Skip to main content
SpringerLink
Log in

Visualising the topography of the acoustic radiation in clinical diffusion tensor imaging scans

  • Functional Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

It has long been thought that the acoustic radiation (AR) white matter fibre tract from the medial geniculate body of the thalamus to the Heschl’s gyrus cannot be reconstructed via single-fibre analysis of clinical diffusion tensor imaging (DTI) scans. A recently developed single-fibre probabilistic method suggests otherwise. The method uses dynamic programming (DP) to compute the most probable paths between two regions of interest. This study aims to observe the ability of single-fibre probabilistic analysis via DP to visualise the AR in clinical DTI scans from legacy pilot cohorts of subjects with normal hearing (NH) and profound hearing loss (HL).

Methods

Single-fibre probabilistic analysis via DP was applied to reconstruct 3D models of the AR in the two cohorts. DTI and T1 data at 1.5 T for subjects with NH (n = 11) and HL (n = 5), as well as 3 T for NH (n = 1) and HL (n = 1), were used.

Results

The topographical features of AR previously observed in post-mortem and multi-fibre analyses can be visualised in DTI scans of 16 subjects and 2 atlases with a success rate of 100%. Relative to MNI coordinates, there was no significant difference in the varifold distances between the topography of the tracts in the 1.5 T cohort.

Conclusion

The AR can be visualised in clinical 1.5 T and 3 T DTI scans using single-fibre probabilistic analysis via DP, hence, the potential for DP to visualise the AR in medical and pre-surgical applications in pathologies such as vestibular schwannoma, multiple sclerosis, thalamic tumours and stroke as well as hearing loss.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1a
Fig. 1b
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

AR:

Acoustic radiation

DP:

Dynamic programming

FA:

Fractional anisotropy

HL:

Hearing loss

HG:

Heschl’s gyrus

LSL:

Listening and spoken Language

MGB:

Medial geniculate body

NH:

Normal hearing

PTA:

Average pure tone audiogram

ROI:

Region of interest

WM:

White matter

References

  1. Johansen-Berg H, Behrens TE (2006) Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Curr Opin Neurol 19(4):379–385. https://doi.org/10.1097/01.wco.0000236618.82086.01

    Article  PubMed  PubMed Central  Google Scholar 

  2. Qiu A, Mori S, Miller MI (2015) Diffusion tensor imaging for understanding brain development in early life. Annu Rev Psychol 66:853–876. https://doi.org/10.1146/annurev-psych-010814-015340

    Article  PubMed  PubMed Central  Google Scholar 

  3. Maffei C, Jovicich J, De Benedictis A, Corsini F, Barbareschi M, Chioffi F, Sarubbo S (2018) Topography of the human acoustic radiation as revealed by ex vivo fibers micro-dissection and in vivo diffusion-based tractography. Brain Struct Funct 223(1):449–459. https://doi.org/10.1007/s00429-017-1471-6

    Article  PubMed  Google Scholar 

  4. Maffei C, Sarubbo S, Jovicich J (2019) A missing connection: a review of the macrostructural anatomy and tractography of the acoustic radiation. Front Neuroanat 13:27. https://doi.org/10.3389/fnana.2019.00027

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zoellner S, Benner J, Zeidler B, Seither-Preisler A, Christiner M, Seitz A, Goebel R, Heinecke A, Wengenroth M, Blatow M, Schneider P (2018) Reduced cortical thickness in Heschl’s gyrus as an in vivo marker for human primary auditory cortex. Hum Brain Mapp 40:1139–1154. https://doi.org/10.1002/hbm.24434

    Article  PubMed  PubMed Central  Google Scholar 

  6. Rademacher J, Bürgel U, Zilles K (2002) Stereotaxic localization, intersubject variability, and interhemispheric differences of the human auditory thalamocortical system. Neuroimage 17(1):142–160

    Article  CAS  Google Scholar 

  7. Simon M, Campbell E, Genest F, MacLean MW, Champoux F, Lepore F (2020) The impact of early deafness on brain plasticity: a systematic review of the white and gray matter changes. Front Neurosci 14:206. https://doi.org/10.3389/fnins.2020.00206

    Article  PubMed  PubMed Central  Google Scholar 

  8. Oishi K, Faria A, van Zijl PCM, Mori S (2011) MRI atlas of human white matter. Elsevier

  9. Stejskal EO, Tanner JE (1965) Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 42(1):288+. https://doi.org/10.1063/1.1695690

