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Large-scale, multimodal phenotypic characterisation is a valuable tool to explore brain function. Poldrack et al. collect and relate MRI, psychological, physiological, metabolic and gene expression data from a single human over an 18 month period, providing a rich resource for future studies.

To mark the 20th anniversary of Nature Reviews Neuroscience, in this Viewpoint article we asked some of the researchers who have authored pieces published in the journal in recent years for their views on how the field, and their areas within it, have developed over the past two decades.

The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.

Russell Poldrack assesses a primer on the implications of advances in brain imaging.

Mumford et al. examine how response time differences can lead to confounds in functional MRI analyses, and they propose a new time-series model to account for response time effects.

This Review evaluates current techniques used to investigate human brain function, discusses the successes and limitations of these techniques to test hypotheses about causal mechanisms, and looks to future directions and implementation of these techniques in real-world problems.

The authors introduce a deep learning framework to reproduce sequences of response times and use it to provide evidence for a stability–flexibility trade-off underlying task-switching costs.

Neuroimagers have collected large datasets and many of these are now available online. In this review the authors discuss the current state of sharing task-based fMRI data and the many challenges it poses.

TemplateFlow is a repository for human and other brain templates and atlases, which operates under the FAIR principles.

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

fMRIPrep is an open-source software tool to ready fMRI datasets for statistical analysis and modeling that is robust to a diversity of inputs and produces standardized outputs, facilitating aggregation of data across studies.

Scientific progress relies on integrating and building on existing knowledge. Here, the authors propose improving cumulative science by developing data-driven ontologies, and they apply this approach to understanding the construct of self-regulation.

Neuroimaging techniques are increasingly applied by the wider neuroscience community. However, problems such as low statistical power, flexibility in data analysis and software issues pose challenges to interpreting neuroimaging data in a meaningful and reliable way. Here, Poldracket al. discuss these and other problems, and suggest solutions.

Functional connectivity estimates vary significantly across different functional magnetic resonance imaging preprocessing pipelines. Due to these variations, using seemingly similar minimal preprocessing does not ensure consistency.

Adolescents are thought to be hypersensitive to rewards. Using model-based fMRI, the authors identify elevated reward prediction error signals in the striatum during adolescence, which could contribute to the increased reward-seeking behavior seen during this stage of development.

Using data from top neuroscience journals, this study finds that women-led work tends to be undercited relative to expectations. This imbalance is driven largely by the citation practices of men and is increasing over time as the field diversifies.

Beam et al. created a data-driven mapping of human brain function, drawing on full texts and coordinate data reported in neuroimaging studies. This validated framework outperformed leading and widely used knowledge frameworks, namely Research Domain Criteria (RDoC) and the Diagnostic and Statistical Manual of Mental Disorders (DSM).

A large study of impulsivity in 14-year-olds finds that substance use and ADHD symptoms are associated with different brain networks that inhibit motor responses. Genetic analysis implicates the noradrenergic system in activity levels in one of these networks, which is centered on the right frontal cortex.

Cognitive function is associated with health and important life outcomes. Here, the authors perform a genome-wide association study for general cognitive function in 300,486 individuals and identify genetic loci that implicate neural and cell developmental pathways in this trait.

Neuronal activity across task states converges onto a low-dimensional manifold. Flow within this attractor space covaries with network-level topology, fluid intelligence, and regional differences in the density of neuromodulatory receptors.

A framework and web interface for the large-scale and automated synthesis of human neuroimaging data extracted from the literature is presented. It is used to generate a large database of mappings between neural and cognitive states and to address long-standing inferential problems in the neuroimaging literature.

Data sharing is an essential component of open science practice. The Brain Imaging Data Structure project has pioneered a way to organize neuroimaging and behavioural data that enables easy sharing and reuse. We present experiences from the BIDS project and highlight how standards can promote open science.

brainlife.io is a one-stop cloud platform for data management, visualization and analysis in human neuroscience. It is web-based and provides access to a variety of tools in a reproducible and reliable manner.

The subjective values of stimuli can be changed through reward-based learning, but here the authors show that the value of food items can be modulated by the concurrent presentation of an irrelevant auditory cue to which subjects must make a motor response. The effects of this pairing lasted at least 2 months, and neuroimaging revealed increased preference-related activity in ventromedial prefrontal cortex.

Meta-analysis of genome-wide association studies for cognitive ability identifies 190 new loci and implicates 939 new genes related to neurogenesis, neuron differentiation and synaptic structure.

ASLPrep is a software suite for reproducible processing of arterial spin labeled magnetic resonance imaging data.

Responding to widespread concerns about reproducibility, the Organization for Human Brain Mapping created a working group to identify best practices in data analysis, results reporting and data sharing to promote open and reproducible research in neuroimaging. We describe the challenges of open research and the barriers the field faces.

Using a large task battery spanning motor, cognitive, social and affective domains, this functional MRI (fMRI) study provides a comprehensive functional map of the human cerebellum, along with a comparison to maps derived from anatomy and resting-state fMRI data.

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.

Many studies focus on neural associations yet understanding the brain will ultimately depend on discovering the causal interactions underlying its functionality. Moving from association to causation will thus be essential for advancing neuroscience.