Biology subjects

Angstadt, M.

Publications and source records attributed to Angstadt, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Fixel-wise Differences in White Matter Fiber-bundle Cross-section identified in Children who Stutter

Developmental stuttering has been repeatedly linked to decreased white matter integrity and structural connectivity differences in major white matter tracts. Prior diffusion-weighted MRI studies reporting such white matter structural differences involved voxel-averaged tensor measures (e.g., fractional anisotropy (FA)). A well-acknowledged limitation of this approach, however, has been that it cannot resolve distinct fiber populations within crossing-fiber regions. Fixel-based analysis addresses this limitation by quantifying individual fiber populations within a voxel and providing complementary measures of fiber density (FD), fiber-bundle cross-section (FC), and their combined measure (FDC).Here we applied Fixel-based analysis to diffusion MRI data from 171 children aged 3 - 13 years, including 84 children who stutter and 87 typically developing controls, to test whether white-matter differences associated with developmental stuttering were expressed as differences in FD, FC, and FDC and whether these measures show group-specific age-related patterns. We evaluated this at both the whole-brain level and within a priori white-matter tracts.FC, but not FD or FDC, showed significant Group and Group by Age effects, indicating that white-matter differences were primarily expressed in fiber-bundle macrostructure. Tract-of-interest analyses identified developmental differences in bilateral arcuate fasciculus FC that remained significant across tracts, with controls showing greater age-related increases in FC than children who stutter. Additional FC differences were observed across corticospinal and thalamocortical pathways, although these tract-wise effects were less robust to across-tract correction. These findings suggest that white-matter differences in developmental stuttering are prominently expressed in fiber-bundle macrostructure rather than fiber density and highlight divergent development of bilateral arcuate pathways. FBA provides fiber-specific information that complements conventional tensor-based diffusion measures and may improve characterization of white-matter development in childhood stuttering.

neuroscience↗

bioRxiv2026

Longitudinal associations between task-control network functioning, cognition, and the environment in early adolescence

Neural maturation in the "task-control" frontoparietal and dorsal attention networks is thought to support the development of adolescents' cognitive and adaptive functioning. Further, theory suggests that family and neighborhood contexts shape brain network development. Yet, few longitudinal studies have examined the co-development of brain network mechanisms with cognitive and adaptive functioning or with environmental factors that could shape the maturation of these mechanisms. The present study addressed these gaps. We used longitudinal data spanning late childhood (Mage=10 years) to early adolescence (Mage=12) from the Adolescent Brain Cognitive Development (ABCD) Study (n=4862). Stable and construct-valid neural signatures of task-control network functioning were estimated using longitudinal bifactor modeling of neural responses to cognitive demand during the n-back task. Multivariate latent change-score models were used to examine co-development of this signature with cognitive efficiency (drift rate from the diffusion model), teacher-reported attention problems, family conflict, and neighborhood safety. Models accounted for baseline family income. Robust reciprocal relations between task-control activation and cognitive efficiency were identified and the latent change scores for efficiency and task-control activation were positively correlated. Similar reciprocal associations were observed between cognitive efficiency and attention problems. Baseline family income predicted the rate of change in task-control network functioning. This study demonstrates that task-control network functioning co-develops with cognition, which in turn supports attentional functioning development across early adolescence. Further, findings highlight the utility of this neural signature of task-control network functioning for understanding the neural basis of cognitive development and the mechanisms through which environmental experiences shape brain and behavior.

neuroscience↗

bioRxiv2026

A parsimonious, age-invariant, and psychometrically validated neural signature of task-control network functioning

Neuroimaging has vast potential for identifying brain features that support individual and developmental differences in cognition, but challenges related to the dimensionality, unreliability, and unknown developmental properties of neuroimaging measures prevent this from being realized. We overcome these challenges by applying latent variable modeling to demand-related responses in the "task-control" frontoparietal and dorsal attention networks (FPN, DAN) within the Adolescent Brain Cognitive Development Study and Human Connectome Project. Task-control activations are well-captured by a bifactor structure with a general factor spanning FPN/DAN and network-specific factors that is remarkably invariant across middle childhood to early adolescence and largely generalizes to adulthood. Trait-state-occasion modeling reveals a general task-control trait that shows robust associations with cognitive abilities, clinical symptoms, and grades. Repeated neuroimaging substantially increases associations. Findings establish a parsimonious neural signature that generalizes across samples, is stable across development, and has broad utility for understanding developmental and individual differences in goal-directed behavior.

