Characterizing brain structures and remodeling after TBI based on information content, diffusion entropy

Abstract

Background: To overcome the limitations of conventional diffusion tensor magnetic resonance imaging resulting from the assumption of a Gaussian diffusion model for characterizing voxels containing multiple axonal orientations, Shannon's entropy was employed to evaluate white matter structure in human brain and in brain remodeling after traumatic brain injury (TBI) in a rat.

Methods: Thirteen healthy subjects were investigated using a Q-ball based DTI data sampling scheme. FA and entropy values were measured in white matter bundles, white matter fiber crossing areas, different gray matter (GM) regions and cerebrospinal fluid (CSF). Axonal densities' from the same regions of interest (ROIs) were evaluated in Bielschowsky and Luxol fast blue stained autopsy (n = 30) brain sections by light microscopy. As a case demonstration, a Wistar rat subjected to TBI and treated with bone marrow stromal cells (MSC) 1 week after TBI was employed to illustrate the superior ability of entropy over FA in detecting reorganized crossing axonal bundles as confirmed by histological analysis with Bielschowsky and Luxol fast blue staining.

Results: Unlike FA, entropy was less affected by axonal orientation and more affected by axonal density. A significant agreement (r = 0.91) was detected between entropy values from in vivo human brain and histologically measured axonal density from post mortum from the same brain structures. The MSC treated TBI rat demonstrated that the entropy approach is superior to FA in detecting axonal remodeling after injury. Compared with FA, entropy detected new axonal remodeling regions with crossing axons, confirmed with immunohistological staining.

Conclusions: Entropy measurement is more effective in distinguishing axonal remodeling after injury, when compared with FA. Entropy is also more sensitive to axonal density than axonal orientation, and thus may provide a more accurate reflection of axonal changes that occur in neurological injury and disease.

Figure 1. Regions of interest for entropy and FA measurements: 1, CSF; 2, cortical gray matter; 3, thalamus; 4, putamen; 5, caudate nucleus; 6, corpus callosum; and 7, frontal white matter.

Figure 2. Comparison between the entropy (A) and the FA map (B) from the same subject.

Gray matter is more visible in the entropy map compared to the FA map.

Figure 3. Entropy vs. FA in CSF, cerebral gray matter (GM), thalamus (TH), putamen (PU), caudate nucleus (CA), FW, and CC.

The details for gray matter, putamen, caudate nucleus are enlarged from the bottom box area. Error bars indicate one standard deviation (N = 10).

Figure 4. Histograms of diffusion attenuation values in CSF (top) shows high probability of occurrence of attenuation values, causing smaller values of entropy.

Gray matter (middle) and white matter (bottom) show more spread of attenuation values, causing larger entropy.

Figure 5. Correlation between entropy and axonal density measured from Bielschowsky and Luxol fast blue staining.

Error bars indicate one standard deviation, and N = 5

Figure 6. Selection of ROIs from frontal white matter (A, FW, top) and corpus callosum (A, CC, bottom), magnified of ROIs from A in FW (B) and CC (C).

The corresponding Bielschowsky and Luxol fast blue staining images in FW (D), and CC (E), respectively, showing axonal density changes in these ROIs.

Figure 7. The FA (A), diffusion entropy (B), q-ball fiber orientation (C, D) maps overlayed onto entropy, and the Bielshowsky and Luxol fast blue immunoreactive staining images (E-H) measured from the fixed animal brain.

The images in F, G are high magnification images from the box areas in image E as indicated by the numbers in the up right corner in the images of E and F.