An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI

Abstract

Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson’s rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The “triple-network” commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.

3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data

“…This study utilizes a recently disseminated CAMRI dataset ( Lee et al, 2021 ), available at https://doi.org/10.18112/openneuro.ds003646.v1.0.0 . The CAMRI dataset consisted of rats aged P80–210 weighing 300–450 g across cohorts of three different rat strains: Sprague-Dawley ( n = 41), Long-Evans ( n = 13), and Wistar ( n = 33); and both sexes: male ( n = 65) and female ( n = 22).…”

“…The whole network is implemented based on Keras ( Chollet, 2015 ) with TensorFlow ( Martín et al, 2016 ) as the backend. We trained and tested the 3D U-Net model for brain extraction performance using a recently released CAMRI rat brain database ( Lee et al, 2021 ), including T2-weighted (T2w) rapid acquisition with relaxation enhancement (RARE) structural MRI (0.2 mm isotropic resolution) and T2*-weighted (T2*w) echo-planar-imaging (EPI) functional MRI (0.4 mm isotropic resolution). The 3D U-Net model was compared with existing rodent brain extraction tools, including RATS, PCNN, SHERM, and 2D U-Net.…”

3D U-Net improves automatic brain extraction for isotropic rat brain MRI data

“…The CAMRI dataset consisted of rats aged P80-210 weighing 300-450 g across cohorts of three different rat strains: Sprague-Dawley (n = 41), Long-Evans (n = 13), and Wistar (n = 33); and both sexes: male (n = 65) and female (n = 22). Detailed information about each cohort can be found in [17]. Each subject contains a T2w RARE and an T2*w EPI, and resolutions were 0.2 mm isotropic and 0.4 mm isotropic, respectively.…”

“…Detailed information about each cohort can be found in (Lee et al, 2021). Each subject contains a T2w RARE and an T2*w EPI, and resolutions were 0.2 mm isotropic and 0.4 mm isotropic, respectively.…”

“…The whole network is implemented based on Keras [15] with TensorFlow [16] as the backend. We trained and tested the 3D U-Net model for brain extraction performance using a recently released CAMRI rat brain database [17], available at https://doi.org/10.18112/openneuro.ds003646.v1.0.0, including T2-weighted (T2w) rapid acquisition with relaxation enhancement (RARE) structural MRI (0.2 mm isotropic resolution) and T2*-weighted (T2*w) echo-planar-imaging (EPI) functional MRI (0.4 mm isotropic resolution). The 3D U-Net model was compared with existing rodent brain extraction tools, including RATS, PCNN, SHERM, and 2D U-Net.…”