Advanced MRI Techniques and Applications

This paper presents an optimized awake mouse fMRI paradigm that reduces stress and motion artifacts, improving functional connectivity detection.

This paper conducts a meta-analysis of 23Na-MRI studies to estimate human brain sodium concentrations by region and method, finding significant variability and providing pooled mean values.

This study uses 1H-MRS to detect metabolic changes in sheep hypothalami under different photoperiod conditions, linking them to adult neurogenesis.

This paper introduces inductively-coupled ear-bars (ICEs) to enhance fMRI signal in rat entorhinal cortex, achieving a 2-fold SNR increase and enabling better connectivity mapping.

This paper introduces a novel hemodynamic deconvolution algorithm for multi-echo fMRI that improves reliability and provides statistical certainty without requiring prior knowledge of experimental paradigms.

The paper shows that over half of the variance in 1H-MRS metabolite estimates is shared across all metabolites, with age and sex affecting this common variance.

This study introduces a new MRI technique to simultaneously measure perfusion parameters of small blood and lymphatic vessels in the human brain, showing faster signal changes in blood vessels than lymphatic ones.

This study investigates neurovascular coupling in preterm infants using EEG and fMRI, finding a relationship between EEG microstate duration and BOLD signal amplitude.

RABIES provides a standardized image processing and quality control platform for rodent fMRI to improve reproducibility and analysis consistency across different acquisition sites and species.

The paper develops and validates a calibration-based correction model for MTsat maps in postmortem 7T MRI data of chimpanzee brains to address B1+ inhomogeneities.

This paper proposes a semi-supervised deep learning method to correct susceptibility distortion in EPI, showing improved performance and faster processing than existing approaches.

This study uses neural networks to reconstruct CO2 and PETCO2 data from respiration waveforms in resting-state fMRI, achieving high correlation with ground truth data.

This study compares metabolite estimation using different macromolecular modeling strategies and finds that cohort-mean measured MM basis functions yield more robust results than subject-specific or parameterized ones.

This study introduces RIDA to reduce between-scan MRI intensity variations and improves longitudinal volume estimates, showing significant benefits for specific brain regions.

This paper introduces a method to normalize MRI intensity values across scans, improving consistency in volume measurements and spatial overlap between images.

The paper validates a new coil array for dog fMRI, showing improved signal-to-noise ratio compared to human knee coils in structural and functional imaging.

The paper quantifies resting-state BOLD hemodynamic response functions (HRF) in white matter, showing significant differences from gray matter and variation along WM pathways.

This paper introduces an MRI technique to assess iron homeostasis in living human brains, showing it varies with age and disease states.

This study uses MRI to track changes in GABA and glutamate levels in the prefrontal cortex across adolescence, finding increased balance between these neurotransmitters.

This study uses non-invasive MRI to measure choroid plexus perfusion and secretory function in rats, finding that anesthesia affects perfusion but not secretory function.

This study examines how different analysis techniques affect age-related differences in GABA concentrations, finding that tissue composition significantly influences results.

PREPAIR corrects physiological noise in fMRI data using phase and magnitude information, outperforming existing methods without external recordings.

This study compares FIX and CompCor noise-reduction techniques in fMRI datasets, finding that FIX better conserves signal while removing similar amounts of noise during both noxious and non-noxious stimulations.

The paper evaluates how lipid suppression, macromolecular models, and baseline stiffness affect reproducibility of metabolite ratios in human brain MRSI data at 9.4 T.

The paper compares two techniques for measuring glucose metabolism in the human brain using deuterium-labeled compounds and MR spectroscopy, estimating TCA cycle and glutamine synthesis rates.

This study benchmarks three MRI sequences to differentiate feedforward and feedback signals in human V1 cortex, finding SE-BOLD provides the best depth-dependent separation.

This paper measures brain lactate levels during sleep-wake cycles in humans using MRS and PSG, finding significant reductions during sleep stages compared to wakefulness.

This paper develops a deep learning method to enhance resolution and correct distortion in single-shot EPI DWI images using a modified GAN architecture and a fractional anisotropy loss.

ORYX-MRSI is an open-source software for 3D 1H-MRSI data analysis that provides automated processing, visualization, and metabolite mapping.

This study shows that baseline cerebral blood flow predicts multiple-demand network activity and fluid intelligence across the lifespan, with non-neuronal CBF effects confounding BOLD signals.