    Article  CAS  Google Scholar 

  10. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1):144–155. https://doi.org/10.1016/j.neuroimage.2006.09.018

    Article  CAS  PubMed  Google Scholar 

  11. Basser PJ, Mattiello J, LeBihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103(3):247–254

    Article  CAS  Google Scholar 

  12. Berman JI, Lanza MR, Blaskey L, Edgar JC, Roberts TP (2013) High angular resolution diffusion imaging probabilistic tractography of the auditory radiation. AJNR Am J Neuroradiol 34(8):1573–1578. https://doi.org/10.3174/ajnr.A3471

  13. Crippa A, Lanting CP, van Dijk P, Roerdink JB (2010) A diffusion tensor imaging study on the auditory system and tinnitus. Open Neuroimaging J 4:16–25. https://doi.org/10.2174/1874440001004010016

    Article  Google Scholar 

  14. Javad F, Warren JD, Micallef C, Thornton JS, Golay X, Yousry T, Mancini L (2014) Auditory tracts identified with combined fMRI and diffusion tractography. Neuroimage 84:562–574. https://doi.org/10.1016/j.neuroimage.2013.09.007

    Article  PubMed  PubMed Central  Google Scholar 

  15. Profant O, Skoch A, Balogova Z, Tintera J, Hlinka J, Syka J (2014) Diffusion tensor imaging and MR morphometry of the central auditory pathway and auditory cortex in aging. Neuroscience 260:87–97. https://doi.org/10.1016/j.neuroscience.2013.12.010

    Article  CAS  PubMed  Google Scholar 

  16. Rueckriegel SM, Homola GA, Hummel M, Willner N, Ernestus RI, Matthies C (2016) Probabilistic fiber-tracking reveals degeneration of the contralateral auditory pathway in patients with vestibular Schwannoma. AJNR Am J Neuroradiol 37(9):1610–1616. https://doi.org/10.3174/ajnr.A4833

  17. Yeh FC, Panesar S, Fernandes D, Meola A, Yoshino M, Fernandez-Miranda JC, Vettel JM, Verstynen T (2018) Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 178:57–68. https://doi.org/10.1016/j.neuroimage.2018.05.027

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zheng X, Zhang J, Dong L, Li F, Sun G, Zhao Y, Liu Y, Xu B (2019) A preliminary investigation report on using probabilistic fiber tractography to track human auditory pathways. World Neurosurg 130:e1–e8. https://doi.org/10.1016/j.wneu.2019.03.066

    Article  PubMed  Google Scholar 

  19. Faria AV, Ratnanather JT, Tward DJ, Lee DS, van den Noort F, Wu D, Brown T, Johnson H, Paulsen JS, Ross CA, Younes L, Miller MI, Predict-HD investigators and coordinators of the Huntington study group (2016) Linking white matter and deep gray matter alterations in premanifest Huntington disease. Neuroimage Clin 11:450–460. https://doi.org/10.1016/j.nicl.2016.02.014

    Article  PubMed  PubMed Central  Google Scholar 

  20. Li M, Ratnanather JT, Miller MI, Mori S (2014) Knowledge-based automated reconstruction of human brain white matter tracts using a path-finding approach with dynamic programming. Neuroimage 88:271–281. https://doi.org/10.1016/j.neuroimage.2013.10.011

    Article  PubMed  Google Scholar 

  21. Ratnanather JT, Lal RM, An M, Poynton CB, Li M, Jiang H, Oishi K, Selemon LD, Mori S, Miller MI (2013) Cortico-cortical, cortico-striatal, and cortico-thalamic white matter fiber tracts generated in the macaque brain via dynamic programming. Brain Connect 3(5):475–490. https://doi.org/10.1089/brain.2013.0143

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tang X, Ross CA, Johnson H, Paulsen JS, Younes L, Albin RL, Ratnanather JT, Miller MI (2018) Regional subcortical shape analysis in premanifest Huntington's disease. Hum Brain Mapp. https://doi.org/10.1002/hbm.24456

  23. Iturria-Medina Y, Canales-Rodriguez EJ, Melie-Garcia L, Valdes-Hernandez PA, Martinez-Montes E, Aleman-Gomez Y, Sanchez-Bornot JM (2007) Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. NeuroImage 36(3):645–660. https://doi.org/10.1016/j.neuroimage.2007.02.012

