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Frontiers in veterinary science2020; 7; 382; doi: 10.3389/fvets.2020.00382

Diffusion Tensor Imaging Tractography of White Matter Tracts in the Equine Brain.

Abstract: Tractography, a noninvasive technique tracing brain pathways from diffusion tensor magnetic resonance imaging (DTI) data, is increasingly being used for brain investigation of domestic mammals. In the equine species, such a technique could be useful to improve our knowledge about structural connectivity or to assess structural changes of white matter tracts potentially associated with neurodegenerative diseases. The goals of the present study were to establish the feasibility of DTI tractography in the equine brain and to provide a morphologic description of the most representative tracts in this species. Postmortem DTI and susceptibility-weighted imaging (SWI) of an equine brain were acquired with a 3-T system using a head coil. Association, commissural, and projection fibers, the three fiber groups typically investigated in tractography studies, were successfully reconstructed and overlaid on SWI or fractional anisotropy maps. The fibers derived from DTI correlate well with their description in anatomical textbooks. Our results demonstrate the feasibility of using postmortem DTI data to reconstruct the main white matter tracts of the equine brain. Further DTI acquisitions and corresponding dissections of equine brains will be necessary to validate these findings and create an equine stereotaxic white matter atlas that could be used in future neuroimaging research.
Publication Date: 2020-07-30 PubMed ID: 32850994PubMed Central: PMC7406683DOI: 10.3389/fvets.2020.00382Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research demonstrated the feasibility of using Diffusion Tensor Imaging (DTI) tractography, which is a non-invasive brain imaging technique, to trace and investigate the white matter tracts in the brain of horses. This could potentially be useful to study structural connectivity and changes related to neurodegenerative diseases.

Introduction and Background

  • The study utilizes Diffusion Tensor Imaging (DTI) tractography, a non-invasive technique that uses magnetic resonance imaging to trace pathways in the brain. This technique has been increasingly employed for the study of brain structures in domestic mammals.
  • Specifically, the research aims to explore the application of this technique to the equine species, where it could provide insights into brain structure and connectivity, as well as inform studies on structural changes related to neurodegenerative diseases.

Study Objectives and Methodology

  • The main objectives of the study were to assess the feasibility of using DTI tractography in the equine brain and to provide a morphological description of the most representative tracts in the horse brain structure.
  • To achieve these goals, the research team acquired DTI and susceptibility-weighted imaging (SWI) of a horse brain using a 3-Tesla system with a head coil. The imagery was then overlaid on SWI or fractional anisotropy maps to reconstruct the fiber pathways in the brain.

Results and Conclusions

  • The researchers successfully reconstructed association, commissural, and projection fibers, which are the three fiber groups typically examined in tractography studies. The derived fibers correlated well with descriptions provided in anatomical textbooks, suggesting the validity of the DTI tractography technique.
  • The study hence demonstrated that DTI data could be used effectively to reconstruct the white matter tracts of the equine brain, indicating further potential for the application of this technique in equine neuroimaging research.
  • The researchers proposed that future work would require additional DTI acquisitions and corresponding dissections of horse brains to further validate these findings. The creation of an equine white matter atlas could support future neuroimaging research efforts.

Cite This Article

APA
Boucher S, Arribarat G, Cartiaux B, Lallemand EA, Péran P, Deviers A, Mogicato G. (2020). Diffusion Tensor Imaging Tractography of White Matter Tracts in the Equine Brain. Front Vet Sci, 7, 382. https://doi.org/10.3389/fvets.2020.00382

Publication

ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 7
Pages: 382

Researcher Affiliations

Boucher, Samuel
  • ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France.
Arribarat, Germain
  • ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France.
Cartiaux, Benjamin
  • INSERM UMR1037, Cancer Research Center of Toulouse, Oncopole, Toulouse, France.
Lallemand, Elodie Anne
  • INTHERES, Université de Toulouse, INRA, ENVT, Toulouse, France.
Péran, Patrice
  • ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France.
Deviers, Alexandra
  • ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, ENVT, Toulouse, France.
Mogicato, Giovanni
  • ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, ENVT, Toulouse, France.

