamocortical projections.

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function. [ABSTRACT FROM AUTHOR]

Animal studies have established that the mediodorsal nucleus (MD) of the thalamus is heavily and reciprocally connected with all areas of the prefrontal cortex (PFC). In humans, however, these connections are difficult to investigate. High-resolution imaging protocols capable of reliably tracing the axonal tracts linking the human MD with each of the PFC areas may thus be key to advance our understanding of the variation, development, and plastic changes of these important circuits, in health and disease. Here, we tested in adult female and male humans the reliability of a new reconstruction protocol based on in vivo diffusion MRI to trace, measure, and characterize the fiber tracts interconnecting the MD with 39 human PFC areas per hemisphere. Our protocol comprised the following three components: (1) defining regions of interest; (2) preprocessing diffusion data; and, (3) modeling white matter tracts and tractometry. This analysis revealed largely separate PFC territories of reciprocal MD–PFC tracts bearing striking resemblance with the topographic layout observed in macaque connection-tracing studies. We then examined whether our protocol could reliably reconstruct each of these MD–PFC tracts and their profiles across test and retest sessions. Results revealed that this protocol was able to trace and measure, in both left and right hemispheres, the trajectories of these 39 area-specific axon bundles with good-to-excellent test-retest reproducibility. This protocol, which has been made publicly available, may be relevant for cognitive neuroscience and clinical studies of normal and abnormal PFC function, development, and plasticity. [ABSTRACT FROM AUTHOR]

Repetitive transcranial magnetic stimulation (TMS) is widely used in neuroscience and clinical settings to modulate human cortical activity. The effects of TMS on neural activity depend on the excitability of specific neural populations at the time of stimulation. Accordingly, the brain state at the time of stimulation may influence the persistent effects of repetitive TMS on distal brain activity and associated behaviors. We applied intermittent theta burst stimulation (iTBS) to a region in the posterior parietal cortex (PPC) associated with grasp control to evaluate the interaction between stimulation and brain state. Across two experiments, we demonstrate the immediate responses of motor cortex activity and motor performance to state-dependent parietal stimulation. We randomly assigned 72 healthy adult participants to one of three TMS intervention groups, followed by electrophysiological measures with TMS and behavioral measures. Participants in the first group received iTBS to PPC while performing a grasping task concurrently. Participants in the second group received iTBS to PPC while in a task-free, resting state. A third group of participants received iTBS to a parietal region outside the cortical grasping network while performing a grasping task concurrently. We compared changes in motor cortical excitability and motor performance in the three stimulation groups within an hour of each intervention. We found that parietal stimulation during a behavioral manipulation that activates the cortical grasping network increased downstream motor cortical excitability and improved motor performance relative to stimulation during rest. We conclude that constraining the brain state with a behavioral task during brain stimulation has the potential to optimize plasticity induction in cortical circuit mechanisms that mediate movement processes. [ABSTRACT FROM AUTHOR]

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients. [ABSTRACT FROM AUTHOR]

This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools. [ABSTRACT FROM AUTHOR]

The N1, Tb, and P2 components of the event‐related potential (ERP) are thought to reflect the sequential processing of auditory stimuli in the human brain. Despite their extensive use in biological, cognitive, and clinical neuroscience, there are no guidelines for how to appropriately power ERP studies using these components. In the present study, we investigated how the number of trials, number of participants, effect magnitude, and study design influenced statistical power. Using Monte Carlo simulations of ERP data from a passive listening task, we determined the probability of finding a statistically significant effect in 58,900 experiments repeated 1,000 times each. We found that as the number of trials, number of participants, and effect magnitude increased, so did statistical power. We also found that increasing the number of trials had a bigger effect on statistical power for within‐subject designs than for between‐subject designs, and that within‐subject designs required a smaller number of trials and participants to provide the same level of statistical power for a given effect magnitude than between‐subject designs. These results show that it is important to carefully consider these factors when designing ERP studies, rather than relying on tradition or anecdotal evidence. To improve the robustness and reproducibility of ERP research, we have built an online statistical power calculator (https://bradleynjack.shinyapps.io/ErpPowerCalculator), which we hope will allow researchers to estimate the statistical power of previous studies, as well as help them design appropriately‐powered studies in the future. Using Monte Carlo simulations of ERP data from a passive listening task, we found that the number of trials, number of participants, effect magnitude, and study design interacted to influence statistical power for the N1, Tb, and P2 components. We hope that these results will improve the robustness and reproducibility of ERP research. [ABSTRACT FROM AUTHOR]

Duane E. Haines, a highly regarded and influential founding member of the International Society for the History of the Neurosciences, passed away in North Carolina on July 1, 2024. He specialized in neuroanatomy, standardized anatomical nomenclature, and contributed to the field's history. Duane received his Ph.D. from Michigan State University and held positions at West Virginia University, the University of Mississippi School of Medicine, and Wake Forest School of Medicine. He published numerous articles, chapters, and books, including a neuroanatomical atlas and a fundamental neuroscience textbook. Duane also collaborated with Régis Olry on a series of columns that were recently reissued as a book on neuroscience terms. He made notable contributions to the Society for the History of the Neurosciences and held various leadership roles within the organization. [Extracted from the article]

