Multivariate BWAS can be replicable with moderate sample sizes

[1]  Timothy O. Laumann,et al.  Multivariate BWAS can be replicable with moderate sample sizes , 2023, Nature.

[2]  B. Yeo,et al.  Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns , 2022, NeuroImage.

[3]  E. Finn,et al.  How to establish robust brain–behavior relationships without thousands of individuals , 2022, Nature Neuroscience.

[4]  Evan M. Gordon,et al.  Brain-behavior correlations: Two paths toward reliability , 2022, Neuron.

[5]  S. Eickhoff,et al.  Linking interindividual variability in brain structure to behaviour , 2022, Nature Reviews Neuroscience.

[6]  Timothy O. Laumann,et al.  Reproducible brain-wide association studies require thousands of individuals , 2022, Nature.

[7]  Alicia R. Martin,et al.  Leveraging fine-mapping and multi-population training data to improve cross-population polygenic risk scores , 2022, Nature Genetics.

[8]  A. Holmes,et al.  Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity , 2022, Science advances.

[9]  J. Haynes,et al.  Performance reserves in brain-imaging-based phenotype prediction , 2022, bioRxiv.

[10]  Aaron F. McDaid,et al.  A Saturated Map of Common Genetic Variants Associated with Human Height from 5.4 Million Individuals of Diverse Ancestries , 2022 .

[11]  Joris Van den Bossche,et al.  Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery , 2021, NeuroImage.

[12]  Luke J. Chang,et al.  Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature , 2021, NeuroImage.

[13]  A. Zalesky,et al.  Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? , 2021, NeuroImage.

[14]  J. Concato,et al.  Bi-Ancestral Depression GWAS in the Million Veteran Program and Meta-Analysis in >1.2 Million Subjects Highlights New Therapeutic Directions , 2021, Nature Neuroscience.

[15]  David B. Leake,et al.  The association between gambling and financial, social and health outcomes in big financial data , 2021, Nature Human Behaviour.

[16]  Danilo Bzdok,et al.  Prediction, Not Association, Paves the Road to Precision Medicine. , 2020, JAMA psychiatry.

[17]  Simon B. Eickhoff,et al.  Meta-matching: a simple framework to translate phenotypic predictive models from big to small data , 2020, bioRxiv.

[18]  Alejandro F Frangi,et al.  The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions , 2020, Nature Communications.

[19]  V. Calhoun,et al.  Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises , 2020, Biological Psychiatry.

[20]  Mark W. Woolrich,et al.  Optimising network modelling methods for fMRI , 2019, NeuroImage.

[21]  Mert R. Sabuncu,et al.  Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics , 2020, NeuroImage.

[22]  Gael Varoquaux,et al.  Establishment of Best Practices for Evidence for Prediction: A Review. , 2019, JAMA psychiatry.

[23]  S. J. Sinclair,et al.  A History of Routine Outcome Measurement in Clinical Practice: A Review of Evidence and Issues , 2019 .

[24]  David C. Funder,et al.  Evaluating Effect Size in Psychological Research: Sense and Nonsense , 2019, Advances in Methods and Practices in Psychological Science.

[25]  Alicia R. Martin,et al.  Clinical use of current polygenic risk scores may exacerbate health disparities , 2019, Nature Genetics.

[26]  Dustin Scheinost,et al.  Ten simple rules for predictive modeling of individual differences in neuroimaging , 2019, NeuroImage.

[27]  Agatha Lenartowicz,et al.  Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis. , 2019, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[28]  Russell A. Poldrack,et al.  The Costs of Reproducibility , 2019, Neuron.

[29]  Peter R. Killeen,et al.  Predict, Control, and Replicate to Understand: How Statistics Can Foster the Fundamental Goals of Science , 2018, Perspectives on Behavior Science.

[30]  Tor D. Wager,et al.  Placebo Effects on the Neurologic Pain Signature: A Meta-analysis of Individual Participant Functional Magnetic Resonance Imaging Data , 2018, JAMA neurology.

[31]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[32]  Gaël Varoquaux,et al.  Cross-validation failure: Small sample sizes lead to large error bars , 2017, NeuroImage.

[33]  Chris Leptak,et al.  What evidence do we need for biomarker qualification? , 2017, Science Translational Medicine.

[34]  Annchen R. Knodt,et al.  The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences , 2017, Behavior Research Methods.

[35]  N. Krieger,et al.  Structural racism and health inequities in the USA: evidence and interventions , 2017, The Lancet.

[36]  Luke J. Chang,et al.  Building better biomarkers: brain models in translational neuroimaging , 2017, Nature Neuroscience.

[37]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[38]  Felix D. Schönbrodt,et al.  At what sample size do correlations stabilize , 2013 .

[39]  Jonathan Flint,et al.  Confidence and precision increase with high statistical power , 2013, Nature Reviews Neuroscience.

[40]  Robert L. McArthur.  Faculty Opinions recommendation of Power failure: why small sample size undermines the reliability of neuroscience. , 2013 .

[41]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[42]  M. Lindquist,et al.  An fMRI-based neurologic signature of physical pain. , 2013, The New England journal of medicine.

[43]  阡陌,et al.  Sense and Nonsense , 2011 .

[44]  J. Ioannidis.  Why Most Discovered True Associations Are Inflated , 2008, Epidemiology.

[45]  L. Eyde,et al.  Psychological testing and psychological assessment. A review of evidence and issues. , 2001, The American psychologist.

[46]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[47]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[48]  C. B. Colby.  The weirdest people in the world , 1973 .