The Development of Socially Directed Attention: An Functional Magnetic Resonance Imaging Study in Infant Monkeys

Socially guided visual attention, such as gaze following and joint attention, represents the building block of higher-level social cognition in primates, although their neurodevelopmental processes are still poorly understood. Atypical development of these social skills has served as early marker of autism spectrum disorder and Williams syndrome. In this study, we trace the developmental trajectories of four neural networks underlying visual and attentional social engagement in the translational rhesus monkey model. Resting-state fMRI (rs-fMRI) data and gaze following skills were collected in infant rhesus macaques from birth through 6 months of age. Developmental trajectories from subjects with both resting-state fMRI and eye-tracking data were used to explore brain-behavior relationships. Our findings indicate robust increases in functional connectivity (FC) between primary visual areas (primary visual cortex [V1] - extrastriate area 3 [V3] and V3 - middle temporal area, ventral motion areas middle temporal area - AST, as well as between TE and amygdala (AMY) as infants mature. Significant FC decreases were found in more rostral areas of the pathways, such as areas temporal area occipital part - TE in the ventral object pathway, V3 - lateral intraparietal (LIP) of the dorsal visual attention pathway and V3 - temporo-parietal area of the ventral attention pathway. No changes in FC were found between cortical areas LIP-FEF and temporo-parietal area - Area 12 of the dorsal and ventral attention pathways or between AST-AMY and AMY-insula. Developmental trajectory of gaze following revealed a period of dynamic changes with gradual increases from 1 to 2 months, followed by slight decreases from 3 to 6 months. Exploratory association findings across the 6-month period showed that infants with higher gaze following had lower FC between primary visual areas V1-V3, but higher FC in the dorsal attention areas V3-LIP, both in the right hemisphere. Together, the first 6 months of life in rhesus macaques represent a critical period for the emergence of gaze following skills associated with maturational changes in FC of socially guided attention pathways.

A precision functional atlas of personalized network topography and probabilities

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Study design features that improve effect sizes in cross-sectional and longitudinal brain-wide association studies

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required to improve the replicability of BWAS because actual effect sizes are much smaller than those reported in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index (RESI) using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for improving standardized effect sizes and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger covariate variance have larger effect size estimates and that the longitudinal studies we examined have systematically larger standardized effect sizes than cross-sectional studies. We propose a cross-sectional RESI to adjust for the systematic difference in effect sizes between cross-sectional and longitudinal studies that allows investigators to quantify the benefit of conducting their study longitudinally. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes to increase between-subject variability and adding a single additional longitudinal measurement per subject can improve effect sizes. However, evaluating these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset shows that commonly used longitudinal models can, counterintuitively, reduce effect sizes. We demonstrate that the benefit of conducting longitudinal studies depends on the strengths of the between- and within-subject associations of the brain and non-brain measures. Explicitly modeling between- and within-subject effects avoids conflating the effects and allows optimizing effect sizes for them separately. These findings underscore the importance of considering study design features to improve the replicability of BWAS.

Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample

Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.

A general exposome factor explains individual differences in functional brain network topography and cognition in youth

Childhood environments are critical in shaping cognitive neurodevelopment. With the increasing availability of large-scale neuroimaging datasets with deep phenotyping of childhood environments, we can now build upon prior studies that have considered relationships between one or a handful of environmental and neuroimaging features at a time. Here, we characterize the combined effects of hundreds of inter-connected and co-occurring features of a child's environment ("exposome") and investigate associations with each child's unique, multidimensional pattern of functional brain network organization ("functional topography") and cognition. We apply data-driven computational models to measure the exposome and define personalized functional brain networks in pre-registered analyses. Across matched discovery (n=5139, 48.5% female) and replication (n=5137, 47.1% female) samples from the Adolescent Brain Cognitive Development study, the exposome was associated with current (ages 9-10) and future (ages 11-12) cognition. Changes in the exposome were also associated with changes in cognition after accounting for baseline scores. Cross-validated ridge regressions revealed that the exposome is reflected in functional topography and can predict performance across cognitive domains. Importantly, a single measure capturing a child's exposome could more accurately and parsimoniously predict cognition than a wealth of personalized neuroimaging data, highlighting the importance of children's complex, multidimensional environments in cognitive neurodevelopment.