References

This article includes 54 references
  1. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review.. J. Mol. Neurosci. (2008) 34:51–61.
    doi: 10.1007/s12031-007-0029-0pubmed: 18157658google scholar: lookup
  2. Catani M, Thiebautdeschotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections.. Cortex (2008) 44:1105–32.
    doi: 10.1016/j.cortex.2008.05.004pubmed: 18619589google scholar: lookup
  3. Moller M, Frandsen J, Andersen G, Gjedde A, Vestergaard-Poulsen P, Ostergaard L. Dynamic changes in corticospinal tracts after stroke detected by fibretracking.. J Neurol Neurosurg Psychiatry (2007) 78:587–92.
    doi: 10.1136/jnnp.2006.100248pmc: PMC2077935pubmed: 17210628google scholar: lookup
  4. Cho S-H, Kim DG, Kim D-S, Kim Y-H, Lee C-H, Jang SH. Motor outcome according to the integrity of the corticospinal tract determined by diffusion tensor tractography in the early stage of corona radiata infarct.. Neurosci Lett (2007) 426:123–7.
    doi: 10.1016/j.neulet.2007.08.049pubmed: 17897782google scholar: lookup
  5. Urbanski M, Thiebaut de Schotten M, Rodrigo S, Oppenheim C, Touzé E, Méder JF. DTI-MR tractography of white matter damage in stroke patients with neglect.. Exp Brain Res (2011) 208:491–505.
    doi: 10.1007/s00221-010-2496-8pubmed: 21113581google scholar: lookup
  6. Cheng C-Y, Hsu C-Y, Huang Y-C, Tsai Y-H, Hsu H-T, Yang W-H. Motor outcome of deep intracerebral haemorrhage in diffusion tensor imaging: comparison of data from different locations along the corticospinal tract.. Neurol. Res. (2015) 37:774–81.
  7. Lee W, Park B, Han K. Classification of diffusion tensor images for the early detection of Alzheimer's disease.. Comput Biol Med (2013) 43:1313–20.
  8. Lee S-H, Coutu J-P, Wilkens P, Yendiki A, Rosas HD, Salat DH. Tract-based analysis of white matter degeneration in Alzheimer's disease.. Neuroscience (2015) 301:79–89.
  9. Tan WQ, Yeoh C-S, Rumpel H, Nadkarni N, Lye W-K, Tan E-K. Deterministic tractography of the nigrostriatal-nigropallidal pathway in Parkinson's disease.. Sci Rep (2015) 5:17283.
    doi: 10.1038/srep17283pmc: PMC4664862pubmed: 26619969google scholar: lookup
  10. Fischer FU, Wolf D, Scheurich A, Fellgiebel A. Altered whole-brain white matter networks in preclinical Alzheimer's disease. NeuroImage Clin. (2015) 8:660–6.
    doi: 10.1016/j.nicl.2015.06.007pmc: PMC4536470pubmed: 26288751google scholar: lookup
  11. Daianu M, Mendez MF, Baboyan VG, Jin Y, Melrose RJ, Jimenez EE. An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer's disease.. Brain Imaging Behav (2016) 10:1038–53.
    doi: 10.1007/s11682-015-9458-5pmc: PMC5167220pubmed: 26515192google scholar: lookup
  12. Berberat J, McNamara J, Remonda L, Bodis S, Rogers S. Diffusion tensor imaging for target volume definition in glioblastoma multiforme.. Strahlenther Onkol (2014) 190:939–43.
    doi: 10.1007/s00066-014-0676-3pubmed: 24823897google scholar: lookup
  13. Gray-Edwards HL, Salibi N, Josephson EM, Hudson JA, Cox NR, Randle AN. High resolution MRI anatomy of the cat brain at 3Tesla.. J Neurosci Methods (2014) 227:10–7.
  14. Stuckenschneider K, Hellige M, Feige K, Gasse H. 3-Tesla magnetic resonance imaging of the equine brain in healthy horses - Potentials and limitations.. Pferdeheilkunde (2014) 30:657–70.
    doi: 10.21836/PEM20140605google scholar: lookup
  15. Schmidt MJ, Knemeyer C, Heinsen H. Neuroanatomy of the equine brain as revealed by high-field (3Tesla) magnetic-resonance-imaging.. PLoS One (2019) 14:e0213814.
  16. Johnson PJ, Janvier V, Luh W-M, FitzMaurice M, Southard T, Barry EF. Equine stereotaxtic population average brain atlas with neuroanatomic correlation.. Front. Neuroanat. (2019) 13:89.
    doi: 10.3389/fnana.2019.00089pmc: PMC6787676pubmed: 31636547google scholar: lookup
  17. Liu X, Tian R, Zuo Z, Zhao H, Wu L, Zhuo Y. A high-resolution MRI brain template for adult Beagle.. Magn Reson Imaging (2020) 68:148–57.
    doi: 10.1016/j.mri.2020.01.003pubmed: 31945416google scholar: lookup
  18. Takahashi E, Dai G, Wang R, Ohki K, Rosen GD, Galaburda AM. Development of cerebral fiber pathways in cats revealed by diffusion spectrum imaging.. NeuroImage (2010) 49:1231–40.
  19. Jacqmot O, Van Thielen B, Fierens Y, Hammond M, Willekens I, Van Schuerbeek P. Diffusion tensor imaging of white matter tracts in the dog brain: diffusion tensor imaging.. Anat Rec (2013) 296:340–9.
