Editorial: '<i>Safety in numbers'</i>? Big data discovery strategies in neuro‐developmental science – contributions and caveats

Sonuga‐Barke, Edmund

doi:10.1111/jcpp.13723

Cited by 6 publications

(6 citation statements)

References 15 publications

(14 reference statements)

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“…With regard to brain imaging research, a recent study 32 used theoretical data to model trade-offs of increasing sample size well into the thousands, demonstrating the risk of latent bias to outweigh the benefit of reduced variance. This concern bears out in the present real-world analysis, which cautions against equating data quantity and quality in youth sMRI studies 33 . The findings also have important implications for large-scale MRI studies of other populations where head motion occurs more frequently, including those with psychiatric and neurological disorders 34 , and those at the extremes of age 35, 36 .…”

Section: Discussionmentioning

confidence: 94%

Uncovering and mitigating bias in large, automated MRI analyses of brain development

Elyounssi

Kunitoki

Clauss

et al. 2023

Preprint

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Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk1, 2. However, MRI studies of youth are especially susceptible to motion and other artifacts3, 4. These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies,5and can potentially introduce non-random noise into clinical association analyses. Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality images, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study6. Compared to the best-rated images (44.9% of the sample), lower-quality images generally associated with decreased cortical thickness and increased cortical surface area measures (Cohen’s d 0.14-2.84). Variable image quality led to counterintuitive patterns in analyses that associated structural MRI and clinical measures, as inclusion of lower-quality scans altered apparent effect sizes in ways that increased risk for both false positives and negatives. Quality-related biases were partially mitigated by controlling for surface hole number, an automated index of topological complexity that differentiated lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00). However, even among the highest-rated images, subtle topological errors occurred during image preprocessing, and their correction through manual edits significantly and reproducibly changed thickness measurements across much of the cortex (d 0.15-0.92). These findings demonstrate that inadequate QC of youth structural MRI scans can undermine advantages of large sample size to detect meaningful associations.

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Section: Discussionmentioning

confidence: 94%

Uncovering and mitigating bias in large, automated MRI analyses of brain development

Elyounssi

Kunitoki

Clauss

et al. 2023

Preprint

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“…Developing work is considering concepts of marginalization and societal justice more broadly (Petty, Hamilton et al., 2023). There are associated implications for individual well‐being, including an autistic person's sense of belonging (Milton & Sims, 2016; Sonuga‐Barke, 2023).…”

Section: Well‐being May Be Different For Autistic Peoplementioning

confidence: 99%

“…Contemporary theorizing provides the opportunity to refocus the purpose of therapeutic interventions, disentangle the need to reduce problematic “symptoms” alone, and promote thriving (Sonuga‐Barke, 2023; van Os et al., 2019). Our mixed‐methods review had deliberately broad inclusion criteria for psychological interventions, outcomes and study designs.…”

Section: Reconceptualized Autistic Well‐beingmentioning

confidence: 99%

A critical reflection on definitions of autistic well‐being

Deakin,

Petty,

Heasman

et al. 2024

Brain and Behavior

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“…Magnetic resonance imaging (MRI) studies in neonates are crucial for assessing brain development and elucidating mechanisms underlying typical and atypical neurodevelopmental outcomes. However, neurodevelopment is complex and influenced by multifactorial, often interacting neurobiological, (epi)genetic and environmental processes (Sonuga□Barke, 2023). Large, representative samples are required to disentangle these processes; this can only be achieved by using data acquired from multiple sites.…”

Section: Introductionmentioning

confidence: 99%

Harmonizing multisite neonatal diffusion-weighted brain MRI data for developmental neuroscience

Bonthrone,

Blesa Cábez,

Edwards

et al. 2024

Preprint

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Large diffusion-weighted brain MRI (dMRI) studies in neonates are crucial for developmental neuroscience. Our aim was to investigate the utility of ComBat, and empirical Bayes tool for multisite harmonization, for removing site effects from white matter (WM) dMRI measures in healthy infants born 37-42+6 weeks from the Theirworld Edinburgh Birth Cohort (n=86) and Developing Human Connectome Project (n=287). Skeletonized fractional anisotropy (FA), mean, axial and radial diffusivity (MD, AD, RD) maps were harmonized. The differences between voxel-wise metrics, skeleton means and histogram widths (5th-95th percentile) were assessed before and after harmonization, as well as variance associated with gestational age at birth. Before harmonization, large cohort differences were observed in all measures. Harmonization removed all voxel-wise differences from MD maps and all metric means and histogram widths, however small voxel-wise differences (<1.5% of voxels) remained in FA, AD and RD. We detected significant relationships between GA at birth and all metrics. When comparing single site and multi-site harmonized datasets of equal sample sizes, harmonized data resulted in smaller standardized regression coefficients. ComBat will enable unprecedented sample sizes in developmental neuroscience, offering new horizons for biomarker discovery and validation, understanding typical and atypical brain development, and assessment of neuroprotective therapies.

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Editorial: 'Safety in numbers'? Big data discovery strategies in neuro‐developmental science – contributions and caveats

Cited by 6 publications

References 15 publications

Uncovering and mitigating bias in large, automated MRI analyses of brain development

Uncovering and mitigating bias in large, automated MRI analyses of brain development

A critical reflection on definitions of autistic well‐being

Harmonizing multisite neonatal diffusion-weighted brain MRI data for developmental neuroscience

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