Techniques to study brain activities have evolved dramatically, yet tremendous challenges remain in acquiring high-throughput electrophysiological recordings minimally invasively. Here, we develop an integrated neuroelectronic array that is filamentary, high-density and flexible. Specifically, with a design of single-transistor multiplexing and current sensing, the total 256 neuroelectrodes achieve only a 2.3 × 0.3 mm2 area, unprecedentedly on a flexible substrate. A single-transistor multiplexing acquisition circuit further reduces noise from the electrodes, decreases the footprint of each pixel, and potentially increases the device's lifetime. The filamentary neuroelectronic array also integrates with a rollable contact pad design, allowing the device to be injected through a syringe, enabling potential minimally invasive array delivery. Successful acute auditory experiments in rats validate the ability of the array to record neural signals with high tone decoding accuracy. Together, these results establish soft, high-density neuroelectronic arrays as promising devices for neuroscience research and clinical applications. [ABSTRACT FROM AUTHOR]

The history of Danish neuroscience starts with an account of impressive contributions made at the 17th century. Thomas Bartholin was the first Danish neuroscientist, and his disciple Nicolaus Steno became internationally one of the most prominent neuroscientists in this period. From the start, Danish neuroscience was linked to clinical disciplines. This continued in the 19th and first half of the 20th centuries with new initiatives linking basic neuroscience to clinical neurology and psychiatry in the same scientific environment. Subsequently, from the middle of the 20th century, basic neuroscience was developing rapidly within the preclinical university sector. Clinical neuroscience continued and was even reinforced during this period with important translational research and a close co‐operation between basic and clinical neuroscience. To distinguish 'history' from 'present time' is not easy, as many historical events continue in present time. Therefore, we decided to consider 'History' as new major scientific developments in Denmark, which were launched before the end of the 20th century. With this aim, scientists mentioned will have been born, with a few exceptions, no later than the early 1960s. However, we often refer to more recent publications in documenting the developments of initiatives launched before the end of the last century. In addition, several scientists have moved to Denmark after the beginning of the present century, and they certainly are contributing to the present status of Danish neuroscience—but, again, this is not the History of Danish neuroscience. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience. [ABSTRACT FROM AUTHOR]

Seventy years ago, Hodgkin and Huxley published the first mathematical model to describe action potential generation, laying the foundation for modern computational neuroscience. Since then, the field has evolved enormously, with studies spanning from basic neuroscience to clinical applications for neuromodulation. Computer models of neuromodulation have evolved in complexity and personalization, advancing clinical practice and novel neurostimulation therapies, such as spinal cord stimulation. Spinal cord stimulation is a therapy widely used to treat chronic pain, with rapidly expanding indications, such as restoring motor function. In general, simulations contributed dramatically to improve lead designs, stimulation configurations, waveform parameters and programming procedures and provided insight into potential mechanisms of action of electrical stimulation. Although the implementation of neural models are relentlessly increasing in number and complexity, it is reasonable to ask whether this observed increase in complexity is necessary for improved accuracy and, ultimately, for clinical efficacy. With this aim, we performed a systematic literature review and a qualitative meta‐synthesis of the evolution of computational models, with a focus on complexity, personalization and the use of medical imaging to capture realistic anatomy. Our review showed that increased model complexity and personalization improved both mechanistic and translational studies. More specifically, the use of medical imaging enabled the development of patient‐specific models that can help to transform clinical practice in spinal cord stimulation. Finally, we combined our results to provide clear guidelines for standardization and expansion of computational models for spinal cord stimulation. [ABSTRACT FROM AUTHOR]

Volumetric assessment of brain structures is an important tool in neuroscience research and clinical practice. The volumetric measurement of normally functioning human brain helps detect age-related changes in some regions, which can be observed at varying degrees. This study aims to estimate the insular volume in the normally functioning human brain in both genders, different age groups, and side variations. A cross-sectional retrospective study was conducted on 42 adult Sudanese participants in Al-Amal Hospital, Sudan, between May to August 2022, using magnetic resonance imaging (MRI) and automatic brain segmentation through a software program (BrainSuite). The statistical difference in total insular volume on both sides of the cerebral hemisphere was small. The insular volume on the right side was greater in males, while the left side showed no difference between both genders. A statistically significant difference between males and females was found (p > 0.05), and no statistical difference in different age groups was found according to the one-way ANOVA test (p>0.05). Adult Sudanese males showed a larger insular volume than females. MRI can be used to morphometrically assess the insula to detect any pathological variations based on volume changes. [ABSTRACT FROM AUTHOR]

Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system's nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces. [ABSTRACT FROM AUTHOR]