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Abstract Psilocybin has shown promise for the treatment of mood disorders, which are often accompanied by cognitive dysfunction including cognitive rigidity. Recent studies have proposed neuropsychoplastogenic effects as mechanisms underlying the enduring therapeutic effects of psilocybin. In an open-label study of 24 patients with major depressive disorder, we tested the enduring effects of psilocybin therapy on cognitive flexibility (perseverative errors on a set-shifting task), neural flexibility (dynamics of functional connectivity or dFC via functional magnetic resonance imaging), and neurometabolite concentrations (via magnetic resonance spectroscopy) in brain regions supporting cognitive flexibility and implicated in acute psilocybin effects (e.g., the anterior cingulate cortex, or ACC). Psilocybin therapy increased cognitive flexibility for at least 4 weeks post-treatment, though these improvements were not correlated with the previously reported antidepressant effects. One week after psilocybin therapy, glutamate and N-acetylaspartate concentrations were decreased in the ACC, and dFC was increased between the ACC and the posterior cingulate cortex (PCC). Surprisingly, greater increases in dFC between the ACC and PCC were associated with less improvement in cognitive flexibility after psilocybin therapy. Connectome-based predictive modeling demonstrated that baseline dFC emanating from the ACC predicted improvements in cognitive flexibility. In these models, greater baseline dFC was associated with better baseline cognitive flexibility but less improvement in cognitive flexibility. These findings suggest a nuanced relationship between cognitive and neural flexibility. Whereas some enduring increases in neural dynamics may allow for shifting out of a maladaptively rigid state, larger persisting increases in neural dynamics may be of less benefit to psilocybin therapy.

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler’s connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants’ age within ± 3.6 months error and a prediction R2 = .51. To characterize the anatomy of predictive networks, we grouped connections into 11 infant-specific resting-state functional networks defined in a data-driven manner. We found that connections between regions of the same network—i.e. within-network connections—predicted age significantly better than between-network connections. Looking ahead, these findings can help characterize changes in functional brain organization in infancy and toddlerhood and inform work predicting developmental outcome measures in this age range.

As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development StudySM (ABCD Study®) increases, so too does the need for resources to benchmark time-dependent effects. Scan-to-scan changes observed with repeated imaging may reflect development but may also reflect practice effects, day-to-day variability in psychological states, and/or measurement noise. Resources that allow disentangling these time-dependent effects will be useful in quantifying actual developmental change. We present an accelerated adult equivalent of the ABCD Study dataset (a-ABCD) using an identical imaging protocol to acquire magnetic resonance imaging (MRI) structural, diffusion-weighted, resting-state and task-based data from eight adults scanned five times over five weeks. We report on the task-based imaging data (n = 7). In-scanner stop-signal (SST), monetary incentive delay (MID), and emotional n-back (EN-back) task behavioral performance did not change across sessions. Post-scan recognition memory for emotional n-back stimuli, however, did improve as participants became more familiar with the stimuli. Functional MRI analyses revealed that patterns of task-based activation reflecting inhibitory control in the SST, reward success in the MID task, and working memory in the EN-back task were more similar within individuals across repeated scan sessions than between individuals. Within-subject, activity was more consistent across sessions during the EN-back task than in the SST and MID task, demonstrating differences in fMRI data reliability as a function of task. The a-ABCD dataset provides a unique testbed for characterizing the reliability of brain function, structure, and behavior across imaging modalities in adulthood and benchmarking neurodevelopmental change observed in the open-access ABCD Study.

T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data.

Interneuron subtypes have distinct properties and spatial distributions. Here, the authors show that the molecular-genetic basis of cortical resting-state brain function is shaped by distributions of interneuron-related transcripts and may capture individual differences in schizophrenia risk.

