The current social determinant landscape in open neuroimaging
Poster No:
591
Submission Type:
Abstract Submission
Authors:
Karolanne Toulouse1, Robert-Paul Juster2, Anna MacKinnon2, Judy Chen1, Ella Sahlas3, Laurence Kirmayer1, Jessica Royer1, Boris Bernhardt3
Institutions:
1McGill University, Montreal, QC, 2University of Montreal, Montreal, Quebec, 3McGill University, Montreal, Quebec
First Author:
Co-Author(s):
Introduction:
Social determinants of health (SDOH) significantly shape the onset, treatment, and prognosis of diseases, in addition to influencing normative developmental and aging processes over the lifespan. Factors such as socioeconomic status, healthcare access, education, ethnicity, and social support can influence prognosis, including both disease progression and response to treatment. Understanding these determinants is essential in neurological disorders, as they affect not only access to care but also underlying neural mechanisms (Szaflarski, 2014). Open-access neuroimaging datasets provide a unique opportunity to explore the relationship between SDOH and brain structure and function. By combining social and demographic data with advanced neuroimaging techniques, we can investigate how external factors contribute to neural changes and health disparities. The primary aim of this study is to identify critical trends, gaps, and emerging concepts in the existing research on SDOH and neuroimaging.
Methods:
We carried out a systematic review of major open-access neuroimaging datasets available globally that met our inclusion criteria: datasets containing human subjects of any age, representing both healthy and disease-related groups, with a sample size of over 20 subjects, and including neuroimaging data, primarily MRI (Li et al., 2019; Madan, 2022; Xia & He, 2017) (Fig1A). We excluded datasets with non-human subjects or post-mortem data, ensuring our focus remained on human-based research. We further categorized the SDOH represented in these datasets into 10 key domains: social status, education, employment, physical environment, lifestyle behaviors, social support, childhood experiences, genetic and biological factors, healthcare access, and race/culture (Fig1B). For each dataset, we gathered all accessible data, including questionnaire responses and supplementary materials, to assess the extent to which these SDOH factors were integrated and available for further study.
Results:
We identified 65 datasets meeting our inclusion criteria, totalling 569,928 participants (7 307± 56 476, n=65). The datasets were distributed across 14 countries, with the largest datasets coming from the UK, followed by the USA, with Canada ranking third, confirming a strong underrepresentation of data from low- and middle-income countries (Fig2A). Furthermore, 8 of these datasets focused on healthy adults, 10 on atypical or typical development, 19 on aging and lifespan, and the rest on neurocognitive and other disorders (Fig2B). Among the SDOH most included in the datasets, we noted categories of education and literacy (69%), biology/genetic endowment (65%), and culture and race (63%). In contrast, access to health services (15%), childhood experiences (20%), and physical environments (20%) were the least integrated. Several datasets, such as the UK Biobank, NKI-RS/eNKI, CamCAN, MAP, and COBRE, included information on most SDOH. However, only the ABCD study integrated all major SDOH. Notably, the SDOH most thoroughly explored through questionnaires elements included social support and coping skills (165±221 elements, n=20), childhood experiences (136±145 elements, n=13), lifestyle and healthy behaviors (124±170 elements, n=31), and biology/genetic endowment (123±165 elements, n=42). This led to varied patterns of dimensional overlap across SDOH domains and dataset categories at risk of biasing targeted investigations of social determinants of brain health (Fig2C).
Conclusions:
While numerous neuroimaging datasets are available, there remains room for improvement in the inclusion of SDOH. Better integration of these factors would enhance the study of their impact on functional connectivity and brain structure, highlighting the need for more tools to support researchers in incorporating SDOH into their datasets.
Education, History and Social Aspects of Brain Imaging:
Education, History and Social Aspects of Brain Imaging 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 2
Keywords:
ADULTS
Aging
DISORDERS
MRI
Open Data
Pediatric Disorders
Other - Social determinants
1|2Indicates the priority used for review
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2. Madan, C. R. (2022). Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics, 20(1), 109-137. https://doi.org/10.1007/s12021-021-09519-6
3. Szaflarski, M. (2014). Social determinants of health in epilepsy. Epilepsy Behav, 41, 283-289. https://doi.org/10.1016/j.yebeh.2014.06.013
Xia, M., & He, Y. (2017). Functional connectomics from a "big data" perspective. Neuroimage, 160, 152-167. https://doi.org/10.1016/j.neuroimage.2017.02.031