Michael P. Milham - US grants

The purpose of the proposed work is to provide a better understanding of the specific role(s) of anterior cingulate cortex (ACC) in selective attention. Of particular interest is the determination of the stages of processing at which the ACC is involved in mediation of conflict resolution (Gehring, 1993) and the degree of conflict required to invoke its activity. The proposed experiments will make use of variants of the Stroop task, as this task provides for direct manipulation of selective attention. Functional MRI will be used to detect task specific changes in the ACC's activity. The first experiment will establish whether or not ACC activity is linked to response interference. This will be accomplished by determining if its activity in the Stroop task is dependent on whether or not the incongruent word names an eligible response. The second experiment will determine if the ACC's activity is dependent on the degree of as semantic conflict present. To do so, increases in ACC activity during the interference condition will be examined a a function of the relative semantic value of the incongruent word. The third experiment will determine if the ACC's activity is dependent on the degree of conflict at the response stage. To do so, increases in ACC activity during the interference condition will be examined as a function of the relative strength of the stimulus to response mappings associated with the incongruent word. The development of a more specific understanding of the ACC's role in selective attention is of importance to the medical field, as recent neuroimaging studies have linked ACC dysfunction to attention deficits observed in clinical populations such as individual suffering from schizophrenia or attention-deficit-hyperactivity-disorder.

Project Summary Children with severe Early Caregiving Adversities (ECAs) are the most vulnerable to psychopathology as a result of prolonged neglect, abuse, and care disruptions that impact neurodevelopment. It is currently estimated that addressing ECAs would lead to a 29.8% reduction in worldwide psychiatric illness. Existing research, including findings from the original grant of this renewal, has demonstrated that there is a very strong link between ECA exposures and increased risk for psychopathology and altered neurodevelopment at the population level; and yet, given the heterogeneity in ECA populations, there is a critical gap in knowledge regarding how ECAs increase any specific risks to an individual child. The proposed research addresses this significant mental health problem by combining sophisitcated data-analysis methods that use experiential and phenotypic heterogeneity together with longitudinal neuroimaging and behavioral assessments in school-age children. This approach will increase precision when linking ECAs and child outcomes associated with the Research Domain Criteria constructs of Negative Valence and Cognitive Control Systems (NVS/CCS). The overarching goal of the present work is to create an explanatory model for the heterogeneous impact of ECAs on neurodevelopmental trajectories of NVS/CCS. This project's premise is that children exposed to ECAs have highly heterogeneous developmental histories as well as heterogeneous outcomes; therefore, prediction of ECA outcomes requires cutting-edge, sophisticated data analytic methods. We hypothesize that data-driven approaches will 1) more precisely define NVS/CCS outcomes for school-aged children with ECAs, and 2) provide a more robust explanatory model for links between ECAs and NVS/CCS trajectories. Aim 1A subtypes children with a history of ECAs based on 2.5-year developmental trajectories of NVS/CCS. 300 6-8 year old children (250 sampled from previous institutional and foster care; 50 community comparisons) will provide neuroimaging, behavioral, and self/caregiver reports every 15 months for 2.5 years. Biclustering methods will be applied to the baseline and follow-up data to identify homogeneous NVS/CCS final outcome clusters of children. Aim 1B develops an explanatory model to predict developmental trajectory subtypes for children with ECAs, from early life profiles and brain/behavior phenotypes at the time of enrollment. Machine learning methods applied to early life profiles, baseline NVS/CCS profiles, and sex, will predict developmental trajectory subtypes. Aim 2 identifies adverse and protective life events during the 2.5-year assessment period that are predictive of 2.5-year follow-up outcomes for children with ECAs. The inclusion of child-sex and current life- events will identify potential divergence in pathways across middle childhood. This prospective design of children exposed to various ECAs is designed to develop predictive models for ECA trajectory subtypes and outcomes, which can inform our understanding of risk and protective factors in accord with the goals of precision medicine.  

SUMMARY A fundamental advance in Neuroimaging in the last 10 years has been the development of methods to derive regional parcellations of the cerebral cortex, or `parcellated connectomes', from structural and/or functional MRI data. Within the Conte Center, the Neuroimaging core will focus on providing single subject and group connectomes of the brain in humans and macaques. Specifically, connectomes will allows for: (1) the localization of recording sites in relation to independently localized human cortical regions and networks; (2) standardization of recording site localization across centers, which will in turn provide a way to compare results across experiments; (3) definition of a macaque monkey cortical parcellation atlas for the comparison, sharing, and meta-analysis of physiological studies within our consortium, but also the wider non-human primates physiological community. The Neuroinformatics core will facilitate data sharing across the Conte Center sites and the neuroscience community through an open source web interface.

ABSTRACT Major psychiatric illnesses are increasingly understood as disorders of brain development, which has led to large-scale studies of youth that combine multi-modal neuroimaging with clinical phenotyping. Together, such data have emphasized the promise of objective ?growth charts? of brain development. However, synergies across major efforts remains unrealized due to use of different clinical instruments, different scanning protocols, challenges in informatics, and difficulties in data integration. In this proposal, we will overcome these obstacles by leveraging advances in multivariate harmonization and analysis techniques to build highly reproducible growth charts of human brain development. To do this, we will aggregate and harmonize eight existing large-scale developmental imaging studies, comprising over 10,000 participants between the age of 5- 24 (Aim 1). We will use this harmonized data to build generalizable indices of normal network brain development (Aim 2). Finally, developmental abnormalities within specific brain networks will be linked to dimensions of psychopathology (Aim 3). Critically, all code, data, and derived indices will be shared publicly, creating a massive new resource to accelerate research in the developmental neuroscience community (Aim 4). In sum, this proposal will have provide a new data resource, yield reproducible growth charts of brain development, and delineate novel mechanisms regarding the developmental basis of psychopathology in youth.

Project Summary/Abstract. Reading Disability (RD) is the most common learning disability, affecting 10 ? 15% of school age children. It incurs major functional impairments at all stages of life, with a wealth of data documenting lifelong disadvantages in educational and occupational attainment. Therefore, identifying effective and affordable treatments for RD is a high priority for reading researchers, clinicians and educators. Problematically, current evidence-based reading interventions largely rely on services by trained specialists, either in well-resourced classrooms or clinical settings. As such, under-resourced schools (or countries) often are unable to provide reading interventions for their students. In recent years, technology-based reading interventions have been proposed as a means of overcoming these challenges, as they can be administered in the home, without direct expert supervision - thereby minimizing resource demands. In the area of reading-focused EdTech, GraphoLearn has emerged as a leader, with the largest evidence-base to date. However, the vast majority of studies to date have been conducted in highly controlled settings, rather than the home environment it was intended for ? leaving open questions about effectiveness. Additionally, similar to any intervention, not all children with RD benefit equally from treatment; however little attention has been given to identifying predictors of treatment response. Here we propose to evaluate the effectiveness of home-administered GraphoLearn through the implementation of a large-scale, randomized controlled trial (RCT) in 450 reading disabled children (boys and girls, ages 6.0- 10.0). To accomplish this goal rapidly and with minimal cost, we will recruit participants from the Healthy Brain Network [HBN], an ongoing study of mental health and learning disorders in children, ages 5.0-21.0, whose family have concerns about behavior and/or learning (target n = 10,000; current n = 3000+). The availability of comprehensive characterizations (e.g., demographic, cognitive, mental health, EEG, multimodal MRI) for all HBN participants makes the sample optimal for exploring an extensive set of participant and environmental factors that may affect treatment outcomes (i.e., demographic, cognitive, emotional, neurobiological, environmental). Specific aims of the proposed work are to: 1) Evaluate the effectiveness of GraphoLearn in a large sample of children with RD, and 2) Identify participant-related and environment-level factors that are significantly associated with GraphoLearn outcomes. To accomplish this latter aim, sophisticated machine learning approaches (Random Forest Regression models) will be employed.

Project Summary Despite the rapid advances in the neuroimaging research workflow over the last decade, the enormous variability between and within data types and specimens impedes integrated analyses. Moreover, the availability of a comprehensive portfolio of software libraries and tools has also resulted in a concerning degree of analytical variability. Generalizing the preprocessing ? that is, the intermediate step between data generation by the measurement device and the subsequent statistical modeling and analysis ? beyond fMRIPrep, we propose a framework called NiPreps (NeuroImaging Preprocessing toolS) that we envision as a workbench for the development of such pipelines. By exclusively addressing the preprocessing of the data, fMRIPrep has successfully allowed researchers to focus their effort and expertise on the portion most relevant to scientific inference (i.e., statistical and computational analyses) and reduce methodological variability. NiPreps expands fMRIPrep to operate on new imaging modalities (diffusion MRI, arterial spin labeling, positron emission tomography, and multi-echo functional MRI) and disciplines (e.g., preclinical imaging). Despite some remarkable analysis workflows that display end-to-end consolidation, integrations across applications (e.g., analyses of human and nonhuman data) remain exceptionally challenging. Hence, we will evolve fMRIPrep into NiPreps, a software framework integrating BIDS and following the BIDS-Apps specifications. First, the project will consolidate the NiPreps foundations, with the generalization of fMRIPrep's driving principles and methods across modalities and domains of application. Second, we will expand the portfolio of end-user NiPreps with dMRIPrep, ASLPrep, PETPrep, and better coverage of multi-echo fMRI by fMRIPrep. Finally, we will address the NiPreps community's consolidation to ensure the sustainability of the framework, converging the communities around each -Prep with hackathons and docusprints. In short, NIPreps will pave the way towards next-generation imaging, ultimately allowing neuroscientists to seek a unified statistical framework capable of rigorously integrating cross-application and cross-species data analysis.