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bioRxiv2026

Cognitive and affective neurodevelopment in youth exposed to deprivation and threat

BackgroundEarly life adversity (ELA) is associated with notable negative consequences across development. Experiences of deprivation may affect neurocognitive development, while experiences of threat may alter emotion processing. Deprivation and threat may also differentially influence reward processing. However, unique consequences of deprivation and threat beyond low family resources are debated. MethodsWe employed an exposure vs. control data analytic approach to isolate deprivation and threat influences from socioeconomic resources. Adolescent Brain Cognitive Development (ABCD(R)) Study youth exposed to neither deprivation nor threat (N=2408-2962) were matched to youth exposed to deprivation-only (N=638-721), threat-only (N=198-232), or threat non-exclusively (threat+: N=382-464) based on family income, parental education, race/ethnicity, sex, and age. Multivariate analyses were used to distinguish each ELA group from their respective control groups in the neurocognitive domain (resting-state connectomic maturation, cognitive task performance, and cortical grey matter thickness at two timepoints) and in the neuroaffective domain (nucleus accumbens and caudate activation to reward anticipation and amygdala and insula activation to fearful faces). ResultsIn the neurocognitive domain, similar latent variables (LVs) differentiated the deprivation and threat+ groups from their respective matched control groups. This LV corresponded to neurocognitive maturation, loading positively on cortical functional maturation and task performance, and negatively on cortical grey matter thickness. This LV was weaker in the deprivation and threat+ groups compared to controls. In the neuroaffective domain, no significant LVs were found. ConclusionBoth threat and deprivation exposure during childhood may delay neurocognitive development in early adolescence beyond their co-occurrence with low socioeconomic resources.

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bioRxiv2025

Volumetric Differences of Thalamic Nuclei are Associated with Post-Trauma Psychopathology

Previous investigations of whole thalamus and thalamic nuclei volumes in post-trauma psychopathology have been sparse, limited in scope, and yielded inconsistent results. To address this, volumetric estimates of whole thalamus and thalamic nuclei were obtained from structural brain MRI scans from 2,058 participants across 20 worldwide sites in the ENIGMA PTSD working group. Thalamic volumes were compared between trauma-exposed participants with posttraumatic stress disorder (PTSD) (n=238), major depressive disorder (MDD) (n=184), comorbid PTSD+MDD (n=618), and trauma-exposed control participants (n=1,018). PTSD and MDD symptom severity, PTSD symptom clusters, and childhood trauma were similarly examined for associations with thalamic volume. Participants with PTSD only compared to controls had smaller thalamic nuclei volumes in sensorimotor nuclei, including the parafascicular (Pf), ventral anterior magnocellular (VAmc), medial pulvinar (PuM), and anterior pulvinar (PuA) nuclei of the thalamus. MDD only and comorbid PTSD+MDD participants exhibited smaller mediodorsal thalamus volumes compared to controls. Overall PTSD and MDD symptom severity negatively correlated with the volume of the mediodorsal thalamus. A significant interaction between PTSD and MDD severity was found, such that MDD severity was positively associated with thalamic volume only among individuals with high PTSD severity. Avoidance and hyperarousal symptoms of PTSD were positively associated with thalamic volume, while re- experiencing and negative mood/cognition symptoms were negatively associated with thalamic volume. Childhood physical and emotional abuse were positively and negatively associated with thalamic volume, respectively. Whole thalamus volume and volumes of the sensorimotor and limbic thalamus may play an important role in the development of PTSD and MDD in the aftermath of trauma exposure. The interaction between PTSD and MDD symptoms and contrasting effects across PTSD symptom clusters and types of childhood adversity suggests multiple neurobiological mechanisms are involved in shaping thalamic volume post-trauma.

