Marlee M. Vandewouw

Rich-club organization is a topographical property of functional brain networks where highly connected regions are more densely interconnected. The rich-club has been shown to be vital for supporting cognitive functioning, and differences have been associated with a wide range of mental health and developmental conditions. This type of organization undergoes complex changes across development, and we have previously shown that males and females have strikingly distinct age-related changes. While disruptions in its development have been related to neurodevelopmental conditions and related mental health conditions such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD), the diagnostic- and sex-specific age-related patterns remain unexplored. These conditions also have heterogeneous and overlapping neurobiology and phenotype, and thus transdiagnostic approaches may be most suitable for addressing the complexities. Here, we combine the temporal advantages of magnetoencephalography (MEG) with a data-driven, transdiagnostic approach to parse the heterogeneity of age-related variation in rich-club organization in 386 neurodivergent (116 ADHD, 231 autism, 39 OCD) and 231 neurotypical children and youth, stratified by biological sex. In males and females, subgroups with distinct age-related patterns of rich-club organization did not align cleanly with the diagnostic categories. These patterns were frequency specific, separating the female subgroups in the alpha and gamma bands and the male subgroups in the theta, alpha, beta, and high gamma bands. While the female subgroups differed in their proportions of autistic and neurotypical individuals, all diagnostic categories were represented across the subgroups. The female subgroups also differed in intelligence, while the male subgroups differed in hyperactivity/impulsivity symptoms. Together, our findings indicate that frequency-specific, age-related variation in rich-club organization is largely shared across neurotypical and neurodivergent children and youth, rather than delineating diagnostic categories, and highlight the importance of considering sex when studying the biological constructs underlying neurodivergence.

Cortical expansion is a defining feature of human evolution and neurodevelopment, involving the tangential growth and gyrification of the cerebral cortex. Although disruptions to the expansion of the cortex are implicated in diverse brain-based disorders, the genetic architecture underlying this process remains undercharacterized. Here, we integrated GWAS data for five neuroanatomical phenotypes measured in up to 73,800 individuals to model a latent genomic factor capturing the shared genetic basis of cortical surface area, folding, curvature, gyrification, and intracranial volume. Using a multivariate framework, we mapped this pleiotropic architecture across biological scales, identifying novel effector genes, neural cell types, and developmental pathways involved in cortical expansion. Functional genomic evidence indicated that a diverse cellular ensemble contributes to the tangential growth and gyrification of the cortex during prenatal neurodevelopment, with progenitor cell lineages prominently involved. Regional analyses further revealed that genetic influences are spatially heterogeneous and organized along canonical anatomical, functional, and developmental gradients. Finally, cortical expansion exhibited substantial genetic overlap with neurodevelopmental, psychiatric, and neurological disorders, with shared genetic influences often concentrated in prefrontal cortical regions. Together, these findings map the genetic architecture of human cortical expansion and implicate it as a common etiological axis linking evolution, neurodevelopment, and health and disease.

In neurodevelopmental research, within-diagnosis heterogeneity and across-diagnosis overlap necessitate a shift from case-control designs to data-driven clustering approaches. However, our understanding of the replicability of these clustering structures across independent datasets remains limited. Our objective was to examine the replicability of clustering structure in measures of brain morphology in neurodiverse children across two independent datasets, namely the Province of Ontario Neurodevelopmental Disorder (POND) Network and the Healthy Brain Network (HBN). POND and HBN data were collected across various institutions in Ontario, Canada, and New York, United States, respectively. Participants were 5-19 years old and had diagnoses of autism, attention deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or were neurotypical. We used measures of cortical volume, surface area, cortical thickness, and subgroup volume from structural MRI data. Principal component analysis (PCA) and clustering were used to examine the replicability of clustering structures across the datasets. Correlations among principle components, measures of clusterability, and alignment between the four brain measures as well as male/female subsets were examined. Brain-behaviour associations were examined using univariate and multivariate approaches. The POND dataset included 747 participants with (autism n = 312, ADHD n = 220, OCD n = 70, neurotypical n = 145). The HBN dataset included 582 participants (autism n = 60, ADHD n = 445, OCD n = 19, neurotypical n = 58). Our results showed significant between-dataset correlations in 82.1% of the principal components derived from brain measures. A two-cluster structure was replicated across datasets, brain measures, and the female/male subsets, however the participant composition of clusters were only aligned between cortical volume and surface area, and cortical thickness and subcortical volume. Regional effect sizes for between-cluster differences were highly correlated across datasets (beta = 0.92+/−0.01, p < 0.0001; adjusted R-squared=0.93). Data-driven clusters did not align with diagnostic labels across datasets. Brain-behaviour associations were only replicated for male subsets and subcortical volume using multivariate analysis. We found evidence of replicability of the clustering structure across two independent datasets; however, caution must be exercised in integrating multiple measures in clustering and interpretation of brain-behaviour associations.

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.

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.

