Posts Tagged Diffusion MRI

[Abstract] Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review

Acquired brain injury (ABI) is associated with a range of cognitive and motor deficits, and poses a significant personal, societal, and economic burden. Rehabilitation programs are available that target motor skills or cognitive functioning. In this review, we summarize the existing evidence that training may enhance structural neuroplasticity in patients with ABI, as assessed using structural magnetic resonance imaging (MRI)–based techniques that probe microstructure or morphology. Twenty-five research articles met key inclusion criteria. Most trials measured relevant outcomes and had treatment benefits that would justify the risk of potential harm. The rehabilitation program included a variety of task-oriented movement exercises (such as facilitation therapy, postural control training), neurorehabilitation techniques (such as constraint-induced movement therapy) or computer-assisted training programs (eg, Cogmed program). The reviewed studies describe regional alterations in white matter architecture and/or gray matter volume with training. Only weak-to-moderate correlations were observed between improved behavioral function and structural changes. While structural MRI is a powerful tool for detection of longitudinal structural changes, specific measures about the underlying biological mechanisms are lacking. Continued work in this field may potentially see structural MRI metrics used as biomarkers to help guide treatment at the individual patient level.

via Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review – Karen Caeyenberghs, Adam Clemente, Phoebe Imms, Gary Egan, Darren R. Hocking, Alexander Leemans, Claudia Metzler-Baddeley, Derek K. Jones, Peter H. Wilson, 2018

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[Abstract+References] Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review

Acquired brain injury (ABI) is associated with a range of cognitive and motor deficits, and poses a significant personal, societal, and economic burden. Rehabilitation programs are available that target motor skills or cognitive functioning. In this review, we summarize the existing evidence that training may enhance structural neuroplasticity in patients with ABI, as assessed using structural magnetic resonance imaging (MRI)–based techniques that probe microstructure or morphology. Twenty-five research articles met key inclusion criteria. Most trials measured relevant outcomes and had treatment benefits that would justify the risk of potential harm. The rehabilitation program included a variety of task-oriented movement exercises (such as facilitation therapy, postural control training), neurorehabilitation techniques (such as constraint-induced movement therapy) or computer-assisted training programs (eg, Cogmed program). The reviewed studies describe regional alterations in white matter architecture and/or gray matter volume with training. Only weak-to-moderate correlations were observed between improved behavioral function and structural changes. While structural MRI is a powerful tool for detection of longitudinal structural changes, specific measures about the underlying biological mechanisms are lacking. Continued work in this field may potentially see structural MRI metrics used as biomarkers to help guide treatment at the individual patient level.