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bioRxiv2024

Somatomotor disconnection links sleep duration with socioeconomic context, screen time, cognition, and psychopathology

BackgroundSleep is critical for healthy brain development and emotional wellbeing, especially during adolescence when sleep, behavior, and neurobiology are rapidly evolving. Theoretical reviews and empirical research have historically focused on how sleep influences mental health through its impact on higher-order brain systems. No studies have leveraged data-driven network neuroscience methods to uncover interpretable, brain-wide signatures of sleep duration in adolescence, their socio-environmental origins, or their consequences for cognition and mental health. MethodsHere, we implement graph theory and component-based predictive modeling to examine how a multimodal index of sleep duration is associated with intrinsic brain architecture in 3,173 youth (11-12 years) from the Adolescent Brain Cognitive DevelopmentSM Study. ResultsWe demonstrate that network integration/segregation exhibit a strong, generalizable multivariate association with sleep duration. We next identify a single component of brain architecture centered on a single network as the dominant contributor of this relationship. This component is characterized by increasing disconnection of a lower-order system - the somatomotor network - from other systems, with shorter sleep duration. Finally, greater somatomotor disconnection is associated with lower socioeconomic resources, longer screen times, reduced cognitive/academic performance, and elevated externalizing problems. ConclusionsThese findings reveal a novel neural signature of shorter sleep in adolescence that is intertwined with environmental risk, cognition, and psychopathology. By robustly elucidating the key involvement of an understudied brain system in sleep, cognition, and psychopathology, this study can inform theoretical and translational research directions on sleep to promote neurobehavioral development and mental health during the adolescent transition.

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bioRxiv2024

Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individuals resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youths connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

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bioRxiv2023

Socioeconomic resources in youth are linked to divergent patterns of network integration and segregation across the brain's transmodal axis

Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully influence intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neurobiological meaning of these widespread alterations remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage the resources of graph theory to precisely characterize multivariate and univariate associations between household SER and the functional integration and segregation (i.e., participation coefficient, within-module degree) of brain regions across major cognitive, affective, and sensorimotor systems during the resting state in 5,821 youth (ages 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are related to greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that the effects of SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread effects of SER on brain organization, indicating that SER levels differentially configure the intrinsic functional architecture of developing unimodal and transmodal systems. This study highlights both sensorimotor and higher-order networks that may serve as neural markers of environmental stress and opportunity, and which may guide efforts to scaffold healthy neurobehavioral development among disadvantaged communities of youth.

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bioRxiv2023

Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation

Efficiency of evidence accumulation (EEA), an individuals ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from over 5,000 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in "task-positive" frontoparietal and dorsal attention networks, which explains 36%-39% of the variance across individuals in EEA. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks flexible adaptation to cognitive demands is a key neural underpinning of EEA.

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bioRxiv2023

Neighborhood air pollution is negatively associated with neurocognitive maturation in early adolescence

