White matter tracts relating to hearing loss in LBC and UK Biobank
Creators: name: Buchanan, Colin
name: Jones, Bethan F
name: Cox, Simon R.
Publisher: name: Open Science Framework
Year: 2023
Subjects: subject: Psychology
subject: FOS: Psychology
subject: Health Psychology
subject: Medicine and Health Sciences
subject: Social and Behavioral Sciences
subject: Mental and Social Health

description: White matter tracts relating to hearing loss in LBC and UK Biobank Background Despite hearing being such a prevalent facet of ageing, relatively little is known about the neurobiological changes that accompany age-related hearing loss. Age-related bilateral changes in hearing (or presbycusis) can be characterised by reduced ability to perceive and discriminate different frequencies and a loss of fidelity that can affect operations such as speech comprehension (J. Wang &amp; Puel, 2020) . (In the UK, more than 40% of people over 50 years old have hearing loss, rising to more than 70% of people over the age of 70; Royal National Institute for Deaf People, 2023). Age-related hearing loss is becoming more prevalent with estimations suggesting by 2050, one in every ten people will have disabling hearing loss (Deafness and Hearing Loss, 2023.). Tinnitus is characterised by phantom ringing in the ears - is estimated to occur in 1 in 7 adults (Deafness and Hearing Loss, 2023). Tinnitus can occur in clinically normal hearing ranges (≤20 dB hearing level) but there is evidence of cochlear damage in tinnitus sufferers; there is also evidence for outer hair cell damage causing tinnitus and elevated listening levels, which is a risk factor for later life hearing loss (Job et al., 2007; Roberts et al., 2010; Weisz et al., 2006). Both hearing loss and tinnitus have implications for social interaction, quality of life, and independent living among older adults and can herald cognitive decline and dementia (Loughrey et al., 2018). Beyond the elevated risk that presbycusis confers on cochlear sensory-neural cell degeneration, there is an increasing interest in the cerebral consequences for both hearing loss and tinnitus, with findings supporting region-specific differences or changes, which have been taken to support atrophic or ‘re-wiring’ accounts of diminished auditory input. Well-powered studies in older adults with concomitant structural and diffusion MRI (dMRI) are currently lacking and have potential to add to our understanding of the neurobiological bases of these ageing-related phenomena. Brain correlates of hearing loss Most cases of hearing loss are driven by changes in the inner ear, and less frequently in the middle ear (as opposed to a minority of cases directly arising from nerve degradation or damage), and several reports indicate that specific brain changes also accompany this phenomenon. It has been proposed that the degradation of the auditory input results in repurposing of the brain regions for recruitment into other types of processing such as visual processing (Glick &amp; Sharma, 2020), though other studies (below) may also support the presence of subtle atrophic effects within the wider auditory network. Studies such as Glick and Sharma (2020) found that in older participants with age related hearing loss, the auditory cortex “rewired” for visual processing and after 6 months of wearing a hearing aid this process was reversed. This suggests the auditory network is far more plastic than previously believed and that without auditory input, the auditory cortex is instead used for visual processing (Ponticorvo et al., 2022). In hearing impaired participants there was significant increase in brain network degree centrality (DC) between the auditory network (superior temporal gyrus (STG), medial geniculate nucleus (MGN) and the visual network; Wei et al., 2021). This was positively correlated with the extent of the hearing impairment. However, there remains conflicting evidence about the extent to which white matter microstructural properties of the brain’s major which matter pathways differ with hearing loss. For example, acoustic radiation such as fractional anisotropy (FA) and mean diffusivity (MD) do not appear to differ among adults with hearing loss, compared to controls (Koops et al., 2021; Profant et al., 2014). On the other hand, Tarabichi et al., (2018) review of dMRI studies of auditory pathways in patients with sensorineural hearing loss found that FA values on the side of hearing loss were significantly reduced in the inferior colliculus (IC) and elevated at the lateral lemniscus compared to the normal hearing side. In unilateral hearing loss patients, greater grey matter volume have been reported in the somatosensory and motor systems, and lower volumes in the auditory (i.e., Heschl's gyrus) and visual systems (i.e., the calcarine cortex; Wang et al., 