Biology subjects

Arnold, P. D.

Publications and source records attributed to Arnold, P. D..

10 recordsLinked to original sources

bioRxiv2026

The Canadian Pediatric Imaging Platform (C-PIP) Reference Sample: Neuroimaging Protocol and Cross-site Validation for Harmonized Multimodal Pediatric Imaging

Understanding how the human brain matures from infancy through adolescence is essential for identifying factors that influence health outcomes. Magnetic resonance imaging (MRI) provides powerful tools for characterizing structural, microstructural, functional, and metabolic aspects of brain development; however, many pediatric imaging studies are constrained by small sample sizes, limited geographic diversity, lack of longitudinal measurement, restricted age ranges, and heterogeneous acquisition protocols. While recent large-scale neuroimaging consortia have begun to address these challenges, such resources remain limited in the Canadian context, creating a critical gap. The Canadian Pediatric Imaging Platform (C-PIP) has developed a multisite neuroimaging framework across three of Canada's largest pediatric hospitals: Alberta Children's Hospital (Calgary), Centre Hospitalier Universitaire Sainte-Justine (Montreal), and the Hospital for Sick Children (Toronto). These three sites were united through a national initiative, leveraging high-performance MRI systems to accelerate Canadian neurodevelopmental research. In addition, C-PIP has implemented a centralized data sharing infrastructure to facilitate open science and collaborative research across institutions. Here, we describe the development and implementation of a multimodal MRI acquisition protocol designed to support multimodal longitudinal studies of typical and atypical brain development spanning infancy through adolescence. Within-site stability and cross-site stability of C-PIP core MRI sequences were evaluated using a travelling-participant study. The C-PIP MRI protocol establishes a harmonized, multisite imaging framework that supports large-scale investigations of pediatric brain development and disorders.

neuroscience↗

bioRxiv2025

Functional organization of multisensory integration network in children and youth with neurodevelopmental disorders predicts clinical sensory issues

Differences in sensory processing in neurodevelopmental conditions, including Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD), cascade into downstream clinical symptomatology. This includes differences in combining sensory information from multiple modalities into a unified percept, known as multisensory integration. Little is known about the functional organization of multisensory network (MSN) in these groups, its relation to clinical sensory issues, or its interaction with other higher-order cortical networks. We examined resting-state fMRI data from 417 participants in the Province of Ontario Neurodevelopmental Network (ASD=174, ADHD=130, Typical Development=113; Mean age=11.96{+/-}4.10). Timeseries data were extracted from the MSN and seven additional resting-state cortical networks (RSNs). Undirected and directed functional connectivity (FC) metrics were computed within the MSN and between the MSN and other RSNs. FC was compared across diagnoses and related to clinical sensory characteristics. The thalamus emerged as a hub region in both undirected and directed FC within the MSN and between the MSN-RSNs. Some diagnosis-related differences were observed, with increased MSN-RSN FC particularly in ADHD; however, associations with sensory characteristics were stronger in both undirected and directed FC within the MSN and between the MSN-RSNs, regardless of diagnosis. Converging evidence was seen in data-driven clusters based on FC metrics, which did not align with diagnosis, but instead mapped on to the overall level of sensory issues reported. That the data-driven clusters sorted not by diagnosis but by sensory characteristics suggests that these sensory characteristics and their underlying neurobiology are transdiagnostic in nature as opposed to specific to ASD or ADHD.

neuroscience↗

bioRxiv2024

Using deep learning to predict internalizing problems from brain structure

Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the biological markers of internalizing problems are not well understood. Here, we used deep learning, a powerful tool for identifying complex and multi-dimensional brain-behaviour relationships, to predict cross-sectional and worsening longitudinal trajectories of internalizing problems. Data were extracted from four large-scale datasets: the Adolescent Brain Cognitive Development study, the Healthy Brain Network, the Human Connectome Project Development study, and the Province of Ontario Neurodevelopmental network. We developed deep learning models that used measures of brain structure (thickness, surface area, and volume) to (a) predict clinically significant internalizing problems cross-sectionally (N=14,523); and (b) predict subsequent worsening trajectories (using the reliable change index) of internalizing problems (N=10,540) longitudinally. A stratified cross-validation scheme was used to tune, train, and test the models, which were evaluated using the area under the receiving operating characteristic curve (AUC). The cross-sectional model performed well across the sample, reaching an AUC of 0.80[95% CI: 0.71,0.88]. For the longitudinal model, while performance was sub-optimal for predicting worsening trajectories in a sample of the general population (AUC=0.66[0.65,0.67]), good performance was achieved in a small, external test set of primarily ND conditions (AUC=0.80[0.78,0.81]), as well as across all ND conditions (AUC=0.73[0.70,0.76]). Deep learning with features of brain structure is a promising avenue for biomarkers of internalizing problems, particularly for individuals who have a higher likelihood of experiencing difficulties.

neuroscience↗

bioRxiv2024

Dataset factors influencing age-related changes in brain structure and function in neurodevelopmental conditions

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets, and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N=1,218) and function (resting-state functional connectivity strength; N=1,254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental network (POND) and the Healthy Brain Network (HBN). We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

neuroscience↗

bioRxiv2021

Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders

BackgroundExternalizing and internalizing behaviors are common and contribute to impairment in children with neurodevelopmental disorders (NDDs). Associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in children with and without clinically significant internalizing/externalizing behaviors. This study examined whether such associations are present across children with different NDDs. MethodsMulti-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6-18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (n range=157-346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined. ResultsNo significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results. ConclusionsThe current study provides evidence in favour of the absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with various NDDs and TDC. Detecting shared brain-behavior relationships in children with NDDs may benefit from the use of different methodological approaches, including incorporation of multi-dimensional behavioral data (i.e. behavioral assessments, neurocognitive tasks, task-based fMRI) or clustering approaches to delineate whether subgroups of individuals with different brain-behavior profiles are present within heterogeneous cross-disorder samples.

neuroscience↗

bioRxiv2020

Visuomotor Activation of Inhibition-Processing in Pediatric Obsessive Compulsive Disorder: A Magnetoencephalography Study

BackgroundThe ability to inhibit a response is a component of executive control that is impaired in many individuals with obsessive compulsive disorder (OCD) and may contribute to clinical symptoms. This study explored whether neural processing during response inhibition, measured using magnetoencephalography (MEG), differed in a sample of medication-naive youth with OCD, compared to typically developing controls (TDC). MethodsData was acquired from 20 medication-naive children and youth with OCD (11.9 {+/-} 2.1 SD years) and 14 TDC (12.3 {+/-} 2.1 SD years). MEG was used to localize and characterize neural activity during a Go/No-Go task. Regional differences in amplitude of activity during a Go and No-Go condition between OCD versus TDC were examined. ResultsIn response to the visual cue presented during the Go condition, participants with OCD showed significantly increased amplitude of activity in the primary motor (MI) cortex compared to TDC. A trend towards decreased amplitude of activity in the orbitofrontal cortex in OCD versus TDC was also found during stop errors in the No-Go condition. ConclusionOur preliminary study in a small medication-naive sample suggests that neural response within motor and orbitofrontal regions may be altered during Go/No-Go Task performance, and that MEG as an imaging tool may be sensitive to detecting such differences.

neuroscience↗

bioRxiv2019

Genome-wide Association Study of Pediatric Obsessive-Compulsive Traits: Shared Genetic Risk between Traits and Disorder

ObjectiveTo identify genetic variants associated with obsessive-compulsive (OC) traits and test for sharing of genetic risks between OC traits and obsessive-compulsive disorder (OCD). MethodsWe conducted a genome-wide association analysis of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of three OCD case-controls samples (cases=3384, controls=8363). Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses. ResultsA locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase {delta}) was significantly associated with OC traits at the genome-wide significance level (p=2.48x10-8). The rs7856850 locus was also associated with OCD in a meta-analysis of three independent OCD case/control genome-wide datasets (p=0.0069). Polygenic risk scores derived from OC traits were significantly associated with OCD in a sample of childhood-onset OCD and vice versa (ps<0.01). OC traits were highly but not significantly genetically correlated with OCD (rg=0.83, p=0.07). ConclusionsWe report the first validated genome-wide significant variant for OC traits. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the importance of the type of measure used to measure traits as well as the feasibility and power of using trait-based approaches in community samples for genetic discovery.

genomics↗

bioRxiv2018

Utility of Attention-Deficit/Hyperactivity Disorder Trait Measure in Population Genetics: A Polygenic Risk Study

