Congenital profound hearing loss (hearing level >90 dB diagnosed at birth or in early childhood) deprives children of spoken language development and has lifelong negative consequences for education, employment, and psychosocial status (Kral and O’Donoghue, 2010). The prevalence of neonatal hearing loss has increased from 1.09 to 1.7 cases per 1000 live births in the past two decades (CDC Early Hearing Detection and Intervention (EHDI) Hearing Screening & Follow-up Survey (HSFS), 2021; Alicia and Marcus, 2010). Most congenital hearing loss is caused by sensorineural impairments in the inner ear, cochlear nerve, and/or central auditory pathway (Korver et al., 2017). The aetiology of these impairments is multifaceted, with genetic factors playing a significant role, alongside external contributors such as congenital infections (Marazita et al., 1993). Hearing loss is categorized as syndromic, with accompanying physical or laboratory findings, or non-syndromic, where hearing loss is the sole symptom and has a highly heterogeneous genetic basis (Korver et al., 2017). This article primarily focuses on non-syndromic sensorineural hearing loss (SNHL).

Inner ear malformations and cochlear nerve deficiencies (IEM&CND), identifiable with CT and MRI, contribute to 15–39% of paediatric SNHL cases (Li et al., 2011; Mafong et al., 2002). The rest is primarily due to cellular-level abnormalities. The aetiology of IEM&CND is complex and largely unknown, yet these abnormalities suggest an earlier developmental arrest compared to cases with normal inner ear structures (Sennaroglu and Saatci, 2002). Addressing IEM&CND is vital as it presents specific challenges in clinical management. Cochlear implantation (CI) and auditory brainstem implantation (ABI) are currently the only solutions for profound SNHL, but the choice between CI and ABI presents a dilemma for many IEM&CND patients. CI directly stimulates the spiral ganglion cells, the first-order neurons of the auditory pathway, while ABI bypasses the cochlear nerve and stimulates the second-order auditory neurons in the cochlear nucleus (CN) when complex IEM or cochlear nerve aplasia makes CI inapplicable (Chen and Oghalai, 2016). Both CI and ABI are capable of providing hearing sensation and language development for children with severe-to-profound prelingual hearing loss, but postoperative outcomes vary among individuals. ABI recipients generally have poorer speech recognition performance and delayed and incomplete language development compared to CI recipients (Sennaroğlu et al., 2016). However, the prognosis of CI may not necessarily be better than that of ABI for certain IEMs, including common cavity, cochlear hypoplasia, incomplete partition-type I, and cochlear aperture abnormalities, as the presence of sufficient cochlear fibres required for CI success is uncertain (Freeman and Sennaroglu, 2018; Sennaroğlu and Bajin, 2017). This uncertainty is hard to address for two reasons: (1) assessing the cochlear nerve through visual inspection of MRI poses challenges, including subjective limitations, image quality, and difficulty distinguishing the cochlear nerve from the cranial nerve VIII (the cochleovestibular nerve) in cases of common cavity; and (2) the absence of the cochlear nerve structurally does not always indicate a lack of functional hearing (Thai-Van et al., 2000). Furthermore, distinguishing certain types of IEMs can be problematic and may impact surgical decision-making. For example, differentiating cochlear aplasia with a dilated vestibule (CADV) from a common cavity is challenging; CADV is a definitive indication for ABI, while a common cavity allows for either CI or ABI. These observations underscore the importance of a deep dive into IEM&CND and highlight the limitations of contemporary clinical imaging in diagnosis, prognosis evaluation, and surgical guidance.

Speech is transmitted through the auditory pathway and understood at the language network (Friederici, 2011). For children with severe-to-profound prelingual hearing loss, the deprivation of auditory inputs into this workflow may affect the structural and functional development of associated brain regions, the degrees of impairment of which may be relevant to later auditory and language performance after surgical reconstruction of hearing. Diffusion tensor imaging (DTI) studies have shown that children with profound SNHL exhibit decreased fractional anisotropy (FA) values along almost the entire auditory pathway, particularly in the inferior colliculus (IC), medial geniculate body (MGB), and auditory radiation (Tarabichi et al., 2018). Additionally, hearing and language outcomes (categories of auditory performance) 6–12 mo following CI were positively correlated with preoperative auditory pathway FA values (Chang et al., 2012; Huang et al., 2015; Wang et al., 2019b; Wu et al., 2016). Similarly, brain structures associated with the language pathway, including the superior temporal gyrus (STG), Broca’s area, superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF), also showed decreased FA values in children with profound hearing loss (Wang et al., 2019b; Wang et al., 2019a; Wu et al., 2016). The FA value of Broca’s area was positively correlated with speech recognition performance after CI implantation (Chang et al., 2012). These results indicate that children with profound SNHL have a delayed or impaired microstructural organization in the central auditory pathway and the language pathway that may affect subsequent implantation outcomes.

However, there are several challenges in this field. Firstly, the human auditory pathway is difficult to inspect non-invasively due to its delicate nodes connected by curving and crossing fibres, especially the parts that are buried deep in the brainstem (Zanin et al., 2019). Functional MRI (fMRI) responses to natural sounds have been used to locate subcortical auditory structures (Sitek et al., 2019), but this method is not applicable to patients with profound hearing loss as no auditory brainstem responses (ABRs) can be detected in these individuals, making it impossible to capture corresponding blood oxygen signals in fMRI. In fact, few studies have located the auditory brainstem of children with profound hearing loss, particularly the CN that ABI targets. Therefore, new localization and tractography methods are needed to precisely map the auditory pathway in vivo for individuals with normal or impaired hearing. Secondly, although earlier studies reported several altered fibre tracts related to language function (Chang et al., 2012; Wang et al., 2019b; Wang et al., 2019a; Wu et al., 2016), there has been insufficient investigation into the language pathway. Its component streams, which are segmented on both structural and functional bases, do not correspond to the major fibre tracts typically studied in whole-brain analyses. Finally, the diffusion tensor model used in these studies performs poorly in estimating regions with crossing fibres (Farquharson et al., 2013), which may lead to problematic tractography (particularly in the auditory pathway) and false interpretations of FA alterations in children with profound hearing loss. To overcome this methodological limitation, the present study implemented a state-of-the-art fixel-based analysis (FBA) approach (Dhollander et al., 2021). A ‘fixel’ refers to an individual fibre population within a voxel, allowing for the quantification of white matter properties in fibre-crossing areas.

Furthermore, previous neuroimaging studies of profound congenital SNHL excluded children with IEM&CND. However, as mentioned earlier, these subjects comprise a significant proportion of patients with congenital hearing loss and are more inclined to be faced with difficult surgical decisions and unsatisfactory post-implantation outcomes. Therefore, it is important to include and focus on children with IEM&CND when studying central adaptations associated with profound hearing loss.

In the present study, we introduced a new pipeline for reconstructing the human auditory pathway and examined the brain structural development of children with profound congenital SNHL at both the acoustic processing level and the speech perception level. Specifically, we included children under the age of six with profound hearing loss, with and without IEM&CND, as well as normal hearing controls. We segmented the subcortical auditory nuclei using super-resolution track density imaging (TDI) maps and T1-weighted images, and tracked the auditory pathway and the language pathway using probabilistic tractography. Then, we used FBA to investigate the fibre properties of these two pathways. We aimed to investigate (1) the alteration of fibre metrics in the auditory-language network of children with profound SNHL; (2) the potential impact of IEM&CND on the pre-implant structural development of this network; and (3) the relationship between the pre-implant structural development of this network and the auditory-language outcomes following CI or ABI implantation. By doing so, we hope to provide new insights into the surgical strategies and rehabilitation of children with profound congenital SNHL.

In this study, we successfully segmented subcortical auditory regions and reconstructed the auditory and language pathways in vivo. Our findings showed decreased FD and FC mainly in the inferior part of the central auditory pathway and the left language pathway. Additionally, we discovered that the correlation between language pathway fibre metrics and peripheral vestibulocochlear nerve structure is stronger and more significant than that in the central auditory pathway, and that the peripheral nerve structure moderated the developmental trajectory of the central auditory and language pathways. Preoperatively evaluating the structure of the auditory-language network helps predict postoperative audiometric and qualitative language outcomes.

Integrating pre-surgical evaluations for optimized auditory-language rehabilitation

Our study delivers novel insight into the quantification of central auditory structural development and advocates an individualized, comprehensive pre-surgical evaluation of the auditory-language network to inform surgical decisions and predict prognosis (see Figure 6). From the correlation results, we observed significant clinical values that bolster the utility of such an evaluation. Further research should explore association and causation between neural and more robust auditory (e.g. speech-in-noise) and language evaluations.

Figure 6

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The subcortical auditory segmentations of the study-specific template (as well as T1-weighted, DEC-TDI, and WM FOD templates) are available on the Open Science Framework: https://osf.io/pxmf5/.

1. 1. Wang Y
   2. Jiang M
   3. Zhu Y

   (2023) Open Science Framework

   Mapping the auditory pathway.

   https://doi.org/10.17605/OSF.IO/PXMF5

1. Website

   1. Alicia C
   2. Marcus G

   (2010) Directors of Speech and Hearing Programs in State Health and Welfare Agencies (DSHPSHWA): Overview and Summary of 1999–2004 DSHPSHWA Data

   Accessed September 19, 2010.

   https://www.cdc.gov/ncbddd/hearingloss/documents/1999-2004_dshpshwa-summary.pdf
2. 1. Allen MC
   2. Nikolopoulos TP
   3. O’Donoghue GM

   (1998) Speech intelligibility in children after cochlear implantation

   The American Journal of Otology 19:742–746.

   https://doi.org/10.1016/j.ijporl.2003.11.006

   • PubMed
   • Google Scholar
3. 1. Archbold S
   2. Lutman ME
   3. Marshall DH

   (1995) Categories of auditory performance

   The Annals of Otology, Rhinology & Laryngology. Supplement 166:312–314.

   https://doi.org/10.3109/03005364000000045

   • PubMed
   • Google Scholar
4. 1. Astuto LM
   2. Bork JM
   3. Weston MD
   4. Askew JW
   5. Fields RR
   6. Orten DJ
   7. Ohliger SJ
   8. Riazuddin S
   9. Morell RJ
   10. Khan S
   11. Riazuddin S
   12. Kremer H
   13. van Hauwe P
   14. Moller CG
   15. Cremers CWRJ
   16. Ayuso C
   17. Heckenlively JR
   18. Rohrschneider K
   19. Spandau U
   20. Greenberg J
   21. Ramesar R
   22. Reardon W
   23. Bitoun P
   24. Millan J
   25. Legge R
   26. Friedman TB
   27. Kimberling WJ

   (2002) CDH23 mutation and phenotype heterogeneity: a profile of 107 diverse families with Usher syndrome and nonsyndromic deafness

   American Journal of Human Genetics 71:262–275.

   https://doi.org/10.1086/341558

   • PubMed
   • Google Scholar
5. 1. Avants BB
   2. Tustison NJ
   3. Song G
   4. Cook PA
   5. Klein A
   6. Gee JC

   (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration

   NeuroImage 54:2033–2044.

   https://doi.org/10.1016/j.neuroimage.2010.09.025

   • PubMed
   • Google Scholar
6. 1. Bavelier D
   2. Dye MWG
   3. Hauser PC

   (2006) Do deaf individuals see better?

