Biology subjects

Fair, D. A.

Publications and source records attributed to Fair, D. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Movie Watching for Individual Functional Brain Network Estimation in Adolescents

Precision functional mapping (PFM) reveals stable individual differences in brain functional network organization and holds promise for advancing neuropsychiatric clinical care. PFM requires sufficient high-quality and densely-sampled functional MRI data at the individual level that is typically acquired during the so-called resting state while passively fixating on a crosshair. However, crosshair fixation acquisitions can be challenging especially for pediatric or patient populations, where alternatives like movie watching might be used to improve data quality and reduce participant burden. Here, we compare the use of resting state crosshair fixation and movie watching fMRI data to derive individual-specific functional brain network topography using PFM data (28-38 minutes of low-motion fMRI data per-participant per-condition) in nineteen adolescents (12-18 years) with a range of transdiagnostic psychiatric diagnoses. Network assignments were largely conserved between crosshair fixation and movie watching, with a smaller subset of reproducible network reassignments which were overwhelmingly border shifts between neighboring networks. This suggests largely conserved global structure of functional network topography between crosshair fixation and movie watching, though border shifts can compound into variable estimates of cortical network size particularly for movie positive (language, salience, and parietal memory) and movie negative networks (default mode, action mode). Functional network assignments derived from movies and crosshair were also similarly individual-specific and consistent within common neuromodulation targets including dorsolateral prefrontal cortex and somatomotor cortex. Key results were replicated in independent pediatric consortia data (Healthy Brain Network, n = 466, 6-22 years) and reproduced across independent PFM data sessions. Acknowledging individual differences and level of analysis, we conclude that movies are a viable alternative to resting state crosshair fixation for individual network definitions.

neuroscience↗

bioRxiv2026

Age-related differences in neurometabolite concentrations during early infancy: A cross-sectional analysis of the HBCD Release 1.0

Background: Developmental trajectories of low-concentration neurometabolites such as the neurotransmitter {gamma}-aminobutyric acid (GABA), and the antioxidants glutathione (GSH) and ascorbate (Asc) across early infancy remain unexplored. Advances in spectral editing enabled the measurement of these key molecules together with high-concentration metabolites like N-acetylaspartate (NAA) and glutamate (Glu) in the HEALthy Brain and Child Development (HBCD) study, the largest longitudinal study of early brain development in the United States. Purpose: To determine the age-associated trajectories of 14 key neurometabolites during early infancy from a cross-sectional 1H-MRS dataset. Materials and Methods: HBCD utilizes ISTHMUS, an integrated MRS sequence that includes both an unedited short-echo-time PRESS acquisition and an advanced 4-step Hadamard-encoded sequence, HERCULES to enable measurement of both high- and low-concentration metabolites. Metabolite quantification was carried out by the HBCD Data Coordinating Center (HDCC) using an automated Osprey pipeline, with data from 201 infants ages 0 - 10 weeks adjusted age included in the tabular imaging results in HBCD data release 1.0. After excluding preterm born infants and data with poor linewidth or model quality metric, we tested for linear associations with adjusted age for each metabolite. Results: Concentration estimates of total NAA (tNAA), total creatine (tCr) and glutamate (Glu) as well as the combined sum of glutamate and glutamine (Glx) significantly increased across ages 0 - 10 weeks, while myo-inositol (mI) decreased. GABA and GSH showed age-related trends, but did not reach significance. Levels of the lipid precursor phosphorylethanolamine (PE) and Asc are higher in the first months than established adult values. Conclusion: Multiple metabolites showed significant age-related changes during early infancy. While GABA and GSH did not, future work will establish whether the trends suggested here contribute to linear or non-linear patterns across the first years of life.

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bioRxiv2026

Precision Confidence Mapping: An approach to determining individualized network topography with limited data

Individualized resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to guide neuromodulation target selection. However, clinical scans are often short and noisy, and standard pipelines for functional network identification do not provide information about confidence of network assignment. With limited data, unstable network assignments can misdirect stimulation toward off-target regions, making it critical to know which assignments can be trusted. We developed Precision Confidence Mapping (PCM), a bootstrap-based framework that makes this uncertainty explicit and actionable. PCM repeatedly resamples the time series and reruns network detection to estimate how consistently each vertex is assigned to a given network. The resulting confidence maps can be thresholded to exclude less stable regions. We evaluated PCM across scan durations from 5 to 70 minutes using positive predictive value (PPV) as the primary measure of network-assignment precision. PPV quantified the proportion of vertices assigned to a network that received the same label in an independent within-subject 70 minute reference map. Confidence thresholding markedly improved PPV across functional networks, with the largest gains for short scan durations. Compared with standard network assignment, PCM significantly increased agreement with this independent reference. Within-subject agreement remained greater than between-subject agreement, indicating that thresholding preserved individual-specific network topography. These precision gains came with modest reductions in reference-network coverage, particularly at shorter scan durations. This tradeoff may be acceptable for neuromodulation applications that prioritize minimizing off-network assignments. By adding a reliability layer to individualized mapping, PCM supports more cautious and precise neuromodulation targeting under real-world clinical scan constraints.

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bioRxiv2026

Trajectories of hippocampal subregion development in the first years of life and their association with school-aged episodic memory outcomes

Brain networks that support episodic memory development in the first years of life remain poorly understood. Protracted growth of regions such as the hippocampus have been suggested as a causal role in episodic memory development, but development of these memory brain networks and their role in episodic memory development is not yet fully elucidated. In this study, subcortical memory network regions (hippocampus, thalamus, amygdala) were segmented from MRI images in 835 visits spanning 0-4 years of age across 322 participants in the Baby Connectome Project. Hippocampal segmentations were further subdivided into head, body, and tail subregions manually for 426 visits, which were used to train models that automatically segmented hippocampal subregions for the remaining visits. 58 participants returned for an early school-age follow-up, including two episodic memory tasks. Volumetric growth trajectories differed across regions and across subregions within the hippocampus, with the head of the hippocampus showing steep growth that plateaued months later than the body or tail of the hippocampus. In the right hemispheres hippocampal head, age- and sex- adjusted volumes positively predicted future early school-age episodic memory performance. After accounting for total brain volume, the right thalamus also predicted memory performance. Total sleep duration at the follow-up visit accounted for performance variance above and beyond brain volume correlations. Altogether, results suggest that trajectories of growth and relationships between volume and episodic memory performance are region and subregion specific, and provide evidence for the important role of sleep in associations between brain networks and early episodic memory development. SignificanceThe hippocampus is a critical structure in episodic memory, yet precise longitudinal developmental trajectories of this structure have yet to be elucidated. This study provides detailed, subregion specific hippocampal trajectories, and demonstrates that variation in these trajectories is associated with variation in later episodic memory performance. This insight fills a current gap in the literature delineating how brain development and episodic memory behaviors are related in the first five years of life. Considering this is the same age range during which adults begin to have long-term memories available from childhood, this gap represents an important opportunity to understand how changes in the brain support the development of basic episodic memory skills.

