The Relationship between Neurite Density Measured with Confocal Microscopy in a Cleared Mouse Brain and Metrics Obtained from Diffusion Tensor and Diffusion Kurtosis Imaging

Abstract

Purpose:Diffusional kurtosis imaging (DKI) enables sensitive measurement of tissue microstructure by quantifying the non-Gaussian diffusion of water. Although DKI is widely applied in many situations, histological correlation with DKI analysis is lacking. The purpose of this study was to determine the relationship between DKI metrics and neurite density measured using confocal microscopy of a cleared mouse brain.Methods:One thy-1 yellow fluorescent protein 16 mouse was deeply anesthetized and perfusion fixation was performed. The brain was carefully dissected out and whole-brain MRI was performed using a 7T animal MRI system. DKI and diffusion tensor imaging (DTI) data were obtained. After the MRI scan, brain sections were prepared and then cleared using aminoalcohols (CUBIC). Confocal microscopy was performed using a two-photon confocal microscope with a laser. Forty-eight ROIs were set on the caudate putamen, seven ROIs on the anterior commissure, and seven ROIs on the ventral hippocampal commissure on the confocal microscopic image and a corresponding MR image. In each ROI, histological neurite density and the metrics of DKI and DTI were calculated. The correlations between diffusion metrics and neurite density were analyzed using Pearson correlation coefficient analysis.Results:Mean kurtosis (MK) (P = 5.2 × 10−9, r = 0.73) and radial kurtosis (P = 2.3 × 10−9, r = 0.74) strongly correlated with neurite density in the caudate putamen. The correlation between fractional anisotropy (FA) and neurite density was moderate (P = 0.0030, r = 0.42). In the anterior commissure and the ventral hippocampal commissure, neurite density and FA are very strongly correlated (P = 1.3 × 10−5, r = 0.90). MK in these areas were very high value and showed no significant correlation (P = 0.48).Conclusion:DKI accurately reflected neurite density in the area with crossing fibers, potentially allowing evaluation of complex microstructures.

Diffusion kurtosis imaging in mild traumatic brain injury and postconcussional syndrome

“…There are however evidence that gives us reason to believe that DKI is more sensitive to changes in areas of more complex tissue microstructure compared with DTI. Studies supporting this view have demonstrated that MK and RK strongly correlated with neurite density in the caudate putamen in mice, while FA was only found to correlate moderately, indicating that DKI is more sensitive to changes in areas of complex WM microstructure such as areas with crossing fibers (Irie et al, ). Other studies have shown that MK could reflect neuronal shrinkage (Wu & Cheung, ), changes in axonal and myelin density (Fieremans, Jensen, & Helpern, ) and astrogliosis (Zhuo et al, ).…”

Neurite atrophy in dorsal hippocampus of rat indicates incomplete recovery of chronic mild stress induced depression

“…The diffusion‐tensor‐based parameter FA was shown to be highly correlated and validated with histology‐based FA ST with 2D and 3D microscopy data . Similar to the FA, kurtosis metrics are not a priori specific to any tissue microstructure component, and therefore the histological underpinning of these metrics cannot be established, although a correlation between kurtosis and ν has been reported …”

“…58 Similar to the FA, kurtosis metrics are not a priori specific to any tissue microstructure component, and therefore the histological underpinning of these metrics cannot be established, although a correlation between kurtosis and ν has been reported. 59 By combining dMRI and immunohistochemistry data we obtain complementary information towards a better understanding of the microstructural recovery from CMS at eight weeks after exposure. The marked axo-dendritic (H ν ) atrophy in the AM, dHP, and vHP regions may be indicative of hypo-responsivity of the region even after eight weeks of spontaneous recovery.…”

Diffusion Tensor Imaging-Based Studies at the Group-Level Applied to Animal Models of Neurodegenerative Diseases

“…A hypothesis-guided approach is performed when using ROIs that are placed in defined anatomical regions and comparing the average DTI metrics values, that is, rotational invariant parameters of the diffusion tensor, within the respective ROI for the cohorts (e.g., Harsan et al, 2010 ; Müller et al, 2019 ) for quantitative comparisons of in-between ROIs or to show differences between various white matter regions. ROI analysis could be extensively performed by placing an arbitrary number of ROIs (e.g., Irie et al, 2018 ) with variable extension. The advantage of ROI analysis is that (in case of accurate anatomical placement) it can also be performed without any prior stereotaxical normalization; in this case, manual ROI identification can be supported by confocal microscopic image (Irie et al, 2018 ).…”

“…ROI analysis could be extensively performed by placing an arbitrary number of ROIs (e.g., Irie et al, 2018 ) with variable extension. The advantage of ROI analysis is that (in case of accurate anatomical placement) it can also be performed without any prior stereotaxical normalization; in this case, manual ROI identification can be supported by confocal microscopic image (Irie et al, 2018 ).…”