2016). These lower grey matter volumes in the primary auditory cortex (i.e., STG and Heschl's gyrus) correlated with poorer hearing ability. Additionally, the lower volumes in structures related to high-level cognitive control functions (i.e., dorsolateral prefrontal cortex and anterior cingulate cortex) correlated positively with hearing loss duration (X. Wang et al., 2016; Wei et al., 2021). The degree of hearing impairment was more significantly associated with mean DC values in the right than left ear hearing impairment (Wei et al., 2021). This is further supported by research which states that those with greater ageing-related hearing loss had lower grey matter cortical volume in the cingulate, superior temporal, superior and medial frontal gyri along with the occipital lobe and hypothalamus in hard-of-hearing compared to normal-hearing participants (Boyen et al., 2013; Husain et al., 2011). This is, however, in contrast to Rosemann and Thiel (2020), who did not find any significant associations between grey matter and hearing loss. Finally, it is notable that the measurement of hearing loss in the studies reported above varies. For example, one study compared pure tone and speech thresholds and found that speech detection threshold was strongly correlated with the best pure tone threshold (Ristovska et al., 2021). These are both measures we are interested in and although not much research has been carried out comparing the methods, generally, it appears they are both getting at broadly the same underlying phenomenon, though unclear how far the brain correlates of hearing loss measured in those two ways compare. That is of course partly confounded by heterogeneity in neuroimaging and other modalities across studies. Brain correlates of tinnitus Tinnitus (the perception of sound without an external source) is another age-related auditory issue whose exact aetiology remains unclear. The acoustic radiation (AR) is the largest white matter tract of the central auditory system and connects the auditory thalamus (or MGN) to the auditory cortex, and intermingles with the fibres of the IC in its posterior portion (Maffei et al., 2019). As such, it is a primary candidate for cerebral correlates of age-related hearing loss and tinnitus; alongside other major white matter tracts, its microstructural properties show decline with age (Koops et al., 2021; Cox et al., 2016). Tinnitus is associated with both a lower cross-sectional size and higher MD in the left acoustic radiation, although the effect size was not substantially different to the contralateral pathway (Koops et al., 2021). This was interpreted as a decrease in myelination of the auditory pathways, potentially reflecting pre-existing markers of tinnitus vulnerability (Koops et al., 2021). ). Though it is worth observing that MD is sensitive to numerous structural differences in the brain’s microstructural environment beyond myelination (Jones et al., 2013). Notably, Koops et al. (2021) also interpreted their finding that age was negatively correlated with axonal density in bilateral acoustic radiation to indicate that this loss of fibre may contribute to the poorer speech understanding and slower auditory processing seen in older adults. However, it is unclear whether this association is confounded by the fact that all white matter (WM) microstructural properties show common ageing patterns to some degree. Thus, the magnitude of unique association between the AR and tinnitus requires further empirical testing. It also appears that the extent, loci and laterality of reported tinnitus-brain correlates is heterogeneous and may also be partially distinct from hearing loss. For example, Tarabichi et al., (2018) systematic review investigated white matter changes associated with tinnitus vs hearing loss – participants without tinnitus had a greater reduction in FA (in the IC, trapezoid body, lateral lemniscus (LL), superior olivary nucleus, MGN, Heschl’s gyrus, AR, and the STG) compared to those with tinnitus (Tarabichi et al., 2018). The fibre connectivity to the auditory cortex from the IC and amygdala was found to be weaker in right hemisphere in participants with tinnitus compared to normal hearing controls. They suggested that tinnitus is a hyperplastic process (disinhibition of the central auditory nuclei) driven by depravation of auditory input. Summary and Hypotheses Whereas there are several reports indicating brain structural differences as a function of hearing loss and tinnitus, the precise loci and nature of those associations are not consistently replicated across studies. Reasons for this include i) relatively small sample sizes (typically N&lt;100) which confers lower statistical power with which to reliably detect small effects; ii) cross-study heterogeneity in sample age and composition. For example, age will likely moderate the prevalence and length of exposure, and possible the susceptibility to ageing-related changes as a result of altered auditory input; iii) heterogeneity in the methods for measuring hearing loss, and for identifying brain grey matter loci and white matter pathways. 