Abstract Background Physical activity is associated with reduced depression in youth and adults. However, our understanding of how different aspects of youth activities—specifically, the degree to which they are social, team-oriented, and physical—relate to mental health in children is less clear. Methods Here we use a data-driven approach to characterize the degree to which physical and non-physical youth activities are social and team-oriented. We then examine the relationship between depressive symptoms and participation in different clusters of youth activities using mixed effect models and causal mediation analyses in 11,875 children from the Adolescent Brain Cognitive Development (ABCD) Study. We test our hypotheses in an original sample (n = 4520, NDA release 1.1) and replication sample of participants (n = 7355, NDA release 2.0.1). Results We show and replicate that social–physical activities are associated with lower depressive symptoms. Next, we demonstrate that social connections, measured by number of close friends, partially mediate the association between social–physical activities and lower depressive symptoms. Conclusions Our results provide a rubric for using data-driven techniques to investigate different aspects of youth activities and highlight the social dynamics of physical activities as a possible protective factor against depression in childhood.

Although the feeling of being stressed is ubiquitous and clinically significant, the underlying neural mechanisms are unclear. Using a novel predictive modeling approach, the authors show that functional hippocampal networks specifically and consistently predict the feeling of stress.

Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity—both across individuals and within individuals over time—we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.

Abstract Introduction Working memory is a critical cognitive ability that affects our daily functioning and relates to many cognitive processes and clinical conditions. Episodic memory is vital because it enables individuals to form and maintain their self‐identities. Our study analyzes the extent to which whole‐brain functional connectivity observed during completion of an N‐back memory task, a common measure of working memory, can predict both working memory and episodic memory. Methods We used connectome‐based predictive models (CPMs) to predict 502 Human Connectome Project (HCP) participants' in‐scanner 2‐back memory test scores and out‐of‐scanner working memory test (List Sorting) and episodic memory test (Picture Sequence and Penn Word) scores based on functional magnetic resonance imaging (fMRI) data collected both during rest and N‐back task performance. We also analyzed the functional brain connections that contributed to prediction for each of these models. Results Functional connectivity observed during N‐back task performance predicted out‐of‐scanner List Sorting scores and to a lesser extent out‐of‐scanner Picture Sequence scores, but did not predict out‐of‐scanner Penn Word scores. Additionally, the functional connections predicting 2‐back scores overlapped to a greater degree with those predicting List Sorting scores than with those predicting Picture Sequence or Penn Word scores. Functional connections with the insula, including connections between insular and parietal regions, predicted scores across the 2‐back, List Sorting, and Picture Sequence tasks. Conclusions Our findings validate functional connectivity observed during the N‐back task as a measure of working memory, which generalizes to predict episodic memory to a lesser extent. By building on our understanding of the predictive power of N‐back task functional connectivity, this work enhances our knowledge of relationships between working memory and episodic memory.

Cognitive states, such as rest and task engagement, share an 'intrinsic' functional network organization that is subject to minimal variation over time and yields stable signatures within an individual. Importantly, there are also transient state-specific functional connectivity (FC) patterns that vary across neural states. Here, we examine functional brain organization differences that underlie distinct states in a cross-sectional developmental sample. We compare FC fMRI data acquired during naturalistic viewing (i.e., movie-watching) and resting-state paradigms in a large cohort of 157 children and young adults aged 6–20. Naturalistic paradigms are commonly implemented in pediatric research because they maintain the child's attention and contribute to reduced head motion. It remains unknown, however, to what extent the brain-wide functional network organization is comparable during movie-watching and rest across development. Here, we identify a widespread FC pattern that predicts whether individuals are watching a movie or resting. Specifically, we develop a model for prediction of multilevel neural effects (termed PrimeNet), which can with high reliability distinguish between movie-watching and rest irrespective of age and that generalizes across movies. In turn, we characterize FC patterns in the most predictive functional networks for movie-watching versus rest and show that these patterns can indeed vary as a function of development. Collectively, these effects highlight a 'core' FC pattern that is robustly associated with naturalistic viewing, which also exhibits change across age. These results, focused here on naturalistic viewing, provide a roadmap for quantifying state-specific functional neural organization across development, which may reveal key variation in neurodevelopmental trajectories associated with behavioral phenotypes.

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [rp]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (rp: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.

