Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1–3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available—with a total sample size of around 50,000 individuals—to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain–phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

BACKGROUND.Beige adipose tissue is associated with improved glucose homeostasis in mice. Adipose tissue contains β3adrenergic receptors (β3-ARs), and this study was intended to determine whether the treatment of obese, insulin-resistant humans with the β3-AR agonist mirabegron, which stimulates beige adipose formation in subcutaneous white adipose tissue (SC WAT), would induce other beneficial changes in fat and muscle and improve metabolic homeostasis. METHODS.Before and after β3-AR agonist treatment, oral glucose tolerance tests and euglycemic clamps were performed, and histochemical analysis and gene expression profiling were performed on fat and muscle biopsies. PET-CT scans quantified brown adipose tissue volume and activity, and we conducted in vitro studies with primary cultures of differentiated human adipocytes and muscle. RESULTS.The clinical effects of mirabegron treatment included improved oral glucose tolerance (P < 0.01), reduced hemoglobin A1c levels (P = 0.01), and improved insulin sensitivity (P = 0.03) and β cell function (P = 0.01). In SC WAT, mirabegron treatment stimulated lipolysis, reduced fibrotic gene expression, and increased alternatively activated macrophages. Subjects with the most SC WAT beiging showed the greatest improvement in β cell function. In skeletal muscle, mirabegron reduced triglycerides, increased the expression of PPARγ coactivator 1 α (PGC1A) (P < 0.05), and increased type I fibers (P < 0.01). Conditioned media from adipocytes treated with mirabegron stimulated muscle fiber PGC1A expression in vitro (P < 0.001). CONCLUSION.Mirabegron treatment substantially improved multiple measures of glucose homeostasis in obese, insulinresistant humans. Since β cells and skeletal muscle do not express β3-ARs, these data suggest that the beiging of SC WAT by mirabegron reduces adipose tissue dysfunction, which enhances muscle oxidative capacity and improves β cell function.TRIAL REGISTRATION. Clinicaltrials.gov NCT02919176.

Magnetic resonance imaging (MRI) continues to drive many important neuroscientific advances. However, progress in uncovering reproducible associations between individual differences in brain structure/function and behavioral phenotypes (e.g., cognition, mental health) may have been undermined by typical neuroimaging sample sizes (median N=25)1,2. Leveraging the Adolescent Brain Cognitive Development (ABCD) Study3 (N=11,878), we estimated the effect sizes and reproducibility of these brain wide associations studies (BWAS) as a function of sample size. The very largest, replicable brain wide associations for univariate and multivariate methods were r=0.14 and r=0.34, respectively. In smaller samples, typical for brain wide association studies, irreproducible, inflated effect sizes were ubiquitous, no matter the method (univariate, multivariate). Until sample sizes started to approach consortium levels, BWAS were underpowered and statistical errors assured. Multiple factors contribute to replication failures4,5,6; here, we show that the pairing of small brain behavioral phenotype effect sizes with sampling variability is a key element in widespread BWAS replication failure. Brain behavioral phenotype associations stabilize and become more reproducible with sample sizes of N>2,000. While investigator initiated brain behavior research continues to generate hypotheses and propel innovation, large consortia are needed to usher in a new era of reproducible human brain wide association studies.

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

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