1. We hypothesise that there will be significant associations between greater hearing loss and ostensibly poorer measures of brain health (lower white matter FA, higher white matter MD, smaller brain volumes) with small-to-medium effect size (according to Cohen). Specifically, there will be a regional patterning across areas of cortical and white matter that indicates consistently (numerically) larger magnitudes of associations with the specific aspects of the auditory brain network which we outline below (Table 1). If associations are specific to these regions, associations will remain significant when correcting for overall brain size or white matter microstructure. 2. That the patterning of associations in brain will exhibit good agreement between cohorts for consistent brain measures. 3. For participants with unilateral hearing loss, there will be greater white matter (WM) degradation in below mentioned regions associated with hearing or tinnitus in the ipsilateral hearing-impaired lobe. Methods Data We will use two rich datasets of middle and older community-dwelling UK adults. The Lothian Birth Cohort 1936 (Deary et al., 2012; Taylor et al., 2018) is a group of older adults born in 1936 who aged 11 took part in a national IQ test and were subsequently followed up every three years from age 70 years old (from 2004). At the third wave of testing, LBC1936 participants had both tests of hearing and brain structural and dMRI data (N=~501). The UK Biobank (UKB) is a large national biobank study with approximately 40,000 participants with both structural and dMRI and hearing data (age range: 45 to 81 years). Two supplementary analyses will address the wider age range in UKB. The first will include an age interaction term (to test our hypothesis that hearing-brain associations increase with older age). In the second, if an age interaction is significant, we will split the sample to examine those from 70+ years in UKB, so as to more accurately match the LBC1936 sample for the purposes of replication. Hearing measurement At Wave 3, LBC1936 participants underwent an objective measures of auditory frequency detection. Hearing aids were removed before the test. The HearCheck screener (Siemens) produced three pure high-frequency tones at 3 kHz (kHz) at decreasing intensities of 75-, 55-, and 35-dB Hearing Level, and three pure mid-frequency tones at 1 kHz at decreasing intensities of 55, 35, and 20 dB HL. Participants were instructed to raise their hand each time they heard a tone. This test was conducted for both ears in turn, giving a score out of 6 for each ear (hearingright1khz, hearingright3khz, hearingrighttot, hearingleft1khz, hearingleft3khz, hearinglefttot). They also provided hearing aid information as subjective measures of participant self-rating hearing problems (Do you have difficulty with your hearing? Yes/No (hearingdiff); Do you have difficulty with the volume of TV programmes? Yes/No (hearingdifftv); Do you have difficulty with your hearing when having a conversation in a group? Yes/No (hearingdiffgrpcon); Have you been to the doctor or hospital about hearing problems? Yes/No (hearingdoctor); hearing aid use (Do you usually wear a hearing aid? Yes-left ear, Yes-right ear, Yes-both ears, I have them but do not wear them, No-I do not have hearing aids) (hearingaid); tinnitus rating (Do you get noises (such as ringing or buzzing) in your head or ears that lasts more than 5 minutes? No-never, not presently-but have in the past, yes-some of the time, yes-most of the time) (hearingnoises). UK Biobank participants also provided a self-rating of hearing (UK Biobank Field IDs: 2247, 10793, 2257) and tinnitus (ID: 4803, 4814), hearing aid use (ID: 3393) and objective measures of hearing with noise digit triplets test (ID: 4243, 4229, 4240, 4236, 4242), where the auditory stimulus is presented in 4 dB increments and is adapted by 2 dB if the participant gets it correct or incorrect. Speech-reception-threshold (SRT; ID: 20021, 20019) estimate was also used and ranges between −7.1 dB ± 0.16 dB. We acknowledge that a small proportion (~9%) of the population are likely to have poorer hearing performance due to conductive or mixed hearing loss, as opposed to the most common cause, sensorineural (damage to the inner ear or auditory nerve; Tanna et al., 2022). The measures above are unable to discriminate between different aetiologies, though prior work indicates that the majority of the cases of hearing loss in these self-selecting samples (Lyall et al., 2022; Taylor et al., 2018) are likely to be due to sensorineural (Tanna et al., 2022). MRI acquisition Details of the LBC1936 MRI protocol have been described previously (Wardlaw et al., 2011). In brief, all participants underwent brain MRI on the same 1.5 T GE Signa Horizon HDx clinical scanner. 