Post-Doc
Radiology

fmokhtar@wakehealth.edu


Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.
Mokhtari F, Rejeski WJ, Zhu Y, Wu G, Simpson SL, Burdette JH, Laurienti PJ.
Neuroimage 2018;173:421-433. doi: 10.1016/j.neuroimage.2018.02.025. [Epub ahead of print]
Tensor-based vs. matrix-based rank reduction in dynamic brain connectivity.
Mokhtari F, Mayhugh RE, Hugenschmidt CE, Rejeski WJ, Laurienti PJ.
Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105740Z (2018); doi: 10.1117/12.2293014.
Baseline gray- and white-matter volume predict successful weight loss in the elderly
Mokhtari F, Paolini BM, Burdette JH, Marsh AP, Rejeski WJ, Laurienti PJ.
Obesity (Silver Spring) 2016; doi:10.1002/oby21652 [Epub Ahead of Print]

Functional Brain Networks Prospectively Predict Intentional Weight Loss is Older Adults.
Fatemeh Mokhtari, Jonathan Burdette, Marsh AP, Rejeski WJ, Paul Laurienti.

Using Higher Order Singular Value Decomposition to Reduce the Dimensionality of fMRI Dynamic Connectivity Tensors.
Fatemeh Mokhtari, Zhu Y, Jonathan Burdette, Wu G, Rejeski WJ, Paul Laurienti.

Using fMRI Dynamic Networks in a Hypergraph Learning Model for Predicting the Success of Lifestyle Weight Loss Interventions in Obese Older Adults
Fatemeh Mokhtari, Jonathan Burdette, Marsh AP, Rejeski WJ, Paul Laurienti.

Graph-Based Semi-Supervised Learning Outperforms Supervised Learning Algorithms in a Small fMRI Dataset
Fatemeh Mokhtari, Zhu Y, Jonathan Burdette, Wu G, Rejeski WJ, Paul Laurienti.

Baseline Gray- and White-Matter Volume Predict Successful Weight Loss in the Elderly.
Fatemeh Mokhtari, Paolini BM, Jonathan Burdette, Marsh AP, Rejeski WJ, Paul Laurienti.