Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data

Abstract

ImportanceNeurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings.ObjectiveTo identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets.Design, Setting, and ParticipantsThis case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study.Main Outcomes and MeasuresThe analyses consisted of a data-driven clustering procedure on measures derived from each participant’s resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested.ResultsOverall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set.Conclusions and RelevanceThe findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.

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

“…Participants were required to have at least 2/3 of their data within the recommended motion thresholds to pass quality control. Of the remaining participants, propensity score matching was used to identify a smaller subset of individuals for which there was not a statistically significant between-dataset differences in median age, sex proportion, and median head motion (Vandewouw et al, 2023). The Schaefer cortical (Schaefer et al, 2018) and Melbourne subcortical (Tian et al, 2020) atlases were used to define 232 brain regions, and pairwise Pearson correlations were computed between region-averaged time series to construct rs-fMRI connectomes.…”

“…While many studies directly examine age-related effects, age is often treated as a nuisance covariate (Hyatt et al, 2020). With increasing awareness of the importance of replicability in neuroimaging studies (Poldrack et al, 2017), it is becoming more common to leverage multiple consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations (Abrol et al, 2023; Grotzinger et al, 2023; Marek et al, 2022; Nicolaisen-Sobesky et al, 2022; Romer et al, 2019; Vandewouw et al, 2023). However, these datasets differ in composition in terms factors which may influence age-related effects, such as participants’ demographics (e.g., age, sex and gender, race and ethnicity, socioeconomic status), diagnosis, co-occurring conditions, and other phenotypic variables.…”

Normative growth modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in children with ADHD

“…These studies motivate the need to discover ADHD biotypes with homogeneous structural or functional signatures. To date, eight studies have identified ADHD biotypes using different neuroimaging measures, such as structural morphology (16,17), functional connectivity (18)(19)(20)(21), and combinations of both features (22,23). These results have already begun to provide insights into the neurophysiological heterogeneity of ADHD.…”

Exploring functional connectivity in clinical and data-driven groups of preterm and term adults

“…Similarly, individuals belonging to distinct diagnostic and non-diagnostic psychiatric groups also exhibit within- and between-group heterogeneity in terms of phenotypic profiles. Recent studies implementing such approaches in psychiatric populations have successfully identified patterns of structural and functional connectivity characterising distinct data-driven behavioural subgroups irrespective of diagnostic labels (Astle et al, 2019; Bathelt et al, 2018; Jones et al, 2021; Mareva et al, 2023; Siugzdaite et al, 2020; Vandewouw et al, 2023). A small number of studies in VPT children followed similar methodological approaches and investigated the underlying brain changes differentiating within-group behavioural heterogeneity.…”