Zengmin Li

TL;DR: Assessment of nuisance regression methods and the effects of motion correction on a large dataset of both task and resting fMRI of anesthetized rat brain showed that neither detrend, motion correction, motion regression nor CSF signal regression could improve specificity despite increasing temporal signal‐to‐noise ratios.

TL;DR: In this article , a deep learning network based on the generative adversarial net (GAN) was used to automatically correct coil inhomogeneity and extract the brain from both spin- and gradient-echo EPI without user intervention.

TL;DR: Using whole-brain simulations of experimental data, a general framework is provided for interpreting localized intervention effects, offering insights that could refine clinical interventions for focal brain disorders by enabling targeted circuit-level neuromodulation strategies.

TL;DR: The ability of multiband DTI to acquire high‐resolution data of the mouse brain with 84 slices covering the whole brain in 0.2 mm isotropic resolution without a coil array at 9.4 T is demonstrated.