    Article  CAS  PubMed  Google Scholar 

  24. Merhof D, Enders F, Hastreiter P, Ganslandt O, Fahlbusch R, Nimsky C (2006) Stamminger M Neuronal fiber connections based on A*-pathfinding. In: Manduca A, Amini AA (eds) Medical Imaging 2006: Physiology, function, and structure from medical images. SPIE, San Diego, pp 61431S–661438S

  25. Merhof D, Richter M, Enders F, Hastreiter P, Ganslandt O, Buchfelder M, Nimsky C, Greiner G (2006) Fast and accurate connectivity analysis between functional regions based on DT-MRI. LNCS 4191:225–233

    Google Scholar 

  26. Zalesky A (2008) DT-MRI fiber tracking: a shortest paths approach. IEEE Trans Med Imaging 27(10):1458–1471. https://doi.org/10.1109/TMI.2008.923644

    Article  PubMed  Google Scholar 

  27. Oishi K, Zilles K, Amunts K, Faria A, Jiang H, Li X, Akhter K, Hua K, Woods R, Toga AW, Pike GB, Rosa-Neto P, Evans A, Zhang J, Huang H, Miller MI, van Zijl PC, Mazziotta J, Mori S (2008) Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage 43(3):447–457. https://doi.org/10.1016/j.neuroimage.2008.07.009

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA, Pekar JJ, van Zijl PC, Mori S (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39(1):336–347. https://doi.org/10.1016/j.neuroimage.2007.07.053

    Article  PubMed  Google Scholar 

  29. Huang H, Zhang J, Jiang H, Wakana S, Poetscher L, Miller MI, van Zijl PC, Hillis AE, Wytik R, Mori S (2005) DTI tractography based parcellation of white matter: application to the mid-sagittal morphology of corpus callosum. Neuroimage 26(1):195–205. https://doi.org/10.1016/j.neuroimage.2005.01.019

    Article  PubMed  Google Scholar 

  30. Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S (2007) Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36(3):630–644. https://doi.org/10.1016/j.neuroimage.2007.02.049

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ratnanather JT, Arguillère S, Kutten KS, Hubka P, Kral A, Younes L (2020) 3D Normal coordinate systems for cortical areas. In: Kushnarev S, Qiu A, Younes L (eds) Mathematics of shapes and applications. World Scientific, Singapore, pp 167–179. https://doi.org/10.1142/9789811200137_0007

    Chapter  Google Scholar 

  32. Ratnanather JT (2019) Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss-scientific and clinical challenges. Wiley Interdiscip Rev Syst Biol Med 12:e1469. https://doi.org/10.1002/wsbm.1469

    Article  PubMed  Google Scholar 

  33. Lim IA, Faria AV, Li X, Hsu JT, Airan RD, Mori S, van Zijl PC (2013) Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures. Neuroimage 82:449–469. https://doi.org/10.1016/j.neuroimage.2013.05.127

    Article  PubMed  PubMed Central  Google Scholar 

  34. Jiang H, van Zijl PC, Kim J, Pearlson GD, Mori S (2006) DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Prog Biomed 81(2):106–116. https://doi.org/10.1016/j.cmpb.2005.08.004

    Article  Google Scholar 

  35. Mori S, Zhang J (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51(5):527–539. https://doi.org/10.1016/j.neuron.2006.08.012

    Article  CAS  PubMed  Google Scholar 

  36. Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2001) Nonrigid multimodality image registration. Medical Imaging: 2001: Image Processing Pts 1-3 4322:1609–1620

    Article  Google Scholar 

  37. Ratnanather JT, Barta PE, Honeycutt NA, Lee N, Morris HM, Dziorny AC, Hurdal MK, Pearlson GD, Miller MI (2003) Dynamic programming generation of boundaries of local coordinatized submanifolds in the neocortex: application to the planum temporale. Neuroimage 20(1):359–377

    Article  CAS  Google Scholar 

  38. Bürgel U, Amunts K, Hoemke L, Mohlberg H, Gilsbach JM, Zilles K (2006) White matter fiber tracts of the human brain: three-dimensional mapping at microscopic resolution, topography and intersubject variability. Neuroimage 29(4):1092–1105. https://doi.org/10.1016/j.neuroimage.2005.08.040

    Article  PubMed  Google Scholar 

  39. Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PC, Mori S (2004) Fiber tract-based atlas of human white matter anatomy. Radiology 230(1):77–87. https://doi.org/10.1148/radiol.2301021640

