Tuesday, Jun 25: 12:00 PM - 1:15 PM
Oral Sessions
COEX
Room: Hall D 2
Presentations
Prenatal drug exposure (PDE) impacts infant brain development with documented long-term consequences (Ross 2015). Functional magnetic resonance imaging (fMRI) studies of infants and youth with PDE reveal aberrant brain functional connectivity (Salzwedel 2020). Animal models demonstrate that PDE timing significantly impacts offspring outcome (Byrnes 2018), but most human fMRI studies use a binary categorization to assess drug exposure, limiting the ability to detect timing effects associated with PDE. Here, we use resting-state fMRI (rsfMRI) to characterize timing-related effects of PDE on the neonatal functional connectome. For the first time, we examine neural mechanisms associated with full PDE across all three trimesters (PDE-F) and partial PDE during only the first and/or second trimester (PDE-T1T2).
Presenter
Janelle Liu, Cedars-Sinai Medical Center Los Angeles, CA
United States
Autism and attention-deficit/hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology, and neuroanatomical alterations have been reported in both [1–3]. Both conditions show significant sex and age modulations on neuroanatomy [3,4], which are not yet fully understood. Normative modelling is an emerging technique that provides a unified framework for studying age- and sex-specific divergence in brain development in a common space [5]. We aimed to characterise regional cortical and global neuroanatomy in autism and ADHD, as well as sex and age differences, benchmarked against models of typical brain development based on a sample of over 75,000 individuals.
Presenter
Saashi Bedford, University of Cambridge Cambridge, Select State/Province
United Kingdom
Attention-Deficit/Hyperactivity Disorder (ADHD) stands as a complex neurodevelopmental disorder, drawing considerable focus in the realm of neuroimaging psychiatry. While aberrations in the neural mechanisms of both brain gray matter and white matter have been extensively pinpointed, the intricate patterns of their structural connectivity coupling and the concurrent gene expression profiles continue to elude comprehensive understanding. Herein, we established machine-learning classifiers based on Gray-White Matter Structural Connectivity Coupling (GWSC) patterns, with a parallel exploration to unravel the underlying transcriptomes.
Presenter
Nanfang Pan, West China Hospital of Sichuan University Chengdu, OH
China
Externalising and internalising disorders are common in youth but are often studied in isolation, preventing an investigation of the transdiagnostic vulnerability which may underlie them. Recent studies have attempted to identify unique versus shared neurobiological alterations across these disorders (e.g., Durham et al., 2021; Gold et al., 2016; Goodkind et al., 2015; Yu et al., 2023), but results have been inconsistent, likely due to heterogeneous sample selection and methods. Using data from the ENIGMA consortium, we conducted a mega-analysis to identify shared and distinct cortical and subcortical alterations between internalising (anxiety disorders and depression) and externalising (attention-deficit/hyperactivity disorder [ADHD] and conduct disorder [CD]) disorders in youth.
Presenter
Sophie Townend, University of Bath Bath, Somerset
United Kingdom
Dynamical structures of brain activity can be quantified from functional magnetic resonance imaging (fMRI), like local regional activity and functional connectivity (FC) between pairs of regions (Fig 1A). To date, most studies use only one of the two representations with a limited set of statistics, like the fractional amplitude of low-frequency fluctuations (fALFF) for regional dynamics and the Pearson correlation coefficient for FC [1]. Emerging work using comprehensive libraries of interdisciplinary time-series features [2,3] suggests that alternative statistics may be more suitable for a given application [3,4], though there is currently no unifying framework for comparing across multiple features and representation types simultaneously. Here, we introduce a systematic approach to quantify diverse types of local and pairwise dynamical structure from fMRI data, comparing the ability of multiple feature-based representations to capture meaningful differences in neuropsychiatric case–control datasets.
Presenter
Annie Bryant, The University of Sydney
School of Physics
Coogee, NSW
Australia
Major depressive disorder (MDD) has a lifetime prevalence of 11.1-14.6% [1,2] and represents the leading cause of disability due to mental health conditions for young people aged 10-24 years worldwide [3,4]. Functional neuroimaging can delineate the neural substrates of psychiatric, cognitive, and neurological disorders and potentially provide targets for treatment [5-8]. Youth MDD research however lag behind that in adults where existing resting-state functional MRI (rs-fMRI) studies have yielded inconsistent findings [9]. Further, mega-analysis, involving compilation of independent cohort datasets and offering unprecedented advantages of improved generalizability and increased statistical power, has remained unexplored in youth MDD.
Presenter
Nga Yan Tse, The University of Melbourne
Department of Psychiatry
Carlton, Victoria
Australia