Association of Poorer Hearing With Longitudinal Change in Cerebral White Matter Microstructure | Geriatrics | JAMA Otolaryngology–Head & Neck Surgery | JAMA Network
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Figure 1.  Dot Plot Representing the t Values of Results From Associations Between Hearing Assessments and Baseline Fractional Anisotropy (FA) and Mean Diffusivity (MD) From Linear Mixed-Effects Models
Dot Plot Representing the t Values of Results From Associations Between Hearing Assessments and Baseline Fractional Anisotropy (FA) and Mean Diffusivity (MD) From Linear Mixed-Effects Models

Orange represents the t values from pure-tone average (PTA). Blue represents the t values from speech recognition thresholds (SRTs). Sup fronto-occ fasc indicates superior fronto-occipital fasciculus; inf fronto-occ fasc, inferior fronto-occipital fasciculus; fasc, fasciculus; CC, corpus callosum; ant, anterior; sup, superior; post, posterior; int, internal. Solid lines represent the thresholds for significance of P < .05 (blue) and P < .01 (orange). Points outside the solid lines represent significant associations. Linear mixed-effects models with random intercepts and slopes included the following fixed effects: PTA, baseline age, sex, race (White vs other), baseline hypertension (systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or treatment with antihypertensive medications), baseline obesity (body mass index ≥30 kg/m2), baseline elevated total cholesterol (≥200 mg/dL), time since first concurrent hearing and magnetic resonance imaging/diffusion tensor imaging assessment, and 2-way interactions of PTA, baseline age, sex, and race with time since first concurrent hearing and magnetic resonance imaging/diffusion tensor imaging assessment.

Figure 2.  Dot Plot Representing the t Values of Results From Associations Between Hearing Assessments and Change in Fractional Anisotropy (FA) and Mean Diffusivity (MD) Over Time From Linear Mixed-Effects Models
Dot Plot Representing the t Values of Results From Associations Between Hearing Assessments and Change in Fractional Anisotropy (FA) and Mean Diffusivity (MD) Over Time From Linear Mixed-Effects Models

Orange represents the t values from pure-tone average (PTA). Blue represents the t values from speech recognition thresholds (SRT). Sup fronto-occ fasc indicates superior fronto-occipital fasciculus; inf fronto-occ fasc, inferior fronto-occipital fasciculus; fasc, fasciculus; CC, corpus callosum; ant, anterior; sup, superior; post, posterior; int, internal. Solid lines represent the thresholds for significance of P < .05 (blue) and P < .01 (orange). Points outside the dashed lines represent significant associations. Association between PTA and change in corpus callosum body as well as the association between signal-to-noise ratio and change in uncinate fasciculus survived false discovery rate correction. Linear mixed-effects models with random intercepts and slopes included the following fixed effects: PTA, baseline age, sex, race (White vs other), baseline hypertension (systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or treatment with antihypertensive medications), baseline obesity (body mass index ≥30 kg/m2), baseline elevated total cholesterol (≥200 mg/dL), time since first concurrent hearing and magnetic resonance imaging/diffusion tensor imaging assessment, and 2-way interactions of PTA, baseline age, sex, and race with time since first concurrent hearing and magnetic resonance imaging/diffusion tensor imaging assessment.

Figure 3.  Regions of Interest in Associations of Pure-Tone Average and Speech-in-Noise Scores With Change in Diffusion Tensor Imaging Metrics
Regions of Interest in Associations of Pure-Tone Average and Speech-in-Noise Scores With Change in Diffusion Tensor Imaging Metrics

A, The significant tracts from the association of pure-tone average with change in white matter microstructure. Teal indicates the body of the corpus callosum, and violet indicates the inferior fronto-occipital fasciculus. B, The significant tract from the association of speech-in-noise score with change in white matter microstructure. Red indicates the uncinate fasciculus. The regions of interest came from the Eve WM atlas,29 and these are superimposed on a T1-weighted image.