    doi: 10.1002/ar.22638pubmed: 23355519google scholar: lookup
  20. Anaya García MS, Hernández Anaya JS, Marrufo Meléndez O, Velázquez Ramírez JL, Palacios Aguiar R. In vivo study of cerebral white matter in the dog using diffusion tensor tractography.. Vet Radiol Ultrasound (2015) 56:188–95.
    doi: 10.1111/vru.12211pmc: PMC4409102pubmed: 25288360google scholar: lookup
  21. Lee W, Lee SD, Park MY, Foley L, Purcell-Estabrook E, Kim H. Functional and diffusion tensor magnetic resonance imaging of the sheep brain.. BMC Vet Res (2015) 11:262.
    doi: 10.1186/s12917-015-0581-8pmc: PMC4606502pubmed: 26467856google scholar: lookup
  22. Robinson JL, Baxi M, Katz JS, Waggoner P, Beyers R, Morrison E. Characterization of structural connectivity of the default mode network in dogs using diffusion tensor imaging.. Sci Rep (2016) 6:36851.
    doi: 10.1038/srep36851pmc: PMC5122865pubmed: 27886204google scholar: lookup
  23. Dai G, Das A, Hayashi E, Chen Q, Takahashi E. Regional variation of white matter development in the cat brain revealed by ex vivo diffusion MR tractography.. Int J Dev Neurosci (2016) 54:32–8.
  24. Jacqmot O, Van Thielen B, Michotte A, Willekens I, Verhelle F, Goossens P. Comparison of several white matter tracts in feline and canine brain by using magnetic resonance diffusion tensor imaging.. Anat Rec (2017) 300:1270–89.
    doi: 10.1002/ar.23579pubmed: 28214332google scholar: lookup
  25. Das A, Takahashi E. Characterization of white matter tracts by diffusion MR tractography in cat and ferret that have similar gyral patterns.. Cereb Cortex (2018) 28:1338–47.
    doi: 10.1093/cercor/bhx048pmc: PMC6059242pubmed: 28334159google scholar: lookup
  26. Pieri V, Trovatelli M, Cadioli M, Zani DD, Brizzola S, Ravasio G. In vivo diffusion tensor magnetic resonance tractography of the sheep brain: an atlas of the ovine white matter fiber bundles.. Front Vet Sci (2019) 6:345.
    doi: 10.3389/fvets.2019.00345pmc: PMC6805705pubmed: 31681805google scholar: lookup
  27. Johnson PJ, Pascalau R, Luh W-M, Raj A, Cerda-Gonzalez S, Barry EF. Stereotaxic diffusion tensor imaging white matter atlas for the in vivo domestic feline brain.. Front Neuroanat (2020) 14:1.
    doi: 10.3389/fnana.2020.00001pmc: PMC7026623pubmed: 32116572google scholar: lookup
  28. Chambers JK, Tokuda T, Uchida K, Ishii R, Tatebe H, Takahashi E. The domestic cat as a natural animal model of Alzheimer's disease.. Acta Neuropathol Commun (2015) 3:78.
    doi: 10.1186/s40478-015-0258-3pmc: PMC4674944pubmed: 26651821google scholar: lookup
  29. Schmidt F, Willems N, Stolzing A. Detection and quantification of A-amyloid, pyroglutamyl AA, and tau in aged canines.. J Neuropathol Exp Neurol (2015) 74:12.
    doi: 10.1097/NEN.0000000000000230pubmed: 26247394google scholar: lookup
  30. Mcfarlane D. Advantages and limitations of the equine disease, pituitary pars intermedia dysfunction as a model of spontaneous dopaminergic neurodegenerative disease.. Ageing Res Rev (2007) 6:54–63.
    doi: 10.1016/j.arr.2007.02.001pubmed: 17374512google scholar: lookup
  31. Chang HT, Rumbeiha WK, Patterson JS, Puschner B, Knight AP. Toxic equine parkinsonism: an immunohistochemical study of 10 horses with nigropallidal encephalomalacia.. Vet Pathol (2012) 49:398–402.
    doi: 10.1177/0300985811406885pubmed: 21527781google scholar: lookup
  32. Bradbury AM, Gurda BL, Casal ML, Ponder KP, Vite CH, Haskins ME. A review of gene therapy in canine and feline models of lysosomal storage disorders.. Human Gene Ther Clin Dev (2015) 26:27–37.
    doi: 10.1089/humc.2015.002pmc: PMC4516914pubmed: 25671613google scholar: lookup
  33. Karageorgos L, Lancaster MJ, Nimmo JS, Hopwood JJ. Gaucher disease in sheep.. J Inherit Metab Dis (2011) 34:209–15.
    doi: 10.1007/s10545-010-9230-3pubmed: 20978939google scholar: lookup
  34. Dickinson PJ, LeCouteur RA, Higgins RJ, Bringas JR, Larson RF, Yamashita Y. Canine spontaneous glioma: a translational model system for convection-enhanced delivery.. Neuro Oncol (2010) 12:928–40.
    doi: 10.1093/neuonc/noq046pmc: PMC2940703pubmed: 20488958google scholar: lookup
  35. Pascalau R, Aldea CC, Padurean VA, Szabo B. Comparative study of the major white matter tracts anatomy in equine, feline and canine brains by use of the fibre dissection technique.. Anat Histol Embryol (2016) 45:373–85.