   Trends in Cognitive Sciences 10:512–518.

   https://doi.org/10.1016/j.tics.2006.09.006

   • PubMed
   • Google Scholar
7. 1. Calamante F
   2. Tournier JD
   3. Jackson GD
   4. Connelly A

   (2010) Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping

   NeuroImage 53:1233–1243.

   https://doi.org/10.1016/j.neuroimage.2010.07.024

   • PubMed
   • Google Scholar
8. 1. Calamante F
   2. Tournier JD
   3. Heidemann RM
   4. Anwander A
   5. Jackson GD
   6. Connelly A

   (2011) Track density imaging (TDI): validation of super resolution property

   NeuroImage 56:1259–1266.

   https://doi.org/10.1016/j.neuroimage.2011.02.059

   • PubMed
   • Google Scholar
9. 1. Calamante F
   2. Tournier JD
   3. Kurniawan ND
   4. Yang Z
   5. Gyengesi E
   6. Galloway GJ
   7. Reutens DC
   8. Connelly A

   (2012) Super-resolution track-density imaging studies of mouse brain: comparison to histology

   NeuroImage 59:286–296.

   https://doi.org/10.1016/j.neuroimage.2011.07.014

   • PubMed
   • Google Scholar
10. 1. Calamante F
    2. Oh SH
    3. Tournier JD
    4. Park SY
    5. Son YD
    6. Chung JY
    7. Chi JG
    8. Jackson GD
    9. Park CW
    10. Kim YB
    11. Connelly A
    12. Cho ZH

    (2013) Super-resolution track-density imaging of thalamic substructures: comparison with high-resolution anatomical magnetic resonance imaging at 7.0T

    Human Brain Mapping 34:2538–2548.

    https://doi.org/10.1002/hbm.22083

    • PubMed
    • Google Scholar
11. Website

    1. CDC Early Hearing Detection and Intervention (EHDI) Hearing Screening & Follow-up Survey (HSFS)

    (2021) Hearing Screening & Follow-up Survey (HSFS)

    Accessed September 19, 2021.

    https://www.cdc.gov/ncbddd/hearingloss/2019-data/01-data-summary.html
12. 1. Chang Y
    2. Lee HR
    3. Paik JS
    4. Lee KY
    5. Lee SH

    (2012) Voxel-wise analysis of diffusion tensor imaging for clinical outcome of cochlear implantation: retrospective study

    Clinical and Experimental Otorhinolaryngology 5 Suppl 1:S37–S42.

    https://doi.org/10.3342/ceo.2012.5.S1.S37

    • PubMed
    • Google Scholar
13. 1. Chen MM
    2. Oghalai JS

    (2016) Diagnosis and management of congenital sensorineural hearing loss

    Current Treatment Options in Pediatrics 2:256–265.

    https://doi.org/10.1007/s40746-016-0056-6

    • PubMed
    • Google Scholar
14. 1. Chen Y
    2. Li Y
    3. Jia H
    4. Gu W
    5. Wang Z
    6. Zhang Z
    7. Xue M
    8. Li J
    9. Shi W
    10. Jiang L
    11. Yang L
    12. Sterkers O
    13. Wu H

    (2023) Simultaneous bilateral cochlear implantation in very young children improves adaptability and social skills: a prospective cohort study

    Ear and Hearing 44:254–263.

    https://doi.org/10.1097/AUD.0000000000001276

    • PubMed
    • Google Scholar
15. 1. Cope TE
    2. Sohoglu E
    3. Sedley W
    4. Patterson K
    5. Jones PS
    6. Wiggins J
    7. Dawson C
    8. Grube M
    9. Carlyon RP
    10. Griffiths TD
    11. Davis MH
    12. Rowe JB

    (2017) Evidence for causal top-down frontal contributions to predictive processes in speech perception

    Nature Communications 8:2154.

    https://doi.org/10.1038/s41467-017-01958-7

    • PubMed
    • Google Scholar
16. Book

    1. Dhollander T
    2. Connelly A

    (2016)

    A Novel iterative approach to reap the benefits of multi-tissue CSD from just single-shell (+B=0) diffusion MRI data

    Proc ISMRM.

    • Google Scholar
17. 1. Dhollander T
    2. Clemente A
    3. Singh M
    4. Boonstra F
    5. Civier O
    6. Duque JD
    7. Egorova N
    8. Enticott P
    9. Fuelscher I
    10. Gajamange S
    11. Genc S
    12. Gottlieb E
    13. Hyde C
    14. Imms P
    15. Kelly C
    16. Kirkovski M
    17. Kolbe S
    18. Liang X
    19. Malhotra A
    20. Mito R
    21. Poudel G
    22. Silk TJ
    23. Vaughan DN
    24. Zanin J
    25. Raffelt D
    26. Caeyenberghs K

    (2021) Fixel-based analysis of diffusion MRI: methods, applications

    Challenges and Opportunities. Neuroimage 241:118417.

    https://doi.org/10.1016/j.neuroimage.2021.118417

    • PubMed
    • Google Scholar
18. 1. Ding N
    2. Melloni L
    3. Zhang H
    4. Tian X
    5. Poeppel D

    (2016) Cortical tracking of hierarchical linguistic structures in connected speech

    Nature Neuroscience 19:158–164.

    https://doi.org/10.1038/nn.4186

    • PubMed
    • Google Scholar
19. 1. Dorman MF
    2. Loiselle LH
    3. Cook SJ
    4. Yost WA
    5. Gifford RH

    (2016) Sound source localization by normal-hearing listeners, hearing-impaired listeners and cochlear implant listeners

    Audiology & Neuro-Otology 21:127–131.

    https://doi.org/10.1159/000444740

    • PubMed
    • Google Scholar
20. 1. Edwards AD
    2. Rueckert D
    3. Smith SM
    4. Abo Seada S
    5. Alansary A
    6. Almalbis J
    7. Allsop J
    8. Andersson J
    9. Arichi T
    10. Arulkumaran S
    11. Bastiani M
    12. Batalle D
    13. Baxter L
    14. Bozek J
    15. Braithwaite E
    16. Brandon J
    17. Carney O
    18. Chew A
    19. Christiaens D
    20. Chung R
    21. Colford K
    22. Cordero-Grande L
    23. Counsell SJ
    24. Cullen H
    25. Cupitt J
    26. Curtis C
    27. Davidson A
    28. Deprez M
    29. Dillon L
    30. Dimitrakopoulou K
    31. Dimitrova R
    32. Duff E
    33. Falconer S
    34. Farahibozorg S-R
    35. Fitzgibbon SP
    36. Gao J
    37. Gaspar A
    38. Harper N
    39. Harrison SJ
    40. Hughes EJ
    41. Hutter J
    42. Jenkinson M
    43. Jbabdi S
    44. Jones E
    45. Karolis V
    46. Kyriakopoulou V
    47. Lenz G
    48. Makropoulos A
    49. Malik S
    50. Mason L
    51. Mortari F
    52. Nosarti C
    53. Nunes RG
    54. O’Keeffe C
    55. O’Muircheartaigh J
    56. Patel H
    57. Passerat-Palmbach J
    58. Pietsch M
    59. Price AN
    60. Robinson EC
    61. Rutherford MA
    62. Schuh A
    63. Sotiropoulos S
    64. Steinweg J
    65. Teixeira RPAG
    66. Tenev T
    67. Tournier J-D
    68. Tusor N
    69. Uus A
    70. Vecchiato K
    71. Williams LZJ
    72. Wright R
    73. Wurie J
    74. Hajnal JV

    (2022) The developing human connectome project neonatal data release

    Frontiers in Neuroscience 16:886772.

    https://doi.org/10.3389/fnins.2022.886772

    • Google Scholar
21. 1. Epprecht L
    2. Qureshi A
    3. Kozin ED
    4. Vachicouras N
    5. Huber AM
    6. Kikinis R
    7. Makris N
    8. Brown MC
    9. Reinshagen KL
    10. Lee DJ

    (2020) Human cochlear nucleus on 7 tesla diffusion tensor imaging: insights into micro-anatomy and function for auditory brainstem implant surgery

    Otology & Neurotology 41:e484–e493.

    https://doi.org/10.1097/MAO.0000000000002565

    • PubMed
    • Google Scholar
22. 1. Fan L
    2. Li H
    3. Zhuo J
    4. Zhang Y
    5. Wang J
    6. Chen L
    7. Yang Z
    8. Chu C
    9. Xie S
    10. Laird AR
    11. Fox PT
    12. Eickhoff SB
    13. Yu C
    14. Jiang T

    (2016) The human brainnetome atlas: a new brain atlas based on connectional architecture

    Cerebral Cortex 26:3508–3526.

    https://doi.org/10.1093/cercor/bhw157

    • PubMed
    • Google Scholar
23. 1. Farquharson S
    2. Tournier JD
    3. Calamante F
    4. Fabinyi G
    5. Schneider-Kolsky M
    6. Jackson GD
    7. Connelly A

    (2013) White matter fiber tractography: why we need to move beyond DTI

    Journal of Neurosurgery 118:1367–1377.

    https://doi.org/10.3171/2013.2.JNS121294

    • PubMed
    • Google Scholar
24. 1. Fonov VS
    2. Evans AC
    3. McKinstry RC
    4. Almli CR
    5. Collins DL

    (2009) Unbiased nonlinear average age-appropriate brain templates from birth to adulthood

    NeuroImage 47:S102.

    https://doi.org/10.1016/S1053-8119(09)70884-5

    • Google Scholar
25. 1. Freeman SR
    2. Sennaroglu L

    (2018) Management of cochlear nerve hypoplasia and aplasia

    Advances in Oto-Rhino-Laryngology 81:81–92.