Adolescence is a key period of neurocognitive maturation. Exposure to high levels of air pollutants have been associated with brain differences in youth, though the relevance of these brain findings to behavioral outcomes such as cognitive development is less clear. In this study, we used the US Environmental Protection Agencys thresholds for unhealthy levels of fine particulate matter (PM2.5), Ozone (O3), and NO2 pollutants to compare youth exclusively exposed to high levels of each pollutant to their respective socioeconomically-matched low-pollution peers over a two-year period in the Adolescent Brain Cognitive Development (ABCD) Study. No youth in ABCD study were found to be at or above the unhealthy threshold for NO2. Separate multivariate analyses for PM2.5 (NHigh=348; Ncontrol=279) and O3 (NHigh=355; Ncontrol=324) resulted in two very similar neurocognitive latent variables loading positively on cortical functional maturation and task performance, and negatively on cortical grey matter thickness. We found a significant difference in this neurocognitive maturation latent variable over time between the high-pollution and control groups from 9-10 to 11-12 years of age, such that maturation of cortical networks, increase in task performance, and cortical thinning were significantly higher in the control groups. These results were adjusted for parental income and education and youths age, sex, race/ethnicity, site, head-motion, scanner, general factor of psychopathology, pubertal status, and area deprivation index, in addition to the matching between high-pollution and control groups. In conclusion, exposure to high levels of PM2.5 and O3 is associated with lags in normative neurocognitive maturation in early adolescence.

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bioRxiv2022

Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD. Significance Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well ([~]75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities ([~]60% AUC). Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation. Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

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bioRxiv2022

Socioeconomic Resources are Associated with Distributed Alterations of the Brain's Intrinsic Functional Architecture in Youth

Little is known about how exposure to limited socioeconomic resources (SER) in childhood gets "under the skin" to shape brain development, especially using rigorous whole-brain multivariate methods in large, adequately powered samples. The present study examined resting state functional connectivity patterns from 5,821 youth in the Adolescent Brain Cognitive Development (ABCD) study, employing multivariate methods across three levels: whole-brain, network-wise, and connection-wise. Across all three levels, SER was associated with widespread alterations across the connectome. However, critically, we found that parental education was the primary driver of neural associations of SER. These parental education associations were robust to additional tests for confounding by head motion, and they exhibited notable concentrations in somatosensory and subcortical regions. Moreover, parental education associations with the developing connectome were partially accounted for by home enrichment activities, childs cognitive abilities, and childs grades, indicating interwoven links between parental education, child stimulation, and child cognitive performance. These results add a new data-driven, multivariate perspective on links between household SER and the childs developing functional connectome.

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bioRxiv2020

Leveraging big data for classification of children who stutter from fluent peers

IntroductionLarge datasets, consisting of hundreds or thousands of subjects, are becoming the new data standard within the neuroimaging community. While big data creates numerous benefits, such as detecting smaller effects, many of these big datasets have focused on non-clinical populations. The heterogeneity of clinical populations makes creating datasets of equal size and quality more challenging. There is a need for methods to connect these robust large datasets with the carefully curated clinical datasets collected over the past decades. MethodsIn this study, resting-state fMRI data from the Adolescent Brain Cognitive Development study (N=1509) and the Human Connectome Project (N=910) is used to discover generalizable brain features for use in an out-of-sample (N=121) multivariate predictive model to classify young (3-10yrs) children who stutter from fluent peers. ResultsAccuracy up to 72% classification is achieved using 10-fold cross validation. This study suggests that big data has the potential to yield generalizable biomarkers that are clinically meaningful. Specifically, this is the first study to demonstrate that big data-derived brain features can differentiate children who stutter from their fluent peers and provide novel information on brain networks relevant to stuttering pathophysiology. DiscussionThe results provide a significant expansion to previous understanding of the neural bases of stuttering. In addition to auditory, somatomotor, and subcortical networks, the big data-based models highlight the importance of considering large scale brain networks supporting error sensitivity, attention, cognitive control, and emotion regulation/self-inspection in the neural bases of stuttering.

