no code implementations • 28 Nov 2023 • Kaichao Wu, Beth Jelfs, Katrina Neville, Qiang Fang
Notably, when predicting post-stroke recovery status, whole-brain recruitment emerged as a robust and reliable feature, achieving an AUC of 85. 93 Significance: Our study offers a comprehensive depiction of dynamic brain behavior in the post-ischemic-stroke brain, with a focus on longitudinal changes concurrent with functional recovery.
no code implementations • 15 Dec 2022 • Kaichao Wu, Beth Jelfs, Katrina Neville, John Q. Fang
In particular, functional connectivity(FC) analyses with fMRI at rest can be employed to reveal the neural connectivity rationale behind this post-stroke motor function impairment and recovery.
no code implementations • 7 Jan 2021 • Beth Jelfs, Shuai Sun, Kamran Ghorbani, Christopher Gilliam
The focus of this paper is the estimation of a delay between two signals.
no code implementations • 10 May 2016 • Qi She, Xiaoli Wu, Beth Jelfs, Adam S. Charles, Rosa H. M. Chan
Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations.