Abstract Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with multiple biological etiologies and highly variable symptoms. Using a novel analytical framework that integrates cortex-wide MRI markers of vertical (i.e., thickness, tissue contrast) and horizontal (i.e., surface area, geodesic distance) cortical organization, we could show that a large multi-centric cohort of individuals with ASD falls into 3 distinctive anatomical subtypes (ASD-I: cortical thickening, increased surface area, tissue blurring; ASD-II: cortical thinning, decreased distance; ASD-III: increased distance). Bootstrap analysis indicated a high consistency of these biotypes across thousands of simulations, while analysis of behavioral phenotypes and resting-state fMRI showed differential symptom load (i.e., Autism Diagnostic Observation Schedule; ADOS) and instrinsic connectivity anomalies in communication and social-cognition networks. Notably, subtyping improved supervised learning approaches predicting ADOS score in single subjects, with significantly increased performance compared to a subtype-blind approach. The existence of different subtypes may reconcile previous results so far not converging on a consistent pattern of anatomical anomalies in autism, and possibly relate the presence of diverging corticogenic and maturational anomalies. The high accuracy for symptom severity prediction indicates benefits of MRI biotyping for personalized diagnostics and may guide the development of targeted therapeutic strategies.
Abstract When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional MRI. We show that even when handling identical data, inter-pipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low inter-pipeline agreement mainly becomes appreciable when the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially, we show that when inter-pipeline agreement is compromised, so too are the consistency of insights from brainwide association studies. We highlight the importance of comparing analytic configurations, as both widely discussed and commonly overlooked decisions can lead to marked variation.
Abstract We created resources to facilitate research on the role of human brain microstructure in the development of mental health disorders, based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N=2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI, and we provide automated quality control (QC) scores for every scan, as part of the data release. To scale QC to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers a resource for transdiagnostic research in brain connectivity and pediatric mental health and serves as a novel tool for automated QC of large datasets.
Abstract Characterizing the complexities of early cortical thickness development has been an ongoing undertaking. Longitudinal studies of Non-Human Primates (NHP) offer unique advantages to identifying cortical growth trajectories. Here, we used latent growth models to characterize the trajectories of typical cortical thickness development in Japanese macaques at each cortical surface vertex (i.e., grayordinate). Cortical thickness from 4-to-36 months showed regional-specific linear and non-linear trajectories and distinct maturation timing across the cortex. We revealed an “accumulation/ablation phenomenon” where the most profound development changes occur in zones surrounding focal points of local maxima in cortical thickness or thinness throughout the brain. We further examined maternal diet and inflammation in the context of typical brain trajectories and known network architecture. A well-controlled NHP model of a maternal “Western-style” diet was used alongside measures of inflammatory cytokine interleukin-6 in the mothers during gestation. We observed that accumulation and ablation zones might be most susceptible to environmental effects. The maternal factors, diet, and inflammation during pregnancy were distinctively associated with different aspects of offspring cortical development reflected in regions related to distinctive functional networks. Our findings characterize the intricacies of typical cortical thickness development and highlight how the maternal environment plays a role in cortical development.
Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field .
In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.
ABSTRACT Importance Screening youth for mental disorders may assist in prevention, promote early identification, and reduce related lifetime impairment and distress. Objective The goal was to survey parents about their comfort and preferences for pediatric mental health screening, as well as factors associated with these preferences. Design The online survey was available July 11-14, 2021 on Prolific Academic. Analyses were conducted from November 2021 to November 2022. Setting Online survey. Participants The survey was administered to English-speaking parents with at least one 5-21-year old child at home. The sample included 972 parents, aged 21 and older, from the United States ( n =265), United Kingdom ( n =282), Canada ( n =171), and Other Countries ( n =254). Exposure(s) None. Main Outcome(s)/Measure(s) Parental preferences regarding the screening content, implementation preferences, and screener reviewing preferences of pediatric mental health screening were assessed in a novel survey. Mixed effects logistic models were employed to evaluate factors that influence parental comfort levels. Results Parents, aged 21 to 65 ( M =39.4; 62.3% female), supported annual mental health screening for their child and preferred reviewing the screening results with professional staff (e.g., physicians). Parents preferred parent-report over child self-reports, though they were generally comfortable with both options. Despite slight variations based on country of residence, screening topic, and child’s age, parents were generally comfortable discussing all 21 topics. The greatest comfort was with sleep problems; the least comfort was with firearms, gender identity, suicidality, and substance use/abuse. Conclusions/Relevance Our data indicated that parents support annual parent- and child self-report mental health screening in primary care settings, but comfort levels differ according to various factors, such as screening topic. Parents preferred screening to occur in the healthcare office and to discuss screening results with professional staff. In addition to parental need for expert guidance, the growing awareness of child mental health needs highlights the importance of addressing mental health concerns early via regular mental health screenings. KEY POINTS Question What are parents’ attitudes towards pediatric mental health screening in primary care settings? Findings The vast majority of parents surveyed online ( N=972) expressed comfort with the screening of children for mental health concerns in the primary care setting. Variations in comfort were noted in relation to age of child and topics included. Parents expressed a preference for parent report over child report, as well as for reviewing screening results with professional medical staff. These findings were robust to the country of residence (e.g., United States, Canada, United Kingdom). Meaning Our findings document parental preferences that should be incorporated to enhance the feasibility of mental health screening in primary care settings.
The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.
ABSTRACT Objective Autism spectrum disorder (ASD) is a pervasive neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long-relative to short-range functional connectivity profiles. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes. Methods We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean±SD age=20.8±8.1 years) and 108 neurotypical controls (NT, 19.2±7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types. Results Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems ( i . e ., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores. Conclusions Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.
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