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bioRxiv2026

White matter reflects the childhood exposome

The childhood environment is critical for brain development. However, most neuroimaging studies examine individual environmental measures (e.g., socioeconomic status) or a limited set of exposures, obscuring how the combination of complex, real-world exposures jointly influence brain development. Here we investigated how white matter shape and tissue properties are linked to the childhood exposome, a multidimensional measure capturing over 300 environmental exposures. Using multi-shell diffusion MRI from 8,183 children (ages 9-10) in the ABCD study, we quantified microstructural and macrostructural properties across 62 person-specific white matter tracts. The exposome showed widespread and highly replicable associations with both white matter microstructure and macrostructure: more advantaged environments were associated with larger tract macrostructure and lower orientation dispersion. Principal component analysis revealed that the dominant axis of exposome-white matter covariation aligns with the cortical sensorimotor-association hierarchy, such that tracts spanning this hierarchy exhibit the strongest associations with the exposome. Multivariate models demonstrated that patterns of white matter features explained 25% of the variance in the exposome in unseen individuals. Notably, white matter-based prediction of cognition was markedly reduced after accounting for the exposome ([~]82% reduction in explained variance), indicating that brain-cognition associations overlap substantially with variance captured by the exposome. These findings generalized to independent data from the Healthy Brain Network (n=869), which differs substantially from ABCD in MRI acquisition, participant selection, and childhood environments. Together, these results suggest that white matter architecture strongly reflects the childhood environment.

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bioRxiv2026

Parallel processing chains span cytoarchitectures to organize association cortex

Task fMRI1 and electrophysiology2 have revealed distributed, linked cortical patches with shared category preferences (e.g., faces, objects, places)1,3-5, smaller than cytoarchitectonic areas. Resting-state functional connectivity (RSFC) similarly showed that somato-cognitive action network (SCAN) nodes interleave with effectors (foot, hand, mouth), subdividing the precentral gyrus6. Here, using multiple precision functional mapping (PFM) modalities (RSFC, task, lags), we discovered that most of association cortex is organized like face processing and SCAN, with small, discrete patches interconnected into chains. Such patch-chains densely tile prefrontal cortex but are largely absent from primary cortex. Cortico-striatal connectivity is organized such that patches of the same chain connect to the same striatal location. Within chains, infra-slow fMRI signals are ordered in time. RSFC-defined chains align with task fMRI localizers (e.g., visual, motor, pain). Chains are absent at birth and emerge in the first year of life, suggesting their formation is at least partially experience-driven. Cytoarchitectonic areas are subdivided by patches, and patches in the same chain are distributed across different cytoarchitectures. Chains represent parallel ordered processing streams that are separated by information domain and behavioral goals, not cytoarchitectonics. Functional subdivision of architectonics into smaller patches, interlinked to form cross-architecture chains, enable greater parallelization and flexible specialization of processing.

neuroscience↗

bioRxiv2026

Menarche onset is an inflection point for mental health and brain development

Menarche is a normative milestone of female puberty, yet its role in adolescent mental health and brain development remains poorly understood. Using longitudinal data from 5,016 females (7 annual visits, ages 10-16 years) in the Adolescent Brain Cognitive Development Study, we found that menarche onset functions as an inflection point for the development of internalizing symptoms and gross brain morphometry. The onset of menarche, largely independent of timing and socio-environmental factors, preceded a significant spike in internalizing symptoms, while altering the rate of ongoing structural brain development. Following menarche onset, individuals with faster declines in gray matter volume and surface area also had heightened internalizing symptoms. These findings suggest that menarche is not only a reproductive milestone but a neuroendocrine driver of adolescent brain and mental health trajectories. This normative and easily identifiable marker could define a critical window for mental health screenings with greater precision than current age-based guidelines.

