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\"Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered\"-- Provided by publisher.

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. This Perspective reviews successful applications of functional magnetic resonance imaging (fMRI) and presents a case for fMRI as a central hub on which to integrate the dispersed subfields of systems, cognitive, computational and clinical neuroscience.

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”.

Purpose To evaluate the effectiveness, compared with usual care, of an interactive online group programme combining pain neuroscience education (PNE) and graded exposure to movement (GEM) for improving quality of life and pain experience in breast cancer survivors with chronic pain. Methods This single-blind randomised controlled trial included a sample of 49 breast cancer survivors who were randomly assigned to two groups (experimental: n  = 22 and control: n  = 27). The experimental group received a 12-week person-centred online programme based on pain neuroscience education and therapeutic yoga as gradual exposure to movement, while the control group continued with their usual care. The primary outcome was quality of life (FACT–B + 4); the secondary outcomes were related to the experience of chronic pain (pain intensity, pain interference, catastrophizing, pain self-efficacy, kinesiophobia, and fear avoidance behaviours). All variables were assessed at four time points (T0, baseline; T1, after PNE sessions; T2, after yoga sessions; T3, at 3-month follow-up). For data analysis, ANOVA (2 × 4) analysis of variance (95% CI) was used when outcomes were normally distributed. If not, within-group and between-group comparisons were calculated. Results Thirty-six participants were included in the analysis (control group, 22; experimental group, 14). A significant time * group effect was observed in favour of the experimental group regarding the global quality of life score ( p  = 0.010, η p 2  = 0.124). Significant differences in favour of the experimental group were observed for pain intensity, pain interference, catastrophizing, and pain self-efficacy. These differences persisted at follow-up. Conclusions An online intervention based on PNE and GEM appears to be more effective than usual care for improving quality of life in breast cancer survivors with chronic pain, as a time per group interaction was reported. In addition, the intervention also significantly improved the participants’ experience of chronic pain. However, due to the study limitations further research is needed. Trial record: NCT04965909 (26/06/2021).

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.

Key Points Modern optogenetics enables temporally precise, acute or chronic, excitatory or inhibitory modulation of neuronal activity with cell type and anatomical specificity that can be tuned to timing and magnitude of naturally occurring patterns within the same experimental subject. Diverse opsin variants exhibit unique spectral and kinetic features that are specifically suited for distinct experimental requirements. Optogenetics can be used in combination with electrophysiological or optical recordings, providing powerful approaches to simultaneously monitor and perturb neural function. Activity-dependent labelling of opsins can be used to reactivate neural ensembles that encode particular behaviours or experiences. New anatomical techniques (such as viral-tracing methods and hydrogel-embedding methods for optical access) enable molecular and anatomical profiling of the same cells that were active in vivo , providing integrative understanding of neural circuitry. Optogenetics is widely used to study the consequences of neuronal activity with high spatiotemporal precision. In this Review, Kim et al . discuss the integration of this approach with other technological and methodological advances to gain insights into neuronal function that were previously inaccessible. Modern optogenetics can be tuned to evoke activity that corresponds to naturally occurring local or global activity in timing, magnitude or individual-cell patterning. This outcome has been facilitated not only by the development of core features of optogenetics over the past 10 years (microbial-opsin variants, opsin-targeting strategies and light-targeting devices) but also by the recent integration of optogenetics with complementary technologies, spanning electrophysiology, activity imaging and anatomical methods for structural and molecular analysis. This integrated approach now supports optogenetic identification of the native, necessary and sufficient causal underpinnings of physiology and behaviour on acute or chronic timescales and across cellular, circuit-level or brain-wide spatial scales.

Interest in deciphering the fundamental mechanisms and processes of the human mind represents a central driving force in modern neuroscience research. Activities in support of this goal rely on advanced methodologies and engineering systems that are capable of interrogating and stimulating neural pathways, from single cells in small networks to interconnections that span the entire brain. Recent research establishes the foundations for a broad range of creative neurotechnologies that enable unique modes of operation in this context. This review focuses on those systems with proven utility in animal model studies and with levels of technical maturity that suggest a potential for broad deployment to the neuroscience community in the relatively near future. We include a brief summary of existing and emerging neuroscience techniques, as background for a primary focus on device technologies that address associated opportunities in electrical, optical and microfluidic neural interfaces, some with multimodal capabilities. Examples of the use of these technologies in recent neuroscience studies illustrate their practical value. The vibrancy of the engineering science associated with these platforms, the interdisciplinary nature of this field of research and its relevance to grand challenges in the treatment of neurological disorders motivate continued growth of this area of study.This review summarizes advances in electrical, optical and microfluidic neural interfaces with characteristics that suggest near-term potential for broad deployment to the neuroscience community.

Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger–Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system’s dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.

Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires — and drives — new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.Models of dendrites have been instrumental in our understanding of their functions. Poirazi and Papoutsi review the major contributions of dendritic models, including those already proved or waiting to be experimentally verified, and highlight successful interactions between the modelling and experimental neuroscience communities.

Most neuroscientists would agree that for brain research to progress, we have to know which experimental manipulations have no effect as much as we must identify those that do have an effect. The dominant statistical approaches used in neuroscience rely on P values and can establish the latter but not the former. This makes non-significant findings difficult to interpret: do they support the null hypothesis or are they simply not informative? Here we show how Bayesian hypothesis testing can be used in neuroscience studies to establish both whether there is evidence of absence and whether there is absence of evidence. Through simple tutorial-style examples of Bayesian t-tests and ANOVA using the open-source project JASP, this article aims to empower neuroscientists to use this approach to provide compelling and rigorous evidence for the absence of an effect.Keysers et al. show why P values do not differentiate inconclusive null findings from those that provide important evidence for the absence of an effect. They provide a tutorial on how to use Bayesian hypothesis testing to overcome this issue.