    Article  PubMed  Google Scholar 

  40. Charon N, Trouvé A (2013) The varifold representation of nonoriented shapes for diffeomorphic registration. SIAM J Imaging Sci 6(4):2547–2580. https://doi.org/10.1137/130918885

    Article  Google Scholar 

  41. Younes L (2019) Shapes and diffeomorphisms. Appl Math Sci, vol 171. Springer, Heidelberg ; New York

  42. Cox TF, Cox MAA (2001) Multidimensional scaling. Monographs on statistics and applied probability, vol 88, 2nd edn. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  43. Li M, Faria AV, Oishi K, Mori S, Ratnanather JT Automated fibertracking of the acoustic radiation tract in normal and hearing-impaired adults. In: Midwinter Meeting of the Association for Research in Otolaryngology, Baltimore, MD, 2013

  44. Fernández L, Velásquez C, García Porrero JA, Marco de Lucas E, Martino J. (2020) Heschl’s gyrus fiber intersection area: a new insight on the connectivity of the auditory-language hub. Neurosurg Focus 48 (2):E7. https://doi.org/10.3171/2019.11.FOCUS19778

  45. ten Donkelaar HJ, Kaga K (2011) The auditory system. In: ten Donkelaar HJ (ed) Clinical neuroanatomy: brain circuitry and its disorders. Springer-Verlag, Berlin Heidelberg, pp 305–329

    Chapter  Google Scholar 

  46. Keifer OP Jr, Gutman DA, Hecht EE, Keilholz SD, Ressler KJ (2015) A comparative analysis of mouse and human medial geniculate nucleus connectivity: a DTI and anterograde tracing study. Neuroimage 105:53–66. https://doi.org/10.1016/j.neuroimage.2014.10.047

    Article  PubMed  Google Scholar 

  47. Maffei C, Sarubbo S, Jovicich J (2019) Diffusion-based tractography atlas of the human acoustic radiation. Sci Rep 9(1):4046. https://doi.org/10.1038/s41598-019-40666-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Raine C (2013) Cochlear implants in the United Kingdom: awareness and utilization. Cochlear Implants Int 14(Suppl 1):S32–S37. https://doi.org/10.1179/1467010013Z.00000000077

    Article  PubMed  PubMed Central  Google Scholar 

  49. Cardin V, Orfanidou E, Ronnberg J, Capek CM, Rudner M, Woll B (2013) Dissociating cognitive and sensory neural plasticity in human superior temporal cortex. Nat Commun 4. https://doi.org/10.1038/ncomms2463

  50. Olulade OA, Koo DS, LaSasso CJ, Eden GF (2014) Neuroanatomical profiles of deafness in the context of native language experience. J Neurosci 34(16):5613–5620. https://doi.org/10.1523/Jneurosci.3700-13.2014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Estabrooks W, MacIver-Lux K, Rhoades EA (2016) Auditory-verbal therapy for young children with hearing loss and their families, and the practitioners who guide them. Plural Publishing, Inc., San Diego

    Google Scholar 

  52. Yoshinaga-Itano C, Sedey AL, Coulter DK, Mehl AL (1998) Language of early- and later-identified children with hearing loss. Pediatrics 102(5):1161–1171. https://doi.org/10.1542/peds.102.5.1161

    Article  CAS  PubMed  Google Scholar 

  53. Estabrooks W, McCaffrey Morrison H, McIver-Lux K (2020) Auditory-verbal therapy: science. Research and Practice. Plural Publishing, Inc., San Diego

    Google Scholar 

  54. Takesian AE, Kotak VC, Sharma N, Sanes DH (2013) Hearing loss differentially affects thalamic drive to two cortical interneuron subtypes. J Neurophysiol 110(4):999–1008. https://doi.org/10.1152/jn.00182.2013

    Article  PubMed  PubMed Central  Google Scholar 

  55. Hughes EG, Orthmann-Murphy JL, Langseth AJ, Bergles DE (2018) Myelin remodeling through experience-dependent oligodendrogenesis in the adult somatosensory cortex. Nat Neurosci 21(5):696–706. https://doi.org/10.1038/s41593-018-0121-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Romeo RR, Segaran J, Leonard JA, Robinson ST, West MR, Mackey AP, Yendiki A, Rowe ML, Gabrieli JDE (2018) Language exposure relates to structural neural connectivity in childhood. J Neurosci 38(36):7870–7877. https://doi.org/10.1523/JNEUROSCI.0484-18.2018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Sharma A, Martin K, Roland P, Bauer P, Sweeney MH, Gilley P, Dorman M (2005) P1 latency as a biomarker for central auditory development in children with hearing impairment. J Am Acad Audiol 16(8):564–573