Cumulative Effects of Resting-State Connectivity Across All Brain Networks Significantly Correlate with Attention-Deficit Hyperactivity Disorder Symptoms

Identification of replicable neuroimaging correlates of attention-deficit hyperactivity disorder (ADHD) has been hindered by small sample sizes, small effects, and heterogeneity of methods. Given evidence that ADHD is associated with alterations in widely distributed brain networks and the small effects of individual brain features, a whole-brain perspective focusing on cumulative effects is warranted. The use of large, multisite samples is crucial for improving reproducibility and clinical utility of brain-wide MRI association studies. To address this, a polyneuro risk score (PNRS) representing cumulative, brain-wide, ADHD-associated resting-state functional connectivity was constructed and validated using data from the Adolescent Brain Cognitive Development (ABCD, N = 5,543, 51.5% female) study, and was further tested in the independent Oregon-ADHD-1000 case-control cohort (N = 553, 37.4% female). The ADHD PNRS was significantly associated with ADHD symptoms in both cohorts after accounting for relevant covariates (p < 0.001). The most predictive PNRS involved all brain networks, though the strongest effects were concentrated among the default mode and cingulo-opercular networks. In the longitudinal Oregon-ADHD-1000, non-ADHD youth had significantly lower PNRS (Cohen's d = -0.318, robust p = 5.5 × 10-4) than those with persistent ADHD (age 7-19). The PNRS, however, did not mediate polygenic risk for ADHD. Brain-wide connectivity was robustly associated with ADHD symptoms in two independent cohorts, providing further evidence of widespread dysconnectivity in ADHD. Evaluation in enriched samples demonstrates the promise of the PNRS approach for improving reproducibility in neuroimaging studies and unraveling the complex relationships between brain connectivity and behavioral disorders.

Perinatal Western-style diet exposure associated with decreased microglial counts throughout the arcuate nucleus of the hypothalamus in Japanese macaques

Perinatal exposure to a high-fat, high-sugar Western-style diet (WSD) is associated with altered neural circuitry in the melanocortin system. This association may have an underlying inflammatory component, as consumption of a WSD during pregnancy can lead to an elevated inflammatory environment. Our group previously demonstrated that prenatal WSD exposure was associated with increased markers of inflammation in the placenta and fetal hypothalamus in Japanese macaques. In this follow-up study, we sought to determine whether this heightened inflammatory state persisted into the postnatal period, as prenatal exposure to inflammation has been shown to reprogram offspring immune function and long-term neuroinflammation would present a potential means for prolonged disruptions to microglia-mediated neuronal circuit formation. Neuroinflammation was approximated in 1-yr-old offspring by counting resident microglia and peripherally derived macrophages in the region of the hypothalamus examined in the fetal study, the arcuate nucleus (ARC). Microglia and macrophages were immunofluorescently stained with their shared marker, ionized calcium-binding adapter molecule 1 (Iba1), and quantified in 11 regions along the rostral-caudal axis of the ARC. A mixed-effects model revealed main effects of perinatal diet (P = 0.011) and spatial location (P = 0.003) on Iba1-stained cell count. Perinatal WSD exposure was associated with a slight decrease in the number of Iba1-stained cells, and cells were more densely located in the center of the ARC. These findings suggest that the heightened inflammatory state experienced in utero does not persist postnatally. This inflammatory response trajectory could have important implications for understanding how neurodevelopmental disorders progress.NEW & NOTEWORTHY Prenatal Western-style diet exposure is associated with increased microglial activity in utero. However, we found a potentially neuroprotective reduction in microglia count during early postnatal development. This trajectory could inform the timing of disruptions to microglia-mediated neuronal circuit formation. Additionally, this is the first study in juvenile macaques to characterize the distribution of microglia along the rostral-caudal axis of the arcuate nucleus of the hypothalamus. Nearby neuronal populations may be greater targets during inflammatory insults.

Neural Circuit Markers of Familial Risk for Depression Among Healthy Youths in the Adolescent Brain Cognitive Development Study

BACKGROUND

Family history of depression is a robust predictor of early-onset depression, which may confer risk through alterations in neural circuits that have been implicated in reward and emotional processing. These alterations may be evident in youths who are at familial risk for depression but who do not currently have depression. However, the identification of robust and replicable findings has been hindered by few studies and small sample sizes. In the current study, we sought to identify functional connectivity (FC) patterns associated with familial risk for depression.

METHODS

Participants included healthy (i.e., no lifetime psychiatric diagnoses) youths at high familial risk for depression (HR) (n = 754; at least one parent with a history of depression) and healthy youths at low familial risk for psychiatric problems (LR) (n = 1745; no parental history of psychopathology) who were 9 to 10 years of age and from the Adolescent Brain Cognitive Development (ABCD) Study sample. We conducted whole-brain seed-to-voxel analyses to examine group differences in resting-state FC with the amygdala, caudate, nucleus accumbens, and putamen. We hypothesized that HR youths would exhibit global amygdala hyperconnectivity and striatal hypoconnectivity patterns primarily driven by maternal risk.

RESULTS

HR youths exhibited weaker caudate-angular gyrus FC than LR youths (α = 0.04, Cohen's d = 0.17). HR youths with a history of maternal depression specifically exhibited weaker caudate-angular gyrus FC (α = 0.03, Cohen's d = 0.19) as well as weaker caudate-dorsolateral prefrontal cortex FC (α = 0.04, Cohen's d = 0.21) than LR youths.

CONCLUSIONS

Weaker striatal connectivity may be related to heightened familial risk for depression, primarily driven by maternal history. Identifying brain-based markers of depression risk in youths can inform approaches to improving early detection, diagnosis, and treatment.

Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study

Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (> 50%).

Personalized functional brain network topography is associated with individual differences in youth cognition

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

XCP-D: A Robust Pipeline for the post-processing of fMRI data

Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NifTI or CIFTI files, following pre-processing with fMRIPrep, HCP, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.

Multi-echo Acquisition and Thermal Denoising Advances Infant Precision Functional Imaging

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development, but precise characterization of systems organization during periods of high plasticity might be most influential towards discoveries promoting lifelong health. Collecting and analyzing precision fMRI data during early development has unique challenges and emphasizes the importance of novel methods to improve data acquisition, processing, and analysis strategies in infant samples. Here, we investigate the applicability of two such methods from adult MRI research, multi-echo (ME) data acquisition and thermal noise removal with Noise reduction with distribution corrected principal component analysis (NORDIC), in precision fMRI data from three newborn infants. Compared to an adult example subject, T2* relaxation times calculated from ME data in infants were longer and more variable across the brain, pointing towards ME acquisition being a promising tool for optimizing developmental fMRI. The application of thermal denoising via NORDIC increased tSNR and the overall strength of functional connections as well as the split-half reliability of functional connectivity matrices in infant ME data. While our findings related to NORDIC denoising are coherent with the adult literature and ME data acquisition showed high promise, its application in developmental samples needs further investigation. The present work reveals gaps in our understanding of the best techniques for developmental brain imaging and highlights the need for further developmentally-specific methodological advances and optimizations, towards precision functional imaging in infants.

Maternal Perinatal Stress Trajectories and Negative Affect and Amygdala Development in Offspring

OBJECTIVE

Maternal psychological stress during pregnancy is a common risk factor for psychiatric disorders in offspring, but little is known about how heterogeneity of stress trajectories during pregnancy affect brain systems and behavioral phenotypes in infancy. This study was designed to address this gap in knowledge.

METHODS

Maternal anxiety, stress, and depression were assessed at multiple time points during pregnancy in two independent low-risk mother-infant cohorts (N=115 and N=2,156). Trajectories in maternal stress levels in relation to infant negative affect were examined in both cohorts. Neonatal amygdala resting-state functional connectivity MRI was examined in a subset of one cohort (N=60) to explore the potential relationship between maternal stress trajectories and brain systems in infants relevant to negative affect.

RESULTS

Four distinct trajectory clusters, characterized by changing patterns of stress over time, and two magnitude clusters, characterized by severity of stress, were identified in the original mother-infant cohort (N=115). The magnitude clusters were not associated with infant outcomes. The trajectory characterized by increasing stress in late pregnancy was associated with blunted development of infant negative affect. This relationship was replicated in the second, larger cohort (N=2,156). In addition, the trajectories that included increasing or peak maternal stress in late pregnancy were related to stronger neonatal amygdala functional connectivity to the anterior insula and the ventromedial prefrontal cortex in the exploratory analysis.

CONCLUSIONS

The trajectory of maternal stress appears to be important for offspring brain and behavioral development. Understanding heterogeneity in trajectories of maternal stress and their influence on infant brain and behavioral development is critical to developing targeted interventions.

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

Objectives

Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations.

Experimental Design

Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance.

Principal Observations

Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better.

Conclusions

BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

Caregiver monitoring, but not caregiver warmth, is associated with general cognition in two large sub-samples of youth

Individual differences in cognitive abilities emerge early during development, and children with poorer cognition are at increased risk for adverse outcomes as they enter adolescence. Caregiving plays an important role in supporting cognitive development, yet it remains unclear how specific types of caregiving behaviors may shape cognition, highlighting the need for large-scale studies. In the present study, we characterized replicable yet specific associations between caregiving behaviors and cognition in two large sub-samples of children ages 9-10 years old from the Adolescent Brain Cognitive Development Study® (ABCD). Across both discovery and replication sub-samples, we found that child reports of caregiver monitoring (supervision or regular knowledge of the child's whereabouts) were positively associated with general cognition abilities, after covarying for age, sex, household income, neighborhood deprivation, and parental education. This association was specific to the type of caregiving behavior (caregiver monitoring, but not caregiver warmth), and was most strongly associated with a broad domain of general cognition (but not executive function or learning/memory). Additionally, we found that caregiver monitoring partially mediated the association between household income and cognition, furthering our understanding of how socioeconomic disparities may contribute to disadvantages in cognitive development. Together, these findings underscore the influence of differences in caregiving behavior in shaping youth cognition. RESEARCH HIGHLIGHTS: Caregiver monitoring, but not caregiver warmth, is associated with cognitive performance in youth Caregiver monitoring partially mediates the association between household income and cognition Results replicated across two large matched samples from the Adolescent Brain Cognitive Development Study® (ABCD).

