The end game: respecting major sources of population diversity

Kopál, Jakub; Uddin, Lucina Q.; Bzdok, Danilo

doi:10.1038/s41592-023-01812-3

Cited by 24 publications

(12 citation statements)

References 34 publications

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“…Inevitably, this geographic bias was also found in other neuroimaging normative references or big data studies, such as those involving cortical morphology growth charts 18 and genome-wide association studies of brain structure across the lifespan 88 . Future efforts should incorporate more neuroimaging cohort studies designed to achieve a balanced representation of diverse ethnic populations 89 . Additionally, it is critical to consider the diversity of environmental factors such as socio-economic status, education level, industrialization, and regional culture, in posing potential challenges for the application of developmental trajectories.…”

Section: Discussionmentioning

confidence: 99%

Functional connectome through the human life span

Sun,

Zhao,

Liang

et al. 2023

Preprint

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The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

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Section: Discussionmentioning

confidence: 99%

Functional connectome through the human life span

Sun,

Zhao,

Liang

et al. 2023

Preprint

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“…Furthermore, it is important to note that in addition to economic factors, the representativeness and diversity of the data sample and their generalizability to subpopulations are also important to consider (Benkarim et al, 2022; Greene et al, 2022; Li et al, 2022; Kopal et al, 2023). Finally, not all studies are interested in cross-sectional relationships between brain and non-brain-imaging phenotypes.…”

Section: Discussionmentioning

confidence: 99%

MRI economics: Balancing sample size and scan duration in brain wide association studies

Ooi,

Orban,

Zhang

et al. 2024

Preprint

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A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size x scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK

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“…With increased attention to equitable distribution of precision medicine and to improve clinical translation and fair outcomes, the neuroimaging community has become aware of the need for improving diversity in participant recruitment efforts. This necessitates the consideration of the variability in factors such as race/ethnicity, sex, and age in brain research, particularly as they pertain to drug development, clinical decision making, and mental health outcomes to ensure adequate representation of different populations and to improve generalizability of research outcomes (Kopal, Uddin, and Bzdok 2023;Li et al 2022). Although incidence rates, prevalence and risks of neurological disorders vary in different racial/ethnic groups, most health research have focused on studying majority white, college-educated populations from North America and European countries (Babulal et al 2019;Haga 2010;Lim et al 2023;Mills and Rahal 2019;Velarde et al 2023).…”

Section: Lack Of Diversity In Publicly Available Datasetsmentioning

confidence: 99%

Creating Diverse and Inclusive Scientific Practices for Research Datasets and Dissemination

Kam,

Badhwar,

Borghesani

et al. 2024

Preprint

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Diversity, equity, and inclusivity (DEI) are important for scientific innovation and progress. This widespread recognition has resulted in numerous initiatives for enhancing DEI in recent years. Although progress has been made to address gender and racial disparities, there remains to be biases that limit the opportunities for historically underrepresented individuals to succeed in academia. As members of the Organization for Human Brain Mapping (OHBM) Diversity and Inclusivity committee (DIC), we identified the most challenging and imminent obstacles towards improving DEI practices in the broader neuroimaging field. These obstacles include the lack of diversity in and accessibility to publicly available datasets, barriers in research dissemination, and/or barriers related to publishing. In order to increase diversity and promote equity and inclusivity in our scientific endeavors, we suggest potential solutions that are practical and actionable to overcome these barriers. We emphasize the importance of the enduring and unwavering commitment required to advance DEI initiatives consistently. By doing so, the OHBM and perhaps other neuroscience communities will strive towards a future that is not only marked by scientific excellence but also characterized by diversity, inclusivity and equitable opportunities for all including historically underrepresented individuals internationally.

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The end game: respecting major sources of population diversity

Cited by 24 publications

References 34 publications

Functional connectome through the human life span

Functional connectome through the human life span

MRI economics: Balancing sample size and scan duration in brain wide association studies

Creating Diverse and Inclusive Scientific Practices for Research Datasets and Dissemination

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