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Link Tejavibulya
Link Tejavibulya
PhD student, Yale University
在 yale.edu 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
392021
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
302022
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
262022
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
252022
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
142024
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
62023
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
62022
Leveraging edge-centric networks complements existing network-level inference for functional connectomes
RX Rodriguez, S Noble, L Tejavibulya, D Scheinost
NeuroImage 264, 119742, 2022
52022
The effects of data leakage on neuroimaging predictive models
M Rosenblatt, L Tejavibulya, R Jiang, S Noble, D Scheinost
42023
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
42022
Power and reproducibility in the external validation of brain-phenotype predictions
M Rosenblatt, L Tejavibulya, H Sun, CC Camp, M Khaitova, BD Adkinson, ...
Nature Human Behaviour, 1-16, 2024
22024
Brain-phenotype predictions can survive across diverse real-world data
BD Adkinson, M Rosenblatt, J Dadashkarimi, L Tejavibulya, R Jiang, ...
bioRxiv, 2024
22024
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
22023
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
22023
The effects of data leakage on connectome-based machine learning models
M Rosenblatt, L Tejavibulya, R Jiang, S Noble, D Scheinost
bioRxiv, 2023
12023
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
12022
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
12022
Big data approaches to identifying sex differences in long-term memory
L Tejavibulya, D Scheinost
Cognitive neuroscience 12 (3-4), 185-186, 2021
12021
Edge-centric network control on the human brain structural network
H Sun, M Rosenblatt, J Dadashkarimi, R Rodriguez, L Tejavibulya, ...
Imaging Neuroscience, 2024
2024
Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function
J Ye, L Tejavibulya, W Dai, LM Cope, JE Hardee, MM Heitzeg, ...
bioRxiv, 2024.09. 06.611627, 2024
2024
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