Development of top-down cortical propagations in youth

Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

Functional maturation in visual pathways predicts attention to the eyes in infant rhesus macaques: Effects of social status

Differences in looking at the eyes of others are one of the earliest behavioral markers for social difficulties in neurodevelopmental disabilities, including autism. However, it is unknown how early visuo-social experiences relate to the maturation of infant brain networks that process visual social stimuli. We investigated functional connectivity (FC) within the ventral visual object pathway as a contributing neural system. Densely sampled, longitudinal eye-tracking and resting state fMRI (rs-fMRI) data were collected from infant rhesus macaques, an important model of human social development, from birth through 6 months of age. Mean trajectories were fit for both datasets and individual trajectories from subjects with both eye-tracking and rs-fMRI data were used to test for brain-behavior relationships. Exploratory findings showed infants with greater increases in FC between left V1 to V3 visual areas have an earlier increase in eye-looking before 2 months. This relationship was moderated by social status such that infants with low social status had a stronger association between left V1 to V3 connectivity and eye-looking than high status infants. Results indicated that maturation of the visual object pathway may provide an important neural substrate supporting adaptive transitions in social visual attention during infancy.

Polyneuro risk scores capture widely distributed connectivity patterns of cognition

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

Using synthetic MR images for distortion correction

Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion and differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) images makes their alignment a challenge. Typically, field map data are used to correct EPI distortions. Alignments achieved with field maps can vary greatly and depends on the quality of field map data. However, many public datasets lack field map data entirely. Additionally, reliable field map data is often difficult to acquire in high-motion pediatric or developmental cohorts. To address this, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image with similar contrast properties to EPI data. This synthetic image acts as an effective reference for individual-specific distortion correction. Using pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club; HCP: Human Connectome Project) data, we demonstrate that Synth performs comparably to field map distortion correction approaches, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.

The role of puberty on physical and brain development: A longitudinal study in male Rhesus Macaques

This study examined the role of male pubertal maturation on physical growth and development of neurocircuits that regulate stress, emotional and cognitive control using a translational nonhuman primate model. We collected longitudinal data from male macaques between pre- and peri-puberty, including measures of physical growth, pubertal maturation (testicular volume, blood testosterone -T- concentrations) and brain structural and resting-state functional MRI scans to examine developmental changes in amygdala (AMY), hippocampus (HIPPO), prefrontal cortex (PFC), as well as functional connectivity (FC) between those regions. Physical growth and pubertal measures increased from pre- to peri-puberty. The indexes of pubertal maturation -testicular size and T- were correlated at peri-puberty, but not at pre-puberty (23 months). Our findings also showed ICV, AMY, HIPPO and total PFC volumetric growth, but with region-specific changes in PFC. Surprisingly, FC in these neural circuits only showed developmental changes from pre- to peri-puberty for HIPPO-orbitofrontal FC. Finally, testicular size was a better predictor of brain structural maturation than T levels -suggesting gonadal hormones-independent mechanisms-, whereas T was a strong predictor of functional connectivity development. We expect that these neural circuits will show more drastic pubertal-dependent maturation, including stronger associations with pubertal measures later, during and after male puberty.

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Carotenoids improve the development of cerebral cortical networks in formula-fed infant macaques

Nutrition during the first years of life has a significant impact on brain development. This study characterized differences in brain maturation from birth to 6 months of life in infant macaques fed formulas differing in content of lutein, β-carotene, and other carotenoids using Magnetic Resonance Imaging to measure functional connectivity. We observed differences in functional connectivity based on the interaction of diet, age and brain networks. Post hoc analysis revealed significant diet-specific differences between insular-opercular and somatomotor networks at 2 months of age, dorsal attention and somatomotor at 4 months of age, and within somatomotor and between somatomotor-visual and auditory-dorsal attention networks at 6 months of age. Overall, we found a larger divergence in connectivity from the breastfeeding group in infant macaques fed formula containing no supplemental carotenoids in comparison to those fed formula supplemented with carotenoids. These findings suggest that carotenoid formula supplementation influences functional brain development.

Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

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.

Learning Strategies for Contrast-agnostic Segmentation via SynthSeg for Infant MRI data

Longitudinal studies of infants' brains are essential for research and clinical detection of neurodevelopmental disorders. However, for infant brain MRI scans, effective deep learning-based segmentation frameworks exist only within small age intervals due to the large image intensity and contrast changes that take place in the early postnatal stages of development. However, using different segmentation frameworks or models at different age intervals within the same longitudinal data set would cause segmentation inconsistencies and age-specific biases. Thus, an age-agnostic segmentation model for infants' brains is needed. In this paper, we present "Infant-SynthSeg", an extension of the contrast-agnostic SynthSeg segmentation framework applicable to MRI data of infants at ages within the first year of life. Our work mainly focuses on extending learning strategies related to synthetic data generation and augmentation, with the aim of creating a method that employs training data capturing features unique to infants' brains during this early-stage development. Comparison across different learning strategy settings, as well as a more-traditional contrast-aware deep learning model (nnU-net) are presented. Our experiments show that our trained Infant-SynthSeg models show consistently high segmentation performance on MRI scans of infant brains throughout the first year of life. Furthermore, as the model is trained on ground truth labels at different ages, even labels that are not present at certain ages (such as cerebellar white matter at 1 month) can be appropriately segmented via Infant-SynthSeg across the whole age range. Finally, while Infant-SynthSeg shows consistent segmentation performance across the first year of life, it is outperformed by age-specific deep learning models trained for a specific narrow age range.

