Biology subjects

Georgiades, S.

Publications and source records attributed to Georgiades, S..

5 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

Assigning Targetable Molecular Pathways to Transdiagnostic Subgroups Across Autism and Related Neurodevelopmental Disorders

The heterogeneity of autism and related neurodevelopmental conditions has impeded accurate prognoses and treatment discovery. Using translational neuroimaging across 135 mouse models (3,515 mice) and two human MRI datasets (n = 1,234 and n = 1,015), we derived participant subgroups from shared neuroanatomical features. These subgroups did not distinguish autism, ADHD, or OCD diagnoses and only modestly differentiated cognitive and behavioural phenotypes. Instead, they mapped onto four molecular pathways: (1) synaptic function; (2) MAPK and Wnt signalling; (3) chromatin modification and cellular stress responses; and (4) broader chromatin, immune, and second-messenger signalling pathways. This framework bridges preclinical models and idiopathic human neurodevelopmental conditions, linking patients to biologically relevant molecular mechanisms.

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

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↗