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bioRxiv2020

Connectomic Alterations Linked to Transdiagnostic Risk for Psychopathology at the Transition to Adolescence

BACKGROUNDConvergent research identifies a general factor ("P factor") that confers transdiagnostic risk for psychopathology. However, brain functional connectivity patterns that underpin the P factor remain poorly understood, especially at the transition to adolescence when many serious mental disorders have their onset. OBJECTIVEIdentify a distributed connectome-wide neurosignature of the P factor and assess the generalizability of this neurosignature in held out samples. DESIGN, SETTING, AND PARTICIPANTSThis study used data from the full baseline wave of the Adolescent Brain and Cognitive Development (ABCD) national consortium study, a prospective, population-based study of 11,875 9- and 10-year olds. Data for this study were collected from September 1, 2016 to November 15, 2018 at 21 research sites across the United States. MAIN OUTCOMES AND MEASURESWe produced whole brain functional connectomes for 5,880 youth with high quality resting state scans. We then constructed a low rank basis set of 250 components that captures interindividual connectomic differences. Multi-level regression modeling was used to link these components to the P factor, and leave-one-site-out cross-validation was used to assess generalizability of P factor neurosignatures to held out subjects across 19 ABCD sites. RESULTSThe set of 250 connectomic components was highly statistically significantly related to the P factor, over and above nuisance covariates alone (ANOVA nested model comparison, incremental R-squared 6.05%, {chi}2(250) =412.1, p<4.6x10-10). In addition, two individual connectomic components were statistically significantly related to the P factor after Bonferroni correction for multiple comparisons (t(5511)= 4.8, p<1.4x10-06; t(5121)= 3.9, p<9.7x10-05). Functional connections linking control networks and default mode network were prominent in the P factor neurosignature. In leave-one-site-out cross-validation, the P factor neurosignature generalized to held out subjects (average correlation between actual and predicted P factor scores across 19 held out sites=0.13; pPERMUTATION<0.0001). Additionally, results remained significant after a number of robustness checks. CONCLUSIONS AND RELEVANCEThe general factor of psychopathology is associated with connectomic alterations involving control networks and default mode network. Brain imaging combined with network neuroscience can identify distributed and generalizable signatures of transdiagnostic risk for psychopathology during emerging adolescence.

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bioRxiv2019

Predictive Modeling Achieves High Test-Retest Reliability with Resting State Functional Connectivity

Recent studies found low test-retest reliability in fMRI, raising serious concerns among researchers, but these studies mostly focused on reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply ten predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared to mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all ten modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume-versus surface-based processing). For the most reliable methods, reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.

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bioRxiv2019

Brain Network Mechanisms of General Intelligence

We identify novel mechanisms of general intelligence involving activation patterns of large-scale brain networks. During hard, cognitively demanding tasks, the fronto-parietal network differentially activates relative to the default mode network, creating greater \"separation\" between the networks, while during easy tasks, network separation is reduced. In 920 adults in the Human Connectome Project dataset, we demonstrate that these network separation patterns across hard and easy task conditions are strongly associated with general intelligence, accounting for 21% of the variance in intelligence scores across individuals. Moreover, we identify the presence of a crossover relationship in which FPN-DMN separation profiles that strongly predict higher intelligence in hard task conditions reverse direction and strongly predict lower intelligence in easy conditions, helping to resolve conflicting findings in the literature. We further clarify key properties of FPN-DMN separation: It is a mediator, and not just a marker, of general intelligence, and FPN-DMN separation profiles during the task state can be reliably predicted from connectivity patterns during rest. We demonstrate the robustness of our results by replicating them in a second task and in an independent large sample of youth. Overall, our results establish FPN-DMN separation as a major locus of individual differences in general intelligence, and raise intriguing new questions about how FPN-DMN separation is regulated in different cognitive tasks, across the lifespan, and in health and disease.

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bioRxiv2019

Observing the origins of human brain development: Automated processing of fetal fMRI

AO_SCPLOWBSTRACTC_SCPLOWFetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite rapid and widespread growth of this approach, at present we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses (gestational age 24-39 weeks, M=30.9, SD=4.2), we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open source workflow for fetal functional MRI data preprocessing.

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bioRxiv2018

Prediction of Neurocognitive Profiles in Youth From Resting State fMRI

Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2,013 9-10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. We found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting individual differences across participants across all three domains. In addition, for General Ability scores specifically, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network inter-connections as a major locus of individual differences in cognitive functioning in early adolescence.

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