Biology subjects

Schachar, R. J.

Publications and source records attributed to Schachar, R. J..

10 recordsLinked to original sources

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↗

bioRxiv2025

Decoding the neural basis of sensory phenotypes in Autism

BackgroundDifferences in sensory processing are a defining characteristic of autism, affecting up to 87% of autistic individuals. These differences cause widespread perceptual changes that can negatively impact cognition, development, and daily functioning. Recent research identified five sensory processing phenotypes with varied behavioural presentations; however, their neural basis remains unclear. This study aims to ground these sensory phenotypes in unique patterns of functional connectivity. MethodsWe analyzed data from 146 autistic participants in the Province of Ontario Neurodevelopmental Network. We classified participants into sensory phenotypes using k-means clustering of scores from the Short Sensory Profile. We then computed a connectivity matrix from 200 cortical and 32 subcortical regions and calculated graph-theoretic measures (betweenness centrality, strength, local efficiency, clustering coefficient) to assess information exchange between these regions. We then trained machine learning models to use these measures to classify between all pairs of sensory phenotypes. ResultsWe replicated that our sample of autistic participants was best categorized into five sensory phenotypes. The machine learning models distinguished 7/10 phenotype pairs using graph-theoretic measures (p < 0.005). Information exchange within and between the somatomotor network, orbitofrontal cortex, posterior parietal cortex, prefrontal cortex and subcortical areas were highly predictive of sensory phenotype. ConclusionsThis study shows that distinct sensory phenotypes in autism correspond with unique patterns of functional connectivity. Cortical, subcortical, and network-level connectivity all play a role in shaping distinct sensory processing styles in autism. These findings lay the groundwork for understanding these phenotypes and highlight opportunities to develop interventions in cases of maladaptive sensory processing.

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

Obsessive Compulsive Disorder and Response Inhibition: Meta-analysis of the Stop-Signal Task

This systematic review and meta-analysis updates evidence pertaining to deficient response inhibition in obsessive-compulsive disorder (OCD) as measured by the stop-signal task (SST). We conducted a meta-analysis of the literature to compare response inhibition in patients with OCD and healthy controls, meta-regressions to determine relative influences of age and sex on response inhibition impairment, and a risk of bias assessment for included studies using the Newcastle-Ottawa Scale (NOS). Stop-signal reaction time (SSRT), which estimates the latency of the stopping process deficit, was significantly longer in OCD samples than in controls, reflecting inferior inhibitory control (Raw mean difference = 23.43ms; p = <0.001; 95% CI = [17.42, 29.45]). We did not observe differences in mean reaction time (MRT) in OCD compared to controls (Raw mean difference = 2.51ms; p = 0.755; 95% CI = [-13.27, 18.30]). Age impacted effect size of SSRT, indicating a greater deficit in older patients than younger ones. We did not observe a significant effect of sex on SSRT or MRT scores. General Scientific SummaryDifficulty inhibiting responses is an hypothesized deficit in Obsessive-Compulsive Disorder (OCD). The results of this systematic review and meta-analysis of studies using the Stop Signal Task support the notion of deficient response inhibition in OCD and indicate that older individuals with OCD show greater impairments than younger ones.

pathology↗

bioRxiv2020

Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD

Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments was used. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information (NMI) was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Such new groups can be evaluated longitudinally for prognostic utility and could be stratified for clinical trials targeted toward each groups unique brain and behavioral profiles.

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↗

bioRxiv2019

BOLD differences normally attributed to inhibitory control predict symptoms, not task-directed inhibitory control in ADHD

BackgroundAltered brain activity that has been observed in attention deficit hyperactivity disorder (ADHD) while performing cognitive control tasks like the stop signal task (SST), has generally been interpreted as reflecting either weak (under-active) or compensatory (over-active) versions of the same functions as in healthy controls. If so, then regional activities that correlate with the efficiency of inhibitory control (i.e. stop signal reaction time, SSRT) in healthy subjects should also correlate with SSRT in ADHD. Here we test the alternate hypothesis that BOLD differences might instead reflect the redirection of neural processing resources normally used for task-directed inhibitory control, toward actively managing symptomatic behavior. If so, then activities that correlate with SSRT in TD should instead correlate with inattentive and hyperactive symptoms in ADHD.\n\nMethodsWe used fMRI in 14 typically developing (TD) and 14 ADHD adolescents performing the SST, and in a replication sample of 14 healthy adults. First we identified significant group BOLD differences during all phases of activity in the SST (i.e. warning, response, reactive inhibition, error detection and post-error slowing). Next, we correlated these phases of activity with SSRT in TD, and with SSRT, inattentive and hyperactive symptom scores in ADHD. We then identified whole brain significant correlations in regions of significant group difference in activity.\n\nResultsOnly three regions of significant group difference were correlated with SSRT in TD and replication groups (left and right inferior frontal gyri (IFG) during error detection, and hypothalamus during post-error slowing). Consistent with regions of altered activity managing symptomatic behavior instead of task-directed behavior, left IFG correlated with greater inattentive score, right IFG correlated with lower hyperactive score, and hypothalamus correlated with greater inattentive score and oppositely correlated with SSRT compared to TD.\n\nConclusionsResults are consistent with stimuli that elicit task-directed integration of neural processing in healthy subjects, instead directing integrated function towards managing symptomatic behavior in ADHD. The ability of the current approach to determine whether altered neural activities reflect comparable functions in ADHD and control groups has broad implications for the development and monitoring of therapeutic interventions.

neuroscience↗

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↗