Multivariate BWAS can be replicable with moderate sample sizes

[1]  T. Wager,et al.  Multivariate BWAS can be replicable with moderate sample sizes , 2023, Nature.

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

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

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

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

[6]  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 .

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

[8]  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.

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

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

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

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

[13]  Steven E. Petersen,et al.  A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum , 2018, NeuroImage.

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

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

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

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

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

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

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

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

[22]  Christopher. Simons,et al.  Machine learning with Python , 2017 .

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

[24]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

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

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

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

[28]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[29]  Olivier Ledoit,et al.  A well-conditioned estimator for large-dimensional covariance matrices , 2004 .