    https://doi.org/10.1159/000485542

    • PubMed
    • Google Scholar
26. 1. Friederici AD

    (2011) The brain basis of language processing: from structure to function

    Physiological Reviews 91:1357–1392.

    https://doi.org/10.1152/physrev.00006.2011

    • PubMed
    • Google Scholar
27. 1. Friederici AD
    2. Chomsky N
    3. Berwick RC
    4. Moro A
    5. Bolhuis JJ

    (2017) Language, mind and brain

    Nature Human Behaviour 1:713–722.

    https://doi.org/10.1038/s41562-017-0184-4

    • PubMed
    • Google Scholar
28. 1. Friedmann N
    2. Rusou D

    (2015) Critical period for first language: the crucial role of language input during the first year of life

    Current Opinion in Neurobiology 35:27–34.

    https://doi.org/10.1016/j.conb.2015.06.003

    • PubMed
    • Google Scholar
29. 1. García-Gomar MG
    2. Strong C
    3. Toschi N
    4. Singh K
    5. Rosen BR
    6. Wald LL
    7. Bianciardi M

    (2019) In vivo probabilistic structural Atlas of the inferior and superior Colliculi, Medial and Lateral Geniculate Nuclei and Superior Olivary Complex in Humans Based on 7 Tesla MRI

    Frontiers in Neuroscience 13:764.

    https://doi.org/10.3389/fnins.2019.00764

    • PubMed
    • Google Scholar
30. 1. Hamilton LS
    2. Oganian Y
    3. Hall J
    4. Chang EF

    (2021) Parallel and distributed encoding of speech across human auditory cortex

    Cell 184:4626–4639.

    https://doi.org/10.1016/j.cell.2021.07.019

    • PubMed
    • Google Scholar
31. 1. Harris KC
    2. Ahlstrom JB
    3. Dias JW
    4. Kerouac LB
    5. McClaskey CM
    6. Dubno JR
    7. Eckert MA

    (2021) Neural presbyacusis in humans inferred from age-related differences in auditory nerve function and structure

    The Journal of Neuroscience 41:10293–10304.

    https://doi.org/10.1523/JNEUROSCI.1747-21.2021

    • PubMed
    • Google Scholar
32. 1. Hickok G
    2. Poeppel D

    (2007) The cortical organization of speech processing

    Nature Reviews. Neuroscience 8:393–402.

    https://doi.org/10.1038/nrn2113

    • PubMed
    • Google Scholar
33. 1. Houston DM
    2. Miyamoto RT

    (2010) Effects of early auditory experience on word learning and speech perception in deaf children with cochlear implants: implications for sensitive periods of language development

    Otology & Neurotology 31:1248–1253.

    https://doi.org/10.1097/MAO.0b013e3181f1cc6a

    • PubMed
    • Google Scholar
34. 1. Huang L
    2. Zheng W
    3. Wu C
    4. Wei X
    5. Wu X
    6. Wang Y
    7. Zheng H
    8. Zhou J

    (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:e0140643.

    https://doi.org/10.1371/journal.pone.0140643

    • Google Scholar
35. 1. Kelsell DP
    2. Dunlop J
    3. Stevens HP
    4. Lench NJ
    5. Liang JN
    6. Parry G
    7. Mueller RF
    8. Leigh IM

    (1997) Connexin 26 mutations in hereditary non-syndromic sensorineural deafness

    Nature 387:80–83.

    https://doi.org/10.1038/387080a0

    • PubMed
    • Google Scholar
36. 1. Korver AMH
    2. Smith RJH
    3. Van Camp G
    4. Schleiss MR
    5. Bitner-Glindzicz MAK
    6. Lustig LR
    7. Usami S-I
    8. Boudewyns AN

    (2017) Congenital hearing loss

    Nature Reviews. Disease Primers 3:16094.

    https://doi.org/10.1038/nrdp.2016.94

    • PubMed
    • Google Scholar
37. 1. Kral A
    2. O’Donoghue GM

    (2010) Profound deafness in childhood

    The New England Journal of Medicine 363:1438–1450.

    https://doi.org/10.1056/NEJMra0911225

    • PubMed
    • Google Scholar
38. 1. Kuhl P
    2. Rivera-Gaxiola M

    (2008) Neural substrates of language acquisition

    Annual Review of Neuroscience 31:511–534.

    https://doi.org/10.1146/annurev.neuro.30.051606.094321

    • PubMed
    • Google Scholar
39. 1. Kwon DH
    2. Paek SH
    3. Kim YB
    4. Lee H
    5. Cho ZH

    (2021) In vivo 3D Reconstruction of the Human Pallidothalamic and Nigrothalamic pathways with super-resolution 7T MR Track density imaging and fiber tractography

    Frontiers in Neuroanatomy 15:739576.

    https://doi.org/10.3389/fnana.2021.739576

    • PubMed
    • Google Scholar
40. 1. Leonard MK
    2. Chang EF

    (2014) Dynamic speech representations in the human temporal lobe

    Trends in Cognitive Sciences 18:472–479.

    https://doi.org/10.1016/j.tics.2014.05.001

    • PubMed
    • Google Scholar
41. 1. Leonard MK
    2. Baud MO
    3. Sjerps MJ
    4. Chang EF

    (2016) Perceptual restoration of masked speech in human cortex

    Nature Communications 7:13619.

    https://doi.org/10.1038/ncomms13619

    • PubMed
    • Google Scholar
42. 1. Li Y
    2. Yang J
    3. Li Y

    (2011)

    Constitute, imaging and auditory characteristics of pediatric patients with congenital malformations of inner ear in sensorineural hearing loss

    Journal of Clinical Otorhinolaryngology, Head, and Neck Surgery 25:1–5.

    • Google Scholar
43. 1. Lin JW
    2. Chowdhury N
    3. Mody A
    4. Tonini R
    5. Emery C
    6. Haymond J
    7. Oghalai JS

    (2011) Comprehensive diagnostic battery for evaluating sensorineural hearing loss in children

    Otology & Neurotology 32:259–264.

    https://doi.org/10.1097/MAO.0b013e31820160fa

    • PubMed
    • Google Scholar
44. 1. Maffei C
    2. Sarubbo S
    3. Jovicich J

    (2019) Diffusion-based tractography atlas of the human acoustic radiation

    Scientific Reports 9:4046.

    https://doi.org/10.1038/s41598-019-40666-8

    • PubMed
    • Google Scholar
45. 1. Mafong DD
    2. Shin EJ
    3. Lalwani AK

    (2002) Use of laboratory evaluation and radiologic imaging in the diagnostic evaluation of children with sensorineural hearing loss

    The Laryngoscope 112:1–7.

    https://doi.org/10.1097/00005537-200201000-00001

    • PubMed
    • Google Scholar
46. 1. Mansour Y
    2. Altaher W
    3. Kulesza RJ

    (2019) Characterization of the human central nucleus of the inferior colliculus

    Hearing Research 377:234–246.

    https://doi.org/10.1016/j.heares.2019.04.004

    • PubMed
    • Google Scholar
47. 1. Marazita ML
    2. Ploughman LM
    3. Rawlings B
    4. Remington E
    5. Arnos KS
    6. Nance WE

    (1993) Genetic epidemiological studies of early-onset deafness in the U.S. school-age population

    American Journal of Medical Genetics 46:486–491.

    https://doi.org/10.1002/ajmg.1320460504

    • PubMed
    • Google Scholar
48. 1. Moore JK
    2. Osen KK

    (1979) The cochlear nuclei in man

    The American Journal of Anatomy 154:393–418.

    https://doi.org/10.1002/aja.1001540306

    • PubMed
    • Google Scholar
49. 1. Moore JK

    (1987) The human auditory brain stem: a comparative view

    Hearing Research 29:1–32.

    https://doi.org/10.1016/0378-5955(87)90202-4

    • PubMed
    • Google Scholar
50. 1. Moore JK
    2. Perazzo LM
    3. Braun A

    (1995) Time course of axonal myelination in the human brainstem auditory pathway

    Hearing Research 87:21–31.

    https://doi.org/10.1016/0378-5955(95)00073-d

    • PubMed
    • Google Scholar
51. 1. Obleser J
    2. Leaver AM
    3. Vanmeter J
    4. Rauschecker JP

    (2010) Segregation of vowels and consonants in human auditory cortex: evidence for distributed hierarchical organization

    Frontiers in Psychology 1:232.

    https://doi.org/10.3389/fpsyg.2010.00232

    • PubMed
    • Google Scholar
52. 1. Obleser J
    2. Kayser C

    (2019) Neural entrainment and attentional selection in the listening brain

    Trends in Cognitive Sciences 23:913–926.

    https://doi.org/10.1016/j.tics.2019.08.004

    • PubMed
    • Google Scholar
53. 1. Peelle JE
    2. Johnsrude IS
    3. Davis MH

    (2010) Hierarchical processing for speech in human auditory cortex and beyond

    Frontiers in Human Neuroscience 4:51.

    https://doi.org/10.3389/fnhum.2010.00051

    • PubMed
    • Google Scholar
54. 1. Rademacher J
    2. Bürgel U
    3. Zilles K

    (2002) Stereotaxic localization, intersubject variability, and interhemispheric differences of the human auditory thalamocortical system

    NeuroImage 17:142–160.

    https://doi.org/10.1006/nimg.2002.1178

    • PubMed
    • Google Scholar
55. 1. Raffelt DA
    2. Smith RE
    3. Ridgway GR
    4. Tournier JD
    5. Vaughan DN
    6. Rose S
    7. Henderson R
    8. Connelly A

    (2015) Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres

    NeuroImage 117:40–55.

    https://doi.org/10.1016/j.neuroimage.2015.05.039

    • PubMed
    • Google Scholar
56. 1. Rosahl SK
    2. Rosahl S

    (2013) No easy target: anatomic constraints of electrodes interfacing the human cochlear nucleus

    Neurosurgery 72:58–64.

    https://doi.org/10.1227/NEU.0b013e31826cde82

    • PubMed
    • Google Scholar
57. 1. Sennaroglu L
    2. Saatci I

    (2002) A new classification for cochleovestibular malformations

    The Laryngoscope 112:2230–2241.