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bioRxiv2026

Cortical Thickness and Curvature in Autism and ADHD: A Mega-Analysis

BackgroundExisting evidence suggests cortical morphometric alterations occur in people with autism and ADHD. However, these findings remain tentative due to small sample sizes, heterogeneous imaging pipelines, varied statistical approaches, and limited harmonization across acquisition sites. Few studies have applied standardized processing to large, clinically enriched datasets or addressed site-related batch effects. MethodsWe leveraged six large-scale brain imaging datasets (n = 9,647; male=5,835; female=3,812; ages 5-64 years), including 1,533 individuals with ADHD, 1,080 with autism spectrum disorder, and 7,034 matched controls. All imaging data were processed using the validated ABCD-HCP pipeline, with cortical parcellation into 360 regions based on the Human Connectome Project (HCP) atlas, and ComBat harmonization was applied to account for variability across 67 acquisition sites. Group-level differences in cortical thickness and sulcal curvature were examined with ANCOVAs, controlling for covariates and using Bonferroni correction for multiple comparisons. ResultsOur analyses revealed distinct neuroanatomical signatures for both autism and ADHD. Individuals with autism exhibited regionally thinner cortex and curvature alterations particularly in the Cingulo-Opercular network. In contrast, individuals with ADHD displayed regionally thicker cortex, particularly in the default mode and somatomotor networks, alongside curvature differences. Control participants showed intermediate patterns, suggesting that autism and ADHD may represent diverging extremes of cortical maturation. ConclusionsCortical thickness and curvature emerge as potential biomarkers that can advance understanding of neurodevelopmental conditions and disentangle heterogeneity across diagnostic groups. These findings highlight the value of harmonized, large-scale, standardized analyses for resolving inconsistencies in the literature.

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bioRxiv2025

Patterns of brain-wide associations reflect socioeconomics

Previous brain-wide association studies (BWAS) cross-sectionally linked a specific behavioral trait, most commonly IQ or psychopathology, to variation in brain function or structure. Here, we expanded the focus of BWAS from effect sizes to interpretability and generalizability by mapping 649 variables to brain function and structure. We compared the resultant BWAS maps to other types of brain data to annotate the BWAS patterns. Socioeconomic status (SES) -- not IQ or psychopathology -- showed the strongest associations with both resting-state functional connectivity (RSFC) and cortical thickness in the Adolescent Brain Cognitive Development (ABCD) Study. A principal exposome brain pattern, anchored to sensory and motor cortex, captured 34% of the variance across all BWAS maps. This exposome pattern was strongly correlated with the SES and IQ BWAS maps and non-BWAS maps of sleep (EEG), norepinephrine (PET), and stimulants (drug trial), but not cognitive activation maps (task fMRI). Adjusting for SES, reduced brain-IQ associations by 40%. Brain with IQ associations did not generalize, as they could no longer be detected in subsamples drawn from only higher SES backgrounds, while brain with SES associations remained strong in higher-IQ-only subsamples. These findings reveal SES as the principal axis of population-level brain variation, possibly stemming from the sleep deprivation and heightened stress associated with lower SES, since socioeconomics can only indirectly affect the brain.

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bioRxiv2025

Diffusion MRI Processing in the HEALthy Brain and Child Development Study: Innovations and Applications

The landmark ongoing HEALthy Brain and Cognitive Development (HBCD) study will longitudinally chart brain development in a large sample (projected n=7,200) of infants through age 10 years with multimodal neuroimaging that includes an advanced diffusion MRI (dMRI) acquisition. Here, we detail advances in dMRI image processing developed for HBCD, incorporated into the widely used QSIPrep pipeline. Major changes to preprocessing include improvements in infant brain extraction, distortion correction, and normalization to infant-specific templates. Additionally, we describe a new software package - QSIRecon - that yields rich derived data including diverse maps of tissue microstructure as well as person-specific white matter bundles. Using dMRI data from a subset of the HBCD 1.0 release where age information was available (n=529 sessions across two time points), we observe critical improvements in data quality with preprocessing and see expected developmental patterns. Moving forward, the publicly-available data from HBCD will rapidly grow to become the largest study of brain development in infancy and early childhood using dMRI. QSIPrep and QSIRecon are openly available and can be applied to other infant and pediatric dMRI datasets.