Table.  Characteristics of Study Sample From the Baltimore Longitudinal Study of Aging (N = 356)
Characteristics of Study Sample From the Baltimore Longitudinal Study of Aging (N = 356)
1.
Lin  FR, Niparko  JK, Ferrucci  L.  Hearing loss prevalence in the United States.   Arch Intern Med. 2011;171(20):1851-1852. doi:10.1001/archinternmed.2011.506PubMedGoogle ScholarCrossref
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Deal  JA, Betz  J, Yaffe  K,  et al; Health ABC Study Group.  Hearing impairment and incident dementia and cognitive decline in older adults: the Health ABC Study.   J Gerontol A Biol Sci Med Sci. 2017;72(5):703-709.PubMedGoogle Scholar
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Lin  FR, Metter  EJ, O’Brien  RJ, Resnick  SM, Zonderman  AB, Ferrucci  L.  Hearing loss and incident dementia.   Arch Neurol. 2011;68(2):214-220. doi:10.1001/archneurol.2010.362PubMedGoogle ScholarCrossref
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Lin  FR, Ferrucci  L, Metter  EJ, An  Y, Zonderman  AB, Resnick  SM.  Hearing loss and cognition in the Baltimore Longitudinal Study of Aging.   Neuropsychology. 2011;25(6):763-770. doi:10.1037/a0024238PubMedGoogle ScholarCrossref
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8.
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9.
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10.
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11.
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12.
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14.
Lin  FR, Ferrucci  L, An  Y,  et al.  Association of hearing impairment with brain volume changes in older adults.   Neuroimage. 2014;90:84-92. doi:10.1016/j.neuroimage.2013.12.059PubMedGoogle ScholarCrossref
15.
Armstrong  NM, An  Y, Doshi  J,  et al.  Association of midlife hearing impairment with late-life temporal lobe volume loss.   JAMA Otolaryngol Head Neck Surg. 2019. doi:10.1001/jamaoto.2019.1610PubMedGoogle Scholar
16.
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Armstrong  NM, An  Y, Beason-Held  L,  et al.  Predictors of neurodegeneration differ between cognitively normal and subsequently impaired older adults.   Neurobiol Aging. 2019;75:178-186. doi:10.1016/j.neurobiolaging.2018.10.024PubMedGoogle ScholarCrossref
21.
Williams  OA, An  Y, Beason-Held  L,  et al.  Vascular burden and APOE ε4 are associated with white matter microstructural decline in cognitively normal older adults.   Neuroimage. 2019;188:572-583. doi:10.1016/j.neuroimage.2018.12.009PubMedGoogle ScholarCrossref
22.
Killion  MC, Niquette  PA, Gudmundsen  GI, Revit  LJ, Banerjee  S.  Development of a quick speech-in-noise test for measuring signal-to-noise ratio loss in normal-hearing and hearing-impaired listeners.   J Acoust Soc Am. 2004;116(4 pt 1):2395-2405. doi:10.1121/1.1784440PubMedGoogle ScholarCrossref
23.
Lauzon  CB, Asman  AJ, Esparza  ML,  et al.  Simultaneous analysis and quality assurance for diffusion tensor imaging.   PLoS One. 2013;8(4):e61737. doi:10.1371/journal.pone.0061737PubMedGoogle Scholar
24.
Tian  Q, Ferrucci  L, Resnick  SM,  et al.  The effect of age and microstructural white matter integrity on lap time variation and fast-paced walking speed.   Brain Imaging Behav. 2016;10(3):697-706. doi:10.1007/s11682-015-9449-6PubMedGoogle ScholarCrossref
25.
Alexander  DC, Pierpaoli  C, Basser  PJ, Gee  JC.  Spatial transformations of diffusion tensor magnetic resonance images.   IEEE Trans Med Imaging. 2001;20(11):1131-1139. doi:10.1109/42.963816PubMedGoogle ScholarCrossref
26.
Chang  LC, Jones  DK, Pierpaoli  C.  RESTORE: robust estimation of tensors by outlier rejection.   Magn Reson Med. 2005;53(5):1088-1095. doi:10.1002/mrm.20426PubMedGoogle ScholarCrossref
27.
Cook  P, Bai  Y, Nedjati-Gilani  S,  et al Camino: open-source diffusion-MRI reconstruction and processing. Paper presented at: 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine; May 6-12, 2006; Seattle, WA.
28.
Plassard  AJ, Hinton  KE, Venkatraman  V, Gonzalez  C, Resnick  SM, Landman  BA.  Evaluation of atlas-based white matter segmentation with Eve.   Proc SPIE Int Soc Opt Eng. 2015;9413:94133E.PubMedGoogle Scholar
29.
Mori  S, Oishi  K, Jiang  H,  et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template.   Neuroimage. 2008;40(2):570-582. doi:10.1016/j.neuroimage.2007.12.035PubMedGoogle ScholarCrossref
30.
Klein  A, Dal Canton  T, Ghosh  SS, Landman  B, Lee  J, Worth  A. Open labels: online feedback for a public resource of manually labeled brain images. Paper presented at: 16th Annual Meeting for the Organization of Human Brain Mapping; June 6-10, 2010; Barcelona, Spain.
31.
Bonekamp  D, Nagae  LM, Degaonkar  M,  et al.  Diffusion tensor imaging in children and adolescents: reproducibility, hemispheric, and age-related differences.   Neuroimage. 2007;34(2):733-742. doi:10.1016/j.neuroimage.2006.09.020PubMedGoogle ScholarCrossref
32.
Pfefferbaum  A, Adalsteinsson  E, Sullivan  EV.  Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain.   J Magn Reson Imaging. 2003;18(4):427-433. doi:10.1002/jmri.10377PubMedGoogle ScholarCrossref
33.
Vollmar  C, O’Muircheartaigh  J, Barker  GJ,  et al.  Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0T scanners.   Neuroimage. 2010;51(4):1384-1394. doi:10.1016/j.neuroimage.2010.03.046PubMedGoogle ScholarCrossref
34.
Venkatraman  VK, Gonzalez  CE, Landman  B,  et al.  Region of interest correction factors improve reliability of diffusion imaging measures within and across scanners and field strengths.   Neuroimage. 2015;119:406-416. doi:10.1016/j.neuroimage.2015.06.078PubMedGoogle ScholarCrossref
35.
Morrell  CH, Brant  LJ, Ferrucci  L.  Model choice can obscure results in longitudinal studies.   J Gerontol A Biol Sci Med Sci. 2009;64(2):215-222. doi:10.1093/gerona/gln024PubMedGoogle ScholarCrossref
36.
Bernal-Rusiel  JL, Greve  DN, Reuter  M, Fischl  B, Sabuncu  MR; Alzheimer’s Disease Neuroimaging Initiative.  Statistical analysis of longitudinal neuroimage data with linear mixed effects models.   Neuroimage. 2013;66:249-260. doi:10.1016/j.neuroimage.2012.10.065PubMedGoogle ScholarCrossref
37.
Lin  FR, Maas  P, Chien  W, Carey  JP, Ferrucci  L, Thorpe  R.  Association of skin color, race/ethnicity, and hearing loss among adults in the USA.   J Assoc Res Otolaryngol. 2012;13(1):109-117. doi:10.1007/s10162-011-0298-8PubMedGoogle ScholarCrossref
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Houtgast  T, Festen  JM.  On the auditory and cognitive functions that may explain an individual’s elevation of the speech reception threshold in noise.   Int J Audiol. 2008;47(6):287-295. doi:10.1080/14992020802127109PubMedGoogle ScholarCrossref
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44.
Lin  Y, Wang  J, Wu  C, Wai  Y, Yu  J, Ng  S.  Diffusion tensor imaging of the auditory pathway in sensorineural hearing loss: changes in radial diffusivity and diffusion anisotropy.   J Magn Reson Imaging. 2008;28(3):598-603. doi:10.1002/jmri.21464PubMedGoogle ScholarCrossref
45.
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    Original Investigation
    September 3, 2020