    https://doi.org/10.1097/00005537-200212000-00019

    • PubMed
    • Google Scholar
58. 1. Sennaroğlu L
    2. Colletti V
    3. Lenarz T
    4. Manrique M
    5. Laszig R
    6. Rask-Andersen H
    7. Göksu N
    8. Offeciers E
    9. Saeed S
    10. Behr R
    11. Bayazıt Y
    12. Casselman J
    13. Freeman S
    14. Kileny P
    15. Lee DJ
    16. Shannon RV
    17. Kameswaran M
    18. Hagr A
    19. Zarowski A
    20. Schwartz MS
    21. Bilginer B
    22. Kishore A
    23. Sennaroğlu G
    24. Yücel E
    25. Saraç S
    26. Ataş A
    27. Colletti L
    28. O’Driscoll M
    29. Moon IS
    30. Gärtner L
    31. Huarte A
    32. Nyberg G
    33. Mocan BÖ
    34. Atay G
    35. Bajin MD
    36. Çınar BÇ
    37. Batuk MÖ
    38. Yaralı M
    39. Aydınlı FE
    40. Aslan F
    41. Kirazlı MC
    42. Özkan HB
    43. Hans JM
    44. Kosaner J
    45. Polak M

    (2016) Consensus statement: Long-term results of ABI in children with complex inner ear malformations and decision making between CI and ABI

    Cochlear Implants International 17:163–171.

    https://doi.org/10.1080/14670100.2016.1208396

    • PubMed
    • Google Scholar
59. 1. Sennaroğlu L
    2. Bajin MD

    (2017) Classification and current management of inner ear malformations

    Balkan Medical Journal 34:397–411.

    https://doi.org/10.4274/balkanmedj.2017.0367

    • PubMed
    • Google Scholar
60. 1. Sherafati A
    2. Dwyer N
    3. Bajracharya A
    4. Hassanpour MS
    5. Eggebrecht AT
    6. Firszt JB
    7. Culver JP
    8. Peelle JE

    (2022) Prefrontal cortex supports speech perception in listeners with cochlear implants

    eLife 11:e75323.

    https://doi.org/10.7554/eLife.75323

    • PubMed
    • Google Scholar
61. 1. Sitek KR
    2. Gulban OF
    3. Calabrese E
    4. Johnson GA
    5. Lage-Castellanos A
    6. Moerel M
    7. Ghosh SS
    8. De Martino F

    (2019) Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T

    eLife 8:e48932.

    https://doi.org/10.7554/eLife.48932

    • PubMed
    • Google Scholar
62. 1. Skeide MA
    2. Brauer J
    3. Friederici AD

    (2016) Brain functional and structural predictors of language performance

    Cerebral Cortex 26:2127–2139.

    https://doi.org/10.1093/cercor/bhv042

    • PubMed
    • Google Scholar
63. 1. Smith RE
    2. Tournier JD
    3. Calamante F
    4. Connelly A

    (2015) SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography

    NeuroImage 119:338–351.

    https://doi.org/10.1016/j.neuroimage.2015.06.092

    • PubMed
    • Google Scholar
64. 1. Sohoglu E
    2. Peelle JE
    3. Carlyon RP
    4. Davis MH

    (2012) Predictive top-down integration of prior knowledge during speech perception

    The Journal of Neuroscience 32:8443–8453.

    https://doi.org/10.1523/JNEUROSCI.5069-11.2012

    • PubMed
    • Google Scholar
65. 1. Stelma F
    2. Bhutta MF

    (2014) Non-syndromic hereditary sensorineural hearing loss: review of the genes involved

    The Journal of Laryngology and Otology 128:13–21.

    https://doi.org/10.1017/S0022215113003265

    • PubMed
    • Google Scholar
66. 1. Strominger NL
    2. Hurwitz JL

    (1976) Anatomical aspects of the superior olivary complex

    The Journal of Comparative Neurology 170:485–497.

    https://doi.org/10.1002/cne.901700407

    • PubMed
    • Google Scholar
67. 1. Tang Y
    2. Sun W
    3. Toga AW
    4. Ringman JM
    5. Shi Y

    (2018) A probabilistic atlas of human brainstem pathways based on connectome imaging data

    NeuroImage 169:227–239.

    https://doi.org/10.1016/j.neuroimage.2017.12.042

    • PubMed
    • Google Scholar
68. 1. Tarabichi O
    2. Kozin ED
    3. Kanumuri VV
    4. Barber S
    5. Ghosh S
    6. Sitek KR
    7. Reinshagen K
    8. Herrmann B
    9. Remenschneider AK
    10. Lee DJ

    (2018) Diffusion tensor imaging of central auditory pathways in patients with sensorineural hearing loss: a systematic review

    Otolaryngology--Head and Neck Surgery 158:432–442.

    https://doi.org/10.1177/0194599817739838

    • PubMed
    • Google Scholar
69. 1. Terr LI
    2. Edgerton BJ

    (1985) Three-dimensional reconstruction of the cochlear nuclear complex in humans

    Archives of Otolaryngology 111:495–501.

    https://doi.org/10.1001/archotol.1985.00800100043003

    • PubMed
    • Google Scholar
70. 1. Thai-Van H
    2. Fraysse B
    3. Berry I
    4. Berges C
    5. Deguine O
    6. Honegger A
    7. Sevely A
    8. Ibarrola D

    (2000)

    Functional magnetic resonance imaging may avoid misdiagnosis of cochleovestibular nerve aplasia in congenital deafness

    The American Journal of Otology 21:663–670.

    • PubMed
    • Google Scholar
71. 1. The Joint Committee on Infant Hearing

    (2019)

    Year 2019 position statement: Principles and guidelines for early hearing detection and intervention programs

    Journal of Early Hearing Detection and Intervention 4:1–44.

    • Google Scholar
72. Conference

    1. Tournier JD
    2. Calamante F
    3. Connelly A

    (2010)

    Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions

    Proceedings of the international society for magnetic resonance in medicine.

    • Google Scholar
73. 1. Tournier JD
    2. Smith R
    3. Raffelt D
    4. Tabbara R
    5. Dhollander T
    6. Pietsch M
    7. Christiaens D
    8. Jeurissen B
    9. Yeh CH
    10. Connelly A

    (2019) MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation

    NeuroImage 202:116137.

    https://doi.org/10.1016/j.neuroimage.2019.116137

    • PubMed
    • Google Scholar
74. 1. Venezia JH
    2. Thurman SM
    3. Richards VM
    4. Hickok G

    (2019) Hierarchy of speech-driven spectrotemporal receptive fields in human auditory cortex

    NeuroImage 186:647–666.

    https://doi.org/10.1016/j.neuroimage.2018.11.049

    • PubMed
    • Google Scholar
75. 1. Verschuur CA
    2. Lutman ME
    3. Ramsden R
    4. Greenham P
    5. O’Driscoll M

    (2005) Auditory localization abilities in bilateral cochlear implant recipients

    Otology & Neurotology 26:965–971.

    https://doi.org/10.1097/01.mao.0000185073.81070.07

    • PubMed
    • Google Scholar
76. 1. Wang A
    2. Liang Y
    3. Fridell RA
    4. Probst FJ
    5. Wilcox ER
    6. Touchman JW
    7. Morton CC
    8. Morell RJ
    9. Noben-Trauth K
    10. Camper SA
    11. Friedman TB

    (1998) Association of unconventional myosin MYO15 mutations with human nonsyndromic deafness DFNB3

    Science 280:1447–1451.

    https://doi.org/10.1126/science.280.5368.1447

    • PubMed
    • Google Scholar
77. 1. Wang S
    2. Chen B
    3. Yu Y
    4. Yang H
    5. Cui W
    6. Li J
    7. Fan GG

    (2019a) Alterations of structural and functional connectivity in profound sensorineural hearing loss infants within an early sensitive period: A combined DTI and fMRI study

    Developmental Cognitive Neuroscience 38:100654.

    https://doi.org/10.1016/j.dcn.2019.100654

    • PubMed
    • Google Scholar
78. 1. Wang H
    2. Liang Y
    3. Fan W
    4. Zhou X
    5. Huang M
    6. Shi G
    7. Yu H
    8. Shen G

    (2019b) DTI study on rehabilitation of the congenital deafness auditory pathway and speech center by cochlear implantation

    European Archives of Oto-Rhino-Laryngology 276:2411–2417.

    https://doi.org/10.1007/s00405-019-05477-7

    • PubMed
    • Google Scholar
79. 1. Wang L
    2. Shen M
    3. Liang W
    4. Dao W
    5. Zhou L
    6. Zhu M

    (2020) Validation of the Mandarin versions of CAP and SIR

    International Journal of Pediatric Otorhinolaryngology 139:110413.

    https://doi.org/10.1016/j.ijporl.2020.110413

    • Google Scholar
80. 1. Winer JA

    (1984) The human medial geniculate body

    Hearing Research 15:225–247.

    https://doi.org/10.1016/0378-5955(84)90031-5

    • PubMed
    • Google Scholar
81. 1. Winkler AM
    2. Ridgway GR
    3. Webster MA
    4. Smith SM
    5. Nichols TE

    (2014) Permutation inference for the general linear model

    NeuroImage 92:381–397.

    https://doi.org/10.1016/j.neuroimage.2014.01.060

    • PubMed
    • Google Scholar
82. 1. Wu C
    2. Huang L
    3. Tan H
    4. Wang Y
    5. Zheng H
    6. Kong L
    7. Zheng W

    (2016) Diffusion tensor imaging and MR spectroscopy of microstructural alterations and metabolite concentration changes in the auditory neural pathway of pediatric congenital sensorineural hearing loss patients

    Brain Research 1639:228–234.

    https://doi.org/10.1016/j.brainres.2014.12.025

    • PubMed
    • Google Scholar
83. 1. Yasunaga S
    2. Grati M
    3. Cohen-Salmon M
    4. El-Amraoui A
    5. Mustapha M
    6. Salem N
    7. El-Zir E
    8. Loiselet J
    9. Petit C

    (1999) A mutation in OTOF, encoding otoferlin, A FER-1-like protein, causes DFNB9, A nonsyndromic form of deafness

    Nature Genetics 21:363–369.

    https://doi.org/10.1038/7693

    • PubMed
    • Google Scholar
84. 1. Yushkevich PA
    2. Piven J
    3. Hazlett HC
    4. Smith RG
    5. Ho S
    6. Gee JC
    7. Gerig G

    (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability

    NeuroImage 31:1116–1128.

    https://doi.org/10.1016/j.neuroimage.2006.01.015

    • PubMed
    • Google Scholar
85. 1. Zanin J
    2. Dhollander T
    3. Farquharson S
    4. Rance G
    5. Connelly A
    6. Nayagam BA

    (2019) Review: Using diffusion-weighted magnetic resonance imaging techniques to explore the microstructure and connectivity of subcortical white matter tracts in the human auditory system

    Hearing Research 377:1–11.

    https://doi.org/10.1016/j.heares.2019.02.014

    • PubMed
    • Google Scholar
86. 1. Zentner GE
    2. Layman WS
    3. Martin DM
    4. Scacheri PC

    (2010) Molecular and phenotypic aspects of CHD7 mutation in CHARGE syndrome

    American Journal of Medical Genetics. Part A 152A:674–686.

    https://doi.org/10.1002/ajmg.a.33323

    • PubMed
    • Google Scholar
87. 1. Zhong Y
    2. Xu T
    3. Dong R
    4. Lyu J
    5. Liu B
    6. Chen X

    (2017) The analysis of reliability and validity of the IT-MAIS, MAIS and MUSS

    International Journal of Pediatric Otorhinolaryngology 96:106–110.

    https://doi.org/10.1016/j.ijporl.2017.03.006

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Impact of Peripheral Auditory Structure on the Development of Auditory-Language Network in Children with Profound Sensorineural Hearing Loss" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Barbara Shinn-Cunningham as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Behavioral data on the speech and language abilities of the children are available and should be reported.