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bioRxiv2025

Precision functional imaging in infants using multi-echo fMRI at 7T

Personalized functional brain developmental trajectories can be studied with Precision Functional Mapping (PFM). Our previous work has demonstrated that PFM can be achieved in infants despite rapid brain growth. However, even with extensive data collection (up to 1 hour of fMRI), the reliability and precision of these maps remain lower than those observed in youth and adults - particularly within subcortical structures. In this work we demonstrate the utility of high-field 7T MRI compared to 3T MRI for facilitating PFM in infants. We showcase data from multi-echo fMRI acquisitions in the same infants at both 7T and 3T and demonstrate that 7T imaging in infants is safe and feasible with our subject-specific safety workflow. Moreover, we demonstrate that the use of a higher magnetic field strength affords a spatial resolution more appropriately matched to infants smaller head and brain sizes, yielding notable improvements in data quality, especially for PFM. The increase in both spatial precision and reliability also suggests that 7T MRI can reduce the amount of data required for PFM. Last, we show how ultra-high field imaging can help us study the development of subcortical-to-cortical connectivity patterns, crucial for understanding brain development during this developmental window. 7T MRI is a promising new avenue for developmental cognitive neuroscience.

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bioRxiv2025

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible Brain-wide Motifs

The Research Domain Criteria (RDoC) framework was introduced to guide psychiatric research using biologically grounded, dimensional constructs of mental function. However, its hierarchical domain structure remains largely unvalidated against individual-level brain and behavioral data. Building on prior group-level work, we applied a multi-stage validation framework to Human Connectome Project (HCP) task-fMRI data to test whether individual-level, data-driven models more accurately capture the organization of brain activity and behavior than RDoC-based models. Using confirmatory factor analysis in two independent cohorts, we found that data-driven bifactor models consistently outperformed RDoC-based models across multiple fit indices. The general factor derived from these models revealed a reproducible, low-dimensional axis spanning visual-attentional to default mode-auditory systems, aligning with canonical macroscale cortical gradients. Community detection further identified reproducible spatial motifs whose centroids corresponded to interpretable functional systems and whose alignment predicted individual performance on working memory and relational reasoning tasks. To assess whether these findings extended beyond neural data, we analyzed behavioral measures in HCP and in an independent transdiagnostic dataset (LA5c). In both datasets, data-driven behavioral models outperformed RDoC-based models, although the relative support for bifactor versus specific factor structure differed by dataset. Extending the neural analyses to LA5c, which included healthy controls and individuals with ADHD, bipolar disorder, and schizophrenia, showed that data-driven bifactor models generalized across diagnostic groups and that alignment with data-driven community centroids related to symptom severity, whereas RDoC-based representations showed weaker or no associations. Finally, topological analysis of task-evoked brain activity revealed that data-driven representations better captured the global organization of brain states than RDoC domains. Together, these findings demonstrate that individual-level, empirically derived models provide a more accurate, generalizable, and behaviorally relevant account of brain organization than the current RDoC framework. By integrating neural, behavioral, and clinical validation, this work advances precision neuroscience and supports the empirical refinement of dimensional psychiatric frameworks.

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bioRxiv2025

Commonality and Variability in Functional Networks In Young Children Under 5 Years Old

Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices. We developed an age-appropriate functional reference that captured common structure across toddlers without averaging away individual variability, enabling estimation of each childs networks from resting-state fMRI. Across cohorts of 8-60-month-old children, we found individualized network organization--including finer-scale subdivisions and emerging language lateralization--well before age five. Network layouts showed longitudinal stability, with greater consistency in sensory than association regions. Within-network connectivity was stronger and explained age-related variance when networks were defined using individualized rather than group-consensus topography. Left-lateralization of language networks tracked age-normalized verbal ability, linking early functional architecture to emerging cognition. These findings show that behaviorally relevant brain networks arise far earlier than previously recognized, providing a foundation for studying typical development and early biomarkers.