    Association of Poorer Hearing With Longitudinal Change in Cerebral White Matter Microstructure

    Author Affiliations
    • 1Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
    • 2Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, Rhode Island
    • 3School of Engineering, Vanderbilt University, Nashville, Tennessee
    • 4Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
    • 5Department of Otolaryngology–Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
    JAMA Otolaryngol Head Neck Surg. 2020;146(11):1035-1042. doi:10.1001/jamaoto.2020.2497
    Key Points

    Question  Is poorer peripheral and central auditory function associated with baseline status or change in white matter (WM) microstructure as measured by metrics from diffusion tensor imaging?

    Findings  In this cohort study of 356 participants from the Baltimore Longitudinal Study of Aging, poorer peripheral and central auditory function was not associated with WM microstructure at baseline. Longitudinally, poorer peripheral hearing was associated with changes in mean diffusivity in the inferior fronto-occipital fasciculus and the body of the corpus callosum; poorer central auditory function was associated with changes in the uncinate fasciculus.

    Meaning  Findings suggest that poorer peripheral and central auditory function is related to change in integrity of specific WM regions involved with auditory processing.

    Abstract

    Importance  There is a dearth of studies that examine the association between poorer hearing and change in cerebral white matter (WM) microstructure.

    Objective  To examine the association of poorer hearing with baseline and change in WM microstructure among older adults.