2) The clinical evaluation and presentation of the children, both genetically and auditorily, is inadequate. Please provide any further details that would be useful for interpreting the other results.

3) The distinction between the groups isn't adequately considered. Please provide further details.

4) The practical relevance of the auditory-language pathways is unclear. Aside from behavioral data (point 1, above), a broader consideration of the studied pathways in the overall processing of hearing and language by the brain is needed.

5) Add discussion of the possibility that the quantitative difference between effects on central auditory and language structures is due to the different ability to image each pathway.

Reviewer #1 (Recommendations for the authors):

Overall, the authors have done a beautiful job creating high-resolution maps of the auditory pathway in these children. This type of imaging is not my speciality and so I cannot comment on the methodology or analysis behind the imaging. However, I am an expert on pediatric hearing loss and so I evaluated this manuscript from that perspective.

Detailed recommendations are below:

1) There are some major deficiencies with respect to the clinical evaluation/presentation of the children in this study which makes interpretation of the results difficult. First of all, there is no diagnosis provided. The causes of pediatric hearing loss is extraordinarily complex with hundreds of genetic forms and environmental causes as well. Hearing loss is not a diagnosis but is instead a symptom of an underlying pathologic change to the peripheral and/or central auditory system. The authors do not provide a diagnosis for the 13 children with hearing loss and as such any conclusions are limited. For example, if some of the children have GJB2 hearing loss, I would expect differences compared to children with CDH23 hearing loss (which has been shown to have some central auditory effect). Similarly, variants in CHD7, are associated with CHARGE syndrome and are a common cause of cochlear nerve deficiency, with some presumed central effect. The authors should provide more clinical history on individual patients, including hearing testing, and should at the least have performed genetic testing for a full evaluation of hearing loss. Ideally, the authors would then separate their subjects into groups (i.e. those with GJB2 hearing loss, versus others). This would make their study more powerful.

2) This study does not include cochlear implant and/or auditory brainstem implant outcomes. Therefore, the authors cannot have any claims to determining or predicting outcomes with these devices until their correlate this finding with those results. This would be an excellent follow-up study and the authors should temper their statements on clinical applicability until this is completed.

3) I assume that the CIs and ABIs were implanted after imaging? This is not specified anywhere that I could find.

4) The figure legends are confusing. Please indicate whether each image is an individual patient or an average and which group each image belongs to.

5) The graphs in Figure 3 are very very difficult to read. The authors should reformat these so that there is a white background and the bars are filled (or at least not use the current color scheme).

6) The authors should be careful about how they use the word 'deaf'. 'Deaf or hard of hearing' is an appropriate way to describe individuals with hearing loss. Severe-to-profound or profound are accurate ways to describe the severity of a hearing loss. The use of the word 'deaf' to describe profound hearing loss is generally discouraged as it is not scientific and may be confused by 'Deaf' (capital D) which describes members of the Deaf community.

Please see above.

Reviewer #2 (Recommendations for the authors):

General comments

– Although the paper tried to justify why focusing on IEM and CND is important, such decision seems still arbitrary to this reviewer. More discussion on what makes these hearing loss etiologies distinct from other etiologies would help.

– The paper does not discuss how these results help with a better understanding of speech sound encoding and decoding in listeners with normal and impaired hearing. It is particularly important to discuss these findings in the context of mapping sound patterns to phonological units.

– Also, there is a rich literature on hearing, language comprehension, and speech perception network from Gregory Hickok, David Poeppel, and Jonathan Peelle which are not discussed in this paper.

Title

– The title needs to be more specific to the questions of this study. Using the general term "Peripheral Auditory Structure" could be misleading. Please revise to reflect both the specific types of hearing loss in this study and also the population that is children with profound hearing loss, but receive CI or ABI as interventions.

Abstract:

– Cochlear implantation and auditory brainstem implantation can provide hearing sensation… it is more accurate to say … provide "partial" hearing sensation …

– "Previous attempts to locate subcortical auditory nuclei using fMRI responses to sounds are not applicable to deaf patients." Please add a reason(s) for why these previous attempts are not applicable.

– Line 3: lifelong "negative" consequences …

– Line 39: please check the in-text citations format

– Line 52: "Secondly, although earlier studies reported several altered fibre tracts related to language function, …" Please add references to support this statement.

– Line 83: mean [SD], age, 30.92 [6.115] months; 9 males, 4 females

– Mean [SD] of age

– You can remove 4 females as it is redundant

– Please consider the same edit for NH

– Line 85: Please indicate the confidence interval in your stats report

Table 1

– I recommend using the term gender instead of sex

Line 137

– FC, FD, and FDC are presented here for the first time in the manuscript. So, their extended version should be used here to avoid possible confusion.

Caption of Figure 3: please report the actual p-values

– Line 139: ie. -->◊ i.e.

Figure 3

– I wonder how the data and results would look if ABI and CI groups were represented separately.

– Line 167-168: please elaborate on why such surgical decisions have limited value.

– Line 193: Report the actual p–values

– Line 451: Provide more details on the nature of click stimuli including frequency.

– Line 453: Clarify the dB HL used for NH decision (20 dB HL?).

– Line 468: If these measurements were not used in this study, it is not clear to this reviewer why they are reported. Please clarify.

– Line 479–481: please add inter–rater reliability to provide evidence on the acceptable agreement between raters.

– Line 503–504: please report how many images were excluded in this process if any.

– Line 511: Does {plus minus}0.010 refer to standard deviation? If so, please indicate that in the paper.

– Table 1 and 2 in appendices: round correlation coefficients to two decimal places. More than two decimal places seem redundant.

Reviewer #3 (Recommendations for the authors):

Recommendations:

1. Enhance the reliability and replicability of research findings by increasing the sample size for both SNHL and NH children. The current sample size is insufficient for between-group comparisons and correlational analyses, particularly when examining sub-groups of SNHL children with only 6 or 7 subjects per group.

2. Incorporate post-surgical behavioral outcome measures into the analysis to assess the value of pre-surgical neuroimaging and neural analyses. This will help elucidate the relationship between pre-surgical neuroimaging and post-surgical behavioral benefits, informing clinical decision-making.

3. Reevaluate data analyses for potential double dipping issues, specifically in lines 153-155 and Figure 3 C&D. The impaired pixels identified by comparing SNHL and NH groups are re-analyzed for the same comparison, potentially inflating effect size by only examining the ROI/pixels derived from the same comparison.

4. To better understand age effects, conduct the analyses presented in Figure 4 for NH children, which will serve as a baseline to demonstrate how different types of SNHL modulate the development of auditory and language pathways in comparison to normal development.

5. Ensure that modulation effects displayed in Figure 4 are controlled for confounding variables, such as residual hearing, sex, gestational weeks, birth weight, etc. to improve the validity of the results.

Essential revisions:

1) Behavioral data on the speech and language abilities of the children are available and should be reported.

Thank you for the insightful comment. We agree that the inclusion of post-implantation behavioral data on speech and language abilities is crucial to further support the value of our findings. We have now incorporated this data into our analysis, which can be found in the ‘Results’ section (see Page 15-17) and ‘Discussion’ section (see Page 23-25). Our analysis shows a promising correlation between the pre-operative MRI metrics and post-implant speech and language outcomes, suggesting the potential of these imaging markers in predicting outcomes.

2) The clinical evaluation and presentation of the children, both genetically and auditorily, is inadequate. Please provide any further details that would be useful for interpreting the other results.

Thank you for your comment. We agree that a more comprehensive clinical and genetic characterization of the children could add valuable context to our study. Accordingly, we have included more comprehensive clinical data, available genetic testing results, and auditory evaluation results for all children in the revised manuscript. Please see Supplementary file 1 and Supplementary file 2 for these updates. For more detailed information, especially about the genetic characterization and limitations of our study, please refer to our response to Reviewer 1's Comment 1 below.

3) The distinction between the groups isn't adequately considered. Please provide further details.

Thank you for your comment. We acknowledge the significance of clearly defining and justifying our choice of groups, particularly emphasizing Inner Ear Malformation (IEM) and Cochlear Nerve Deficiency (CND) to enhance the contextual understanding of our research. We have carefully reviewed reviewer 2's comment 1 and incorporated it into our revision process. We have added a comprehensive discussion on the significance of focusing on IEM&CND, the challenges it poses in clinical management, and the complexities associated with its etiology in the ‘Introduction’ section (Page 2, Line 15-43). These additions aim to provide a clearer understanding of the differentiation between the groups under study.

4) The practical relevance of the auditory-language pathways is unclear. Aside from behavioral data (point 1, above), a broader consideration of the studied pathways in the overall processing of hearing and language by the brain is needed.

Thank you for your feedback. We have expanded our discussion to encompass a broader consideration of the auditory and language pathways in speech processing. This includes an exploration of both hierarchical and parallel theories of acoustic-phonological mapping involving HG and STG, as well as the dynamic interactions between STG and higher-order brain regions during speech perception. We have also addressed the implications of our findings within this context (Page 23-24, Line 584-619). For more details, please refer to our response to Reviewer 2's Comment 2 below.

5) Add discussion of the possibility that the quantitative difference between effects on central auditory and language structures is due to the different ability to image each pathway.

