Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

Does fMRI repetition suppression reveal mirror neuron activity in the human brain? Insights from univariate and multivariate analysis

Mirror neurons (MN) have been proposed as the neural substrate for a wide range of clinical, social and cognitive phenomena. Over the last decade, a commonly used tool for investigating MN activity in the human brain has been functional magnetic resonance (f MRI ) repetition suppression (RS ) paradigms. However, the available evidence is mixed, largely owing to inconsistent application of the methodological criteria necessary to infer MN properties. This raises concerns about the degree to which one can infer the presence (or absence) of MN activity from earlier accounts that adopted RS paradigms. We aimed to clarify this issue using a well‐validated f MRI RS paradigm and tested for mirror properties by rigorously applying the widely accepted criteria necessary to demonstrate MN activity using traditional univariate techniques and Multivariate Pattern Analysis (MVPA ). While univariate whole brain analysis in healthy adults showed uni‐modal RS effects within the supplementary motor area, no evidence for cross‐modal RS effects consistent with mirror neuron activity was found. MVPA on the other hand revealed a region along the anterior intraparietal sulcus that met the criteria for MN activity. Taken together, these results clarify disparate evidence from earlier RS studies, highlighting that traditional univariate analysis of RS data may not be sensitive for detecting MN activity when rigorously applying the requisite criteria. In light of these findings, we recommend that short of increasing sample sizes substantially, future studies using RS paradigms to investigate MN s across the human brain consider the use of MVPA

Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images

Abstract Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible