ArticlePLoS computational biology2021
Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- Spontaneous brain regional dynamics contribute to generalizable brain-behaviour associations.Nature human behaviour · 2026Article
- Developmental variations in recurrent spatiotemporal brain propagations from childhood to adulthood.Nature communications · 2026Article
- MIND versus MSN: A systematic evaluation of test-retest reliability and age sensitivity for T1-weighted structural similarity networks.Network neuroscience (Cambridge, Mass.) · 2026Article
- Magnetic Resonance Imaging-Guided Neuronavigation for Transcranial Magnetic Stimulation in Mood Disorders: Technical Foundation, Advances, and Emerging Tools.Human brain mapping · 2025Review
- Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.Nature neuroscience · 2025Article
- White Matter Bundle Reconstruction From Single-Shell Diffusion Magnetic Resonance Imaging: Test-Retest Reliability and Predictive Capability Across Orientation Distribution Function Reconstruction Methods.Human brain mapping · 2025Article
- Comparing reliability-based measures of functional connectivity between movie and rest: An ROI-based approach.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Is Pearson's correlation coefficient enough for functional connectivity in fMRI?Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Challenges in measuring individual differences of brain function.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- GAMing the Brain: Investigating the Cross-modal Relationships between Functional Connectivity and Structural Features using Generalized Additive Models.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- When no answer is better than a wrong answer: A causal perspective on batch effects.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- Why experimental variation in neuroimaging should be embraced.Nature communications · 2024Review
- Moving beyond processing- and analysis-related variation in resting-state functional brain imaging.Nature human behaviour · 2024Article
- Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs.Communications biology · 2024Article
- DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.Patterns (New York, N.Y.) · 2024Article
- Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: A multiverse analysis using the monetary incentive delay task.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Test-Retest Reliability of Functional Connectivity in Adolescents With Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- Living on the edge: network neuroscience beyond nodes.Trends in cognitive sciences · 2023Review
- ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences.Nature methods · 2023Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations-such as measurement error-as compared to systematic deviations-such as individual differences-are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual's samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.