Biology subjects

Vandewouw, M. M.

Publications and source records attributed to Vandewouw, M. M..

5 recordsLinked to original sources

bioRxiv2024

Using deep learning to predict internalizing problems from brain structure

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.

neuroscience↗

bioRxiv2024

Measuring the neurodevelopmental trajectory of excitatory-inhibitory balance via visual gamma oscillations

Disruption of the balance between excitatory and inhibitory neurotransmission (E-I balance) underlies theories of many neurodevelopmental disorders, however, its study is typically restricted to adults, animal models and the lab-bench. Neurophysiological oscillations in the gamma frequency band relate closely to E-I balance, and a new technology - OPM-MEG - offers the possibility to measure such signals across the lifespan. We used OPM-MEG to measure gamma oscillations induced by visual stimulation in >100 participants, aged 2-34 years. We demonstrate a significantly changing spectrum with age, with low amplitude broadband gamma oscillations in children and high amplitude band limited oscillations dominating in adults. We used a canonical cortical microcircuit to model these gamma signals, revealing significant age-related shifts in E-I balance in superficial pyramidal neurons in visual cortex. Our findings detail the first MEG metrics of gamma oscillations and their underlying generators from toddlerhood, providing a benchmark against which future studies can contextualise.

neuroscience↗

bioRxiv2024

The development of aperiodic and periodic resting-state power between early childhood and adulthood: new insights from optically pumped magnetometers

Neurophysiological signals, comprised of both periodic (e.g., oscillatory) and aperiodic (e.g., non-oscillatory) activity, undergo complex developmental changes between childhood and adulthood. With much of the existing literature primarily focused on the periodic features of brain function, our understanding of aperiodic signals is still in its infancy. Here, we are the first to examine age-related changes in periodic (peak frequency and power) and aperiodic (slope and offset) activity using optically pumped magnetometers (OPMs), a new, wearable magnetoencephalography (MEG) technology that is particularly well-suited for studying development. We examined age-related changes in these spectral features in a sample (N=65) of toddlers (1-3 years), children (4-5 years), young adults (20-26 years), and adults (27-38 years). Consistent with the extant literature, we found significant age-related decreases in the aperiodic slope and offset, and changes in peak frequency and power that were frequency-specific; we are the first to show that the effect sizes of these changes also varied across brain regions. This work not only adds to the growing body of work highlighting the advantages of using OPMs, especially for studying development, but also contributes novel information regarding the variation of neurophysiological changes with age across the brain.

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↗

bioRxiv2020

The developing relations between networks of cortical myelin and neurophysiological connectivity

Recent work identified that patterns of distributed brain regions sharing similar myeloarchitecture are related to underlying functional connectivity, demonstrating cortical myelins plasticity to changes in functional demand. However, the changing relation between functional connectivity and structural architecture throughout child and adulthood is poorly understood. We show that structural covariance connectivity measured using T1-weighted/T2-weighted ratio and functional connectivity measured using magnetoencephalography exhibit nonlinear developmental changes. We then show significant relations between structural and functional connectivity, which have both shared and distinct characteristics dependent on the neural oscillatory frequency. Increases in structure-function coupling are visible during the protracted myelination observed throughout childhood and adolescence, and are followed by decreases near the onset of adulthood to potentially support increasing cognitive flexibility and functional specialization in adulthood. Our work lays the foundation for understanding the mechanisms by which myeloarchitecture supports brain function, enabling future investigations into how clinical populations may deviate from normative patterns.

neuroscience↗