    doi: 10.1111/ahe.12208pubmed: 26394884google scholar: lookup
  36. Shatil AS, Matsuda KM, Figley CR. A method for whole brain ex vivo magnetic resonance imaging with minimal susceptibility artifacts.. Front. Neurol. (2016) 7:208.
    doi: 10.3389/fneur.2016.00208pmc: PMC5126074pubmed: 27965620google scholar: lookup
  37. Labelle F, Monteil S, Ken S, Audigie F, Gros H, Peran P. Diffusion tensor imaging of white matter tracts in the normal equine brain.. In: Proceedings of the 31st Conference of the European Association of Veterinary Anatomists. Vienna: (2016). 45 p..
  38. Manjón JV, Coupé P, Concha L, Buades A, Collins DL, Robles M. Diffusion weighted image denoising using overcomplete local PCA.. PLoS One (2013) 8:e73021.
  39. Avants BB, Tustison N, Song G. Advanced normalization tools (ANTS).. Insight J. (2009) 2:1–35.
  40. Basser PJ, Mattiello J, Lebihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo.. J Magn Reson B (1994) 103:247–54.
    doi: 10.1006/jmrb.1994.1037pubmed: 8019776google scholar: lookup
  41. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging.. Biophys. J. (1994) 66:259–67.
  42. Yeh F-C, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng W-YI. Deterministic diffusion fiber tracking improved by quantitative anisotropy.. PLoS One (2013) 8:e80713.
  43. Jbabdi S, Johansen-Berg H. Tractography: where do we go from here?. Brain Connect. (2011) 1:169–83.
    doi: 10.1089/brain.2011.0033pmc: PMC3677805pubmed: 22433046google scholar: lookup
  44. Bao Y, Wang Y, Wang W, Wang Y. The superior fronto-occipital fasciculus in the human brain revealed by diffusion spectrum imaging tractography: an anatomical reality or a methodological artifact?. Front Neuroanat. (2017) 11:119.
    doi: 10.3389/fnana.2017.00119pmc: PMC5733543pubmed: 29321729google scholar: lookup
  45. Meola A, Comert A, Yeh F-C, Stefaneanu L, Fernandez-Miranda JC. The controversial existence of the human superior fronto-occipital fasciculus: connectome-based tractographic study with microdissection validation.. Hum Brain Mapp. (2015) 36:4964–71.
    doi: 10.1002/hbm.22990pmc: PMC4715628pubmed: 26435158google scholar: lookup
  46. Schmierer K, Wheeler-Kingshott CA, Tozer DJ, Boulby PA, Parkes HG, Yousry TA. Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation.. Magn Reson Med. (2008) 59:268–77.
    doi: 10.1002/mrm.21487pmc: PMC2241759pubmed: 18228601google scholar: lookup
  47. Rane S, Duong TQ. Comparison of in vivo and ex vivo diffusion tensor imaging in rhesus macaques at short and long diffusion times.. Open Neuroimag J. (2011) 5:172–8.
    doi: 10.2174/1874440001105010172pmc: PMC3258009pubmed: 22253659google scholar: lookup
  48. Guilfoyle DN, Helpern JA, Lim KO. Diffusion tensor imaging in fixed brain tissue at 7.0 T.. NMR Biomed. (2003) 16:77–81.
    doi: 10.1002/nbm.814pubmed: 12730948google scholar: lookup
  49. Sun S-W, Neil JJ, Song S-K. Relative indices of water diffusion anisotropy are equivalent in live and formalin-fixed mouse brains.. Magn Reson Med. (2003) 50:743–8.
    doi: 10.1002/mrm.10605pubmed: 14523960google scholar: lookup
  50. Sun SW, Neil JJ, Liang HF, He YY, Schmidt RE, Hsu CY. Formalin fixation alters water diffusion coefficient magnitude but not anisotropy in infarcted brain.. Magn Reson Med. (2005) 53:1447–51.
    doi: 10.1002/mrm.20488pubmed: 15906292google scholar: lookup
  51. D'Arceuil HE, Westmoreland S, de Crespigny JA. An approach to high resolution diffusion tensor imaging in fixed primate brain.. Neuroimage (2007) 35:553–65.
  52. D'Arceuil H, de Crespigny A. The effects of brain tissue decomposition on diffusion tensor imaging and tractography.. Neuroimage (2007) 36:64–8.
  53. Dyrby TB, Baaré WFC, Alexander DC, Jelsing J, Garde E, Søgaard LV. An ex vivo imaging pipeline for producing high-quality and high-resolution diffusion-weighted imaging datasets.. Hum Brain Mapp. (2011) 32:544–63.
    doi: 10.1002/hbm.21043pmc: PMC6870191pubmed: 20945352google scholar: lookup
  54. Dai J-K, Wang S-X, Shan D, Niu H-C, Lei H. A diffusion tensor imaging atlas of white matter in tree shrew.. Brain Struct Funct. (2017) 222:1733–51.
    doi: 10.1007/s00429-016-1304-zpubmed: 27624528google scholar: lookup

