Post-Doc
Radiology

fmokhtar@wakehealth.edu


Dynamic fMRI Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity.
Mokhtari F, Laurienti PJ, Rejeski WJ, Ballard G.
Brain Connect. 2019; 9(1): 95-112. doi: 10.1089/brain.2018.0605
Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.
Mokhtari F, Akhlaghi MI, Simpson SL, Wu G, Laurienti PJ.
Neuroimage. 2019; 189:655-66. doi: 10.1016/j.neuroimage.2019.02.001
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: 421-33. doi: 10.1016/j.neuroimage.2018.02.025
Tensor-based vs. matrix-based rank reduction in dynamic brain connectivity.
Mokhtari F, Mayhugh RE, Hugenschmidt CE, Rejeski WJ, Laurienti PJ.
SPIE dig lib. 2018; 10574. doi: 10.1117/12.2293014

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.