Affective disorders such as major depression are common but serious illnesses characterized by altered processing of emotional information. Although the frequency and severity of depressive symptoms increase dramatically over the course of childhood and adolescence, much of our understanding of their neurobiological bases comes from work characterizing adults’ responses to static emotional information. As a consequence, relationships between depressive brain phenotypes and naturalistic emotional processing, as well as the manner in which these associations emerge over the lifespan, remain poorly understood. Here, we apply static and dynamic inter-subject correlation analyses to examine how brain function is associated with clinical and non-clinical depressive symptom severity in 112 children and adolescents (7–21 years old) who viewed an emotionally evocative clip from the film Despicable Me during functional MRI. Our results reveal that adolescents with greater depressive symptom severity exhibit atypical fMRI responses during movie viewing, and that this effect is stronger during less emotional moments of the movie. Furthermore, adolescents with more similar item-level depressive symptom profiles showed more similar brain responses during movie viewing. In contrast, children’s depressive symptom severity and profiles were unrelated to their brain response typicality or similarity. Together, these results indicate a developmental change in the relationships between brain function and depressive symptoms from childhood through adolescence. Our findings suggest that depressive symptoms may shape how the brain responds to complex emotional information in a dynamic manner sensitive to both developmental stage and affective context.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.

Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. In this study, we used the visual attentional blink (VAB) as a test of the functional generalizability of the brain’s attentional networks. In a VAB task, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to limitations in attentional capacity or selection, VAB deficits attenuate when participants are distracted or deploy attention diffusely. The VAB is also behaviorally and theoretically dissociable from other attention tasks. Here we used Connectome-based Predictive Models (CPMs), which associate individual differences in task performance with functional connectivity patterns, to test their ability to predict performance for multiple attentional tasks. We constructed visual attentional blink (VAB) CPMs, and then used them and a sustained attention network model (saCPM; Rosenberg et al., 2016a) to predict performance. The latter model had been previously shown to successfully predict performance across tasks involving selective attention, inhibitory control, and even reading recall. Participants (n ​= ​73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from VAB task data (behavior and fMRI) successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse performance for selective and sustained attention tasks, and vice versa. Predictions from applying the saCPM to the data mirrored these results, with the network’s negative edges predicting better VAB performance. The vabCPM’s positive edges partially yet significantly overlapped with the saCPM’s negative edges, and vice versa. Many positive edges from the vabCPM involved the default mode network, whereas many negative edges involved the salience/ventral attention network. We conclude that the vabCPM and saCPM networks reflect general attentional functions that influence performance on many tasks. The networks may indicate an individual’s propensity to deploy attention in a more diffuse or a more focused manner.

This review summarizes how predictive modeling, a method that uses brain features to predict individual differences in behavior, is used to understand developmental periods. Rosenberg et al focus specifically on adolescence and examples of characteristic adolescent behaviors such as risk-taking.

Abstract Introduction Connectome‐based predictive modeling (CPM) is a recently developed machine‐learning‐based framework to predict individual differences in behavior from functional brain connectivity (FC). In these models, FC was operationalized as Pearson's correlation between brain regions’ fMRI time courses. However, Pearson's correlation is limited since it only captures linear relationships. We developed a more generalized metric of FC based on information flow. This measure represents FC by abstracting the brain as a flow network of nodes that send bits of information to each other, where bits are quantified through an information theory statistic called transfer entropy. Methods With a sample of individuals performing a sustained attention task and resting during functional magnetic resonance imaging (fMRI) (n = 25), we use the CPM framework to build machine‐learning models that predict attention from FC patterns measured with information flow. Models trained on n − 1 participants’ task‐based patterns were applied to an unseen individual's resting‐state pattern to predict task performance. For further validation, we applied our model to two independent datasets that included resting‐state fMRI data and a measure of attention (Attention Network Task performance [n = 41] and stop‐signal task performance [n = 72]). Results Our model significantly predicted individual differences in attention task performance across three different datasets. Conclusions Information flow may be a useful complement to Pearson's correlation as a measure of FC because of its advantages for nonlinear analysis and network structure characterization.

Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.