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bioRxiv2025

Connectome-Based Predictive Modeling of Concurrent and Prospective Substance Use in Adolescence

Understanding the neural mechanisms of adolescent substance use is a critical public health issue, with direct implications for bolstering prevention and treatment strategies. Yet this effort is challenging because substance use is multi-faceted, commonly used brain network features are not optimized to capture both local and global aspects of intrinsic connectivity, and because substance use facets change over time. In this study, we operationalized adolescent substance use along three dimensions--intent, access, and family-developmental history--and trained predictive models of each facet using resting-state connectivity. Trait impulsivity, a known risk factor, was also examined. Using Baseline and 2 Year Follow-Up data from the ABCD Bids Community Collection (ABCC), we found that prediction was more successful at follow-up than baseline. At baseline, predictive accuracy was modest and intent to use substances was the most accurately predicted facet. Prediction accuracies at follow-up were much higher, with access and family-developmental history being better predicted, signaling a developmental shift in the brain-behavior mapping of substance use vulnerabilities. These findings suggest that the neurobiological correlates of substance use are dynamic across adolescence, possibly reflecting changing phenotype. More broadly, these results underscore the importance of modeling distinct substance use facets and accounting for developmental timing to understand risk trajectories, while contributing to a growing literature that shows early-developing individual differences are predictive of later outcomes.

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bioRxiv2025

Precision Functional Neuroimaging Reveals Individually Specific Auditory Responses in Infants

Adaptively responding to salient stimuli in the environment is a fundamental feature of cognitive development in early life, which is enabled by the developing brain. Understanding individual variability in how the brain supports this fundamental process is essential for uncovering neurodevelopmental trajectories and potential neurodevelopmental risks. In the present study, we used a precision functional imaging approach to probe activation in response to salient auditory stimuli and its relation to brain functional networks in individual infants. A minimum of 60 minutes of fMRI BOLD data with an auditory oddball paradigm were collected in ten infants with a mean postmenstrual age of 48 weeks. Results demonstrate the feasibility of performing a precision functional imaging study to investigate individual specific responses to salient stimuli in infants. While responses to the auditory oddball were consistent between individuals in auditory processing areas, responses across the rest of the brain differed across individuals in their magnitude and shape. Individual specific response patterns appeared to be relatively stable and differed from other participants response patterns, despite fluctuations across runs. Commonalities and differences between individuals demonstrated in this sample contribute to our understanding of how the developing brain instantiates processing of salient stimuli. Our findings suggest that during early development, early unimodal processing is well conserved across individuals, however subsequent perceptual processing is still being personally defined. In this context, individual specific response patterns could be a promising target for biomarkers of normative brain and cognitive development.

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bioRxiv2025

Precision Functional Mapping of the Individual Human Brain Near Birth

Cortical areas are a fundamental organizational property of the brain, but their development in humans is not well understood. Key unanswered questions include whether cortical areas are fully established near birth, the extent of individual variation in the arrangement of cortical areas, and whether any such individual variation in cortical area location is greater in later-developing association areas as compared to earlier-developing sensorimotor areas. To address these questions, we used functional MRI to collect precision functional mapping (PFM) data in eight individual neonates (mean 42.7 weeks postmenstrual age) over 2-5 days (mean 77.9 minutes of low motion data per subject [framewise displacement <0.1]). Each subjects dataset was split into two roughly equal halves of data from different days of data collection to measure within-subject reliability and across-subject similarity. Whole-brain patterns of functional connectivity (FC) reached a mean within-subject, across-day reliability of r=0.78 with 41.9 minutes of data. Across subject similarity of whole-brain FC was r=0.62 on average and significantly lower than within-subject similarity (t=5.9, p<0.001). Using established methods to identify transitions in FC across the cortical surface, we identified sets of cortical areas for each individual that were subject-specific and highly reliable across split-halves (mean z=4.4, SD=1.4). The arrangement of cortical areas was thus individually specific across the entire cortical surface, and this individual specificity did not vary as a function of the sensorimotor-association axis. This study establishes the feasibility of neonatal PFM and suggests that cortical area arrangement is individually specific and largely established shortly following birth. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/663543v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@b6c63aorg.highwire.dtl.DTLVardef@1903235org.highwire.dtl.DTLVardef@1d05733org.highwire.dtl.DTLVardef@10d0770_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIIt is feasible to obtain precision functional mapping (PFM) in neonates, acquiring 60+ minutes of fMRI data in individual neonates over multiple days. C_LIO_LINeonatal functional connectivity (FC) measures obtained through PFM are moderately reliable with 40 minutes of data. C_LIO_LIPatterns of neonatal FC across the brain are individually specific. C_LIO_LICortical areas, based on patterns of homogenous FC, can be reliably identified across the entire cortical surface in individual neonates. C_LIO_LIThe arrangement of cortical areas at birth is individually specific across the entire brain. C_LI