Thank you for your valuable comment. We agree that the inherent challenges in imaging each pathway might indeed contribute to the observed differences in their structural effects. To address this concern, we have added an in-depth discussion on this point in the sections where we discuss the respective results of the auditory and language pathways (Page 22, Line 524-545). Specifically, we have included considerations on factors such as the severe distortion in diffusion images of the auditory pathway, particularly within the brainstem, the complex fiber crossing issue that is more pronounced within the auditory pathway, and the inherent differences in fiber numbers between the auditory and language pathways.

“Indeed, the quantitative differences observed between the effects on the central auditory and language structures may be influenced by the varying ability to accurately image each pathway with current neuroimaging technologies. The central auditory pathway, largely situated in the brainstem, faces severe distortion in diffusion images due to the inherent properties of the echo-planar imaging (EPI) sequence used. Despite distortion correction methods, it is challenging to entirely eliminate these effects. In contrast, the language pathways, primarily located in the cerebrum, have less distortion, allowing for more accurate imaging. Additionally, the auditory pathways in the brain (e.g., the auditory radiations) intersect with numerous cerebral fibre tracts, complicating accurate tractography. While the Constrained Spherical Deconvolution (CSD) modelling approach we employed is currently the most advanced method for addressing crossing fibres, it is not entirely exempt from these issues. The language pathways, on the other hand, intersect with fewer fibre tracts, facilitating more precise reconstruction. Lastly, the language pathways are more extensive and possess more fibres than the auditory pathways. From a statistical perspective, this enables more "sampling" when extracting average metrics such as fibre density, resulting in more stable outcomes. Therefore, the differences in our ability to visualize and quantify these pathways might have influenced the comparative effects observed. As neuroimaging technologies and methodologies continue to advance, we anticipate that our understanding of the differential effects of hearing loss on the central auditory and language structures will continue to evolve and improve.”

Additionally, we have included a summary of this discussion in the 'Limitations' section of our paper (Page 27, Line 654-658) to underscore its potential impact on our results. We hope that these additions sufficiently address your comment.

Reviewer #1 (Recommendations for the authors):

Overall, the authors have done a beautiful job creating high-resolution maps of the auditory pathway in these children. This type of imaging is not my speciality and so I cannot comment on the methodology or analysis behind the imaging. However, I am an expert on pediatric hearing loss and so I evaluated this manuscript from that perspective.

Detailed recommendations are below:

1) There are some major deficiencies with respect to the clinical evaluation/presentation of the children in this study which makes interpretation of the results difficult. First of all, there is no diagnosis provided. The causes of pediatric hearing loss is extraordinarily complex with hundreds of genetic forms and environmental causes as well. Hearing loss is not a diagnosis but is instead a symptom of an underlying pathologic change to the peripheral and/or central auditory system. The authors do not provide a diagnosis for the 13 children with hearing loss and as such any conclusions are limited. For example, if some of the children have GJB2 hearing loss, I would expect differences compared to children with CDH23 hearing loss (which has been shown to have some central auditory effect). Similarly, variants in CHD7, are associated with CHARGE syndrome and are a common cause of cochlear nerve deficiency, with some presumed central effect. The authors should provide more clinical history on individual patients, including hearing testing, and should at the least have performed genetic testing for a full evaluation of hearing loss. Ideally, the authors would then separate their subjects into groups (i.e. those with GJB2 hearing loss, versus others). This would make their study more powerful.

We appreciate the reviewer's insightful comment regarding the complex etiologies of pediatric hearing loss, and the importance of understanding the specific diagnoses, including potential genetic causes, to fully interpret our results. We agree that the genetic basis of hearing loss could potentially influence the outcomes we have studied, and that this is a limitation of our current study. As stated in our response to the Essential Revision, we have now included more detailed clinical, auditory, and available genetic data for all patients in our study.

To further elucidate our study participants' audiological profiles, we have included a detailed table in the supplements. This comprehensive table delineates each patient's audiological history and current status, including newborn hearing screening results, initial diagnosis parameters, intervention details, and recent preoperative audiometric evaluations. We believe this enriched data set will provide valuable context for interpreting our study's results (see Supplementary file 1).

Notably, all children included in our study have been clinically characterized as having non-syndromic hearing loss (see Supplementary file 2). Comprehensive family histories have been obtained, with no reported congenital hearing loss or known genetic disorders within their immediate families. In addition, full-body examinations have been performed to exclude any syndromic forms of hearing loss. Therefore, while we appreciate the importance of genetic factors as pointed out by the reviewer, it is important to note that our cohort is composed of children clinically diagnosed with non-syndromic hearing loss. To enhance clarity, we have included this information in the first paragraph of the ‘Introduction’ (Page 2, Line 9-14).

“The etiology of these impairments is multifaceted, with genetic factors playing a significant role, alongside external contributors such as congenital infections (Marazita et al., 1993). Hearing loss is categorized as syndromic, with accompanying physical or laboratory findings, or non-syndromic, where hearing loss is the sole symptom and has a highly heterogeneous genetic basis (Korver et al., 2017). This article primarily focuses on non-syndromic sensorineural hearing loss.”

We have added the available genetic testing results for half of our patients (seven out of thirteen, see Supplementary file 2) and have included a discussion on the possible implications of these results for our study's findings (see Page 21, Line 469-490). However, it is not feasible for us to recall the remaining patients for this testing due to practical constraints. We acknowledge in the manuscript that the absence of genetic testing results for all patients limits our ability to draw definitive conclusions regarding the impact of specific genetic variants on our findings (see Page 27, Line 649-653).

(Page 21, Line 469-490) “Our findings revealed a stronger and more significant correlation between language pathway fibre metrics and peripheral nerve tissue density than with central auditory metrics. This might seem counterintuitive given the structural and functional connections between the central auditory pathway and the peripheral cochlear nerve. For patients with profound SNHL, who present normal cochlear nerves and inner ear structures, the primary pathology is often located at the cellular level within the cochlea. However, in non-syndromic profound SNHL patients exhibiting IEM&CND, more severe genetic abnormalities are typically suspected. In our study cohort, genetic testing results were acquired for seven participants, revealing mutations in GJB2 (two patients), OTOF (one patient), and MYO15A (one patient). These mutations are largely associated with peripheral auditory deficits, given these genes' critical roles in inner ear development and function (Kelsell et al., 1997; Stelma & Bhutta, 2014; Wang et al., 1998; Yasunaga et al., 1999). Consequently, we hypothesized that observed central auditory pathway alterations could be adaptive changes due to these peripheral deficits. Nevertheless, it remains uncertain whether the detected central auditory anomalies stem from genetic factors directly or whether they are the result of underdevelopment due to auditory deprivation following peripheral dysfunction. Our findings suggest a weak association between profound hearing loss-related impaired fixels in the central auditory pathway and cn.VIII tissue density, casting doubt on the former hypothesis. It is important to consider, however, the potential of certain gene mutations such as CDH23 or CHD7 – typically linked with syndromic forms of hearing loss – to directly affect central auditory pathways (Astuto et al., 2002; Zentner et al., 2010). Although our study primarily included non-syndromic hearing loss patients, these potential genetic influences highlight the complexities inherent in interpreting auditory function and call for further in-depth investigation.”

(Page 27, Line 649-653) “We also acknowledge some limitations to this study: (1) The genetic dataset in our study is incomplete and heterogeneous. While two patients underwent extensive testing of over 200 genes, five were tested for select common genes. For a thorough understanding of the genetic factors in auditory deficits, future studies should employ uniform and comprehensive genetic testing across participants;”

Subsequent adjustments to the methodological and Results sections have been made accordingly, which can be found in Page 28, Line 701-705 (Methods) and Page 5, Line 117-119 (Results).

We hope that these additions address the reviewer's concerns, and we thank them for prompting us to more thoroughly discuss these important issues.

2) This study does not include cochlear implant and/or auditory brainstem implant outcomes. Therefore, the authors cannot have any claims to determining or predicting outcomes with these devices until their correlate this finding with those results. This would be an excellent follow-up study and the authors should temper their statements on clinical applicability until this is completed.

We appreciate your suggestion to include post-implantation outcomes to strengthen our findings and their clinical applicability. As addressed in our response to the essential revisions, we have now incorporated these outcomes into our analysis (see Page 15-17). Additionally, we have introduced a section titled “Integrating pre-surgical evaluations for optimized auditory-language rehabilitation” in the ‘Discussion’ to elaborate on the implications and limitations of these results (see Page 23-25). This inclusion lends more substantiation to our assertions regarding the predictive value of the pre-operative MRI metrics for CI/ABI outcomes.

3) I assume that the CIs and ABIs were implanted after imaging? This is not specified anywhere that I could find.

Yes, both CT and MRI scans were conducted preoperatively during the evaluation phase for CI or ABI implantation. To enhance clarity, we have now restructured our 'Methods' section into five major subsections, namely: 'Study sample,' 'Pre-operative clinical and imaging evaluation', 'Post-operative outcomes assessment,' 'MRI data analysis,' and 'Statistical analysis.' The 'Clinical data' and 'CT, MRI acquisition, and quality assessment' aspects have been incorporated under the 'Pre-operative clinical and imaging evaluation' subsection.

4) The figure legends are confusing. Please indicate whether each image is an individual patient or an average and which group each image belongs to.

Thank you for your comment. We have made revisions to the legends in Figure 1 and Figure 2 to provide clearer information. We apologize for any confusion caused. If there are any other specific points that require clarification or if you have further suggestions, please let us know, and we will address them accordingly. Your feedback is greatly appreciated.

5) The graphs in Figure 3 are very very difficult to read. The authors should reformat these so that there is a white background and the bars are filled (or at least not use the current color scheme).

Thank you for your comment. We apologize for any difficulties in interpretation caused by the current color scheme and formatting. We have made the necessary adjustments. If you have any other specific recommendations or concerns, please let us know, and we will address them accordingly.

6) The authors should be careful about how they use the word 'deaf'. 'Deaf or hard of hearing' is an appropriate way to describe individuals with hearing loss. Severe-to-profound or profound are accurate ways to describe the severity of a hearing loss. The use of the word 'deaf' to describe profound hearing loss is generally discouraged as it is not scientific and may be confused by 'Deaf' (capital D) which describes members of the Deaf community.

Please see above.

We appreciate this comment. The revised terms are now highlighted in green color throughout the manuscript. We consistently use 'profound hearing loss' to describe the patients in this study (hearing level over 95 dB at both sides before operation) and choose accurate descriptions when referencing the work of other authors.

