Cerebral volume and diffusion MRI changes in children with sensorineural hearing loss

Moon, Peter K.; Qian, Z Jason; McKenna, Emily; Xi, Kevin; Rowe, Nathan C.; Ng, Nathan; Zheng, Jimmy; Tam, Lydia; MacEachern, Sarah J.; Ahmad, Iram; Cheng, Alan G.; Forkert, Nils D.; Yeom, Kristen W.

doi:10.1016/j.nicl.2020.102328

Cited by 10 publications

(11 citation statements)

References 47 publications

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“…CHv and CHa exhibited decreased connectivity with the hippocampus and posterior cingulate, respectively. These findings further support interactions between hearing loss and memory (McCoy et al, 2005;Moon et al, 2020;Rabbitt 1991;Shang et al, 2018;Zhang et al, 2018) and hearing loss and the default mode network (Jung, Colletta, Coalson, Schlaggar, & Lieu, 2017;.…”

Section: Striatal Network Associated With Hearing Losssupporting

confidence: 73%

Striatal networks for tinnitus treatment targeting

Larson

Sabes

Mizuiri

et al. 2021

Human Brain Mapping

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Neuromodulation treatment effect size for bothersome tinnitus may be larger and more predictable by adopting a target selection approach guided by personalized striatal networks or functional connectivity maps. Several corticostriatal mechanisms are likely to play a role in tinnitus, including the dorsal/ventral striatum and the putamen. We examined whether significant tinnitus treatment response by deep brain stimulation (DBS) of the caudate nucleus may be related to striatal network increased functional connectivity with tinnitus networks that involve the auditory cortex or ventral cerebellum. The first study was a cross‐sectional 2‐by‐2 factorial design (tinnitus, no tinnitus; hearing loss, normal hearing, n = 68) to define cohort level abnormal functional connectivity maps using high‐field 7.0 T resting‐state fMRI. The second study was a pilot case–control series (n = 2) to examine whether tinnitus modulation response to caudate tail subdivision stimulation would be contingent on individual level striatal connectivity map relationships with tinnitus networks. Resting‐state fMRI identified five caudate subdivisions with abnormal cohort level functional connectivity maps. Of those, two connectivity maps exhibited increased connectivity with tinnitus networks—dorsal caudate head with Heschl's gyrus and caudate tail with the ventral cerebellum. DBS of the caudate tail in the case‐series responder resulted in dramatic reductions in tinnitus severity and loudness, in contrast to the nonresponder who showed no tinnitus modulation. The individual level connectivity map of the responder was in alignment with the cohort expectation connectivity map, where the caudate tail exhibited increased connectivity with tinnitus networks, whereas the nonresponder individual level connectivity map did not.

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Section: Striatal Network Associated With Hearing Losssupporting

confidence: 73%

Striatal networks for tinnitus treatment targeting

Larson

Sabes

Mizuiri

et al. 2021

Human Brain Mapping

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“…The caudate nucleus was reported to be involved in the center of language control, monitoring and controlling vocabulary and language selection [17]. Prior study indicated that the GM density of the caudate nucleus in children with CSSHL was reduced compared with children with normal hearing [18]. Some studies also confirmed that the caudate nucleus is the center of habitual learning, and its microstructural alteration may be the basis of learning difficulties and decreased academic performance in CSSHL children [19].…”

Section: Discussionmentioning

confidence: 99%

Gray matter density changes in children with congenital severe sensorineural deafness: a voxel-based morphometric study

2023

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Early hearing loss could cause abnormal brain development, which has been linked to the complex process known as cross-modal neuroplasticity. However, previous studies investigating the brain structure of infants with congenital severe sensorineural hearing loss (CSSHL) are scarce and have yielded inconsistent results. This study aimed to further explore the gray matter (GM) density changes in children with CSSHL. Fifteen children aged 0–5 years with CSSHL and 11 healthy children as controls (aged 0–5 years) were recruited. Each participant underwent a structural MRI scan. The voxel-based morphometry method was performed to evaluate GM density for each participant and analyze their characteristics. It was discovered that: (1) GM density of the right superior temporal gyrus and caudate in the CSSHL group was smaller than that of healthy controls (HC). However, GM density was larger in the left posterior central gyrus, superior frontal gyrus, inferior parietal lobule and right cerebellum in the CSSHL group compared with HC. (2) The GM density value of the left superior frontal gyrus and inferior parietal lobule was negatively correlated with age. However, the GM density value of the right superior temporal gyrus in the CSSHL group was positively correlated with age. Compared with HC, the GM density of CSSHL children was larger in somatosensory areas (including left superior frontal gyrus, posterior central gyrus, inferior parietal lobule and right cerebellum), whereas GM density was smaller in auditory-related areas (such as the right superior temporal gyrus and caudate). Moreover, GM density change was influenced by the duration of hearing deprivation.

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“…Previous studies reported significant differences in the MRI-based cerebral volume and gray matter microstructure in children with SNHL ( Moon et al, 2020 ). Hearing loss may affect white matter tracts linking the eighth cranial nerve to subcortical nuclei (i.e., cochlear) and primary auditory cortices (i.e., Heschl’s gyri) and the gray matter of primary auditory cortices ( Feng et al, 2018 ; Tarabichi et al, 2018 ).…”

Section: Introductionmentioning

confidence: 91%

Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

Song

Jin

et al. 2022

Front. Comput. Neurosci.

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Identification of congenital sensorineural hearing loss (SNHL) and early intervention, especially by cochlear implantation (CI), are crucial for restoring hearing in patients. However, high accuracy diagnostics of SNHL and prognostic prediction of CI are lacking to date. To diagnose SNHL and predict the outcome of CI, we propose a method combining functional connections (FCs) measured by functional magnetic resonance imaging (fMRI) and machine learning. A total of 68 children with SNHL and 34 healthy controls (HC) of matched age and gender were recruited to construct classification models for SNHL and HC. A total of 52 children with SNHL that underwent CI were selected to establish a predictive model of the outcome measured by the category of auditory performance (CAP), and their resting-state fMRI images were acquired. After the dimensional reduction of FCs by kernel principal component analysis, three machine learning methods including the support vector machine, logistic regression, and k-nearest neighbor and their voting were used as the classifiers. A multiple logistic regression method was performed to predict the CAP of CI. The classification model of voting achieves an area under the curve of 0.84, which is higher than that of three single classifiers. The multiple logistic regression model predicts CAP after CI in SNHL with an average accuracy of 82.7%. These models may improve the identification of SNHL through fMRI images and prognosis prediction of CI in SNHL.

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Cerebral volume and diffusion MRI changes in children with sensorineural hearing loss

Cited by 10 publications

References 47 publications

Striatal networks for tinnitus treatment targeting

Striatal networks for tinnitus treatment targeting

Gray matter density changes in children with congenital severe sensorineural deafness: a voxel-based morphometric study

Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

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