Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy Niloufar Fozouni 1,6 , Michael Chopp 1,6 , Siamak P. Nejad-Davarani 1 , Zheng Gang Zhang 1,6 , Norman L. Lehman 3 , Steven Gu 1 , Yuji Ueno 1 , Mei Lu 2 , Guangliang Ding 1 , Lian Li 1 , Jiani Hu 5 , Hassan Bagher-Ebadian 1 , David Hearshen 4 , Quan Jiang 1,6 * 1 Department of Neurology, Henry Ford Hospital, Detroit, Michigan, United States of America, 2 Department of Biostatistics and Research Epidemiology, Henry Ford Hospital, Detroit, Michigan, United States of America, 3 Department of Pathology, Henry Ford Hospital, Detroit, Michigan, United States of America, 4 Department of Radiology, Henry Ford Hospital, Detroit, Michigan, United States of America, 5 MR Center, Harper Hospita, Detroit, Michigan, United States of America, 6 Department of Physics, Oakland University, Rochester, Michigan, United States of America 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. Citation: Fozouni N, Chopp M, Nejad-Davarani SP, Zhang ZG, Lehman NL, et al. (2013) Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy. PLoS ONE 8(10): e76343. doi:10.1371/journal.pone.0076343 Editor: Stefano L. Sensi, University G. D’Annunzio, Italy Received May 17, 2013; Accepted August 23, 2013; Published October 15, 2013 Copyright: ß 2013 Fozouni et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by National Institutes of Health grants RO1 NS64134, RO1 AG037506. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: qjiang1@hfhs.org Introduction Diffusion Tensor Imaging (DTI), developed more than a decade ago [1], has been successfully used for the study of brain anatomy and in clinical neurodiagnostics, the latter especially for disease processes involving the white matter, such as multiple sclerosis (MS) [2,3], amyotrophic lateral sclerosis (ALS) [4], cerebral ischemia [5,6], brain tumors [7,8], and head trauma [9,10,11]. The diffusivity from traditional DTI is derived from a symmetric rank-2, positive tensor[12]. The most important indices that can be derived from DTI are diffusivity, Relative Anisotropy (RA), Fractional Anisotropy (FA), color-coded fiber direction maps and 3-D fiber tractography [13]. Amongst these, FA is the most widely used index for quantitatively characterizing neurodegener- ative conditions, such as aging, Parkinson’s disease, developmental disorders [14], and white matter disease [15,16,17]. Despite its popular application, conventional DTI has short- comings resulting from its two underlying assumptions. First, the use of a ‘single’ diffusion tensor for characterizing a pixel volume, which may contain thousands of tissue components, results in a diffusion tensor representing only an average of these multiple tissue components (compartments). Examples of DTI model failure in analyzing areas of fiber crossing in white matter have been documented [18,19]. For example, areas of white matter containing two (or more) fiber systems passing within the same pixel appear hypo-intense in FA. Conventional DTI thus PLOS ONE | www.plosone.org 1 October 2013 | Volume 8 | Issue 10 | e76343