Beyond diagnosis: Cross-diagnostic features in canonical resting-state networks in children with neurodevelopmental
Eun Jung Choi1, Marlee M Vandewouw2, Margot J Taylor3
1Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Neurosciences & Mental Health, Research Institute, The Hospital for Sick Children, Toronto, Canada.
Insights
Neurodevelopmental disorders (NDDs) show similar brain connectivity patterns, challenging traditional diagnoses. Dimensional analysis reveals shared neurobiology across conditions, suggesting a need for new diagnostic approaches beyond current categories.
Area of Science:
- Neuroscience
- Developmental Psychology
- Psychiatry
Background:
- Children with neurodevelopmental disorders (NDDs) often exhibit shared behavioral symptoms despite distinct diagnostic criteria.
- Understanding the common neurobiological underpinnings of NDDs is crucial for developing effective interventions.
Purpose of the Study:
- To investigate resting-state network connectivity in children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and pediatric obsessive-compulsive disorder (OCD) compared to typically developing (TD) controls.
- To explore both diagnosis-based and dimensional approaches to uncover neurobiological similarities and differences across NDDs.
Main Methods:
- Utilized functional network graphs and five graph metrics from resting-state fMRI data in a large sample (N=407).
- Applied diagnosis-based comparisons (ANCOVA) and a dimensional approach correlating network metrics with behavioral measures.
- Employed data-driven k-means clustering to identify subgroups irrespective of diagnostic labels.
Main Results:
- Diagnosis-based comparisons showed no significant differences between NDD groups and TD controls, or among NDDs.
- Dimensional analysis revealed significant correlations between subcortical functional connectivity and general adaptive functioning across all participants.
- Clustering identified two distinct subgroups with heterogeneous diagnoses, indicating neurobiological overlap rather than distinct categorical separation.
Conclusions:
- Neurobiological patterns in NDDs may not align with current diagnostic categories, suggesting a need for transdiagnostic approaches.
- Functional connectivity, particularly in subcortical regions, is associated with adaptive functioning and behavioral challenges across NDDs.
- Future research should consider dimensional and data-driven methods to better understand the neurobiology of NDDs.
Abstract:
Children with neurodevelopmental disorders (NDDs) share common behavioural manifestations despite distinct categorical diagnostic criteria. Here, we examined canonical resting-state network connectivity in three diagnostic groups (autism spectrum disorder, attention-deficit/hyperactivity disorder and paediatric obsessive-compulsive disorder) and typically developing controls (TD) in a large single-site sample (N = 407), applying diagnosis-based and dimensional approaches to understand underlying neurobiology across NDDs. Each participant's functional network graphs were computed using five graph metrics. In diagnosis-based comparisons, an analysis of covariance was performed to compare all NDDs to TD, followed by pairwise comparisons between NDDs. In the dimensional approach, participants' functional network graphs were correlated with continuous behavioural measures, and a data-driven k-means clustering analysis was applied to determine if subgroups of participants were seen, without diagnostic information having been included. In the diagnosis-based comparisons, children with NDDs did not differ significantly from the TD group and the NDD categorical groups also did not differ significantly from each other, across all graph metrics. In the dimensional, diagnostic-independent approach, however, subcortical functional connectivity was significantly correlated with participants' general adaptive functioning across all participants. The clustering analysis identified an optimal solution of two clusters, and participants assigned in the same data-driven cluster were highly heterogeneous in diagnosis. Neither cluster exclusively contained a specific diagnostic group, nor did NDDs separate cleanly from TDs. Each participant's distance ratio between the two clusters was significantly correlated with general adaptive functioning, social deficits and attentional problems. Our results suggest the neurobiological similarity and dissimilarity between NDDs need to be investigated beyond DSM/ICD-based, behaviourally-defined diagnostic categories.