    Article  Google Scholar 

  58. Shiell MM, Champoux F, Zatorre RJ (2015) Reorganization of auditory cortex in early-deaf people: functional connectivity and relationship to hearing aid use. J Cogn Neurosci 27(1):150–163. https://doi.org/10.1162/jocn_a_00683

    Article  PubMed  Google Scholar 

  59. Soares JM, Marques P, Alves V, Sousa N (2013) A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci 7:31. https://doi.org/10.3389/fnins.2013.00031

    Article  PubMed  PubMed Central  Google Scholar 

  60. Chesters J, Baghai-Ravary L, Mottonen R (2015) The effects of delayed auditory and visual feedback on speech production. J Acoust Soc Am 137(2):873–883. https://doi.org/10.1121/1.4906266

    Article  PubMed  PubMed Central  Google Scholar 

  61. Jang SH, Bae CH, Seo JP (2018) Injury of auditory radiation and sensorineural hearing loss from mild traumatic brain injury. Brain Inj 33:1–4. https://doi.org/10.1080/02699052.2018.1539243

    Article  CAS  Google Scholar 

  62. Chang Y, Lee SH, Lee YJ, Hwang MJ, Bae SJ, Kim MN, Lee J, Woo S, Lee H, Kang DS (2004) Auditory neural pathway evaluation on sensorineural hearing loss using diffusion tensor imaging. Neuroreport 15(11):1699–1703

    Article  Google Scholar 

  63. Lin Y, Wang J, Wu C, Wai Y, Yu J, Ng S (2008) Diffusion tensor imaging of the auditory pathway in sensorineural hearing loss: changes in radial diffusivity and diffusion anisotropy. J Magn Reson Imaging 28(3):598–603. https://doi.org/10.1002/jmri.21464

    Article  PubMed  Google Scholar 

  64. Zheng W, Wu C, Huang L, Wu R (2017) Diffusion kurtosis imaging of microstructural alterations in the brains of paediatric patients with congenital sensorineural hearing loss. Sci Rep 7(1):1543. https://doi.org/10.1038/s41598-017-01263-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Huang L, Zheng W, Wu C, Wei X, Wu X, Wang Y, Zheng H (2015) Diffusion tensor imaging of the auditory neural pathway for clinical outcome of cochlear implantation in pediatric congenital sensorineural hearing loss patients. PLoS One 10(10):e0140643. https://doi.org/10.1371/journal.pone.0140643

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wang H, Liang Y, Fan W, Zhou X, Huang M, Shi G, Yu H, Shen G (2019) DTI study on rehabilitation of the congenital deafness auditory pathway and speech center by cochlear implantation. Eur Arch Otorhinolaryngol 276(9):2411–2417. https://doi.org/10.1007/s00405-019-05477-7

    Article  PubMed  PubMed Central  Google Scholar 

  67. Kurtcan S, Hatiboglu MA, Alkan A, Toprak H, Seyithanoglu MH, Aralasmak A, Atasoy B, Uysal O (2018) Evaluation of auditory pathways using DTI in patients treated with gamma knife radiosurgery for acoustic neuroma: a preliminary report. Clin Neuroradiol 28(3):377–383. https://doi.org/10.1007/s00062-017-0572-1

    Article  PubMed  Google Scholar 

  68. Lutz J, Hemminger F, Stahl R, Dietrich O, Hempel M, Reiser M, Jager L (2007) Evidence of subcortical and cortical aging of the acoustic pathway: a diffusion tensor imaging (DTI) study. Acad Radiol 14(6):692–700. https://doi.org/10.1016/j.acra.2007.02.014

    Article  PubMed  Google Scholar 

  69. Emmorey K, Allen JS, Bruss J, Schenker N, Damasio H (2003) A morphometric analysis of auditory brain regions in congenitally deaf adults. Proc Natl Acad Sci U S A 100(17):10049–10054. https://doi.org/10.1073/pnas.1730169100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Penhune VB, Cismaru R, Dorsaint-Pierre R, Petitto LA, Zatorre RJ (2003) The morphometry of auditory cortex in the congenitally deaf measured using MRI. Neuroimage 20(2):1215–1225. https://doi.org/10.1016/S1053-8119(03)00373-2