BackgroundValid and genetically-informative trait measures of psychopathology collected in the general population would provide a powerful complement to case/control genetic designs. We report the convergent, predictive and discriminant validity of the parent- and the self-report versions of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN) for attention-deficit/hyperactivity disorder (ADHD) traits. We tested if SWAN ADHD scores were associated with ADHD diagnosis, ADHD polygenic risk, as well as with traits and polygenic risk for co-occurring disorders such as anxiety and obsessive-compulsive disorder (OCD).\n\nMethodsWe collected parent- and self-report SWAN scores in a community sample (n=15,560; 6-18 years of age) and created norms. Sensitivity-specificity analyses determined SWAN cut-points that discriminated those with a community ADHD diagnosis (n=972) from those without a community diagnosis. We validated cut-points from the community sample in a clinical sample (266 ADHD cases; 36 controls). We tested if SWAN scores were associated with anxiety and obsessive-compulsive (OC) traits and polygenic risk for ADHD, OCD and anxiety disorders.\n\nResultsBoth the parent- and the self-report SWAN measures showed high convergent validity with established ADHD measures and distinguished ADHD participants with high sensitivity and specificity in the community sample. Cut-points established in the community sample discriminated ADHD clinic cases from controls with a sensitivity of 86% and specificity of 94%. High parent- and self-report SWAN scores and scores above the community-based cut-points were associated with polygenic risk for ADHD. High ADHD traits were associated with high anxiety traits, but not OC traits. SWAN scores were not associated with OCD or anxiety disorder polygenic risk.\n\nConclusionThe parent- and self-report SWAN are potentially useful in genetic research because they predict ADHD diagnoses and are associated with ADHD polygenic risk.

genetics↗

bioRxiv2018

Factor Structure and Heritability of Obsessive-Compulsive Traits in Children and Adolescents in the General Population

BackgroundObsessive-compulsive disorder (OCD) is a heritable childhood-onset psychiatric disorder that may represent the extreme of obsessive-compulsive (OC) traits that are widespread in the general population. We studied the factor structure and heritability of the Toronto Obsessive Compulsive Scale (TOCS), a new measure designed to assess traits associated with OCD in children and adolescents. We also examined the degree to which genetic effects are unique and shared between dimensions.\n\nMethodsOC traits were measured using the TOCS in 16,718 children and adolescents (6 to 18 years) at a local science museum. Factor analysis was conducted to identify OC trait dimensions. Univariate and multivariate twin modeling was performed to estimate the heritability of OC trait dimensions in a subset of twins (220 pairs).\n\nResultsSix OC dimensions were identified: Cleaning/Contamination, Hoarding, Rumination, Superstition, Counting/Checking, and Symmetry/Ordering. The TOCS total score (74%) and OC trait dimensions were heritable (30-77%). Hoarding was phenotypically distinct but shared genetic effects with other OC dimensions. Most of the genetic effects were shared between dimensions while unique environment accounted for the majority of dimension-specific variance, except for hoarding which had considerable unique genetic factors. A latent trait did not account for the shared variance between dimensions.\n\nConclusionsOC traits and individual OC dimensions were heritable, although the degree of shared and dimension-specific etiological factors varied by dimension. The TOCS is useful for genetic research of OC traits and OC dimensions should be examined individually and together along with total trait scores to characterize OC genetic architecture.

genetics↗

bioRxiv2017

Examination of the Shared Genetic Basis of Anorexia Nervosa and Obsessive-Compulsive Disorder

Anorexia nervosa (AN) and obsessive-compulsive disorder (OCD) are often comorbid and likely to share genetic risk factors. Hence, we examine their shared genetic background using a crossdisorder GWAS meta-analysis of 3,495 AN cases, 2,688 OCD cases and 18,013 controls. We confirmed a high genetic correlation between AN and OCD (rg = 0.49 {+/-} 0.13, p = 9.07x10-7) and a sizable SNP heritability (SNP h2 = 0.21 {+/-} 0.02) for the cross-disorder phenotype. Although no individual loci reached genome-wide significance, the cross-disorder phenotype showed strong positive genetic correlations with other psychiatric phenotypes (e.g., bipolar disorder, schizophrenia, neuroticism) and negative correlations with metabolic phenotypes (e.g., BMI, triglycerides). Follow-up analyses revealed that although AN and OCD overlap heavily in their shared risk with other psychiatric phenotypes, the relationship with metabolic and anthropometric traits is markedly stronger for AN than for OCD. We further tested whether shared genetic risk for AN/OCD was associated with particular tissue or cell-type gene expression patterns and found that the basal ganglia and medium spiny neurons were most enriched for AN/OCD risk, consistent with neurobiological findings for both disorders. Our results confirm and extend genetic epidemiological findings of shared risk between AN and OCD and suggest that larger GWASs are warranted.

genetics↗