While disordered brain circuits should manifest in psychiatric symptoms and cognitive deficits, how they jointly impact multiple behaviors remains poorly understood. Connectome-based predictive (CPM) modeling can identify functional networks associated with specific behavioral measures. The derived networks provide evidence of where an individual’s disordered circuits are, and prediction strength indexes network modeling accuracy. Here, we used CPM to predict a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varies substantially across instruments, with cognitive tests yielding stronger models than clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits associated with symptomatology reside in regions where networks overlap, we constructed predictive models using these sparsely shared circuits. They strongly predict cognitive performance and are primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings demonstrate that constraining predictive models to features shared between multiple phenotypes in training data can improve network localization precision. Simon et al. leverage connectome-based predictive modeling to identify brain circuits related to cognitive deficits that co-occur with self-reported psychiatric symptoms. They find that the sparsely shared networks strongly predict cognition and are often localized within the frontoparietal and between the frontoparietal and default mode networks.

Abstract Brain networks mature in a hierarchical sequence that parallels the emergence of cognitive functions. However, precisely how brain structural maturation supports the ordered development of cognitive functions remains largely unknown. Here, leveraging network control theory and 3712 developmental diffusion magnetic resonance imaging scans, we investigate how the brain’s structural effort to activate cognitive states — quantified as control energy — changes from infancy through adolescence. A total of 100 cognitive states were defined as meta-analytic activation maps from NeuroSynth, prioritized by their frequency in major neurodevelopmental behavioral assessments. We show that the control energy to drive most cognitive tasks decreases during development (for 96 out of 100 cognitive states). Ages to achieve optimal energy efficiency for each state concentrate around school age and late adolescence, whereas social and perceptual functions reach efficiency earlier (mean optimal age = 100.2 months) than higher-order cognitive functions (mean optimal age = 205.5 months). Further, we estimated the influence of molecular-level neurodevelopmental events on control energy by coupling control inputs to each event’s gene expression profile. We find that such influences vary in both temporal breadth and cognitive scope. Prenatal events (neuron differentiation and migration) exert effects mostly in infancy, while the prolonged process of myelination shapes the energy landscape across all developmental periods and the widest range of cognitive domains. Moreover, the transition energy architecture remains stable across development but becomes progressively modularized, such that transitions within the same category of cognitive states become increasingly favored. Together, these findings provide a comprehensive growth chart of how brain structural maturation supports the hierarchical emergence of cognitive abilities across early life, and establish a normative framework that enables systematic approaches to activate targeted brain circuits and facilitate selective cognitive transitions.

We depart from the feed-forward approach of brain-behavior modeling, which identifies the functional brain connectivity networks associated with performance on external tests, and instead introduce a feedback approach that reveals the brain systems those external tests reflect. In fMRI data from n = 302 demographically and clinically diverse participants, we a priori define connectivity networks for six cognitive constructs and employ kernel ridge regression to quantify each network's contribution to test performance. This approach provides a ranking of test scores according to the predictive power of each cognitive network, revealing which tests probe which brain networks. It further identifies combinations of measures that optimally probe predefined brain systems and evaluates how specific subtests influence composite scores, revealing when subset inclusion reinforces or weakens specific brain circuit and composite score relationships. This work opens an avenue of research by providing a framework for the development of test instruments guided by quantitative brain metrics.

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on neuroimaging data are increasingly used as tools for predicting behavioural phenotypes, enhancing precision medicine and improving generalizability compared with traditional MRI studies. However, the high dimensionality of brain connectivity data makes model interpretation challenging. Prevailing practices rely on selecting features and, implicitly, interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Despite its widespread use, how univariate feature selection balances the trade-off between simplification for optimizing modelling and oversimplification that misrepresents true neurobiology remains understudied. Here, using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that edges discarded by feature selection can achieve significant prediction accuracies while yielding different neurobiological interpretations. These results are observed across cognitive, developmental and psychiatric phenotypes, extend to both functional connectivity (functional MRI) and structural (diffusion tensor imaging) connectomes, and remain evident in external validation. They suggest that focusing on only the top features may simplify the neurobiological bases of brain-behaviour associations. Such interpretations present only the tip of the iceberg when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results reinforce that subtle brain-wide signals should not be ignored.

Abstract Accumulating evidence implicates sleep and circadian rhythm disruption in substance use disorders, including opioid use disorder (OUD). To understand whether weaker light exposure time cues are observed in patients with OUD, we compared the amplitudes of personal light exposure in patients with OUD ( n = 73) and healthy controls ( n = 49). Participants monitored personal light exposure and rest/activity via wrist-worn actigraphy for 1 week. We calculated light and physical activity amplitude for each day with non-orthogonal spectral analysis. Patients with OUD demonstrated significantly lower light exposure amplitudes and significantly higher physical activity amplitudes compared to healthy controls, suggesting lower light exposure amplitudes are not accounted for by sedentary behavior in patients with OUD. Blunted light exposure amplitudes, indicative of a weaker time cue to the circadian clock, characterize patients with OUD and may represent a novel target for improving sleep and circadian health in OUD.