    Article  PubMed  Google Scholar 

  71. Shibata DK (2007) Differences in brain structure in deaf persons on MR imaging studied with voxel-based morphometry. AJNR Am J Neuroradiol 28(2):243–249

    CAS  PubMed  Google Scholar 

  72. Huttenlocher PR, Dabholkar AS (1997) Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 387(2):167–178

    Article  CAS  Google Scholar 

  73. Maffei C (2017) Finding the missing connection: diffusion-based tractography reconstruction of the acoustic radiation and other applications. PhD. thesis, University of Trento

Download references

Acknowledgements

The 1.5 T and 3 T scans for the HL subjects were acquired with assistance of Dr. Boatman and Dr. Rapp respectively. Technical support from Drs. Ceritoglu and Mori is also appreciated.

Funding

The 1.5 T scans for the HL subjects were acquired via a grant from the National Organization for Hearing Research. Scans and atlases for the NH subjects were acquired via NIH/NIBIB Grant P41 EB015909. The work for processing and analysis was supported in part by the same NIH grant.

Author information

Authors and Affiliations

  1. Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA

    S. Bryn Dhir, Kwame S. Kutten & J. Tilak Ratnanather

  2. Vanderbilt University, Nashville, TN, 37235, USA

    Muwei Li

  3. Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA

    Andreia V. Faria

  4. Center for Imaging Science and Institute for Computational Medicine, Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA

    Laurent Younes

Authors
  1. S. Bryn Dhir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwame S. Kutten
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreia V. Faria
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laurent Younes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. Tilak Ratnanather
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JTR and AVF conceived the idea and design of the study and supervised data collection and interpretation. SBD, KSK, LY and ML carried out the study and data collection, analysis and interpretation. SBD and JTR drafted the manuscript. All authors participated in study design and data interpretation, and provided critical manuscript revisions, read and approved the final manuscript.

Corresponding author

Correspondence to J. Tilak Ratnanather.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The retrospective imaging studies involving human subjects were previously approved by institutional review boards for Johns Hopkins Medical Institutions and Kennedy Krieger Institute.

Informed consent

Informed consent was obtained from all participants of the retrospective imaging studies.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

APPENDIX

APPENDIX

It is instructive to assess the axonal and myelination abnormalities of the AR and volumes of the HG in people with HL. Thus, from the binary segmentations of the left and right HG, volumes were calculated, and from the generated left and right AR, fractional anisotropy (FA) values were calculated at each voxel from the eigenvalues [8]. For the 1.5 T cohort only, the FA values were in the NH, and HL groups were compared using the Wilcoxon-Rank Sum test with 95% confidence interval due to the small sample size and reported with asymptomatic p values with continuity correction. FA values of the AR and HG volumes from subjects with NH (n = 10) and HL (n = 5) are shown in Fig. 4. In the NH group, the FA for the left AR is larger than in the right (0.43 and 0.41, respectively; p value 0.054). The HG volume of the right HG is significantly larger in the HL group compared with the NH group (1122 mm3 and 909 mm3 respectively; p value 0.019).

The lower FA values are similar to those reported with similar age and hearing loss [62,62,64]. Lower but not significantly different FA values were also noted in children and adults prior to cochlear implantation [65, 66]. Similarly, lower FA bilaterally for patients with acoustic neuromas [67] has been reported and may be associated with degenerative changes seen in older adults [68]. Also, low values were obtained from tracts originating in the inferior colliculus and terminating in the auditory cortex [15], and therefore, comparisons cannot be made, but these lower FA values need to be reconciled with the larger HG volumes observed bilaterally [69,69,71] which may be a consequence of the altered synaptic pruning in the developing auditory cortex [72]. Finally, an unpublished study [73] also reported a reduction bilaterally in FA in subjects with congenital HL who used sign language. Therefore, the results highlight the importance of understanding the effects of LSL and sign language on WM properties of the AR.

Fig. 4
figure 5

FA values and HG volumes from 1.5 T cohort of subjects with NH (n = 10) and HL (n = 5). Vertical lines denote maximum and minimum values. The plus sign symbol denotes the mean, and horizontal lines inside boxes indicate the median. Note that volumes of the MGB were not reported due to variability in factors (anatomical size and location), and difficulty of delineation

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhir, S.B., Kutten, K.S., Li, M. et al. Visualising the topography of the acoustic radiation in clinical diffusion tensor imaging scans. Neuroradiology 62, 1157–1167 (2020). https://doi.org/10.1007/s00234-020-02436-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-020-02436-6

Keywords

Search

Navigation