| Brainhack: Developing a culture of open, inclusive, community-driven neuroscience R Gau, S Noble, K Heuer, KL Bottenhorn, IP Bilgin, YF Yang, ... Neuron 109 (11), 1769-1775, 2021 | 34 | 2021 |
| Functional connectome–based predictive modeling in autism C Horien, DL Floris, AS Greene, S Noble, M Rolison, L Tejavibulya, ... Biological psychiatry 92 (8), 626-642, 2022 | 22 | 2022 |
| Large-scale differences in functional organization of left-and right-handed individuals using whole-brain, data-driven analysis of connectivity L Tejavibulya, H Peterson, A Greene, S Gao, M Rolison, S Noble, ... Neuroimage 252, 119040, 2022 | 21 | 2022 |
| Predicting the future of neuroimaging predictive models in mental health L Tejavibulya, M Rolison, S Gao, Q Liang, H Peterson, J Dadashkarimi, ... Molecular psychiatry 27 (8), 3129-3137, 2022 | 20 | 2022 |
| A protocol for working with open-source neuroimaging datasets C Horien, K Lee, ML Westwater, S Noble, L Tejavibulya, T Kayani, ... STAR protocols 3 (1), 101077, 2022 | 7 | 2022 |
| Machine learning and prediction in fetal, infant, and toddler neuroimaging: A review and primer D Scheinost, A Pollatou, AJ Dufford, R Jiang, MC Farruggia, M Rosenblatt, ... Biological psychiatry 93 (10), 893-904, 2023 | 6 | 2023 |
| Predicting transdiagnostic social impairments in childhood using connectome-based predictive modeling AJ Dufford, V Kimble, L Tejavibulya, J Dadashkarimi, K Ibrahim, ... medRxiv, 2022.04. 07.22273518, 2022 | 4 | 2022 |
| Leveraging edge-centric networks complements existing network-level inference for functional connectomes RX Rodriguez, S Noble, L Tejavibulya, D Scheinost NeuroImage 264, 119742, 2022 | 2 | 2022 |
| Data leakage inflates prediction performance in connectome-based machine learning models M Rosenblatt, L Tejavibulya, R Jiang, S Noble, D Scheinost Nature Communications 15 (1), 1829, 2024 | 1 | 2024 |
| Power and reproducibility in the external validation of brain-phenotype predictions M Rosenblatt, L Tejavibulya, CC Camp, R Jiang, ML Westwater, S Noble, ... bioRxiv, 2023 | 1 | 2023 |
| Heightened sensitivity to high-calorie foods in children at risk for obesity: insights from behavior, neuroimaging, and genetics KM Rapuano, L Tejavibulya, EN Dinc, A Li, H Davis, R Korn, RL Leibel, ... Brain imaging and behavior 17 (5), 461-470, 2023 | 1 | 2023 |
| Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available J Dadashkarimi, A Karbasi, Q Liang, M Rosenblatt, S Noble, M Foster, ... Medical Image Analysis 88, 102864, 2023 | 1 | 2023 |
| Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available J Dadashkarimi, A Karbasi, Q Liang, M Rosenblatt, S Noble, M Foster, ... bioRxiv, 2022.07. 19.500642, 2022 | 1 | 2022 |
| Foxa2 and Pet1 Direct and Indirect Synergy Drive Serotonergic Neuronal Differentiation B Aydin, M Sierk, M Moreno-Estelles, L Tejavibulya, N Kumar, N Flames, ... Frontiers in Neuroscience 16, 903881, 2022 | 1 | 2022 |
| Big data approaches to identifying sex differences in long-term memory L Tejavibulya, D Scheinost Cognitive neuroscience 12 (3-4), 185-186, 2021 | 1 | 2021 |
| Brain-handedness associations depend on how and when handedness is measured L Tejavibulya, C Horien, C Fredricks, B Ficek-Tani, ML Westwater, ... bioRxiv, 2024.02. 15.580563, 2024 | | 2024 |
| Brain-phenotype predictions can survive across diverse real-world data BD Adkinson, M Rosenblatt, J Dadashkarimi, L Tejavibulya, R Jiang, ... bioRxiv, 2024.01. 23.576916, 2024 | | 2024 |
| The effects of data leakage on connectome-based machine learning models M Rosenblatt, L Tejavibulya, R Jiang, S Noble, D Scheinost bioRxiv, 2023.06. 09.544383, 2023 | | 2023 |
| The effects of data leakage on neuroimaging predictive models M Rosenblatt, L Tejavibulya, R Jiang, S Noble, D Scheinost | | 2023 |
| Combining task connectomes can emphasize or deemphasize sex differences J Dadashkarimi, L Tejavibulya, S Gao, AS Greene, S Noble, ... | | |