Reviewer #2 (Recommendations for the authors):

General comments

– Although the paper tried to justify why focusing on IEM and CND is important, such decision seems still arbitrary to this reviewer. More discussion on what makes these hearing loss etiologies distinct from other etiologies would help.

Thank you for your valuable feedback. We understand the reviewer's concern regarding the justification for focusing on IEM&CND and the need to address it more comprehensively.

Emphasizing IEM & CND is crucial because it poses increased challenges in clinical decision-making compared to cases with normal inner ear structures, particularly in determining the suitable surgical intervention.

It is worth noting that the etiology of IEM&CND involves both genetic and environmental factors, similar to patients with normal inner ears. However, the genetic basis of non-syndromic IEM&CND is not extensively studied, which makes establishing clear genetic correlations challenging. Nonetheless, it is generally understood that the developmental arrest associated with IEM&CND occurs earlier compared to cases with normal inner ears.

To address these concerns, we have revised the second paragraph of the ‘Introduction’ (see Page 2, Line 15-43). This revised section elaborates on the challenges in clinical management posed by IEM&CND, provides an explanation for its complex etiology, and highlights the limitations of contemporary clinical imaging in diagnosing, evaluating prognosis, and guiding surgical strategies for profound SNHL patients, particularly those with IEM&CND.

“Inner ear malformations and cochlear nerve deficiencies (IEM&CND), identifiable with CT and MRI, contribute to 15-39% of pediatric sensorineural hearing loss (SNHL) cases (Li et al., 2011; Mafong et al., 2002). The rest is primarily due to cellular-level abnormalities. The etiology of IEM&CND is complex and largely unknown, yet these abnormalities suggest an earlier developmental arrest compared to cases with normal inner ear structures (Sennaroglu & Saatci, 2002). Addressing IEM&CND is vital as it presents specific challenges in clinical management. Cochlear implantation (CI) and auditory brainstem implantation (ABI) are currently the only solutions for profound SNHL, but the choice between CI and ABI presents a dilemma for many IEM&CND patients. CI directly stimulates the spiral ganglion cells, the first-order neurons of the auditory pathway, while ABI bypasses the cochlear nerve and stimulates the second-order auditory neurons in the cochlear nucleus when complex inner ear malformation or cochlear nerve aplasia makes CI inapplicable (Chen & Oghalai, 2016). Both CI and ABI are capable of providing hearing sensation and language development for children with severe-to-profound prelingual hearing loss, but postoperative outcomes vary among individuals. ABI recipients generally have poorer speech recognition performance and delayed and incomplete language development compared to CI recipients (Sennaroğlu et al., 2016). However, the prognosis of CI may not necessarily be better than that of ABI for certain IEMs, including common cavity, cochlear hypoplasia, incomplete partition-type I, and cochlear aperture abnormalities, as the presence of sufficient cochlear fibres required for CI success is uncertain (Freeman & Sennaroglu, 2018; Sennaroğlu & Bajin, 2017). This uncertainty is hard to address for two reasons: (1) assessing the cochlear nerve through visual inspection of MRI poses challenges, including subjective limitations, image quality, and difficulty distinguishing the cochlear nerve from the cranial nerve VIII (the cochleovestibular nerve) in cases of common cavity; and (2) the absence of the cochlear nerve structurally does not always indicate a lack of functional hearing (Thai-Van et al., 2000). Furthermore, distinguishing certain types of IEMs can be problematic and may impact surgical decision-making. For example, differentiating cochlear aplasia with a dilated vestibule (CADV) from a common cavity is challenging; CADV is a definitive indication for ABI, while a common cavity allows for either CI or ABI. These observations underscore the importance of a deep dive into IEM&CND, and highlight the limitations of contemporary clinical imaging in diagnosis, prognosis evaluation, and surgical guidance.”

Additionally, the penultimate paragraph of the introduction reinforces the inclusion of IEM&CND in our study (Page 3, Line 83-88).

“Furthermore, previous neuroimaging studies of profound congenital SNHL excluded children with IEM&CND. However, as mentioned earlier, these subjects comprise a significant proportion of patients with congenital hearing loss, and are more inclined to be faced with difficult surgical decisions and unsatisfactory post-implantation outcomes. Therefore, it is important to include and focus on children with IEM&CND when studying central adaptations associated with profound hearing loss.”

We hope that these modifications effectively justify the significance of focusing on IEM&CND and adequately address the concerns raised by the reviewer.

– The paper does not discuss how these results help with a better understanding of speech sound encoding and decoding in listeners with normal and impaired hearing. It is particularly important to discuss these findings in the context of mapping sound patterns to phonological units.

– Also, there is a rich literature on hearing, language comprehension, and speech perception network from Gregory Hickok, David Poeppel, and Jonathan Peelle which are not discussed in this paper.

We appreciate your feedback regarding the need to contextualize our findings in the understanding of speech processing in listeners with normal and impaired hearing.

We opted to employ Angela D. Friederici's language model, given its explicit elucidation of the neuroanatomical fiber bundles associated with specific language functions. This aspect is particularly aligned with our study's emphasis on white matter structure. While Hickok and Poeppel's dual-stream model offers valuable insights, it does not directly associate the processing streams with specific neuroanatomical fiber bundles. However, it is noteworthy that there are considerable similarities between the two models, as both recognize the role of ventral streams in semantic processing and dorsal streams in sensorimotor integration. Additionally, Friederici's model includes a dorsal stream dedicated to syntactic processing. Our reason for basing our language pathway tracking on Friederici's model has been added in the Discussion section (Page 20, Line 437-439).

Further, we have expanded our discussion to elaborate on how our findings contribute to a better understanding of speech processing. We have included a discussion on both the hierarchical and parallel theories of acoustic-phonological mapping in HG and STG. Additionally, we have highlighted the significance of the dynamic interplay between STG and higher-level brain areas in speech perception, particularly under challenging conditions such as the use of auditory implants. We have discussed the implications of our findings in relation to these aspects. Please refer to Page 23-24, Line 584-619 for the revised content.

Title

– The title needs to be more specific to the questions of this study. Using the general term "Peripheral Auditory Structure" could be misleading. Please revise to reflect both the specific types of hearing loss in this study and also the population that is children with profound hearing loss, but receive CI or ABI as interventions.

Thank you for your feedback. We agree that the term "Peripheral Auditory Structure" may not accurately convey the focus of our study. As a result, we have revised the title to "Impact of Inner Ear Malformations and Cochlear Nerve Deficiencies on the Development of Auditory-Language Network in Children with Profound Sensorineural Hearing Loss" to address this concern.

Regarding the decision not to include "receive CI or ABI" in the title, our intention was to avoid any potential ambiguity that could lead readers to assume that our study primarily focuses on post-implantation developmental changes in the auditory-language network. However, what we analyzed are the pre-implant MRI structures. We sincerely appreciate your valuable feedback, and we have taken your comments into account to enhance the clarity and accuracy of our study.

Additionally, we have updated the title of the fifth subsection in 'Results,' changing it from 'Peripheral auditory structure moderated the structural development of central pathways' to 'Peripheral nerve structure…' for enhanced clarity (Page 12, Line 200).

Abstract:

– Cochlear implantation and auditory brainstem implantation can provide hearing sensation… it is more accurate to say … provide "partial" hearing sensation …

We agree with this comment and have added the word “partial”.

– "Previous attempts to locate subcortical auditory nuclei using fMRI responses to sounds are not applicable to deaf patients." Please add a reason(s) for why these previous attempts are not applicable.

We appreciate this comment and have added the reason as follows.

“Previous attempts to locate subcortical auditory nuclei using fMRI responses to sounds are not applicable to patients with profound hearing loss as no auditory brainstem responses can be detected in these individuals, making it impossible to capture corresponding blood oxygen signals in fMRI.”

– Line 3: lifelong "negative" consequences …

We agree with this comment and have added the word “negative”.

– Line 39: please check the in-text citations format

Thank you for your comment. We have followed the APA style, as suggested by eLife. In regard to the two references published in 2019 with different authors named "Wang," we used their initials to differentiate them. However, we have now updated the references to use the first and second authors for proper distinction (see Page 3, Line 57). If you have any specific requirements or further recommendations regarding this scenario, please let us know.

“(H. Wang et al., 2019; S. Wang, et al., 2019; Wu et al., 2016)” >> “(Wang, Liang, et al., 2019; Wang, Chen, et al., 2019; Wu et al., 2016)”

– Line 52: "Secondly, although earlier studies reported several altered fibre tracts related to language function, …" Please add references to support this statement.

Thank you for your comment. To address any potential misunderstanding, we have included the references again in Line 72-73, even though they were previously mentioned in the paragraph from Line 54-59.

– Line 83: mean [SD], age, 30.92 [6.115] months; 9 males, 4 females

– Mean [SD] of age

– You can remove 4 females as it is redundant

– Please consider the same edit for NH

– Line 85: Please indicate the confidence interval in your stats report

We agree with these two comments and have revised them in Page 5, Line 105-110.

“Twenty-three children aged under six years old including thirteen patients with bilateral profound congenital sensorineural hearing loss (mean [SD] of age, 30.92 [6.115] months; 9 males) and ten normal hearing volunteers (mean [SD] of age, 42.90 [4.270] months; 5 males) matched on age and gender were included (Mann-Whitney U test: p-value for age = 0.077, 95% confidence interval for the difference in age = [-32.0, 6.5] months; p-value for gender = 0.446, 95% confidence interval for the difference in male counts = [-1, 4]).”

Table 1

– I recommend using the term gender instead of sex

We appreciate this comment and have revised “sex” to “gender” through the manuscript.

Line 137

– FC, FD, and FDC are presented here for the first time in the manuscript. So, their extended version should be used here to avoid possible confusion.

We agree with this comment and have provided the extended version in Page 10, Line 167-168.

“In the central auditory pathway, FBA results demonstrated reduced fibre density (FD), fibre cross-section (FC), and fibre density and cross-section (FDC) in TB and decreased FC in LL in patients with profound SNHL.”

Caption of Figure 3: please report the actual p-values

Thank you. We have revised it in Figure 3 as well as in Figure 4.

– Line 139: ie. -->◊ i.e.

Thank you. We have corrected this (Page 10, Line 170).

Figure 3

– I wonder how the data and results would look if ABI and CI groups were represented separately.