    Design, Setting, and Participants  This was a prospective cohort study that evaluated speech-in-noise, pure-tone audiometry, and WM microstructure, as measured by mean diffusivity (MD) and fractional anisotropy (FA), both of which were evaluated by diffusion tensor imaging (DTI) in 17 WM regions. Data were collected between October 2012 and December 2018 and analyzed between March 2019 and August 2019 with a mean follow-up time of 1.7 years. The study evaluated responses to the Baltimore Longitudinal Study of Aging among 356 cognitively normal adults who had at least 1 hearing assessment and DTI session. Excluded were those with baseline cognitive impairment, stroke, head injuries, Parkinson disease, and/or bipolar disorder.

    Exposures  Peripheral auditory function was measured by pure-tone average in the better-hearing ear. Central auditory function was measured by signal-to-noise ratio score from a speech-in-noise task and adjusted by pure-tone average.

    Main Outcomes and Measures  Linear mixed-effects models with random intercepts and slopes were used to examine the association of poorer peripheral and central auditory function with baseline and longitudinal DTI metrics in 17 WM regions, adjusting for baseline characteristics (age, sex, race, hypertension, elevated total cholesterol, and obesity).

    Results  Of 356 cognitively normal adults included in the study, the mean (SD) age was 73.5 (8.8) years, and 204 (57.3%) were women. There were no baseline associations between hearing and DTI measures. Longitudinally, poorer peripheral hearing was associated with increases in MD in the inferior fronto-occipital fasciculus (β = 0.025; 95% CI, 0.008-0.042) and the body (β = 0.050; 95% CI, 0.015-0.085) of the corpus callosum, but there were no associations of peripheral hearing with FA changes in these tracts. Poorer central auditory function was associated with longitudinal MD increases (β = 0.031; 95% CI, 0.010-0.052) and FA declines (β = −1.624; 95% CI, −2.511 to −0.738) in the uncinate fasciculus.

    Conclusions and Relevance  Findings of this cohort study suggest that poorer hearing is related to change in integrity of specific WM regions involved with auditory processing.

    Introduction

    Hearing impairment (HI), a prevalent condition affecting two-thirds of adults 70 years and older,1 is a risk factor for cognitive decline and dementia.2-7 Hearing consists of peripheral and central components. Peripheral auditory function is characterized by transduction and encoding of sound in the cochlea. In contrast, central auditory function reflects the central processing and decoding of the auditory signal by the cerebral cortex. While detecting simple tones in quiet (eg, pure-tone audiometry) primarily reflects peripheral auditory function, understanding complex sounds (eg, speech in noise) depends both on peripheral auditory encoding and on central auditory processing and decoding of the signal.8,9 Central auditory function could be associated with a broader range of cerebral regions, including those involved in executive and attentional functions.10 Previous studies have demonstrated that hearing speech in noise requires additional attentional and cortical resources to be diverted to aid in speech understanding, which is suggestive of a degradation of the ascending auditory signal.11,12

    Brain structural change is a potential mechanism underlying the association of HI with cognitive decline and dementia.13 Hearing impairment is associated with longitudinal declines in gray matter, especially within temporal lobes,14,15 but there is limited information on HI and white matter (WM) microstructure among older adults.16 The microstructure of WM can be measured by diffusion tensor imaging (DTI) with derived parameters including fractional anisotropy (FA) and mean diffusivity (MD).17,18 Fractional anisotropy quantifies the directionality of water diffusion, while MD quantifies the magnitude of water diffusion within brain tissue. When WM structures become damaged or decline, FA reduces, as the directional order imposed is lost, and MD increases, as there are fewer barriers restricting the overall magnitude of diffusion.19

    Given the dearth of studies on the association of HI with longitudinal change in WM microstructural integrity,16 we evaluated the associations of both peripheral and central auditory function with baseline and change in WM microstructure. We hypothesize that poorer peripheral and central auditory function are associated with declines in WM microstructure, especially in tracts associated with hearing and language (ie, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus). We also examined these associations by hemisphere as an exploratory analysis.

    Methods
    Study Participants

    Participants, ranging from ages 55 through 99 years, were community-dwelling Baltimore Longitudinal Study of Aging (BLSA) participants free of cognitive impairment. Enrollment procedures are detailed elsewhere.20,21 Briefly, participants were excluded based on significant health conditions that could affect brain structure or function (ie, stroke, closed head injury, cranial/brain surgery, malignant cancer, gliomas, intracranial cyst with brain tissue displacement, seizure, and bipolar disorders). For study inclusion, participants had 1 or more concurrent audiometric and neuroimaging sessions (N = 356) between 2012 and 2018 and were followed up thereafter with serial magnetic resonance imaging (MRI) assessments.