Background: Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making prescribing challenging and potentially exposing individuals to multiple medication trials. This study developed artificial intelligence (AI) models to predict prescribing patterns of stimulants, anti-depressants, and anti-psychotics. Methods: Models were built to predict cross-sectional medication usage from the Child Behaviour Checklist. Data from the Province of Ontario Neurodevelopmental (POND) network ( N =598) trained and tested the models, while data from the Healthy Brain Network (HBN N =1,764) and Adolescent Brain Cognitive Development (ABCD N =2,396) studies were used for external validation. For the electronic medical record (EMR) cohort, data from the Psychopharmacology Program (PPP N =312) at Holland Bloorview Kids Rehabilitation Hospital were used to predict longitudinal success. Stacked ensemble models were built separately for each medication class, and area under the receiver operating characteristic curve (AU-ROC) evaluated performance. Results: The research cohorts demonstrated feasibility, with internal testing (POND) achieving an AU-ROC (median [IQR]) of 0.75 [0.73,0.80] for stimulants, 0.83 [0.78,0.87] for anti-depressants, and 0.79 [0.72,0.86] for anti-psychotics. Performance in external testing sets (HBN and ABCD) confirmed generalizability. In the EMR cohort (PPP), AU-ROCs were 0.84 [0.81,0.88] for stimulants, 0.82 [0.77,0.87] for anti-depressants, and 0.87 [0.83,0.91] for anti-psychotics. Limitations: The study’s findings should be interpreted with caution due to several factors, including the use of medication choice as a proxy for treatment response and the omission of key sociodemographic variables, which may introduce bias. The model was trained solely on North American data, limiting global generalizability, and predictions were restricted to broad medication classes rather than within-class choices. Conclusions: Findings demonstrate that AI can accurately learn expert prescribing patterns and predict medication prescribing decisions, supporting the potential of data-driven tools to guide personalized medication management for neurodevelopmental conditions and reducing the trial-and-error burden in clinical practice.

Significant genetic, behavioural and neuroanatomic heterogeneity is common in autism spectrum- and related- neurodevelopmental disorders (NDDs). This heterogeneity constrains the development of effective therapies for diverse patients in precision medicine paradigms. This has led to the search for subgroups of individuals having common etiologic factors/biology (e.g., genetic pathways), thus creating potential uniformity in prognosis and/or treatment response. Despite NDDs having a strong genetic component, only ~15-20% of individuals will present with a specific rare genetic variant considered clinically pathogenic, and therefore, subtyping efforts tend to focus on using clinical, cognitive, and/or brain imaging phenotypes to group individuals. Here we delineated mechanisms via mouse to human translational neuroscience. Using MRI derived structural neuroanatomy and a spatial transcriptomic comparison, we linked subgroups of 135 NDD relevant mouse models (3,515 individual mice) separately to two human databases, with 1,234 and 1,015 human individuals with NDDs, composed of autism, attention-deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), other related NDDs, and typically developing controls. Subgroups were significantly linked by consistent neuroanatomy across all three datasets, mouse and human, indicating that direct cross-species subgrouping and translation is consistent and reproducible. Ultimately, four specific neuroanatomical clusters were found and linked to precise molecular mechanisms: two showing a chromatin/transcription motif, with one of those showing specific links to G-protein coupled receptors (GPCR) and Notch signalling, and another two being mainly synaptic in origin, with one off those showing specific connections to axon guidance and Wnt signaling. Assigning molecular pathways, and thus genetic information, from the mouse to individual participants provides an insight into undetected and/or related genetic variants that could be working in combination or interacting with an environmental influence. Moreover, the subgroups found are transdiagnostic, including participants with autism, ADHD, and OCD, which indicates that NDDs as a whole can be subdivided into consistent neuroanatomical clusters with cohesive underlying biological mechanisms. This work allows us to bridge the gap between preclinical models and human disorders, linking previously idiopathic human patients to pertinent genetics, molecular mechanisms, and pathways.

Facial expressions are fundamental to social communication, with emotional face processing developing throughout childhood. However, the neural mechanisms underlying this process in young children remain underexplored due to challenges in neuroimaging this population. Optically pumped magnetometers (OPMs), a wearable magnetoencephalography (MEG) technology, offer potential advantages for studying these responses early in life. This study investigated evoked responses and functional connectivity in 45 children (3–5 years) during an emotional face processing task using OPM-MEG. The M170 component, a key marker of face processing, and whole-brain functional connectivity of eight regions of interest were assessed. Children exhibited a robust M170 response to emotional faces in the bilateral fusiform gyri. Peak amplitude increased with age, but no significant latency changes were observed. A significant network of increased connectivity following emotional face onset, involving connections between the amygdalae, insulae, occipital, and frontal regions was found. This study provides the first evidence of M170 responses and large-scale connectivity to emotional faces in young children using OPM-MEG. Findings highlight the feasibility of OPMs for developmental neuroimaging and provide insights into the maturation of emotion-related neural circuits in early childhood. These results establish a foundation for future face processing research in clinical paediatric populations, such as autism.