Friday, Jun 27: 11:30 AM - 12:45 PM
Oral Sessions
Brisbane Convention & Exhibition Centre
Room: M3 (Mezzanine Level)
Presentations
Major mental illnesses are increasingly understood as disorders of brain development [1]. Neuroimaging studies of brain development can help track healthy brain maturation and have the potential to identify deviations from normal development linked to psychopathology. However, large and diverse samples are required to capture reliable neurodevelopmental patterns on the population level [2]. While it is possible to aggregate data across multiple resources, data aggregation is not a straightforward process given the differences in neuroimaging and psychiatric phenotyping protocols used by independent studies. To this end, we introduce Reproducible Brain Charts (RBC), an open data resource that integrates several of the largest studies of brain development in youth.
Presenter
Golia Shafiei, PhD, University of Pennsylvania
Psychiatry
Philadelphia, PA
United States
Brain landmark localization is an important step in many neuroimaging and clinical workflows. For instance, identifying salient regions like the anterior (AC) and posterior commissure (PC) is common during brain alignment [1], and the AC-PC line is used as a reference during stereotactic targeting [2].
We extended the search for salient brain regions by creating [3] and validating [4] an open-access protocol for annotating 32 landmarks, called anatomical fiducials (AFIDs). However, manual localization is time intensive, making automation necessary for application in large-scale datasets and clinical use.
In this work, we introduce AutoAFIDs (github.com/afids), a BIDS App for automatic landmark detection using deep learning. We demonstrate its broad utility in three downstream applications: 1) image registration, 2) stereotactic targeting and 3) brain charting.
Presenter
Alaa Taha, University of Western Ontario
School of Biomedical Engineering
London, Ontario
Canada
Estimating effect size is a critical step in power analyses, and can help inform experimental design. However, effect size estimation is particularly difficult for fMRI data due to the complexity of the data and the analysis techniques. Further, it is difficult to obtain estimates from the literature, and small sample sizes of pilot studies may not provide precise enough estimates. When similar studies can be found in the literature, effect sizes are often not reported across the whole brain, limiting utility for study design. To facilitate the estimation and exploration of effect sizes for fMRI, we estimated effects for "typical" study designs with large datasets.
Presenter
Hallee Shearer, Northeastern University
Psychology
Somerville, MA
United States
The infancy stage (0-2 years old) is a critical period for brain development. The precise pediatric brain atlas could serve as fundamental tools for understanding the intricate developmental regulation of the pediatric brain during first several years in life. However, majority of pediatric atlases have been derived by warping adult atlases or manually delineated single subject's atlas into pediatric brain spaces, which does not account for the distinct characteristics of infant brain data. While recent years have seen the emergence of atlases constructed from pediatric brain imaging databases, these efforts often fall short in fine age resolution, spatial continuity across different age points and individualized brain mapping technique. Therefore, in this study, we aim to propose a novel individualized infant brain atlas mapping technique and constructed precise infant brain atlas in individual-level and age-specific population-level infant atlases.
Presenter
Meizhen Han, Beijing Normal University Beijing, Beijing
China
Meta-analyses play an instrumental role in the field of neuroimaging to summarize and generate consensus (or lack thereof!) across studies. The outcomes of these meta-analyses can inform clinicians about a subfield of neuroscience and help researchers generate novel hypotheses. Following the standards of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), meta-analyses are both time and resource intensive. However, this does not need to be the case. By leveraging automation and crowdsourcing the work of active users, we've created a platform that makes running a meta-analysis open, reproducible, and efficient. Here, we present Neurosynth Compose, a powerful and intuitive platform for curating and annotating studies as well as specifying and executing meta-analyses openly and reproducibly.
Presenter
James Kent, UT Austin Austin, TX
United States
The UK Biobank (UKB) is an essential resource for research in neuroimaging and population mental health (Dutt et al., 2021). However, the dataset has become increasingly complex to navigate due to the ongoing collection and release of follow-up data. In this study, we aimed to establish an up-to-date roadmap for neuroimaging research in mental health using the UKB dataset, with a focus on updating the univariate effect sizes for associations between imaging-derived phenotypes (IDPs) and all available mental health phenotypes (MHPs). To set the stage for future longitudinal studies, we also calculated effect sizes for repeated measures correlations between IDPs and MHPs assessed at two timepoints (Bakdash & Marusich, 2017).
Presenter
Xuqian Li, PhD, The University of Queensland
Australian Institute for Bioengineering and Nanotechnology
Brisbane, Queensland
Australia