A new era in cognitive neuroscience: the tidal wave of artificial intelligence (AI).

Translating artificial intelligence techniques into the realm of cognitive neuroscience holds promise for significant breakthroughs in our ability to probe the intrinsic mechanisms of the brain. The recent unprecedented development of robust AI models is changing how and what we understand about the brain. In this Editorial, we invite contributions for a BMC Neuroscience Collection on "AI and Cognitive Neuroscience".

Reductionism and proxy failure: From neuroscience to target-based drug discovery.

Reductionist methodologies reduce phenomena to some of their lower-level components. Researchers gradually shift their focus away from observing the actual object of study toward investigating and optimizing such lower-level proxies. Following reductionism, these proxies progressively diverge further from the original object of study. We vividly illustrate this in the evolution of target-based drug discovery from rational and phenotypic drug discovery.

Neuroscience fundamentals relevant to neuromodulation: Neurobiology of deep brain stimulation in Parkinson's disease.

Deep Brain Stimulation (DBS) has become a pivotal therapeutic approach for Parkinson's Disease (PD) and various neuropsychiatric conditions, impacting over 200,000 patients. Despite its widespread application, the intricate mechanisms behind DBS remain a subject of ongoing investigation. This article provides an overview of the current knowledge surrounding the local, circuit, and neurobiochemical effects of DBS, focusing on the subthalamic nucleus (STN) as a key target in PD management. The local effects of DBS, once thought to mimic a reversible lesion, now reveal a more nuanced interplay with myelinated axons, neurotransmitter release, and the surrounding microenvironment. Circuit effects illuminate the modulation of oscillatory activities within the basal ganglia and emphasize communication between the STN and the primary motor cortex. Neurobiochemical effects, encompassing changes in dopamine levels and epigenetic modifications, add further complexity to the DBS landscape. Finally, within the context of understanding the mechanisms of DBS in PD, the article highlights the controversial question of whether DBS exerts disease-modifying effects in PD. While preclinical evidence suggests neuroprotective potential, clinical trials such as EARLYSTIM face challenges in assessing long-term disease modification due to enrollment timing and methodology limitations. The discussion underscores the need for robust biomarkers and large-scale prospective trials to conclusively determine DBS's potential as a disease-modifying therapy in PD.

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Unlocking the potential of adeno-associated virus in neuroscience: a brief review.

Adeno-associated virus (AAV) has emerged as a pivotal tool in neuroscience research, owing to its remarkable versatility and efficiency in delivering genetic material to diverse cell types within the nervous system. This mini review aims to underscore the advanced applications of AAV vectors in neuroscience and their profound potential to revolutionize our understanding of brain function and therapeutic interventions for neurological disorders. By providing a concise overview of the latest developments and strategies employing AAV vectors, this review illuminates the transformative role of AAV technology in unraveling the complexities of neural circuits and paving the way for innovative treatments. Through elucidating the multifaceted capabilities of AAV-mediated gene delivery, this review underscores its pivotal role as a cornerstone in contemporary neuroscience research, promising remarkable insights into the intricacies of brain biology and offering new avenues for therapeutic intervention.

What does the mean mean? A simple test for neuroscience.

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

Effects of Manual Therapy Plus Pain Neuroscience Education with Integrated Motivational Interviewing in Individuals with Chronic Non-Specific Low Back Pain: A Randomized Clinical Trial Study.

Background and Objectives: Chronic non-specific low back pain (CNLBP) persists beyond 12 weeks. Manual therapy recommended for CNLBP demonstrates short-term efficacy. Pain Neuroscience Education (PNE) teaches patients to modify pain perception through explanations, metaphors, and examples, targeting brain re-education. Motivational Interviewing (MI) enhances motivation for behavioral change, steering patients away from ambivalence and uncertainty. These approaches collectively address the multifaceted nature of CNLBP for effective management. The aim of this study was to investigate a manual therapy intervention combined with PNE with MI on pain, pressure pain threshold (PPT), disability, kinesiophobia, catastrophizing, and low back functional ability in individuals experiencing CNLBP. Materials and Methods: Sixty adults with CNLBP were randomly divided into three equal groups (each n = 20). The first group received manual therapy and PNE with integrated MI (combined therapy group), the second group underwent only manual therapy (manual therapy group), and the third group followed a general exercise program at home (control group). Pain in the last 24 h was assessed using the Numeric Pain Rating Scale (NPRS), functional ability with the Roland-Morris Disability Questionnaire (RMDQ), PPT in the lumbar region through pressure algometry, kinesiophobia with the Tampa Scale for Kinesiophobia (TSK), catastrophizing with the Pain Catastrophizing Scale (PCS), and performance using the Back Performance Scale (BPS) at baseline, in the fourth week, and six months post-intervention. Results: Statistically significant differences between the intervention groups and the control group were found in both the fourth-week measurement and the six-month follow-up, as evident in the NPRS and RMDQ scores, as well as in the total values of tested PPTs (p < 0.05). Differences were also observed between the two intervention groups, with a statistically greater improvement in the combined therapy group at both time points (fourth week and six-month follow-up) (p < 0.05). Regarding the TSK and PCS scores in the fourth week, statistically significant differences were observed between the two intervention groups compared to the control group, as well as between the two intervention groups (p < 0.05). However, in the six-month follow-up, statistically significant differences were found only between the combined therapy group and the other two groups, with the combined therapy group showing significant improvements (p < 0.05). In relation to BPS, both intervention groups exhibited statistically significant differences compared to the control group in the fourth week, without any significant differences between the two intervention groups. However, in the six-month follow-up, significant differences were noted between the combined therapy group and the other two groups (p < 0.05), with combined therapy demonstrating greater improvement. Conclusions: The addition of PNE with integrated MI enhanced the positive effects of a manual therapy intervention in all outcome measures. The combination of manual therapy plus PNE with integrated MI appeared to provide greater improvements compared to the isolated application of manual therapy, and these improvements also lasted longer. These short- and long-term positive effects are likely attributed to the combination of PNE with integrated MI, which contributed to increasing the effectiveness of the treatment. Further studies are required to investigate the optimum dosage of manual therapy and PNE with integrated MI in individuals with CNLBP.

Understanding the heterogeneity of anxiety using a translational neuroscience approach.

Anxiety disorders affect millions of people worldwide and present a challenge in neuroscience research because of their substantial heterogeneity in clinical presentation. While a great deal of progress has been made in understanding the neurobiology of fear and anxiety, these insights have not led to effective treatments. Understanding the relationship between phenotypic heterogeneity and the underlying biology is a critical first step in solving this problem. We show translation, reverse translation, and computational modeling can contribute to a refined, cross-species understanding of fear and anxiety as well as anxiety disorders. More specifically, we outline how animal models can be leveraged to develop testable hypotheses in humans by using targeted, cross-species approaches and ethologically informed behavioral paradigms. We discuss reverse translational approaches that can guide and prioritize animal research in nontraditional research species. Finally, we advocate for the use of computational models to harmonize cross-species and cross-methodology research into anxiety. Together, this translational neuroscience approach will help to bridge the widening gap between how we currently conceptualize and diagnose anxiety disorders, as well as aid in the discovery of better treatments for these conditions.

Bibliometric analysis of neuroscience publications quantifies the impact of data sharing.

SUMMARY: Neural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions. AVAILABILITY AND IMPLEMENTATION: The application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.

Functional genomics and systems biology in human neuroscience.

Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.

Functional neuroimaging as a catalyst for integrated neuroscience.

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.