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Biology subjects

Stevenson, R.

Publications and source records attributed to Stevenson, R..

6 recordsLinked to original sources

Divergence in neurodivergent brains: mapping the relationship between developmental trajectories in functional connectivity and clinical outcomes

Brain network development differs between neurodivergent (ND) and neurotypical (NT) youth, but its developmental trajectory, and relationship to clinical outcomes, remains unclear. We examined age-related changes in functional network connectivity and clinical symptoms in 861 participants (5-21 years) from the Healthy Brain Network, including NT youth and those with Neurodevelopmental Disorders (NDDs) and generalized anxiety disorder (GAD). Functional connectivity during movie-watching was modeled using generalized additive models across eight large-scale networks to characterize nonlinear developmental trajectories. Divergence in connectivity profiles between ND and NT groups first emerged during late childhood and were largest during adolescence. Importantly, divergence in connectivity patterns followed a similar trajectory path, but preceded, divergence in clinical outcomes. These findings identify adolescence as a sensitive period when differences in patterns of brain activity and clinical differences become increasingly pronounced, highlighting a potential window for targeted intervention.

neuroscience↗

Astrocyte dysfunction distinguishes monozygotic twin C9orf72 expansion carriers discordant for amyotrophic lateral sclerosis.

Hexanucleotide repeat expansions in C9orf72 are the most common genetic cause of amyotrophic lateral sclerosis, yet many carriers remain asymptomatic for decades or never develop disease. This incomplete penetrance suggests that phenoconversion from genetic susceptibility to symptomatic disease onset is governed by epigenetic, environmental and cell-intrinsic modifiers. Astrocytes are key mediators of non-cell-autonomous neurodegeneration in ALS, but whether they undergo disease-associated phenoconversion in C9orf72 expansion carriers remains unclear. We investigated astrocyte-associated mechanisms of phenoconversion using a unique C9orf72 pedigree comprising monozygotic twins discordant for ALS and their asymptomatic father. This enabled analysis across a continuum from non-penetrance to late-stage neurodegeneration while controlling for inherited genetic background. Notably, the unaffected twin exhibited hypermethylation of the expanded C9orf72 allele, identifying an epigenetic correlate of non-penetrance. To define symptomatic and asymptomatic-associated astrocyte states, fibroblasts from this pedigree were directly reprogrammed into astrocytes and subjected to molecular, functional, electrophysiological and translatome analyses. Astrocytes derived from symptomatic ALS individuals were found to exhibit astrocyte-mediated toxicity towards motor neurons, canonical C9orf72 molecular pathology and connexin-related membrane dysfunction. Specifically, RNA foci and dipeptide repeat protein burden peaked in astrocytes obtained early in disease in the symptomatic twin and declined in astrocytes derived from samples obtained at advanced disease stages, whereas motor neuron toxicity increased progressively, demonstrating a dissociation between aggregate burden and functional neurotoxicity. In contrast, connexin-mediated electrophysiological dysfunction emerged with symptomatic disease and closely tracked with maximal toxicity. Translatome profiling revealed early global translational repression and impaired proteostasis in symptomatic astrocytes, whereas unaffected and non-penetrant C9-carriers retained enrichment of protein homeostasis pathways. These findings define distinct astrocyte states associated with asymptomatic, early symptomatic and late-stage disease. Notably, the affected twin reported substantially higher lifetime strenuous physical activity compared to the unaffected twin, consistent with a potential role for sustained exercise-related stress in accelerating disease onset in genetically susceptible individuals. Experimentally modelling increased stress induced aberrant upregulation of connexin-mediated currents in C9orf72 astrocytes, including in asymptomatic carriers, indicating that physiological stress can unmask latent astrocyte-intrinsic vulnerability and precipitate dysfunction in cellular homeostasis. Together, these findings redefine phenoconversion in C9orf72-associated amyotrophic lateral sclerosis to be associated with a significant upregulation of astrocyte toxicity, failure of resilience and demonstrate that the astrocyte disease state can be potentially induced by external stressors.

neuroscience↗

VasoTracker 2: An Open-source Platform for Quantitative Analysis of Vascular Reactivity and Function

VasoTracker 2 is an open-source platform for studying blood vessel dynamics, featuring versatile diameter-tracking software and complementary low-cost hardware components. This system surpasses existing tools through accessible, high-resolution analysis across multiple imaging modalities, enabling comprehensive assessment of vascular dynamics in both real-time and pre-recorded experiments. Advanced algorithms enable multi-point diameter tracking in branched vessels, automated pressure-response protocols, and reliable edge detection. The software can assess vessels imaged by brightfield microscopy, fluorescence imaging, and in ultrasound recordings, supporting diverse applications from isolated vessel studies to in vivo assessment. For ex vivo applications, VasoTracker 2 includes modular open-source hardware components that can be used to create a low-cost pressure myograph system: a confocal-compatible vessel chamber and a programmable pressure controller, VasoMoto. By combining powerful analytical capabilities with an open-access approach, VasoTracker 2 provides free software and low-cost hardware alternatives to commercial systems, democratizing access to advanced vascular research tools for scientists worldwide.

physiology↗

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↗

Using Machine Learning to Identify Neural Mechanisms Underlying the Development of Cognition in Children and Adolescents with ADHD

Children and adolescents diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) often show deficits in various measures of higher-level cognition, such as, memory and executive functioning. Poorer high-level cognitive functioning in children with ADDH has been associated with differences in functional connectivity across the brain, including the frontoparietal network. However, little is known about the developmental changes to cortical functional connectivity profiles associated with higher-order cognitive abilities in this cohort. To characterize changes in the functional brain connectivity profiles related to higher-order cognitive functioning, we analyzed a large dataset(n=479) from the publicly available Healthy Brain Network which included fMRI data collected while children and adolescents between the ages of 6 and 16 watched a short movie-clip. The cohort was divided into two groups, neurotypical youth (n=106), and children and adolescents with ADHD (n=373). We applied machine learning models to functional connectivity profiles generated from the fMRI data to identify patterns of network connectivity that differentially predict cognitive abilities in our cohort of interest. We found, using out-of-sample cross validation, models using functional connectivity profiles in response to movie-watching successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children ages 6 to 11, but not in adolescents with ADHD. The models identified connections with the default mode, memory retrieval, and dorsal attention networks as driving prediction during early and middle childhood, but connections with the somatomotor, cingulo-opercular, and frontoparietal networks were more important in middle childhood. This work demonstrated that computational models applied to neuroimaging data in response to naturalistic stimuli can identify distinct neural mechanisms associated with cognitive abilities at different developmental stages in children and adolescents with ADHD.

neuroscience↗

A Platform for Oncogenomic Reporting and Interpretation

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we created the Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework created to facilitate the interpretation and reporting of somatic variants in cancer. PORI is unique in its integration of reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents one of the first open-source platform alternatives to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 TCGA tumours to the graph knowledge base, revealing that 88.2% have at least one potentially targetable alteration, and making available reports describing select individual samples.

bioinformatics↗