How representative are neuroimaging samples? Large-scale evidence for trait anxiety differences between fMRI and behaviour-only research participants

Charpentier, Caroline J.; Faulkner, Paul; Pool, Eva; Ly, Verena; Tollenaar, Marieke S.; Kluen, Lisa Marieke; Fransen, Aniek; Yamamori, Yumeya; Lally, Níall; Mkrtchian, Anahit; Valton, Vincent; Huys, Quentin J. M.; Sarigiannidis, Ioannis; Morrow, Kelly A.; Krenz, Valentina; Kalbe, Felix; Cremer, Anna Lena; Zerbes, Gundula; Kausche, Franziska Magdalena; Wanke, Nadine; Giarrizzo, Alessio; Pulcu, Erdem; Murphy, Susannah E.; Kaltenboeck, Alexander; Browning, Michael; Paul, Lynn; Cools, Roshan; Roelofs, Karin; Pessoa, Luiz; Harmer, C J; Chase, Henry W.; Grillon, Christian; Schwabe, Lars; Roiser, Jonathan P.; Robinson, Oliver J.; O’Doherty, John P.

doi:10.1093/scan/nsab057

Cited by 37 publications

(21 citation statements)

References 63 publications

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“… 20 Where possible, researchers should incorporate data from the full UK Biobank cohort, seek replication cohorts and acknowledge and adjust for potential healthy bias and restrictions of range. 21 Methods for adjustment include post-stratification, raking, calibration, raking with lasso variable selection, regression for estimating response propensity and Bayesian additive regression trees (BART) for estimating response propensity and raking, where there is evidence BART is most effective. 22 …”

Section: Discussionmentioning

confidence: 99%

Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample

Lyall

Quinn

Anderson

et al. 2022

Brain Communications

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UK Biobank is a prospective cohort study of around half-a-million general population participants, recruited between 2006 and 2010, with baseline studies at recruitment and multiple assessments since. From 2014 to date magnetic resonance imaging (MRI) has been pursued in a participant sub-sample, with the aim to scan around n = 100k. This sub-sample is studied widely and therefore understanding its relative characteristics is important for future reports. We aimed to quantify psychological and physical health in the UK Biobank imaging sub-sample, compared with the rest of the cohort. We used t-tests and chi-squares for continuous/categorical variables respectively, to estimate average differences on a range of cognitive, mental and physical health phenotypes. We contrasted baseline values of participants who attended imaging (vs. had not), and compared their values at the imaging visit vs. baseline values of participants who were not scanned. We also tested the hypothesis that the associations of established risk factors with worse cognition would be underestimated in the (hypothesized) healthier imaging group compared with the full cohort. We tested these interactions using linear regression models. On a range of cognitive, mental health, cardiometabolic, inflammatory and neurological phenotypes we found that the 47,920 participants who were scanned by January 2021 showed consistent statistically significant ‘healthy’ bias compared with the ∼450,000 who were not scanned. These effect sizes were small to moderate based on Cohen’s d/Cramer’s V metrics (range = 0.02 to -0.21 for Townsend, the largest effect size). We found evidence of interaction, where stratified analysis demonstrated that associations of established cognitive risk factors were smaller in the imaging sub-sample compared with the full cohort. Of the ∼100,000 participants who ultimately will undergo MRI assessment within UK Biobank, the first ∼50,000 showed some ‘healthy’ bias on a range of metrics at baseline. Those differences largely remained at the subsequent (first) imaging visit, and we provide evidence that testing associations in the imaging sub-sample alone could lead to potential underestimation of exposure/outcome estimates.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample

Lyall

Quinn

Anderson

et al. 2022

Brain Communications

View full text Add to dashboard Cite

show abstract

“…Where possible, researchers should incorporate data from the full UK Biobank cohort, seek replication cohorts, and acknowledge and adjust for potential healthy bias and restrictions of range 21 .…”

Section: Discussionmentioning

confidence: 99%

Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample.

Lyall¹,

Quinn²,

Lyall³

et al. 2021

Preprint

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Background: UK Biobank is a prospective cohort study of around half-a-million general population participants, recruited between 2006 and 2010, with baseline studies at recruitment and multiple assessments since. From 2014 to date magnetic resonance imaging (MRI) has been pursued in a participant sub-sample, with the aim to scan around n=100k. This sub-sample are studied widely and therefore understanding their relative characteristics is important for future reports. We aimed to quantify psychological and physical health in the UK Biobank imaging sub-sample, compared with the rest of the cohort. Methods: We used t-tests and chi-squares for continuous/categorical variables respectively, to estimate average differences on a range of cognitive, mental and physical health phenotypes. We contrasted baseline values of participants who attended imaging (vs. had not), and compared their values at the imaging visit vs. baseline values of participants who were not scanned. We also tested the hypothesis that the associations of established risk factors with worse cognition would be underestimated in the (hypothesized) healthier imaging group compared with the full cohort. We tested these interactions using linear regression models.Results: On a range of cognitive, mental health, cardiometabolic, inflammatory and neurological phenotypes we found that the 47,920 participants who were scanned by January 2021 showed consistent statistically significant ‘healthy’ bias compared with the ~450,000 who were not scanned. These effect sizes were small to moderate based on Cohen’s d/Cramer’s V metrics. We found evidence of interaction, where stratified analysis demonstrated that associations of established cognitive risk factors were smaller in the imaging sub-sample compared with the full cohort. Conclusion: Of the ~100,000 participants who ultimately will undergo MRI assessment within UK Biobank, the first ~50,000 showed some ‘healthy’ bias on a range of metrics at baseline. Those differences largely remained at the subsequent imaging visit, and we provide evidence that testing associations in the imaging sub-sample alone could lead to potential underestimation of exposure/outcome estimates.

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“…When reviewing the contexts of creation and use within pain research, four distinct but inter-related problems consistently emerge: (1) a lack of representative datasets, (2) a tendency toward scientific reductionism and essentialism, (3) harmful assumptions around scientific objectivity and associated expertise or legitimacy, and (4) the potential for unchecked application or use of findings. As we have seen, large language models and sensor datasets are known to be non-representative; the same is also true for many neuroimaging datasets ( 364 – 367 ). This has implications for downstream contexts of use, as it could introduce bias into models or findings, potentially making future treatment decisions unfair, exacerbating existing pain care disparities, or even resulting in harm or death.…”

Section: Discussionmentioning

confidence: 90%

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Berger

Baria

2022

Front. Pain Res.

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Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today—with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care—requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

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How representative are neuroimaging samples? Large-scale evidence for trait anxiety differences between fMRI and behaviour-only research participants

Cited by 37 publications

References 63 publications

Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample

Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample

Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

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