    The BLSA imaging and visit schedules have varied over time. Participants younger than 60 years are evaluated quadrennially. Participants aged 60 through 79 years have biennial visits, and participants 80 years and older have annual visits. Baseline was defined as the first concurrent audiometric and neuroimaging session. The institutional review board of the National Institute of Environmental Health Sciences of the National Institutes of Health approved the research protocol for this study, and written informed consent was obtained at each visit from all participants.

    Peripheral Audiometry

    Pure-tone audiometric testing, a measure of the sensitivity of the peripheral auditory system,21 was performed by a trained examiner with the participant in a sound-attenuating booth using an AD629 audiometer with ER3A insert earphones (Interacoustics). Audiometric thresholds were obtained at frequencies between 0.5 and 8 kHz. A speech-frequency pure-tone average (PTA) of air-conduction thresholds at 0.5, 1, 2, and 4 kHz was calculated for each ear. Peripheral hearing was analyzed as a continuous measure for PTA per 10 dB hearing level (HL) in the better-hearing ear.

    Central Auditory Function

    QuickSIN is a common clinical tool used to quantify a person’s ability to understand speech in the presence of background noise.22 Participants completed 1 practice list and 2 test lists. Each list contains 6 sentences, which each contains 5 keywords for the participant to repeat for a total score out of 30 (ie, 5 words per sentence). Participants are instructed to repeat sentences in the presence of background noise. The background noise begins softly with the first sentence and increases with each subsequent sentence until it is the same presentation level as the sentence. The total correct score is then converted into a derived signal-to-noise (SNR) level (defined as the signal intensity in dB relative to noise at which an individual would be expected to understand 50% of the words). The SNR level from the 2 test lists was averaged for a summary SNR score.

    MRI Acquisition

    Magnetic resonance imaging data were acquired on a 3T Achieva scanner (Philips) at the National Institute on Aging. Protocol for acquisition of DTI was as follows: number of gradient directions = 32, number of b0 images = 1, max b-factor = 700 s/mm2, repetition time/echo time = 7454/75 msec, number of slices = 70, voxel size = 0.81 × 0.81 × 2.2 mm, reconstruction matrix = 320 × 320, acquisition matrix = 116 × 115, field of view = 260 × 260 mm, and flip angle = 90°. Two separate DTI scans were acquired for each participant at each session and subsequently combined to generate images with a number of signal averages of 2 to improve SNR.23

    Image Analysis

    Tensor fitting and quality assessment was carried out using a protocol described previously.23,24 Briefly, diffusion-weighted volumes were affine coregistered to a b0 target image, using Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) eddy_correct tool. This tool uses an affine transformation to correct for image distortion caused by image stretching, shearing, and translation. Each data set was registered and normalized to its own reference scan. These scans were aligned, and all data were processed together in a unified space. The gradient tables were corrected for the identified rotational component using finite strain.25 Each diffusion-weighted image was normalized by its own reference image prior to tensor fitting for the combination of 2 DTI sessions with different unknown intensity normalization constants. To improve robustness, iteratively reweighted least squares fitting with outlier rejection (in the form of RESTORE26 from the Camino toolkit27) was used to estimate tensors on a voxel-wise basis.

    The quality assurance reports were visually examined to assess for motion/artifact, corrupted scans, or other acquisition abnormalities. Additionally, all quantitative quality metrics were exported. Outliers (>2 standard deviations [SDs]) were visually assessed to identify processing failures. Quality assurance review identified concerns for 27 sessions, including motion artifacts and corrupted scans, and these sessions were removed from further analyses, leaving 1722 good-quality DTI sessions for analysis.

    White Matter Regions of Interest

    Following Plassard et al,28 we used a single WM atlas to identify high-resolution regions of interest (ROIs) followed by tissue-class correction based on a multiatlas whole-brain T1 segmentation. Briefly, the Eve WM atlas29 was nonrigidly registered to the target image using Advanced Normalization Tool Symmetric Image Normalization (ANTs SyN). To segment the WM regions of interest (ROIs), the Eve WM atlas was combined with corresponding WM labels from a multiatlas segmentation using 35 manually labeled atlases from NeuroMorphometrics with the BrainCOLOR protocol30 and FA-mapped MRI. The Eve WM labels were intersected with the WM segmentation from BrainCOLOR to remove registration errors of the single-atlas approach. Then the resulting intersected labels were iteratively grown to fill the remaining areas identified as WM in the multiatlas approach. The WM ROI labels obtained from the T1-weighted image for each visit were affine-registered to the FA and MD images and used to extract average FA and MD values for each ROI.

