1310
Symposium
The rapidly evolving landscape of neurological disorders, coupled with increasing demands for precise diagnostic and treatment approaches, makes this symposium particularly timely. Recent advances in neuroimaging technologies and analytical methods have opened new possibilities for understanding brain function and dysfunction, yet integrating these innovations into clinical practice remains challenging. From the development of accessible biomarkers for pediatric populations to sophisticated analyses of brain connectivity in aging and disease, there is a critical need to bridge the gap between methodological innovations and practical applications. This symposium addresses this need by showcasing diverse approaches that span the developmental spectrum and varying levels of technological complexity.
The learning outcomes focus on equipping attendees with practical knowledge about emerging neuroimaging methods and their clinical applications. Participants will gain insights into how different imaging modalities can be optimally utilized for specific research questions and clinical scenarios. They will learn about the latest developments in machine learning applications for neuroimaging data, advanced connectivity analyses, and high-resolution brain mapping techniques. Importantly, attendees will understand the strengths and limitations of various approaches, helping them make informed decisions about methodology selection for their own research or clinical practice. This knowledge is crucial as the field moves toward more personalized approaches in neurology and psychiatry, where precise brain measurements increasingly guide treatment decisions. By bringing together basic research innovations and clinical applications, this symposium aims to accelerate the translation of cutting-edge neuroimaging techniques into meaningful healthcare solutions.
1. Evaluate how different analytical approaches (machine learning, directed information flow analysis, multi-modal integration) can enhance our understanding of brain organization and improve clinical applications across development and disease.
2. Describe how context and methodology influence brain connectivity measurements, from the effects of sensory conditions on psychedelic-induced brain changes to the importance of spatial resolution in mapping subcortical structures.
This symposium is designed for neuroscientists, neuroimaging researchers, and clinicians interested in advanced neuroimaging methods and their applications in understanding brain function across development, aging, and disease. The content will be particularly valuable for both early-career researchers seeking to understand state-of-the-art neuroimaging approaches and experienced investigators interested in novel analytical methods and multi-modal integration strategies.
Presentations
Developing reliable biomarkers to effectively measure child brain health, particularly in neurodevelopmental disorders, is a key challenge highlighted by the World Health Organization (WHO). Recently, the WHO has recognized electroencephalography (EEG) as a promising solution due to its affordability, non-invasive nature, and capacity for objective monitoring across all childhood ages. This talk presents our research on advancing EEG-based diagnostic and prognostic methods, from infancy to adolescence, utilizing machine learning (ML), artificial intelligence (AI) algorithms and computational modelling. Our approach leverages the flexibility of EEG to derive objective markers, ranging from low-cost, limited (2-channel) EEG for predicting brain age to the combined use of quantitative EEG (qEEG) and connectomics with age-binned head models applied to clinical (19-channel) and higher-density (128-channel) EEG configurations. We will also discuss the challenges involved in developing these diagnostic and prognostic algorithms in this population, including training, validation, and site harmonization confounders that are crucial for ensuring accuracy and generalizability. By employing advanced ML/AI tools, our goal is to create scalable solutions that enhance current diagnostic capabilities and enable proactive, personalized healthcare for children. Our efforts aim to pave the way for future innovations in child brain health monitoring, enabling objective, cost-effective EEG assessments on a larger scale, ultimately improving outcomes for pediatric populations, including those with neurodevelopmental disorders.
Reference: [1] Iyer et al. 2024. EBioMedicine; [2] Slater et al. 2023. The Lancet Digital Health; [3] Iyer et al. 2023. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); [4] Stevenson et al. 2017. Scientific reports; [5] Stevenson et al. 2020. Annals of clinical and translational neurology
The brain relies on a constant supply of glucose and oxygen to fuel complex cognitive processes. However, until the recent development of high temporal resolution functional (18F)-fluorodeoxyglucose positron emission tomography (fPET), it has not been possible to measure the brain’s metabolic connectome in individuals. Although connectomes derived from the BOLD signal in fMRI undergo a reconfiguration in ageing, those BOLD signal changes may reflect age-related alterations in one or more metabolic, haemodynamic and vascular components of neuronal activity. Here we use simultaneous fPET/fMRI to compare metabolic and functional connectivity and test their predictive utility for healthy ageing and cognition. Whole-brain fPET connectomes showed moderate topological similarities to fMRI connectomes in 40 younger (mean age 27.9 years; range 20-42) and 46 older (mean 75.8; 60-89) adults. The within-network connectivity was lower and between-network connectivity less variable in the fPET than the fMRI connectomes. However, within and between-network connectivity and graph metrics showed more wide-spread age differences and were significantly associated with performance in more cognitive domains in fPET than fMRI. These results likely reflect that fPET directly measures dynamic glucose metabolism at the post-synaptic neuron, whereas the fMRI signal indirectly indexes neuronal metabolism and has multiple physiological underpinnings. We conclude that metabolic connectivity has greater predictive utility for age and cognition than functional connectivity and offers promise as a means for indexing glucodynamic changes in ageing and disease.
Presenter
Hamish Deery, Monash University Melbourne, Victoria
Australia
Introduction: Homotopic connectivity is an integral component of the brain’s functional architecture, typically quantified using the Pearson correlation between mirrored voxels or parcels [1]. Despite mounting evidence for left-right structural asymmetry in health [2,3] and disease [4,5], interhemispheric information flow is seldom directly assessed.
Methods: We quantified homotopic directed information (DI) from functional magnetic resonance imaging (fMRI) to measure directed interhemispheric coupling for the first time [6]. All resting-state fMRI data were obtained the Human Connectome Project (N=100) and were preprocessed and parcellated into the Desikan-Killiany cortical atlas in [7]. For each of 34 cortical regions (one per hemisphere), we computed DI with a Gaussian density estimator using pyspi [8] in Python.