1. O’Rance, L, Fortune, N. Disability in Australia: Acquired Brain Injury. Canberra, AustraliaAustralian Institute of Health and Welfare2007:124Google Scholar
2. Rabinowitz, A, Levin, HS. Cognitive sequale of traumatic brain injury. Psychiatr Clin North Am. 2014;37:111Google ScholarCrossrefMedline
3. Kuhtz-Buschbeck, JP, Hoppe, B, Gölge, M, Dreesmann, M, Damm-Stünitz, U, Ritz, A. Sensorimotor recovery in children after traumatic brain injury: analyses of gait, gross motor, and fine motor skills. Dev Med Child Neurol. 2003;45:821828Google ScholarCrossrefMedline
4. Hayes, JP, Bigler, ED, Verfaellie, M. Traumatic brain injury as a disorder of brain connectivity. J Int Neuropsychol Soc. 2016;22:120137. doi:10.1017/S1355617715000740. Google ScholarCrossrefMedline
5. Drijkoningen, D, Caeyenberghs, K, Vander Linden, C, Van Herpe, K, Duysens, J, Swinnen, SP. Associations between muscle strength asymmetry and impairments in gait and posture in young brain-injured patients. J Neurotrauma. 2015;32:13241332. doi:10.1089/neu.2014.3787. Google ScholarCrossrefMedline
6. Nocentini, U, Bozzali, M, Spanò, B. Exploration of the relationships between regional grey matter atrophy and cognition in multiple sclerosis. Brain Imaging Behav. 2014;8:378386. doi:10.1007/s11682-012-9170-7. Google ScholarCrossrefMedline
7. Hulkower, MB, Poliak, DB, Rosenbaum, SB, Zimmerman, ME, Lipton, ML. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol. 2013;34:20642074. doi:10.3174/ajnr.A3395. Google ScholarCrossrefMedline
8. Caeyenberghs, K, Wenderoth, N, Smits-Engelsman, BC, Sunaert, S, Swinnen, SP. Neural correlates of motor dysfunction in children with traumatic brain injury: exploration of compensatory recruitment patterns. Brain. 2009;132(pt 3):684694Google ScholarCrossrefMedline
9. Chen, H, Epstein, J, Stern, E. Neural plasticity after acquired brain injury: evidence from functional neuroimaging. PM R. 2010;2(12 suppl 2):S306S312. doi:10.1016/j.pmrj.2010.10.006. Google ScholarCrossrefMedline
10. Choo, PL, Gallagher, HL, Morris, J, Pomeroy, VM, van Wijck, F. Correlations between arm motor behavior and brain function following bilateral arm training after stroke: a systematic review. Brain Behav. 2015;5:e00411. doi: 10.1002/brb3.411. Google ScholarCrossrefMedline
11. Matthews, PM, Johansen-Berg, H, Reddy, H. Non-invasive mapping of brain functions and brain recovery: applying lessons from cognitive neuroscience to neurorehabilitation. Restor Neurol Neurosci. 2004;22:245260Google ScholarMedline
12. Prosperini, L, Piattella, MC, Giannì, C, Pantano, P. Functional and structural brain plasticity enhanced by motor and cognitive rehabilitation in multiple sclerosis. Neural Plast. 2015;2015:481574. doi:10.1155/2015/481574. Google ScholarCrossrefMedline
13. Reid, LB, Boyd, RN, Cunnington, R, Rose, SE. Interpreting intervention induced neuroplasticity with fMRI: the case for multimodal imaging strategies. Neural Plast. 2016;2016:2643491. doi:10.1155/2016/2643491.Google ScholarCrossrefMedline
14. Richards, LG, Stewart, KC, Woodbury, ML, Senesac, C, Cauraugh, JH. Movement-dependent stroke recovery: a systematic review and meta-analysis of TMS and fMRI evidence. Neuropsychologia. 2008;46:311Google ScholarCrossrefMedline
15. Mechelli, A, Crinion, JT, Noppeney, U. Neurolinguistics: structural plasticity in the bilingual brain. Nature. 2004;431:757. doi:10.1038/431757a. Google ScholarCrossrefMedline
16. Takeuchi, H, Sekiguchi, A, Taki, Y. Training of working memory impacts structural connectivity. J Neurosci. 2010;30:32973303. doi:10.1523/JNEUROSCI.4611-09.2010. Google ScholarCrossrefMedline
17. van Tulder, M, Furlan, A, Bombardier, C, Bouter, L; Editorial Board of the Cochrane Collaboration Back Review Group. Updated method guidelines for systematic reviews in the cochrane collaboration back review group. Spine (Phila Pa 1976). 2003;28:12901299Google ScholarCrossrefMedline
18. Fritz, NE, Cheek, FM, Nichols-Larsen, DS. Motor-cognitive dual-task training in persons with neurologic disorders: a systematic review. J Neurol Phys Ther. 2015;39:142153. doi:10.1097/NPT.0000000000000090. Google ScholarCrossrefMedline
19. Guzmán, J, Esmail, R, Karjalainen, K, Malmivaara, A, Irvin, E, Bombardier, C. Multidisciplinary rehabilitation for chronic low back pain: systematic review. BMJ. 2001;322:15111516Google ScholarCrossrefMedline
20. Karjalainen, K, Malmivaara, A, van Tulder, M. Multidisciplinary biopsychosocial rehabilitation for subacute low back pain in working-age adults: a systematic review within the framework of the Cochrane Collaboration Back Review Group. Spine (Phila Pa 1976). 2001;26:262269Google ScholarCrossrefMedline
21. Gauthier, LV, Taub, E, Perkins, C, Ortmann, M, Mark, VW, Uswatte, G. Remodeling the brain: plastic structural brain changes produced by different motor therapies after stroke. Stroke. 2008;39:15201525. doi:10.1161/STROKEAHA.107.502229. Google ScholarCrossrefMedline
22. Schlaug, G, Marchina, S, Norton, A. Evidence for plasticity in white-matter tracts of patients with chronic Broca’s aphasia undergoing intense intonation-based speech therapy. Ann N Y Acad Sci. 2009;1169:385394. doi:10.1111/j.1749-6632.2009.04587.x. Google ScholarCrossrefMedline