    Included in the analysis of association were 9 WM ROIs: superior longitudinal fasciculus, superior fronto-occipital fasciculus, inferior fronto-occipital fasciculus, sagittal stratum, cingulum gyrus, cingulum (hippocampus), fornix/stria terminalis, uncinate fasciculus, and the column and body of the fornix; 3 commissural WM ROIs: genu, body, and splenium of the corpus callosum; and 5 projection WM ROIs: anterior and posterior limbs of the internal capsule and anterior, superior, and posterior corona radiata. Values of MD and FA from all ROIs were averaged across both hemispheres. Reliability of DTI measures has been established by other studies.31-33 Mean intraclass correlations of 0.66 for MD and 0.76 for FA were reported previously in older BLSA participants.34

    Statistical Analysis

    Linear mixed-effects models with random intercepts and an unstructured variance-covariance structure were used to assess associations of peripheral and central auditory processing with baseline and longitudinal change in WM microstructure. They can account for variability in visits, follow-up time, and unequal number of measurements and age.35 Also, they are suitable in handling the serial measurements of individuals being positively correlated and the between-subject variance being variable over time due to possible diverging trajectories.36 We used restricted maximum likelihood, allowing for post hoc correction to obtain unbiased estimates of the variance.36 Adjustment baseline covariates included demographic characteristics (age, sex, race [white vs nonwhite]37) and presence of vascular risk factors (hypertension, obesity, and elevated total cholesterol21), as these could have confounded the associations.

    Fixed effects included baseline hearing assessment (PTA/SNR), adjustment covariates, time since baseline, and 2-way interactions of hearing assessment and demographic characteristics with time since baseline. We excluded years of education and 2-way interactions among vascular risk factors and time because these covariates were not significant. In analyses of the associations of central auditory function with baseline and longitudinal change in WM microstructure, we included 2 additional terms: PTA and 2-way interaction between PTA and time because PTA accounts for 62% to 78% of the variability of the SNR.38

    As a secondary analysis, we examined the associations of peripheral and central auditory function with baseline and longitudinal change in right and left WM regions separately. Estimates of FA were multiplied by 1000, while MD estimates were multiplied by 10 000. Type I error was set to 0.05 and 0.01 for ROI analyses. Additionally, to control for the false discovery rate (FDR), we corrected P values at .05 using the Benjamini and Hochberg method,39 and these are noted in the Results section. The significant findings at P < .01 are reported in the Results section. Statistical analyses were performed using Stata SE version 15.0 (StataCorp).

    Results
    Characteristics of Study Sample

    There were 356 BLSA participants (mean [SD] age, 73.5 [8.8] years; 204 [57.3%] women; 251 [70.5%] White) with 882 DTI assessments (Table). The mean (SD) follow-up time of the sample was 1.7 (1.7) years, ranging from 0 to 7.1 years of follow-up. There were 294 BLSA participants with at least 2 repeated MRI assessments with a mean (SD) follow-up time of 1.8 (1.6) years ranging from 0.9 to 7.1 years. The baseline mean (SD) PTA was 28.9 (1.4) dB HL.

    Association of Peripheral Auditory Function With Baseline and Change in WM Tract Integrity

    Figures 1 and 2, as well as eTable 1 in the Supplement, show the associations of peripheral hearing with baseline and change in DTI metrics. There were no significant baseline associations. Longitudinally, poorer baseline peripheral auditory function was associated with MD increases in the inferior fronto-occipital fasciculus (β = 0.025; 95% CI, 0.008-0.042) and the body (β = 0.050; 95% CI, 0.015-0.085) of the corpus callosum (Figure 2). The latter association survived FDR correction. Figure 3 highlights these 2 tracts superimposed on a T-1 weighted image. There were no longitudinal associations between peripheral auditory function and FA.

    We also examined the associations of baseline peripheral hearing with baseline level and change in MD and FA by hemisphere (eTable 2 in the Supplement). Cross-sectionally, greater peripheral auditory function was associated with higher (better) FA of the left inferior fronto-occipital fasciculus (β = 3.852; 95% CI, 1.229-6.475) and lower (worse) FA of right genu of the corpus callosum (β = −5.924; 95% CI, −10.279 to −1.568).