Results: Homotopic DI magnitudes varied across the brain, ranging from the lowest average magnitude in the entorhinal cortex (1.23 × 10-2 ± 8.59 × 10-3) to the highest in the lateral occipital cortex (2.39 ± 0.96). Generally, medial occipital and superior parietal regions exhibited higher homotopic DI while inferior frontal and temporal regions exhibited lower homotopic DI. We also compared left-right asymmetries, finding that lateral regions exhibit greater DI from left to right while medial regions exhibit greater DI from right to left.
Conclusions: Our findings suggest that there are gradients of both the magnitude and direction of interhemispheric information flow, warranting further characterization of the functional implications for health and disease.
References: [1] Mancuso et al. 2019. Scientific Reports; [2] Wan et al. 2024. bioRxiv; [3] Kong, et al. 2022. Human Brain Mapping; [4] Chen et al. 2024. Brain Communications; [5] Roe et al. 2021. Nature Communications; [6] Lizier et al. 2011, Journal of Computational Neuroscience; [7] Fallon et al. 2020. Network Neuroscience; [8] Cliff et al. 2023. Nature Computational Science
Presenter
Annie Bryant, The University of Sydney Sydney, New South Wales
Australia
Background: Serotonergic psychedelic psilocybin induces alterations in consciousness described as profoundly meaningful experiences with significant clinical implications [1-3]. However, their effects on the brain are variable [4] and highly context dependent [5-8]. This study collected the largest psychedelic dataset to date to elucidate hemodynamic response and electrophysiological activity change across diverse conditions during acute psychedelic-altered conscious states.
Methods: In an open-label, placebo-controlled study, at Monash Biomedical Imaging, fMRI and EEG data were collected from sixty healthy adults (18-58 years) during rest, meditation, music, and naturalistic stimuli, before and after 19mg psilocybin. Additional assessments included measurement of mindset, personality, music and mindfulness before, during and longitudinally after imaging. We analysed effective and functional connectivity, focusing on changes in resting-state networks and global functional connectivity (GFC) using a high-resolution cortical surface template. Furthermore, we conducted spatiotemporal analyses of 64-channel EEG data under the same conditions immediately post-MRI sessions.
Results: fMRI analysis revealed increased GFC in associative areas and decreased GFC in sensory areas. Under naturalistic stimuli, this pattern reversed, with a global increase in GFC observed. EEG data demonstrated power reductions across lower frequency bands. Effective and functional connectivity analyses confirmed default-mode network modulation. Behavioural assessments revealed that most participants had transformative subjective experiences, linked to positive mindset changes and interconnectedness.
Conclusions: Our multi-modal approach provides a comprehensive view of psilocybin's acute effects on brain connectivity, electrophysiology, and hierarchical modulation by sensory context. These findings provide crucial insights into the neurobiological mechanisms underlying psychedelic-induced brain changes and their potential clinical significance.
References: [1] Stoliker et al. 2024. Biological Psychiatry; [2] Griffiths et al. 2016. Journal of Psychopharmacology; [3] Stoliker et al. 2022. Pharmacological Reviews;
[4] Siegel et al. 2024. Nature; [5] Griffiths et al. 2018. Journal of Psychopharmacology; [6] Carhart-Harris et al. 2018. Journal of Psychopharmacology; [7] Kaelen et al. 2016. European Neuropsychopharmacology; [8] Kaelen et al. 2015. Psychopharmacology
Introduction: Human subcortex comprises multiple subcortical grey matter (SGM) structures, many with several nuclei and specialised sub-regions dedicated to highly specific functions. Histology-driven brain atlases provide detailed delineation of these sub-regions; however, those cannot be directly applied to in-vivo MRI studies. Here, we integrate the information from anatomy, diffusion micro-environment, and directions of white matter (WM) fibres within SGM, from multi-contrast MRI, to segregate the nuclei and specialised sub-regions of human subcortex.
Methods: Minimally pre-processed T1w, T2w, and Diffusion MRI (dMRI) data obtained at 3T were downloaded from Human Connectome Project [1-3] for 50 healthy subjects. For each subject, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and myelin index (T1w/T2w) were computed following the processing pipeline in [4], orientation of the WM fibre tracts were computed within each voxel [5]. The parametric maps were combined by computing track-weighted imaging maps [6,7] with 0.7mm isotropic super-resolution. SGM structures were masked using FSL [8], data corresponding to SGM structures were first analysed by principal component analysis and then used by k-means clustering to parcellate SGM.
Results and Conclusions: We have identified and mapped 56 newly delineated nuclei, sub-nuclei, and sub-regions within human SGM. Our SydSGM parcellation demonstrated remarkable resemblance to the gold-standard histology-based atlas by Mai et al. [9]. SydSGM parcellation can be directly applied to subject-specific and/or group average MRI dataset as a standalone atlas or by incorporating it into existing brain atlases. We demonstrate SydSGM parcellation’s advantage by revealing declined structural connectivity of highly resolved SGM subnuclei in patients with early Parkinson’s disease.
References: [1] Van Essen et al. 2013. NeuroImage; [2] Glasser et al. 2013. NeuroImage; [3] Sotiropoulos et al. 2013. NeuroImage; [4] Ali et al. 2022. Magnetic Resonance in Medicine; [5] Dhollander et al. 2015. Proc Intl Soc Mag Reson Med; [6] Calamante et al. 2012. NeuroImage; [7] Calamante et al. 2017. MAGMA; [8] Patenaude et al. 2011. NeuroImage; [9] Mai et al., 2015.
Presenter
Tonima Ali, PhD, University of Sydney Sydney, New South Wales
Australia