23. Breier, J, Juranek, J, Papanicolaou, A. Changes in maps of language function and the integrity of the arcuate fasciculus after therapy for chronic aphasia. Neurocase. 2011;17:506517. doi:10.1080/13554794.2010.547505. Google ScholarCrossrefMedline
24. Caria, A, Weber, C, Brötz, D. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology. 2011;48:578582. doi:10.1111/j.1469-8986.2010.01117.x. Google ScholarCrossrefMedline
25. Nordvik, JE, Schanke, AK, Walhovd, K, Fjell, A, Grydeland, H, Landrø, NI. Exploring the relationship between white matter microstructure and working memory functioning following stroke: a single case study of computerized cognitive training. Neurocase. 2012;18:139151. doi:10.1080/13554794.2011.568501.Google ScholarCrossrefMedline
26. Borstad, AL, Bird, T, Choi, S, Goodman, L, Schmalbrock, P, Nichols-Larsen, DS. Sensorimotor training induced neural reorganization after stroke: a case series. J Neurol Phys Ther. 2013;37:2736. doi:10.1097/NPT.0b013e318283de0d. Google ScholarCrossrefMedline
27. Lazaridou, A, Astrakas, L, Mintzopoulos, D. Diffusion tensor and volumetric magnetic resonance Imaging using an MR-compatible hand-induced robotic device suggests training-induced neuroplasticity in patients with chronic stroke. Int J Mol Med. 2013;32:9951000. doi:10.3892/ijmm.2013.1476. Google ScholarCrossrefMedline
28. Särkämö, T, Ripollés, P, Vepsäläinen, H. Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study. Front Hum Neurosci. 2014;8:245. doi:10.3389/fnhum.2014.00245. Google ScholarCrossrefMedline
29. Wan, CY, Zheng, X, Marchina, S, Norton, A, Schlaug, G. Intensive therapy induces contralateral white matter changes in chronic stroke patients with Broca’s aphasia. Brain Lang. 2014;136:17. doi:10.1016/j.bandl.2014.03.011. Google ScholarCrossrefMedline
30. Fan, YT, Lin, KC, Liu, HL, Chen, YL, Wu, CY. Changes in structural integrity are correlated with motor and functional recovery after post-stroke rehabilitation. Restor Neurol Neurosci. 2015;33:835844. doi:10.3233/RNN-150523. Google ScholarCrossrefMedline
31. Young, BM, Stamm, JM, Song, J. Brain-computer interface training and stroke affects patterns of brain-behavior relationships in corticospinal motor fibers. Front Hum Neurosci. 2016;10:457Google ScholarCrossrefMedline
32. Wilkins, KB, Owen, M, Ingo, C, Carmona, C, Dewald, J, Yao, J. Neural plasticity in moderate to severe chronic stroke following a device-assisted task-specific arm/hand intervention Front Neurol. 2017;8:284. doi:10.33389/fneur.2017.00284. Google ScholarCrossrefMedline
33. Yang, HE, Kyeong, S, Lee, SH. Structural and functional improvements due to robot-assisted gait training in the stroke-injured brain. Neurosci Lett. 2017;637:114119Google ScholarCrossrefMedline
34. Ibrahim, I, Tintera, J, Skoch, A. Fractional anisotropy and mean diffusivity in the corpus callosum of patients with multiple sclerosis: the effect of physiotherapy. Neuroradiology. 2011;53:917926. doi:10.1007/s00234-011-0879-6. Google ScholarCrossrefMedline
35. Filippi, M, Riccitelli, G, Mattioli, F. Multiple sclerosis: effects of cognitive rehabilitation on structural and functional MR imaging measures—an explorative study. Radiology. 2012;262:932940. doi:10.1148/radiol.11111299. Google ScholarCrossrefMedline
36. Bonzano, L, Tacchino, A, Brichetto, G. Upper limb motor rehabilitation impacts white matter microstructure in multiple sclerosis. Neuroimage. 2014;90:107116. doi:10.1016/j.neuroimage.2013.12.025. Google ScholarCrossrefMedline
37. Prosperini, L, Fanelli, F, Petsas, N. Multiple sclerosis: changes in microarchitecture of white matter tracts after training with a video game balance board. Radiology. 2014;273:529538. doi:10.1148/radiol.14140168. Google ScholarCrossrefMedline
38. Rasova, K, Prochazkova, M, Tintera, J, Ibrahim, I, Zimova, D, Stetkarova, I. Motor programme activating therapy influences adaptive brain functions in multiple sclerosis: clinical and MRI study. Int J Rehabil Res. 2015;38:4954. doi:10.1097/MRR.0000000000000090. Google ScholarCrossrefMedline
39. Ernst, A, Sourty, M, Roquet, D. Functional and structural cerebral changes in key brain regions after facilitation programme for episodic future thought in relapsing-remitting multiple sclerosis patients. Brain Cogn. 2016;105:3445Google ScholarCrossrefMedline
40. Cruickshank, TM, Thompson, JA, Domínguez, D. The effect of multidisciplinary rehabilitation on brain structure and cognition in Huntington’s disease: an exploratory study. Brain Behav. 2015;5:e00312. doi:10.1002/brb3.312. Google ScholarCrossrefMedline
41. Metzler-Baddeley, C, Cantera, J, Coulthard, E, Rosser, A, Jones, DK, Baddeley, RJ. Improved executive function and callosal white matter microstructure after rhythm exercise in Huntington’s disease. J Huntingtons Dis. 2014;3:273283. doi:10.3233/JHD-140113. Google ScholarCrossrefMedline
42. Sehm, B, Taubert, M, Conde, V. Structural brain plasticity in Parkinson’s disease induced by balance training. Neurobiol Aging. 2014;35:232239. doi:10.1016/j.neurobiolaging.2013.06.021. Google ScholarCrossrefMedline