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  2. Williamson JN, Peng RH, Sung J, Rajabtabar Darvish M, Chen X, Ali M, Li S, Yang Y. Neuroengineering approaches assessing structural and functional changes of motor descending pathways in stroke. Prog Biomed Eng (Bristol) 2025 Sep 11;7(4).
    doi: 10.1088/2516-1091/adfeaapubmed: 40845894google scholar: lookup
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    doi: 10.5114/ppn.2025.149952pubmed: 40376283google scholar: lookup
  4. Inglis FM, Taylor PA, Andrews EF, Pascalau R, Voss HU, Glen DR, Johnson PJ. A diffusion tensor imaging white matter atlas of the domestic canine brain. Imaging Neurosci (Camb) 2024 Aug 1;2:1-21.
    doi: 10.1162/imag_a_00276pubmed: 39301427google scholar: lookup
  5. Behroozi M, Graïc JM, Gerussi T. Beyond the surface: how ex-vivo diffusion-weighted imaging reveals large animal brain microstructure and connectivity. Front Neurosci 2024;18:1411982.
    doi: 10.3389/fnins.2024.1411982pubmed: 38988768google scholar: lookup
  6. Mohammadi S, Ghaderi S. Advanced magnetic resonance neuroimaging techniques: feasibility and applications in long or post-COVID-19 syndrome - a review. Ann Med Surg (Lond) 2024 Mar;86(3):1584-1589.
    doi: 10.1097/MS9.0000000000001808pubmed: 38463042google scholar: lookup
  7. Porcu M, Cocco L, Cau R, Suri JS, Mannelli L, Manchia M, Puig J, Qi Y, Saba L. Correlation of Cognitive Reappraisal and the Microstructural Properties of the Forceps Minor: A Deductive Exploratory Diffusion Tensor Imaging Study. Brain Topogr 2024 Jan;37(1):63-74.
    doi: 10.1007/s10548-023-01020-4pubmed: 38062326google scholar: lookup
  8. Cartiaux B, Amara A, Pailloux N, Paumier R, Malek A, Elmehatli K, Kachout S, Bensmida B, Montel C, Arribarat G, Mogicato G. Diffusion tensor imaging tractography in the one-humped camel (Camelus dromedarius) brain. Front Vet Sci 2023;10:1231421.
    doi: 10.3389/fvets.2023.1231421pubmed: 37649566google scholar: lookup
  9. Yen C, Lin CL, Chiang MC. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life (Basel) 2023 Jun 29;13(7).
    doi: 10.3390/life13071472pubmed: 37511847google scholar: lookup
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