Siyuan Gao
Title
Cited by
Cited by
Year
Task-induced brain state manipulation improves prediction of individual traits
AS Greene, S Gao, D Scheinost, RT Constable
Nature communications 9 (1), 1-13, 2018
1482018
Ten simple rules for predictive modeling of individual differences in neuroimaging
D Scheinost, S Noble, C Horien, AS Greene, EMR Lake, M Salehi, S Gao, ...
NeuroImage 193, 35-45, 2019
1032019
Combining multiple connectomes improves predictive modeling of phenotypic measures
S Gao, AS Greene, RT Constable, D Scheinost
Neuroimage 201, 116038, 2019
352019
Rclens: Interactive rare category exploration and identification
H Lin, S Gao, D Gotz, F Du, J He, N Cao
IEEE transactions on visualization and computer graphics 24 (7), 2223-2237, 2017
272017
Adaptively exploring population mobility patterns in flow visualization
F Wang, W Chen, Y Zhao, T Gu, S Gao, H Bao
IEEE Transactions on Intelligent Transportation Systems 18 (8), 2250-2259, 2017
212017
Distributed patterns of functional connectivity predict working memory performance in novel healthy and memory-impaired individuals
EW Avery, K Yoo, MD Rosenberg, AS Greene, S Gao, DL Na, D Scheinost, ...
Journal of cognitive neuroscience 32 (2), 241-255, 2020
192020
How tasks change whole-brain functional organization to reveal brain-phenotype relationships
AS Greene, S Gao, S Noble, D Scheinost, RT Constable
Cell reports 32 (8), 108066, 2020
82020
Braingnn: Interpretable brain graph neural network for fmri analysis
X Li, J Duncan
bioRxiv, 2020
52020
Task integration for connectome-based prediction via canonical correlation analysis
S Gao, AS Greene, RT Constable, D Scheinost
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 87-91, 2018
52018
A mass multivariate edge-wise approach for combining multiple connectomes to improve the detection of group differences
J Dadashkarimi, S Gao, E Yeagle, S Noble, D Scheinost
International Workshop on Connectomics in Neuroimaging, 64-73, 2019
32019
A hitchhiker’s guide to working with large, open-source neuroimaging datasets
C Horien, S Noble, AS Greene, K Lee, DS Barron, S Gao, D O’Connor, ...
Nature human behaviour 5 (2), 185-193, 2021
22021
A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data
S Gao, G Mishne, D Scheinost
International Conference on Information Processing in Medical Imaging, 631-643, 2019
22019
Combining multiple connectomes via canonical correlation analysis improves predictive models
S Gao, AS Greene, RT Constable, D Scheinost
International Conference on Medical Image Computing and Computer-Assisted …, 2018
22018
Poincaré embedding reveals edge-based functional networks of the brain
S Gao, G Mishne, D Scheinost
International Conference on Medical Image Computing and Computer-Assisted …, 2020
12020
Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease
DS Barron, S Gao, J Dadashkarimi, AS Greene, MN Spann, S Noble, ...
bioRxiv, 638825, 2019
12019
Brainhack: developing a culture of open, inclusive, community-driven neuroscience
R Gau, S Noble, K Heuer, K Bottenhorn, IP Bilgin, YF Yang, J Huntenburg, ...
PsyArXiv, 2021
2021
Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders
DS Barron, S Gao, J Dadashkarimi, AS Greene, MN Spann, S Noble, ...
Cerebral Cortex, 2020
2020
Inference of Dynamic Graph Changes for Functional Connectome
D Ji, J Lu, Y Zhang, S Gao, H Zhao
International Conference on Artificial Intelligence and Statistics, 3230-3240, 2020
2020
Predicting BMI From Whole-Brain Functional Connectivity
E Yeagle, J Dadashkarimi, V Duan, A Greene, D Barron, S Gao, ...
Biological Psychiatry 87 (9), S323, 2020
2020
Non-linear manifold learning in fMRI uncovers a low-dimensional space of brain dynamics
S Gao, G Mishne, D Scheinost
bioRxiv, 2020
2020
The system can't perform the operation now. Try again later.
Articles 1–20