43. Díez-Cirarda, M, Ojeda, N, Peña, J. Increased brain connectivity and activation after cognitive rehabilitation in Parkinson’s disease: a randomized controlled trial. Brain Imaging Behav. 2017;11:16401651Google ScholarCrossrefMedline
44. Burciu, RG, Fritsche, N, Granert, O. Brain changes associated with postural training in patients with cerebellar degeneration: a voxel-based morphometry study. J Neurosci. 2013;33:45944604. doi:10.1523/JNEUROSCI.3381-12.2013. Google ScholarCrossrefMedline
45. Han, K, Davis, RA, Chapman, SB, Krawczyk, DC. Strategy-based reasoning training modulates cortical thickness and resting-state functional connectivity in adults with chronic traumatic brain injury. Brain Behav. 2017;7:e00687. doi:10.1002/brb3.687. Google ScholarCrossrefMedline
46. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. Hilsdale, NJLawrence Earlbaum1988Google Scholar
47. Borenstein, M, Hedges, LV, Higgins, JPT, Rothstein, HR. Multiple outcomes or time-points within a study. In: Borenstein, M, ed. Introduction to Meta-Analysis. Chichester, EnglandWiley2009:225238Google ScholarCrossref
48. Thomas, C, Baker, CI. Teaching an adult brain new tricks: a critical review of evidence for training-dependent structural plasticity in humans. Neuroimage. 2013;73:225236. doi:10.1016/j.neuroimage.2012.03.069. Google ScholarCrossrefMedline
49. Smith, S, Rao, A, De Stefano, N. Longitudinal and cross-sectional analysis of atrophy in Alzheimer’s disease: cross-validation of BSI, SIENA and SIENAX. Neuroimage. 2007;36:12001206. doi:10.1016/j.neuroimage.2007.04.035. Google ScholarCrossrefMedline
50. Heiervang, E, Behrens, TE, Mackay, CE, Robson, MD, Johansen-Berg, H. Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage. 2006;33:867877. doi:10.1016/j.neuroimage.2006.07.037. Google ScholarCrossrefMedline
51. Wakana, S, Caprihan, A, Panzenboeck, MM. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage. 2007;36:630644. doi:10.1016/j.neuroimage.2007.02.049. Google ScholarCrossrefMedline
52. Scholz, J, Klein, MC, Behrens, TE, Johansen-Berg, H. Training induces changes in white-matter architecture. Nat Neurosci. 2009;12:13701371. doi:10.1038/nn.2412. Google ScholarCrossrefMedline
53. Taubert, M, Draganski, B, Anwander, A. Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J Neurosci. 2010;30:1167011677. doi:10.1523/JNEUROSCI.2567-10.2010. Google ScholarCrossrefMedline
54. Hofstetter, S, Tavor, I, Tzur Moryosef, S, Assaf, Y. Short-term learning induces white matter plasticity in the fornix. J Neurosci. 2013;33:1284411280. doi:10.1523/JNEUROSCI.4520-12.2013. Google ScholarCrossrefMedline
55. Cercignani, M, Bammer, R, Sormani, MP, Fazekas, F, Filippi, M. Inter-sequence and inter-imaging unit variability of diffusion tensor MR imaging histogram-derived metrics of the brain in healthy volunteers. AJNR Am J Neuroradiol. 2003;24:638643Google ScholarMedline
56. Price, R, Axel, L, Morgan, T. Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM Nuclear Magnetic Resonance Task Group No. 1. Med Phys. 1990;17:287295. doi:10.1118/1.596566. Google ScholarCrossrefMedline
57. Bookstein, FL. “Voxel-based morphometry” should not be used with imperfectly registered images. Neuroimage. 2001;14:14541462Google ScholarCrossrefMedline
58. Thompson, WK, Holland, D; Alzheimer’s Disease Neuroimaging Initiative. Bias in tensor based morphometry Stat-ROI measures may result in unrealistic power estimates. Neuroimage. 2011;57:14. doi:10.1016/j.neuroimage.2010.11.092. Google ScholarCrossrefMedline
59. Zatorre, RJ, Fields, RD, Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15:528536. doi:10.1038/nn.3045. Google ScholarCrossrefMedline
60. Eriksson, S, Free, S, Thom, M. Quantitative grey matter histological measures do not correlate with grey matter probability values from in vivo MRI in the temporal lobe. J Neurosci Methods. 2009;181:111118. doi:10.1016/j.jneumeth.2009.05.001. Google ScholarCrossrefMedline
61. Jones, DK, Knösche, TR, Turner, R. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage. 2011;73:239254. doi:10.1016/j.neuroimage.2012.06.081. Google ScholarCrossref
62. Jeurissen, B, Leemans, A, Jones, DK, Tournier, JD, Sijbers, J. Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Hum Brain Mapp. 2011;32:461479. doi:10.1002/hbm.21032. Google ScholarCrossrefMedline
63. Caeyenberghs, K, Metzler-Baddeley, C, Foley, S, Jones, DK. Dynamics of the human structural connectome underlying working memory training. J Neurosci. 2016;36:40564066. doi:10.1523/jneurosci.1973-15.2016. Google ScholarCrossrefMedline
64. Metzler-Baddeley, C, Foley, S, de Santis, S. Dynamics of white matter plasticity underlying working memory training: multimodal evidence from diffusion MRI and relaxometry. J Cogn Neurosci. 2017;29:15091520. doi:10.1162/jocn_a_01127. Google ScholarCrossrefMedline
65. Assaf, Y, Basser, PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27:4858. doi:10.1016/j.neuroimage.2005.03.042. Google ScholarCrossrefMedline
66. Deoni, SC, Rutt, BK, Jones, DK. Investigating exchange and multicomponent relaxation in fully-balanced steady-state free precession imaging. J Magn Reson Imaging. 2008;27:14211429. doi:10.1002/jmri.21079. Google ScholarCrossrefMedline

via Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review – Karen Caeyenberghs, Adam Clemente, Phoebe Imms, Gary Egan, Darren R. Hocking, Alexander Leemans, Claudia Metzler-Baddeley, Derek K. Jones, Peter H. Wilson, 2018

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[ARTICLE] Diffusion MRI and the detection of alterations following traumatic brain injury – Full Text

Abstract

This article provides a review of brain tissue alterations that may be detectable using diffusion magnetic resonance imaging MRI (dMRI) approaches and an overview and perspective on the modern dMRI toolkits for characterizing alterations that follow traumatic brain injury (TBI). Noninvasive imaging is a cornerstone of clinical treatment of TBI and has become increasingly used for preclinical and basic research studies. In particular, quantitative MRI methods have the potential to distinguish and evaluate the complex collection of neurobiological responses to TBI arising from pathology, neuroprotection, and recovery. dMRI provides unique information about the physical environment in tissue and can be used to probe physiological, architectural, and microstructural features. Although well-established approaches such as diffusion tensor imaging are known to be highly sensitive to changes in the tissue environment, more advanced dMRI techniques have been developed that may offer increased specificity or new information for describing abnormalities. These tools are promising, but incompletely understood in the context of TBI. Furthermore, model dependencies and relative limitations may impact the implementation of these approaches and the interpretation of abnormalities in their metrics. The objective of this paper is to present a basic review and comparison across dMRI methods as they pertain to the detection of the most commonly observed tissue and cellular alterations following TBI.

