MR-μScope: a multi-scale and simultaneous MRI-miniaturized microscopy system for elucidating neurovascular coupling
Zhu, X.; Li, R.; Li, H.; Gu, L.; Chen, L.; Xu, J.; Chen, J.; Li, Y.; Rudin, M.; Thompson, G. J.; Zhou, N.; Ren, W.
Show abstract
Blood Oxygen Level Dependent functional MRI (BOLD-fMRI) revolutionized non-invasive brain mapping, yet the physiological origins of its signal, linking microscopic neurovascular activity to macroscopic hemodynamics, remain elusive. To bridge this gap, we developed MR-Scope: a simultaneous and multimodal platform that integrates preclinic high-field MRI (9.4T) and miniaturized fluorescence microscopy (Miniscope). Our system contains an MR-compatible Miniscope with electromagnetic shielding, a customized radiofrequency coil with a central optical window, and an adjustable animal cradle, enabling artifact-free acquisition of whole-brain fMRI alongside microscopic vascular dynamics. Phantom validation confirmed negligible cross-modal interference (fMRI SNR [≥] 15 dB; optical SNR [≥] 26 dB). MR-Scope captured stimulus-evoked microvascular dilation and blood flow velocity changes in the somatosensory cortex concurrent with BOLD signals, revealing vessel-size-dependent neurovascular coupling. MR-Scope provides an effective solution for deciphering multi-scale neurovascular interactions and offers unique potential for advancing research into brain function and disease mechanisms involving vascular pathology.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- MRS-measured Glutamate versus GABA reflects excitatory versus inhibitory neural activities in awake mice 94%
- Coupling between cerebrovascular oscillations and CSF flow fluctuation during wakefulness: An fMRI study 93%
- A novel model to quantify blood transit time in cerebral arteries using ASL-based 4D magnetic resonance angiography with example clinical application in moyamoya disease 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.