An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD)

As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development Study^SM (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.

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Reproducible brain-wide association studies require thousands of individuals

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.

Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., "scrubbing") and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

Demographic and mental health assessments in the adolescent brain and cognitive development study: Updates and age-related trajectories

The Adolescent Brain Cognitive Development (ABCD) Study of 11,880 youth incorporates a comprehensive range of measures assessing predictors and outcomes related to mental health across childhood and adolescence in participating youth, as well as information about family mental health history. We have previously described the logic and content of the mental health assessment battery at Baseline and 1-year follow-up. Here, we describe changes to that battery and issues and clarifications that have emerged, as well as additions to the mental health battery at the 2-, 3-, 4-, and 5-year follow-ups. We capitalize on the recent release of longitudinal data for caregiver and youth report of mental health data to evaluate trajectories of dimensions of psychopathology as a function of demographic factors. For both caregiver and self-reported mental health symptoms, males showed age-related decreases in internalizing and externalizing symptoms, while females showed an increase in internalizing symptoms with age. Multiple indicators of socioeconomic status (caregiver education, family income, financial adversity, neighborhood poverty) accounted for unique variance in both caregiver and youth-reported externalizing and internalizing symptoms. These data highlight the importance of examining developmental trajectories of mental health as a function of key factors such as sex and socioeconomic environment.

Baseline brain function in the preadolescents of the ABCD Study

The Adolescent Brain Cognitive Development (ABCD) Study® is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N = 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development.

Maternal Interleukin-6 Is Associated With Macaque Offspring Amygdala Development and Behavior

Human and animal cross-sectional studies have shown that maternal levels of the inflammatory cytokine interleukin-6 (IL-6) may compromise brain phenotypes assessed at single time points. However, how maternal IL-6 associates with the trajectory of brain development remains unclear. We investigated whether maternal IL-6 levels during pregnancy relate to offspring amygdala volume development and anxiety-like behavior in Japanese macaques. Magnetic resonance imaging (MRI) was administered to 39 Japanese macaque offspring (Female: 18), providing at least one or more time points at 4, 11, 21, and 36 months of age with a behavioral assessment at 11 months of age. Increased maternal third trimester plasma IL-6 levels were associated with offspring's smaller left amygdala volume at 4 months, but with more rapid amygdala growth from 4 to 36 months. Maternal IL-6 predicted offspring anxiety-like behavior at 11 months, which was mediated by reduced amygdala volumes in the model's intercept (i.e., 4 months). The results increase our understanding of the role of maternal inflammation in the development of neurobehavioral disorders by detailing the associations of a commonly examined inflammatory indicator, IL-6, on amygdala volume growth over time, and anxiety-like behavior.

Chronic psychosocial stress and experimental pubertal delay affect socioemotional behavior and amygdala functional connectivity in adolescent female rhesus macaques

In females, pubertal onset appears to signal the opening of a window of increased vulnerability to the effects of stress on neurobehavioral development. What is the impact of pubertal timing on this process? We assessed the effects of pubertal timing and stress on behavior and amygdala functional connectivity (FC) in adolescent female macaques, whose social hierarchy provides an ethologically valid model of chronic psychosocial stress. Monkeys experienced puberty spontaneously (n = 34) or pubertal delay via Lupron treatment from age 16-33 months (n = 36). We examined the effects of stress (continuous dimension spanning dominant/low-stress to subordinate/high-stress) and experimental pubertal delay (Lupron-treated vs. Control) on socioemotional behavior and FC at 43-46 months, after all animals had begun puberty. Regardless of treatment, subordinate monkeys were more submissive and less affiliative, and exhibited weaker FC between amygdala and dorsolateral prefrontal cortex and stronger FC between amygdala and temporal pole. Regardless of social rank, Lupron-treated monkeys were also more submissive and less affiliative but were less anxious and exhibited less displacement behavior in a "Human Intruder" task than untreated monkeys; they exhibited stronger FC between amygdala and orbitofrontal cortex. No interactions between rank and Lupron treatment were observed. These similar behavioral outcomes may reflect the common factor of delayed puberty - whether this is stress-related (untreated subordinate animals) or pharmacologically-induced (treated animals). In the brain, however, delayed puberty and subordination stress had separable effects, suggesting that the overlapping socioemotional outcomes may be mediated by distinct neuroplastic mechanisms. To gain further insights, additional longitudinal studies are required.

Toward a Revised Nosology for Attention-Deficit/Hyperactivity Disorder Heterogeneity

Attention-deficit/hyperactivity disorder (ADHD) is among the many syndromes in the psychiatric nosology for which etiological signal and clinical prediction are weak. Reducing phenotypic and mechanistic heterogeneity should be useful to arrive at stronger etiological and clinical prediction signals. We discuss key conceptual and methodological issues, highlighting the role of dimensional features aligned with Research Domain Criteria and cognitive, personality, and temperament theory as well as neurobiology. We describe several avenues of work in this area, utilizing different statistical, computational, and machine learning approaches to resolve heterogeneity in ADHD. We offer methodological and conceptual recommendations. Methodologically, we propose that an integrated approach utilizing theory and advanced computational logic to address targeted questions, with consideration of developmental context, can render the heterogeneity problem tractable for ADHD. Conceptually, we conclude that the field is on the cusp of justifying an emotionally dysregulated subprofile in ADHD that may be useful for clinical prediction and treatment testing. Cognitive profiles, while more nascent, may be useful for clinical prediction and treatment assignment in different ways depending on developmental stage. Targeting these psychological profiles for neurobiological and etiological study to capture different pathophysiological routes remains a near-term opportunity. Subtypes are likely to be multifactorial, cut across multiple dimensions, and depend on the research or clinical outcomes of interest for their ultimate selection. In this context parallel profiles based on cognition, emotion, and specific neural signatures appear to be on the horizon, each with somewhat different utilities. Efforts to integrate such cross-cutting profiles within a conceptual dysregulation framework are well underway.

Obesogenic diet-associated C-reactive protein predicts reduced central dopamine and corticostriatal functional connectivity in female rhesus monkeys

Alterations in dopamine (DA) signaling and reductions in functional connectivity (FC; a measure of temporal correlations of activity between different brain regions) within dopaminergic reward pathways are implicated in the etiology of psychopathology and have been associated with increased concentrations of inflammatory markers, including C-reactive protein. Peripheral and central inflammatory cytokines that have been shown to disrupt DA signaling and corticostriatal FC are associated with C-reactive protein, an acute phase reactant that is used translationally as a marker of systemic inflammation. One factor that can significantly increase systemic inflammation to produce neuroadaptations in reward pathways is a diet that results in fat mass accumulation (e.g. obesogenic diet). The current study in female rhesus monkeys maintained in a standard laboratory chow (n = 18) or on obesogenic diet (n = 16) for 12-months tested the hypothesis that an obesogenic diet would alter central DA and homovanillic acid (HVA) concentrations, and be associated with increased CRP concentrations and decreased FC between corticostriatal regions at 12-months following dietary intervention. We specifically assessed FC between the nucleus accumbens (NAcc) and two sub-regions of the prefrontal cortex (PFC) previously associated with CRP concentrations, the ventromedial PFC (vmPFC) and the orbitofrontal cortex (OFC), which are also involved in emotional and motivational salience assessment, and in goal-directed behavior, impulse control and the salience/value of food, respectively. Results showed that CSF DA concentrations were decreased (p = 0.002), HVA:DA ratios were increased (p = 0.016), and body mass index was increased (p = 0.047) over the 12-months of consuming an obesogenic diet. At 12-months, females maintained in the obesogenic diet exhibited higher CRP concentrations than females consuming chow-only (p = 0.008). Linear regression analyses revealed significant CRP by dietary condition interactions on DA concentrations (β = -5.10; p = 0.017) and HVA:DA ratios (β = 5.14; p = 0.029). Higher CRP concentrations were associated with lower CSF DA concentrations (r = -0.69; p = 0.004) and greater HVA:DA ratios only in females maintained in the obesogenic dietary condition (r = 0.58; p = 0.024). Resting-state magnetic resonance neuroimaging (rs-fMRI) in a subset of females from each diet condition (n = 8) at 12-months showed that higher CRP concentrations were associated decreased FC between the NAcc and subregions of the prefrontal cortex (PFC; p's < 0.05). Decreased FC between the NAcc and PFC subregions were also associated with lower concentrations of DA and greater HVA:DA ratios (p's < 0.05). Overall, these data suggest that increased inflammatory signaling driving heightened CRP levels may mediate the adverse consequences of obesogenic diets on DA neurochemistry and corticostriatal connectivity.

Early Developmental Trajectories of Functional Connectivity Along the Visual Pathways in Rhesus Monkeys

Early social interactions shape the development of social behavior, although the critical periods or the underlying neurodevelopmental processes are not completely understood. Here, we studied the developmental changes in neural pathways underlying visual social engagement in the translational rhesus monkey model. Changes in functional connectivity (FC) along the ventral object and motion pathways and the dorsal attention/visuo-spatial pathways were studied longitudinally using resting-state functional MRI in infant rhesus monkeys, from birth through early weaning (3 months), given the socioemotional changes experienced during this period. Our results revealed that (1) maturation along the visual pathways proceeds in a caudo-rostral progression with primary visual areas (V1-V3) showing strong FC as early as 2 weeks of age, whereas higher-order visual and attentional areas (e.g., MT-AST, LIP-FEF) show weak FC; (2) functional changes were pathway-specific (e.g., robust FC increases detected in the most anterior aspect of the object pathway (TE-AMY), but FC remained weak in the other pathways (e.g., AST-AMY)); (3) FC matures similarly in both right and left hemispheres. Our findings suggest that visual pathways in infant macaques undergo selective remodeling during the first 3 months of life, likely regulated by early social interactions and supporting the transition to independence from the mother.

Lateralized Connectivity between Globus Pallidus and Motor Cortex is Associated with Freezing of Gait in Parkinson's Disease

Freezing of gait (FoG) is a brief, episodic absence or marked reduction of forward progression of the feet, despite the intention to walk, that is common in people with Parkinson's disease (PD). We hypothesized that not only motor, but higher level cognitive and attention areas may be impaired in freezers. In this study, we aimed to characterize differences in cortical and subcortical functional connectivity specific to FoG. We examined resting state neuroimaging and objective measures of FoG severity and gait from 103 individuals (28 PD + FoG, 36 PD - FoG and 39 healthy controls). Inertial sensors were used to quantify freezing severity and gait. Groups with and without FoG were matched on age, disease severity, cognitive status, and levodopa medication. MRI data was processed using surface-based registration. High-quality imaging data were used to characterize differences in connectivity specific to FoG using a pre-defined set of Regions of Interest (ROIs) and validated using whole-brain connectivity analysis. Associations between functional connectivity and objective measures of FoG were determined via predictive modeling using hold-out cross validation. We found that connectivity between the left globus pallidus (GP) and left somatosensory cortex and between two brain areas in the default and insular/vestibular networks exhibited significant differences specific to FoG and were also strong and significant predictors of FoG severity. Our findings suggest that the interplay among motor, default and vestibular areas of the left cortex are critical in the pathology of FoG.

Methods and Challenges for Assessing Heterogeneity

The widely acknowledged homogeneity assumption limits progress in refining clinical diagnosis, understanding mechanisms, and developing new treatments for mental health disorders. This homogeneity assumption drives both a comorbidity and a heterogeneity problem, where two different approaches tackle the problems. One, a unifying approach, tackles the comorbidity problem by assuming that a single general psychopathology factor underlies multiple disorders. Another, a multifactorial approach, tackles the heterogeneity problem by assuming that disorders comprise multiple subtypes driven by multiple discrete factors. We show how each of these approaches can make useful contributions to mental health-related research and clinical practice. For example, the unifying approach can develop a rapid assessment tool that may be clinically valuable for triaging cases. The multifactorial approach can reveal subtypes that are differentially responsive to treatments and highlight distinct mechanisms leading to similar phenotypes. Because both approaches tackle different problems, both have different limitations. We describe the statistical frameworks that incorporate and adjudicate between both approaches (e.g., the bifactor model, normative modeling, and the functional random forest). Such frameworks can identify whether sets of disorders are more affected by heterogeneity or comorbidity. Therefore, future studies that incorporate such frameworks can provide further insight into the nature of psychopathology.

Behavioral and Neural Signatures of Working Memory in Childhood

Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during human development, however, are not well understood. Here, we establish associations between working memory, other cognitive abilities, and functional MRI (fMRI) activation in data from over 11,500 9- to 10-year-old children (both sexes) enrolled in the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity during a working memory challenge indexes working memory performance. This relationship is domain specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task (SST), and reward processing during a monetary incentive delay (MID) task does not track memory abilities. Together, these results inform our understanding of individual differences in working memory in childhood and lay the groundwork for characterizing the ways in which they change across adolescence.SIGNIFICANCE STATEMENT Working memory is a foundational cognitive ability that changes over time and varies across individuals. Here, we analyze data from over 11,500 9- to 10-year-olds to establish relationships between working memory, other cognitive abilities, and frontoparietal brain activity during a working memory challenge, but not during other cognitive challenges. Our results lay the groundwork for assessing longitudinal changes in working memory and predicting later academic and other real-world outcomes.

Long-term alterations in brain and behavior after postnatal Zika virus infection in infant macaques

Zika virus (ZIKV) infection has a profound impact on the fetal nervous system. The postnatal period is also a time of rapid brain growth, and it is important to understand the potential neurobehavioral consequences of ZIKV infection during infancy. Here we show that postnatal ZIKV infection in a rhesus macaque model resulted in long-term behavioral, motor, and cognitive changes, including increased emotional reactivity, decreased social contact, loss of balance, and deficits in visual recognition memory at one year of age. Structural and functional MRI showed that ZIKV-infected infant rhesus macaques had persistent enlargement of lateral ventricles, smaller volumes and altered functional connectivity between brain areas important for socioemotional behavior, cognitive, and motor function (e.g. amygdala, hippocampus, cerebellum). Neuropathological changes corresponded with neuroimaging results and were consistent with the behavioral and memory deficits. Overall, this study demonstrates that postnatal ZIKV infection in this model may have long-lasting neurodevelopmental consequences.

Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD

BACKGROUND

Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in these conditions is atypical executive function (EF). Inconsistent findings highlight that EF features may be shared or distinct across ADHD and ASD. With ADHD and ASD each also being heterogeneous, we hypothesized that there may be nested subgroups across disorders with shared or unique underlying mechanisms.

METHODS

Participants (N = 130) included adolescents aged 7-16 with ASD (n = 64) and ADHD (n = 66). Typically developing (TD) participants (n = 28) were included for a comparative secondary sub-group analysis. Parents completed the K-SADS and youth completed an extended battery of executive and other cognitive measures. A two stage hybrid machine learning tool called functional random forest (FRF) was applied as a classification approach and then subsequently to subgroup identification. We input 43 EF variables to the classification step, a supervised random forest procedure in which the features estimated either hyperactive or inattentive ADHD symptoms per model. The FRF then produced proximity matrices and identified optimal subgroups via the infomap algorithm (a type of community detection derived from graph theory). Resting state functional connectivity MRI (rs-fMRI) was used to evaluate the neurobiological validity of the resulting subgroups.

RESULTS

Both hyperactive (Mean absolute error (MAE) = 0.72, Null model MAE = 0.8826, (t(58) = -4.9, p < .001) and inattentive (MAE = 0.7, Null model MAE = 0.85, t(58) = -4.4, p < .001) symptoms were predicted better than chance by the EF features selected. Subgroup identification was robust (Hyperactive: Q = 0.2356, p < .001; Inattentive: Q = 0.2350, p < .001). Two subgroups representing severe and mild symptomology were identified for each symptom domain. Neuroimaging data revealed that the subgroups and TD participants significantly differed within and between multiple functional brain networks, but no consistent "severity" patterns of over or under connectivity were observed between subgroups and TD.

CONCLUSION

The FRF estimated hyperactive/inattentive symptoms and identified 2 distinct subgroups per model, revealing distinct neurocognitive profiles of Severe and Mild EF performance per model. Differences in functional connectivity between subgroups did not appear to follow a severity pattern based on symptom expression, suggesting a more complex mechanistic interaction that cannot be attributed to symptom presentation alone.

Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

Identifying reproducible individual differences in childhood functional brain networks: An ABCD study

The 21-site Adolescent Brain Cognitive Development (ABCD) study provides an unparalleled opportunity to characterize functional brain development via resting-state functional connectivity (RSFC) and to quantify relationships between RSFC and behavior. This multi-site data set includes potentially confounding sources of variance, such as differences between data collection sites and/or scanner manufacturers, in addition to those inherent to RSFC (e.g., head motion). The ABCD project provides a framework for characterizing and reproducing RSFC and RSFC-behavior associations, while quantifying the extent to which sources of variability bias RSFC estimates. We quantified RSFC and functional network architecture in 2,188 9-10-year old children from the ABCD study, segregated into demographically-matched discovery (N = 1,166) and replication datasets (N = 1,022). We found RSFC and network architecture to be highly reproducible across children. We did not observe strong effects of site; however, scanner manufacturer effects were large, reproducible, and followed a "short-to-long" association with distance between regions. Accounting for potential confounding variables, we replicated that RSFC between several higher-order networks was related to general cognition. In sum, we provide a framework for how to characterize RSFC-behavior relationships in a rigorous and reproducible manner using the ABCD dataset and other large multi-site projects.

Postnatal Zika virus infection is associated with persistent abnormalities in brain structure, function, and behavior in infant macaques

The Zika virus (ZIKV) epidemic is associated with fetal brain lesions and other serious birth defects classified as congenital ZIKV syndrome. Postnatal ZIKV infection in infants and children has been reported; however, data on brain anatomy, function, and behavioral outcomes following infection are absent. We show that postnatal ZIKV infection of infant rhesus macaques (RMs) results in persistent structural and functional alterations of the central nervous system compared to age-matched controls. We demonstrate ZIKV lymphoid tropism and neurotropism in infant RMs and histopathologic abnormalities in the peripheral and central nervous systems including inflammatory infiltrates, astrogliosis, and Wallerian degeneration. Structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) show persistent enlargement of lateral ventricles, maturational changes in specific brain regions, and altered functional connectivity (FC) between brain areas involved in emotional behavior and arousal functions, including weakened amygdala-hippocampal connectivity in two of two ZIKV-infected infant RMs several months after clearance of ZIKV RNA from peripheral blood. ZIKV infection also results in distinct alterations in the species-typical emotional reactivity to acute stress, which were predicted by the weak amygdala-hippocampal FC. We demonstrate that postnatal ZIKV infection of infants in this model affects neurodevelopment, suggesting that long-term clinical monitoring of pediatric cases is warranted.