1 INTRODUCTION

Despite the long history of traumatic brain injury (TBI) as a prevalent cause of death and disability in humans, defining the neurobiological underpinnings of damage and recovery following TBI remains a central challenge. The complex collection of physiological, cellular, and molecular changes that follow TBI can appear to be remarkably heterogeneous, but at the same time they are highly organized into coordinated responses such as neurodegeneration, inflammation, and regeneration. The corpus of histological studies spanning a variety of experimental animal models of TBI have provided crucial insights about the pathomechanisms and cellular alterations that accompany posttraumatic tissue change, but considerable work remains to determine the spatiotemporal evolution of abnormalities, interrelationships among different tissue responses, and their impact on health and behavioral outcomes. Noninvasive imaging in animal models has the potential to build on what is known from histology by providing longitudinal and whole-brain information, but for this approach to be successful it is essential to first improve the understanding of how imaging abnormalities correspond to tissue and cellular changes.

Diffusion magnetic resonance imaging (dMRI) methods are particularly promising for the development of imaging markers of TBI pathology because they are sensitive to microscale water displacement as a proxy for tissue environment geometry and provide a range of quantitative scalar metrics across the whole brain. Furthermore, dMRI may be combined with other conventional or advanced magnetic resonance imaging (MRI) methods such as arterial spin labeling, susceptibility-weighted imaging, or a variety of contrast agent MRI approaches to provide complementary and comprehensive outcome measures. Standard dMRI methods and especially diffusion tensor imaging (DTI) have already demonstrated sensitive detection of abnormalities in a number of experimental models of TBI. In the past decade, multiple advanced dMRI approaches have extended beyond the conventional models with the goals of improving the physical description of water diffusion (e.g., by modeling “non-Gaussian” diffusion) or parameterizing dMRI with respect to the expected biological environment (e.g., by modeling cellular compartments and/or fiber geometry). These new tools will be valuable if they are able to improve the sensitivity or specificity of dMRI following TBI; however, we lack a systematic understanding of how dMRI methods differ from one another for detecting and describing tissue alterations.

A number of excellent reviews exist to describe the current understanding of cellular mechanisms of TBI in general (Bramlett & Dietrich, 2015; Pekna & Pekny, 2012) and within particular areas of neurobiology including neurodegeneration (Johnson, Stewart, & Smith, 2013; Stoica & Faden, 2010), inflammation (Burda, Bernstein, & Sofroniew, 2016; Ziebell & Morganti-Kossmann, 2010), and myelin changes (Armstrong, Mierzwa, Marion, & Sullivan, 2016), among others. As well, several existing reviews have been published regarding MRI and DTI to study human TBI (Brody, Mac Donald, & Shimony, 2015; Duhaime et al., 2010; Hulkower, Poliak, Rosenbaum, Zimmerman, & Lipton, 2013), and recently a pertinent overview and summary of advanced dMRI tools and their relevance to clinical outcomes was published (Douglas et al., 2015). The focus of the present review is to combine what is known from work in experimental models of TBI about tissue and cellular alterations that may affect the physical tissue environment with a comparative description of the major methods for dMRI that may be differentially sensitive to TBI-related tissue change alongside several important caveats for their use and interpretation. The first section provides a categorical summary of cellular response to trauma, emphasizing alterations with microstructural, architectural, or neuroanatomical manifestations that may give rise to detectable dMRI abnormalities, including a review of the existing dMRI studies in experimental TBI models. The second section contains a comparative overview of presently available dMRI methods from standard approaches to advanced techniques. The objective of this article is to provide a reference for the current understanding of these topics as well as a perspective to help guide selection of dMRI tools based on particular aspects of TBI questions.

Continue —> Diffusion MRI and the detection of alterations following traumatic brain injury – Hutchinson – 2017 – Journal of Neuroscience Research – Wiley Online Library

Figure 2. Cross-model comparison of scalar maps in the injured brain. A range of tissue and injury-related contrasts may be visually observed in this collage of 16 representative metrics in the same slice from different dMRI models. This cross-model view of scalar maps demonstrates the potential for nonredundant information about regions of injury that may be gleaned from different models. DTI metrics of fractional anisotropy (FA), trace (TR), axial and radial diffusivity (Dax and Drad), directionally encoded color (DEC) map weighted by lattice index, DEC weighted by Westin linear anisotropy (WL) and DEC weighted by Westin planar anisotropy (WP), DKI metrics of mean kurtosis (MK), axial and radial kurtosis (AK and RK) and kurtosis FA (KFA), MAP-MRI metrics of return to the origin, axis, and plane probabilities (RTOP, RTAP, and RTPP), propagator anisotropy (PA) and non-Gaussianity (NG) and NODDI metrics of compartment volume fractions for isotropic free water (Viso), intracellular water (Vic) and intracellular restricted water (Vir), and orientation dispersion index (ODI). Insets of each map show tissue near the injury site where dMRI values are expected to be abnormal.

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