3D T1-weighted inversion-recovery prepared, fast spoiled gradient-echo volumes were acquired at voxel dimensions of 1 × 1 × 1.3 mm. For the dMRI protocol, single-shot spin-echo echo-planar volumes were acquired in 64 noncollinear directions resulting in 2 mm isotropic voxels. Details of the UKB MRI protocol are freely available (https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf; Alfaro-Almagro et al., 2018; Miller et al., 2016). Imaging was performed at four UK sites (all Siemens Skyra 3T scanner). 3D T1-weighted volumes were acquired using a magnetization-prepared rapid gradient-echo sequence at 1 × 1 × 1 mm voxel resolution. The dMRI volumes were acquired using a multi-shell, spin-echo echo-planar imaging sequence resulting in 100 distinct diffusion-encoding directions and 2 mm isotropic voxels. Previous MRI processing All T1-weighted volumes have previously been segmented into distinct neuroanatomical regions-of-interest (ROIs) using the FreeSurfer image analysis suite and the Desikan-Killiany cortical atlas (http://surfer.nmr.mgh.harvard.edu; Desikan et al., 2006). LBC1936 FreeSurfer data has previously undergone careful quality checking (QC) and manual editing, and UKB data underwent automated QC. Additionally, the UKB imaging team obtained segmentations of the subcortex and various thalamic nuclei including the MGN (Cox et al., 2019; Iglesias et al., 2018). All dMRI volumes have been pre-processed to correct for patient motion, eddy currents and systematic distortions using the FSL toolkit ( Wellcome Centre for Integrative Neuroimaging, FMRIB, Oxford University: http://www.fmrib.ox.ac.uk). These pre-processing steps differed between cohorts due to the different acquisitions and the FSL version available at the time of processing (Alfaro-Almagro et al., 2018; Wardlaw et al., 2011). For all dMRI volumes, diffusion tensors were fitted, and water diffusion measures were estimated for MD and FA. Additionally, neurite orientation dispersion and density imaging (NODDI) measures were computed in UKB (Zhang et al., 2012). For UKB data, automatic mapping of 27 major white matter tracts was conducted using the AutoPtx plugin for FSL (de Groot et al., 2013). AutoPtx data has not yet been computed in LBC1936. Proposed MRI processing On receiving access to the data, we intend to map the major white matter tracts for all LBC1936 Wave 3 participants using AutoPtx. Manual QC will be performed to ensure harmonisation with the existing UKB AutoPtx tracts. QC will involve visual inspection of the tractography outputs and any participants with gross tractography errors will be excluded from dMRI analysis. The derived tract measures will be: [Acoustic Radiation, uncinate fasciculus, Cingulum gyrus, superior longitudinal fasciculus, inferior longitudinal fascicules, anterior thalamic radiation, corticospinal tract, Inferior fronto-occipital fasciculus.name all the outputs you are interested in using here. So dMRI-derived measures of FA, MD in both UKB and LBC for the tracts identified in Fig 1]. Additionally, the NODDI measures available in UKB may provide more specific information on brain tissue microstructure than FA and MD for these tracts. From this we will then describe cross-sectional hearing measures such as tinnitus, uni or bilateral hearing loss and hearing aid use in LBC and UK Biobank, accounting for age and sex. Table 1 Regions of Interest Outcomes Superior temporal gyrus1,2,3 Hearing loss / tinnitus Increased DC values, decreased grey matter for hearing loss compared to tinnitus participants Insula 10 Hearing loss Decrease grey matter Medial geniculate nucleus 1,4,5 Hearing loss / tinnitus Increased DC values Isthmus cingulate cortex 6,10 Hearing loss Decrease grey matter volume in the cingulate cortex Posterior cingulate cortex 6,10 Hearing loss Decrease grey matter volume in the cingulate cortex anterior cingulate cortex 6,10 Hearing loss Decrease grey matter volume in the cingulate cortex Rostral anterior cingulate cortex 6,10 Hearing loss Decrease grey matter volume in the cingulate cortex Transverse temporal lobe Hearing loss Decreased thickness * Dorsolateral Prefrontal Cortex 6 Hearing loss / tinnitus Decrease grey matter volume Hippocampus 3,7,10 tinnitus Lower grey matter integrity White Matter Tracts Acoustic Radiation 4,8,9 Hearing loss / tinnitus Atrophy of the left acoustic radiation and decrease in MD in the left AR in hearing-loss and tinnitus groups uncinate fasciculus 7,11 Hearing loss Lower WM microstructural integrity. No changes in FA values Cingulum bundles 3,11 