We determined brain microstructure alterations in early-stage Parkinson's disease (PD) using diffusion tensor imaging and neurite orientation dispersion and density imaging (NODDI). Additionally, dopamine transporter (DAT) PET in PD was also performed to evaluate whether microstructural changes and dopaminergic losses within the brain contribute independently to PD motor severity. Mean diffusivity was significantly higher in PD in many mid-brain, nigrostriatal, sub-cortical, cortical and white matter regions. However, Viso (NODDI outcome for cerebrospinal-fluid volume fraction) in the motor cortices and parietal lobe were more strongly correlated (r ~ 0.5, p < 0.01) with PD motor severity. Most DAT PET and diffusion MRI measures were uncorrelated and stepwise multiple linear regression analysis determined a combination of DAT availability in the putamen and Viso in the precentral gyrus (motor cortex) as the best predictor of PD motor severity (55% variance explained); inclusion of Viso in precentral gyrus independently accounted for 11% variance in motor severity.

Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates of autistic features. Little work has focused on the optimal brain states to reveal brain-phenotype relationships. Here, using connectome-based predictive modeling, we interrogated four datasets to determine scanning conditions that boost prediction of clinically relevant phenotypes and assess generalizability. In dataset one, a sample of youth with autism and neurotypical participants (n = 63), we found that a sustained attention task resulted in high prediction performance of autistic traits compared with a free-viewing social attention task and a resting-state condition. In dataset two (n = 25), we observed the predictive network model of autistic traits generated from the sustained attention task generalized to predict measures of attention in neurotypical adults. In datasets three and four, we determined the same predictive network model further generalized to predict measures of social responsiveness in the Autism Brain Imaging Data Exchange (n = 229) and the Healthy Brain Network (n = 643). Our data suggest an in-scanner sustained attention challenge can help delineate robust markers of autistic traits.

Functional neuroimaging aims to uncover brain processes underlying behavior and disease, yet studies are often underpowered to detect these effects. How this literature has shaped our understanding of brain function remains unknown, and little guidance exists for planning better powered studies. An underappreciated barrier is that commonly reported effect sizes across the brain are inflated, biasing study planning. Here, we introduce a correction for this inflation bias and show how more accurate studies can be planned using corrected effect size benchmarks from a mega-analysis of 63 typical studies across seven large datasets (52,979 participants). We find that common methods of planning studies based on uncorrected effects lead to roughly half the expected detections at typical sample sizes, with limited spatial overlap with original findings. These missed effects collectively explain meaningful additional variance in the desired outcome. We show how to recover missed effects by planning not only for power but also for a target number of detections via corrected benchmarks, or by taking a whole-brain approach with multivariate effects that individual research groups can detect (n < 50 compared to n > 1,000 for a typical univariate effect). These findings lay the groundwork for more informed study planning and a richer understanding of the widespread nature of brain effects, with implications for shared challenges (and solutions) across biomedicine.

Neuroimaging studies rarely test whether the variance structure is equivalent across population subgroups. Here, in 4,736 participants from the Adolescent Brain Cognitive Development (ABCD) cohort, we examine racialized heteroscedasticity (i.e., differences in variance across racialized groups) in neuroimaging and behavioral data and test how these differences in variance propagate into predictive modeling. Across neuroimaging modalities, behaviors, and predictive frameworks, variance differences exhibited consistent patterns, indicating that variance structure is a stable property across domains within the dataset. Simulation analyses demonstrated that such differences directly induce subgroup disparities in prediction error and reliability, even in the absence of mean differences. Across neuroimaging modalities, multiple measures demonstrated greater variance in Black participants, particularly in functional imaging modalities. Similar variance patterns were observed in behavioral measures, and predictive models exhibited greater residual dispersion and prediction variance in Black participants even when overall performance metrics were comparable. These findings position variance structure, rather than central tendency, as a critical determinant of model performance, generalizability, and reliability across diverse populations.

Low statistical power in neuroimaging often undermines research in the field, leading to missed effects, wasted resources, and reduced reproducibility. Performing power analyses during the study design phase is extremely important, but often prohibitively difficult due to a lack of analytical solutions and high computational costs. We present PRISME (Power Resampling Infrastructure for Statistical Method Evaluation), a MATLAB toolbox for neuroimaging power benchmarking. PRISME provides a computational framework for empirical power analysis independent of inference methods, enabling large scale power benchmarking and method comparison. The toolbox supports diverse neuroimaging data types, including both voxel-based activation and functional connectivity analyses, with a non-parametric, flexible algorithm and unified data representations. Furthermore, unlike previous empirical power approaches, PRISME supports multiple test types, such as association and difference tests with behavioral and clinical measures. Finally, PRISME's 25× speedup from algorithmic optimizations enables larger-scale power benchmarking, including the first power analysis for the ABCD dataset. Overall, PRISME is the first method- and data-type-agnostic power benchmarking tool for neuroimaging, providing a single solution for power analysis across diverse study designs.

Sustained attention is an important neurobiological process. Difficulties with attention play a key role in neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD) and autism. Here, we identified functional connections consistently associated with sustained attention across datasets, participant populations, and fMRI scan types. We interrogated five transdiagnostic, previously published connectome-based models predicting attention and autistic phenotypes. All models were related to sustained attention, including in samples comprising participants with autism. As expected, we observed that models predicting attention phenotypes shared more similar features with each other than models predicting autism symptoms. Interestingly, we observed no statistically significant model similarities when considering factors such as age, functional run type, or diagnosis. This suggests that functional connectivity patterns predicting individual differences in behavior tend to be phenotype-specific, regardless of age or clinical diagnosis. Our results underscore the importance of searching for consistent markers of transdiagnostic sustained attention phenotypes in neurodevelopmental conditions.