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bioRxiv2025

Task-evoked functional connectivity exhibits novel and strengthened relationships with executive function relative to the resting state

Executive functioning in children has been linked to intrinsic brain network organization assessed during the resting state, as well as to brain network organization during the performance of cognitive tasks. Prior work has established that task-based brain networks are stronger predictors of behavior than resting state networks, yet it is unclear if tasks only strengthen relationships that exist weakly at rest or if tasks also evoke unique relationships. A lack of discernment regarding how tasks and the resting state commonly and uniquely support executive functions precludes a holistic understanding of the neurobiological basis of executive functions. This project investigated differences in brain network organization and relationships with executive function ability between the resting state and two executive function tasks, a stop signal task and an emotional n-back task, using the Adolescent Brain and Cognitive Development (ABCD) Study dataset. Both executive function tasks evoked a more integrated network organization than the resting state, and executive function ability was related to different aspects of brain network organization during the resting state and during the tasks. Further, task-related shifts in brain network organization evoked several new relationships with executive function that were not detectable during the resting state and strengthened a relationship with executive function that existed weakly during the resting state. Overall, this study establishes a distinction between common and unique features of intrinsic and task-evoked brain function that facilitate executive function in children. SignificanceExecutive functions, which encompass goal-directed behaviors critical for life success, emerge from interactions within and between networks of brain regions. Here, we tested how executive functions are linked to functional brain network interactions during the resting state, in which there are no external cognitive demands, and during executive function tasks in late childhood. We found that brain network organization during the performance of executive function tasks evoked a combination of new and strengthened relationships with executive function ability compared to the resting state. This suggests that the brain facilitates executive function performance in children through the recruitment of specific functional interactions during the enactment of executive function.

neuroscience↗

bioRxiv2025

Stimulant medications affect arousal and reward, not attention

Prescription stimulants such as methylphenidate are being used by an increasing portion of the population, primarily children. These potent norepinephrine and dopamine reuptake inhibitors promote wakefulness, suppress appetite, enhance physical performance, and are purported to increase attentional abilities. Prior functional magnetic resonance imaging (fMRI) studies have yielded conflicting results about the effects of stimulants on the brains attention, action/motor, and salience regions that are difficult to reconcile with their proposed attentional effects. Here, we utilized resting-state fMRI (rs-fMRI) data from the large Adolescent Brain Cognitive Development (ABCD) Study to understand the effects of stimulants on brain functional connectivity (FC) in children (n = 11,875; 8-11 years old) using network level analysis (NLA). We validated these brain-wide association study (BWAS) findings in a controlled, precision imaging drug trial (PIDT) with highly-sampled (165-210 minutes) healthy adults receiving high-dose methylphenidate (Ritalin, 40 mg). In both studies, stimulants were associated with altered FC in action and motor regions, matching patterns of norepinephrine transporter expression. Connectivity was also changed in the salience (SAL) and parietal memory networks (PMN), which are important for reward-motivated learning and closely linked to dopamine, but not the brains attention systems (e.g. dorsal attention network, DAN). Stimulant-related differences in FC closely matched the rs-fMRI pattern of getting enough sleep, as well as EEG- and respiration-derived brain maps of arousal. Taking stimulants rescued the effects of sleep deprivation on brain connectivity and school grades. The combined noradrenergic and dopaminergic effects of stimulants may drive brain organization towards a more wakeful and rewarded configuration, explaining improved task effort and persistence without direct effects on attention networks.

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