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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”.

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.

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.

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.

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models that decompose cognition into functional components. Computational neuroscience has modeled how interacting neurons can implement elementary components of cognition. It is time to assemble the pieces of the puzzle of brain computation and to better integrate these separate disciplines. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. Here we review recent work in the intersection of cognitive science, computational neuroscience and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.

Current BCI technology is mainly focused on therapeutic outcomes, such as helping people with spinal-cord injuries. It might take years or even decades until BCI and other neurotechnologies are part of our daily lives. Such advances could revolutionize the treatment of many conditions, from brain injury and paralysis to epilepsy and schizophrenia, and transform human experience for the better. But the technology could also exacerbate social inequalities and offer corporations, hackers, governments or anyone else new ways to exploit and manipulate people.

After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging on a shared view of the computational foundations of intelligence that promotes valuable cross-disciplinary exchanges on questions, methods, and results. We chart advances over the past several decades that address challenges of perception and action under uncertainty through the lens of computation. Advances include the development of representations and inferential procedures for large-scale probabilistic inference and machinery for enabling reflection and decisions about tradeoffs in effort, precision, and timeliness of computations. These tools are deployed toward the goal of computational rationality: identifying decisions with highest expected utility, while taking into consideration the costs of computation in complex real-world problems in which most relevant calculations can only be approximated. We highlight key concepts with examples that show the potential for interchange between computer science, cognitive science, and neuroscience.

Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.

Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions. •Neuroscience started collecting multi-modal datasets from thousands of individuals.•Non-parametric models will increase neurobiological insight as data accumulate.•Generative models will reveal candidate mechanisms underlying behavior and disease.•The advantages of frequentist and Bayesian modeling will be more often combined.•Null-hypothesis testing and out-of-sample generalization will draw formal inference.