Stephanie Noble
Affiliations: | 2019 | Interdepartmental Neuroscience Program | Yale University, New Haven, CT |
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| Rosenblatt M, Tejavibulya L, Jiang R, et al. (2024) Data leakage inflates prediction performance in connectome-based machine learning models. Nature Communications. 15: 1829 |
| Adkinson BD, Rosenblatt M, Dadashkarimi J, et al. (2024) Brain-phenotype predictions can survive across diverse real-world data. Biorxiv : the Preprint Server For Biology |
| Rosenblatt M, Tejavibulya L, Jiang R, et al. (2023) The effects of data leakage on connectome-based machine learning models. Biorxiv : the Preprint Server For Biology |
| Rosenblatt M, Tejavibulya L, Camp CC, et al. (2023) Power and reproducibility in the external validation of brain-phenotype predictions. Biorxiv : the Preprint Server For Biology |
| Camp CC, Noble S, Scheinost D, et al. (2023) Test-retest reliability of functional connectivity in depressed adolescents. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging |
| Sun H, Jiang R, Dai W, et al. (2023) Network controllability of structural connectomes in the neonatal brain. Nature Communications. 14: 5820 |
| Rosenblatt M, Rodriguez RX, Westwater ML, et al. (2023) Connectome-based machine learning models are vulnerable to subtle data manipulations. Patterns (New York, N.Y.). 4: 100756 |
| Dadashkarimi J, Karbasi A, Liang Q, et al. (2023) Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Medical Image Analysis. 88: 102864 |
| Shinn M, Hu A, Turner L, et al. (2023) Functional brain networks reflect spatial and temporal autocorrelation. Nature Neuroscience |
| Jiang R, Noble S, Sui J, et al. (2023) Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet. Digital Health |