Systematic misestimation of machine learning performance in neuroimaging studies of depression
出版年份 2021 全文链接
标题
Systematic misestimation of machine learning performance in neuroimaging studies of depression
作者
关键词
-
出版物
NEUROPSYCHOPHARMACOLOGY
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-05-07
DOI
10.1038/s41386-021-01020-7
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Sample Size, Model Robustness, and Classification Accuracy in Diagnostic Multivariate Neuroimaging Analyses
- (2018) Andres H. Neuhaus et al. BIOLOGICAL PSYCHIATRY
- The Marburg-Münster Affective Disorders Cohort Study (MACS): A quality assurance protocol for MR neuroimaging data
- (2018) Christoph Vogelbacher et al. NEUROIMAGE
- Neurobiology of the major psychoses: a translational perspective on brain structure and function—the FOR2107 consortium
- (2018) Tilo Kircher et al. EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE
- Cross-validation failure: Small sample sizes lead to large error bars
- (2017) Gaël Varoquaux NEUROIMAGE
- Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
- (2017) Gaël Varoquaux et al. NEUROIMAGE
- Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
- (2017) Mohammad R. Arbabshirani et al. NEUROIMAGE
- Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies
- (2016) Andre F. Marquand et al. BIOLOGICAL PSYCHIATRY
- Machine Learning and the Profession of Medicine
- (2016) Alison M. Darcy et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Predictive analytics in mental health: applications, guidelines, challenges and perspectives
- (2016) T Hahn et al. MOLECULAR PSYCHIATRY
- Tech giants enter mental health
- (2016) Harris A. Eyre et al. World Psychiatry
- Detecting Neuroimaging Biomarkers for Psychiatric Disorders: Sample Size Matters
- (2016) Hugo G. Schnack et al. Frontiers in Psychiatry
- Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction
- (2015) Meenal J. Patel et al. INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY
- Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy
- (2015) Etienne Combrisson et al. JOURNAL OF NEUROSCIENCE METHODS
- Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience
- (2015) John D.E. Gabrieli et al. NEURON
- NCAN Cross-Disorder Risk Variant Is Associated With Limbic Gray Matter Deficits in Healthy Subjects and Major Depression
- (2015) Udo Dannlowski et al. NEUROPSYCHOPHARMACOLOGY
- Machine learning: Trends, perspectives, and prospects
- (2015) M. I. Jordan et al. SCIENCE
- Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
- (2015) Tjeerd van der Ploeg et al. BMC Medical Research Methodology
- Multimodal imaging of a tescalcin (TESC)-regulating polymorphism (rs7294919)-specific effects on hippocampal gray matter structure
- (2014) U Dannlowski et al. MOLECULAR PSYCHIATRY
- Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder
- (2012) Benson Mwangi et al. BRAIN
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