Thank you for your comment. We recognize the merit in examining the differences between the ABI and CI groups in Figure 3. Although the primary objective of Figure 3 was to compare the hearing-impaired patients as a collective group against the normal control group, we have addressed your request by incorporating an additional figure (Figure 3—figure supplement 1), which compares the ABI, CI, and NH groups. Initially, we employed a one-way ANOVA to assess FD, FC, and FDC at the fixel-level with FWE correction, but no significant fixels were observed, possibly due to the small sample size. Subsequently, tract-level measures were used to illustrate the trend of differences among these three groups in Figure 3—figure supplement 1. We did not perform an ANOVA or post hoc tests at the tract-level to avoid the double dipping issue, especially since the fixel-level ANOVA did not yield significant results.

– Line 167-168: please elaborate on why such surgical decisions have limited value.

Thank you for the comment. We have elaborated on the challenges faced in making surgical decisions for patients with IEM&CND in a new paragraph in the ‘Introduction’ section, where we discuss the limitations of relying solely on visual inspection through CT and MRI (please refer to our response to your first comment). In light of this addition, we have decided to remove this sentence in the ‘Results’ section as the issue is now addressed in the ‘Introduction’.

– Line 193: Report the actual p–values

Thank you. We have reported the actual p-values through the section ‘Peripheral nerve structure moderated the structural development of central pathways’ to improve clarity (Page 12).

– Line 451: Provide more details on the nature of click stimuli including frequency.

Thank you for pointing out this omission. We used standard brief acoustic signals, also known as "clicks," for our study. These click stimuli have a duration of 100 microseconds and cover a broad frequency range from 100 Hz to 10,000 Hz. We have included these details in the ‘Methods’ section in Page 28, Line 695-698.

– Line 453: Clarify the dB HL used for NH decision (20 dB HL?).

Yes, we have added the standard (see Page 28, Line 682).

“The controls had normal hearing in both ears (click ABR threshold/PTA < 20 dB HL).”

– Line 468: If these measurements were not used in this study, it is not clear to this reviewer why they are reported. Please clarify.

Thank you for your observation. We acknowledge the lack of clarity in our initial manuscript. As indicated in our responses to the essential revisions and Reviewer 1's comment, we have now included post-implant behavioral outcome measures into our analysis (see Page 15-17 and Page 23-25). This update serves to validate the utility of our pre-operative MRI metrics for predicting post-implant behavioral outcomes.

– Line 479–481: please add inter–rater reliability to provide evidence on the acceptable agreement between raters.

Thank you for your suggestion. We have added the inter-rater reliability information to the relevant section (see Page 28, Line 713) of our manuscript to provide additional evidence for the reliability of our findings.

– Line 503–504: please report how many images were excluded in this process if any.

Thank you for your comment. It's important to clarify that no images were excluded during the quality control process. In one instance, an inappropriate head position was observed at the beginning of the scanning session, but it was promptly identified and corrected to ensure reliable data collection. As this issue was promptly resolved and did not impact the final dataset, it was not mentioned in the manuscript.

– Line 511: Does {plus minus}0.010 refer to standard deviation? If so, please indicate that in the paper.

Yes, we have clarified it (see Page 29, Line 753-756).

“The Cn.VIII was segmented on each axial section using ITK-SNAP (Yushkevich et al., 2006) by two independent raters. The inter-rater agreement was assessed using the DICE coefficient, yielding a value of 0.977±0.010 (mean± SD).”

– Table 1 and 2 in appendices: round correlation coefficients to two decimal places. More than two decimal places seem redundant.

We agree with this comment and have rounded correlation coefficients to two decimal places (now in Figure 4-source data 1).

Reviewer #3 (Recommendations for the authors):

Recommendations:

1. Enhance the reliability and replicability of research findings by increasing the sample size for both SNHL and NH children. The current sample size is insufficient for between-group comparisons and correlational analyses, particularly when examining sub-groups of SNHL children with only 6 or 7 subjects per group.

Thank you for your valuable comments. We wholeheartedly agree that a larger sample size can provide more robust and generalizable results. However, in the context of our current study, we are confronted with practical constraints, including the unique characteristics of our target population and limited available resources. Expanding the participant pool requires specific procedures such as patient recruitment, informed consent, data collection, and data processing. Given the scope of our current study and resources, it is challenging to significantly expand our sample size at this stage. Despite this limitation, we believe our findings are valuable, shedding light on an area with limited prior research. We have exercised rigorous statistical controls and analyses to maximize the validity and reliability of our findings within the confines of our sample size. We recognize the necessity for further research with larger, more diverse cohorts, and have acknowledged this need in the limitations section of our paper. Thank you once again for your constructive suggestions.

2. Incorporate post-surgical behavioral outcome measures into the analysis to assess the value of pre-surgical neuroimaging and neural analyses. This will help elucidate the relationship between pre-surgical neuroimaging and post-surgical behavioral benefits, informing clinical decision-making.

We appreciate your suggestion to incorporate post-surgical behavioral outcome measures into our analysis. As we mentioned in our responses to previous comments, we have incorporated these measures into our analysis (see Page 15-17 and Page 23-25). The correlation between pre-operative MRI metrics and post-implant behavioral outcomes further supports the potential utility of our imaging markers in informing clinical decision-making.

3. Reevaluate data analyses for potential double dipping issues, specifically in lines 153-155 and Figure 3 C&D. The impaired pixels identified by comparing SNHL and NH groups are re-analyzed for the same comparison, potentially inflating effect size by only examining the ROI/pixels derived from the same comparison.

Thank you for your comment. We recognize that there is indeed a risk of inflating the effect size by re-analyzing the impaired pixels identified from the same comparison.

In our study, we opted for ROI-based analysis to visually demonstrate the fiber structure of each segment in the auditory pathway, as it offers a clear and intuitive representation. We used FWE correction as a statistical control to mitigate the likelihood of false positives in the ROI analysis. However, we acknowledge that FWE correction does not directly address the potential double dipping issue.

We have acknowledged this limitation and included a note in the Results section of our paper (Page 10, Line 186-189). Your feedback has prompted us to provide a more explicit explanation of the limitations and considerations associated with our analysis methods. We sincerely appreciate your valuable input.

“It is worth noting, however, that in our tract-of-interest analysis, there is the potential for double dipping as we re-analysed the impaired pixels identified from the same comparison, which could have impacted the reported effect size.”

4. To better understand age effects, conduct the analyses presented in Figure 4 for NH children, which will serve as a baseline to demonstrate how different types of SNHL modulate the development of auditory and language pathways in comparison to normal development.

Thank you for your comment and suggestion. We appreciate your suggestion about studying the relationship between peripheral nerve development and central auditory and language pathways in normal condition.

However, we would like to mention that in our study, the normal hearing control group did not undergo scanning with the CISS sequence, which is necessary for measuring peripheral nerve indicators. Consequently, we currently lack the data required for a direct comparison of the development of auditory and language pathways between NH children and those with different types of sensorineural hearing loss.

We have acknowledged this limitation in our study and have discussed it in the relevant section of discussion (Page 22, Line 541-545). We sincerely appreciate your feedback, and it will undoubtedly guide our future research efforts.

“Another limitation is that the normal hearing control group did not undergo scanning with the CISS sequence, precluding the establishment of a baseline for comparing the development of auditory and language pathways in SNHL to normal development, which requires future research to include a baseline assessment.”

5. Ensure that modulation effects displayed in Figure 4 are controlled for confounding variables, such as residual hearing, sex, gestational weeks, birth weight, etc. to improve the validity of the results.

Thank you for your valuable comment. We acknowledge that these variables could potentially impact the validity of the results. We acknowledge the importance of considering residual hearing as a potential factor; however, it is worth noting that all patients in our study exhibited click ABR and/or pure tone thresholds (at 0.5k, 1k, 2k, and 4k Hz) that were over 95 dB HL bilaterally, indicating a profound degree of hearing loss. In fact, the pure tone audiometry results showed thresholds over 95 dB HL at each frequency of 0.5k, 1k, 2k, and 4k Hz for all patients who took the exam. Given this, we did not include residual hearing as a covariate in our analysis. However, we have re-calculated the moderation analyses while controlling for other variables including gender, gestational weeks, and birth weights. The results of these analyses can be found in Figure 4-source data 2, and are described in the relevant sections under ‘Results’ (Page 12, Line 239-241) and ‘Discussion’ (Page 22, Line 517-519). The moderation effect became non-significant after controlling these variables, potentially due to a relatively small sample size in comparison to the number of control variables. We appreciate your insightful suggestion, as it has urged us to interpret the results with greater caution.

We hope that the revisions and our responses adequately address the reviewers' comments and meet the standards of eLife. We are open to further suggestions and look forward to your response.

Author details

1. Yaoxuan Wang

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Contributed equally with

   Mengda Jiang and Yuting Zhu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1002-7242

2. Mengda Jiang

   Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Contributed equally with

   Yaoxuan Wang and Yuting Zhu

   Competing interests

   No competing interests declared

3. Yuting Zhu

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Formal analysis, Validation, Visualization, Methodology, Writing – review and editing

   Contributed equally with

   Yaoxuan Wang and Mengda Jiang

   Competing interests

   No competing interests declared

4. Lu Xue

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Validation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Wenying Shu

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

6. Xiang Li

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

7. Hongsai Chen

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Validation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Yun Li

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Data curation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

9. Ying Chen

   1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
   3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

   Contribution

   Data curation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

10. Yongchuan Chai

    1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

    Contribution

    Resources, Data curation, Supervision, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

11. Yu Zhang

    1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

    Contribution

    Data curation, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

12. Yinghua Chu

    MR Collaboration, Siemens Healthineers Ltd, Shanghai, China

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    Yinghua Chu is affiliated with Siemens Healthineers Ltd. The author has no financial interests to declare

13. Yang Song

    MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, China

    Contribution

    Data curation, Methodology, Project administration, Writing – review and editing

    Competing interests

    Yang Song is affiliated with Siemens Healthineers Ltd. The author has no financial interests to declare

14. Xiaofeng Tao

    Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Contribution

    Resources, Data curation, Supervision, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

15. Zhaoyan Wang

    1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Project administration, Writing – review and editing

    For correspondence

    wzyent2019@163.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0977-0920

16. Hao Wu

    1. Department of Otolaryngology, Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    2. Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    3. Shanghai Key Laboratory of Translational Medicine on Ear and Nose diseases, Shanghai, China

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Project administration, Writing – review and editing

    For correspondence

    wuhao@shsmu.edu.cn

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5317-902X

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