    Longitudinally, poorer peripheral auditory function was associated with increasing MD of left inferior fronto-occipital fasciculus (β = 0.012; 95% CI, 0.003-0.021), left uncinate fasciculus (β = 0.033; 95% CI, 0.007-0.058), and left body (β = 0.035; 95% CI, 0.016-0.055) of the corpus callosum. Also, poorer peripheral auditory function was associated with FA declines of the left posterior limb of internal capsule (β = −0.976; 95% CI, −1.645 to −0.307) and the left inferior fronto-occipital fasciculus (β = −0.897; 95% CI, −1.515 to −0.278).

    Association of Central Auditory Function With Baseline and Change in WM Tract Integrity

    Figures 1 and 2, as well as eTable 3 in the Supplement, show the associations of baseline central auditory processing with baseline and change in DTI metrics. There were no significant baseline associations. Longitudinally, poorer central auditory processing was associated with MD increases (β = 0.031; 95% CI, 0.010-0.052) and FA declines (β = −1.624; 95% CI, −2.511 to −0.738) in the uncinate fasciculus. The latter association survived FDR correction. Figure 3 highlights the uncinate fasciculus superimposed on a T-1 weighted image.

    Next, we examined the associations of central auditory processing with baseline and change in DTI metrics by hemisphere (eTable 4 in the Supplement). There were no significant baseline associations. In analyses by hemisphere, we found that poorer central auditory processing was associated longitudinally with increasing MD of the left cingulum (hippocampus) (β = 0.013; 95% CI, 0.005-0.021) and left uncinate fasciculus (β = 0.023; 95% CI, 0.012-0.035), as well as FA declines in left cingulum (hippocampus) (β = −0.831; 95% CI, −1.411 to −0.251), left (β = −0.837; 95% CI, −1.374 to −0.301) and right (β = −0.931; 95% CI, −1.440 to −0.421) uncinate fasciculus, and left column and body of the fornix (β = −0.555; 95% CI, −0.958 to −0.151).

    Discussion

    To our knowledge, this study provides novel information on the associations of both peripheral and central auditory function with baseline and change in regional WM microstructure. Although baseline associations for both hearing components did not reach significance, poorer peripheral auditory function was consistently associated with increases (worsening) in MD in the inferior fronto-occipital fasciculus and the body of the corpus callosum. In contrast, there were no associations with longitudinal FA declines. Poorer central auditory processing was associated with MD increases (worsening) and FA declines (worsening) in the uncinate fasciculus. We also found associations of poorer peripheral auditory function with FA declines in left posterior limb of internal capsule and inferior fronto-occipital fasciculus. Poorer central auditory processing was associated with MD increases in left cingulum (hippocampus) and uncinate fasciculus and FA declines in the left cingulum (hippocampus), bilateral uncinate fasciculus, and left column and body of the fornix. These findings suggest more prominent associations in the left than the right hemisphere.

    The cross-sectional findings support those of Profant et al40 in that there were no significant cross-sectional associations. Rigters et al41 reported cross-sectional associations of peripheral auditory function with lower FA in superior longitudinal fasciculus and the cingulate gyrus part of the cingulum as well as association of central auditory function with lower FA in the uncinate fasciculus. However, methodological differences existed between the 2 studies, both in approach (ROI-based vs voxel-based) and in type of speech-in-noise test administered (language speech in noise vs digits in noise). Other studies reporting cross-sectional relationships between peripheral auditory function and WM microstructure were composed of small sample sizes and mostly involved young to middle-aged adults.42-44

    To our knowledge, these longitudinal findings are novel. We found relationships of poorer baseline peripheral and central auditory function with longitudinal decline in WM association fibers that are directly or indirectly involved in auditory processing (ie, the inferior fronto-occipital fasiculus and uncinate fasiculus). Poorer peripheral auditory function was associated with MD increases (worsening) in the inferior fronto-occipital fasciculus and the body of the corpus callosum, but there were no associations with FA declines. The inferior fronto-occipital tract connects the ipsilateral frontal and occipital lobes with the ipsilateral frontal and posterior parietal and temporal lobes while interconnecting with the uncinate fasciculus.45 Its primary function is the integration of auditory and visual association areas with prefrontal cortex. The inferior fronto-occipital fasciculus includes 2 subcomponents: a superficial and a deep layer.46,47 The superficial layer could be specifically involved in bridging semantic memory with verbal systems,48-51 while the deep layer could be important for object semantic processing because it projects into the fusiform gyrus and dorsolateral prefrontal cortex.52 Poorer peripheral auditory function is associated with volumetric declines in fusiform gyrus,15 suggesting that poorer peripheral auditory function may affect the inferior fronto-occipital fasciculus through its connection with the fusiform gyrus.

    Poorer central auditory processing at baseline was associated with declines in WM microstructure integrity in the uncinate fasciculus, an association tract that connects the anterior temporal lobe with the orbitofrontal cortex.53,54 The uncinate fasciculus is important in the retrieval of context-relevant semantic properties for a given context or task, as the orbitofrontal cortex has been implicated in the executive control system. This tract has been implicated in semantic processing (ie, recalling names,55 making mnemonic associations,56 and the development of language in children57). Central auditory processing was associated with executive function, which aligns with the prefrontal cortex.10

    We also examined the associations of peripheral and central auditory function with baseline and change in WM ROIs by hemisphere separately. Although poorer peripheral auditory function was associated with better WM integrity of the left inferior fronto-occipital fasciculus, there were steeper declines in FA of the left inferior fronto-occipital fasciculus longitudinally, suggesting that within-subject changes may be more sensitive to detecting decline in WM integrity. The patterns of our longitudinal results favor associations in the left hemisphere, although the mechanisms underlying this observation require further exploration. Language processing primarily occurs in the left hemisphere, suggesting that decreased auditory comprehension is correlated with weakened integrity of WM ROIs associated with auditory function in the left hemisphere.

    Limitations

    There are several limitations of the study. First, there is limited heterogeneity of the sample because BLSA participants are highly educated and have a high socioeconomic status. Second, an unmeasured variable, tinnitus, could have affected the associations of peripheral and central auditory processing with baseline and change in WM microstructure. A third limitation could be the atlas-based approach we used to extract regionally specific DTI metrics. Diffusion tensor imaging tractography methods produce more accurate, subject-specific renderings of WM tracts. However, the findings of this study will guide selection of a smaller number of ROIs to apply tract-based spatial statistics58 in future work and explore the associations between DTI metrics and HI in greater detail. An additional limitation is the short follow-up period.

    Conclusions

    In summary, we observed associations of decreased peripheral and central auditory function with declines in WM microstructure in tracts involved with auditory and semantic processing. We did not find significant associations of poorer peripheral and central auditory function with other tracts that were not directly or indirectly associated with audition. We found associations with tracts located in the left hemisphere, supporting evidence of declines in language processing due to decreases in peripheral and central auditory function. Not only is there an association between HI and morphological changes,14,15 but there is also an association of HI with declines in WM microstructure. It could be that changes in brain structure, including WM microstructure, mediate the association between HI and dementia. Further studies are needed to examine these longitudinal relationships among older adults.

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    Article Information

    Accepted for Publication: June 30, 2020.

    Corresponding Author: Susan M. Resnick, PhD, Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute of Aging, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD 21224-6825 (resnicks@mail.nih.gov).

    Published Online: September 3, 2020. doi:10.1001/jamaoto.2020.2497

    Author Contributions: Dr Armstrong and Dr Resnick had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Armstrong, Lin, Resnick.

    Acquisition, analysis, or interpretation of data: Armstrong, Williams, Landman, Deal, Resnick.

    Drafting of the manuscript: Armstrong

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Armstrong.

    Obtained funding: Resnick.

    Administrative, technical, or material support: Williams, Landman, Lin.

    Supervision: Resnick

    Conflict of Interest Disclosures: Dr Landman reported personal fees from Silver Maple and nonfinancial support from International Business Machines, 12 Sigma, the Institute of Electrical and Electronics Engineers, and SPIE outside the submitted work. Dr Lin reported personal fees from Frequency Therapeutics and Caption Call outside the submitted work, as well as being the director of a public health research center funded in part by a philanthropic donation from Cochlear Ltd. to the Johns Hopkins Bloomberg School of Public Health. No other disclosures were reported.

    Funding/Support: This research was supported fully by the Intramural Research Program of the National Institutes of Health’s National Institute on Aging.

    Role of the Funder/Sponsor: The authors of this article include employees of the Intramural Research Program of the National Institute on Aging, who participated in all aspects of the project.

    Additional Contributions: We would like to thank the participants and staff of the Baltimore Longitudinal Study of Aging, the neuroimaging staff of the Laboratory of Behavioral Neuroscience, and the staff of the Johns Hopkins and National Institute on Aging magnetic resonance imaging facilities. The staff were paid their regular salaries.

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