Hearing loss Changes in FA values in the left middle cingulum. Lower FA values in hearing loss compared to controls. superior longitudinal fasciculus 3,11 Hearing loss Rosemann and Thiel expected decreased FA values in longitudinal fasciculus of hearing impaired participants, but did not find any significant difference. Husain: Decrease in FA values in hearing loss inferior longitudinal fascicules 3,11 Hearing loss Rosemann and Thiel expected decreased FA values in longitudinal fasciculus of hearing impaired participants, but did not find any significant difference. Husain: Decrease in FA values in hearing loss anterior thalamic radiation 3 Hearing loss Decrease in FA values in hearing loss corticospinal tract 3 Hearing loss Decrease in FA values in hearing loss Inferior fronto-occipital fasciculus 3 Hearing loss Decrease in FA values in hearing loss Note. Brain regions of interest and their hypothesised relevance for differences in hearing loss and tinnitus. 1 (Wei et al., 2021); 2 (Boyen et al., 2013); 3 (Husain et al., 2011); 4 (Maffei et al., 2019); 5 (Tarabichi et al., 2018); 6 (X. Wang et al., 2016); 7 (Croll et al., 2020); 8 (Koops et al., 2021); 9 (Profant et al., 2014); 10 (Belkhiria et al., 2020); 11 (Rosemann &amp; Thiel, 2020). * This has not been studied in adults with age-related hearing loss, but has been found in children with hearing loss. Statistical Analyses Characterisation of hearing measures by age, laterality, and sex in LBC1936 and UK Biobank We will test and illustrate the prevalence and associations of hearing loss outcomes with age and sex, and also test the degree to which hearing scores are correlated between ears. Brain correlates of hearing loss Hypothesis 1. Associations between all brain MRI measures (the volume of all cortical and subcortical regions, and the FA and MD of white matter tracts) and hearing measures will be investigated using linear regression. Exposures will be hearing acuity (ID: 20019, 20021, 4243, 4232, 4229, 4240 ; hearingright1khz, hearingright3khz, hearingrighttot, hearingleft1khz, hearingleft3khz, hearinglefttot) or self-reported presence of tinnitus (ID: 4803, 4814), outcomes will be the brain MRI derived phenotypes (shown in Table 1), and covariates will include age, sex, scanner site (UKB only) and head position in the scanner (X,Y,Z coordinates). The primary outcome measure will be the total number of tones correctly heard; UK Biobank is a score out of 30, and LBC is a total score out of 12). We will characterise the nature of asymmetry by plotting a histogram of the differences between left vs right scores in both cohorts and correlate the differences between the two. Models involving volumetric MRI data will also include head size as a covariate. We will run separate sets of models for grey matter and white matter components, and correct p-values (alpha = 0.05) using FDR within family, according to tissue type. Hypothesis: associations between hearing measures and brain MRI outcomes will be FDR significant, and numerically strongest in areas identified a priori as auditory network components, as specified in Table 1. We will also ascertain if these a priori brain loci (auditory network components) show specific associations with hearing measures by including, as covariates, global measures of the corresponding brain measurement drawn from the remaining brain (for example, mean FA from those WM tracts not included in the target network, mean grey matter (GM) volume of non-network cortical areas). Prior reports suggest hearing aid use could moderate the effect of poor hearing on brain structures (Jiang et al., 2023). We will therefore re-run the models above including a hearing score x hearing aid use interaction term. Hypothesis: hearing-brain associations will be significantly stronger in those who have hearing loss and do not wear hearing aids, when compared to those that do wear hearing aids. Finally, we will quantitatively compare the spatial patterning of the findings across cohorts by correlating (Pearson’s r) the standardised regression coefficients from the associations in LBC1936 and UKB, within tissue type. As above, we note that whereas different measures of hearing acuity in older age have been used and may well index the same underlying phenomenon, there has been no formal test of their cerebral correlates to our knowledge. Bearing in mind the wider age range of the UK Biobank sample in contrast to the older and tight age range of the LBC1936, we will include supplementary models in which we include a hearing x age interaction term in our UK Biobank analyses, and further check the spatial patterning between LBC1936 and a range restricted UK Biobank dataset using participants aged 70+years only. Hypothesis: we expect that the magnitudes of brain cortical and white matter associations in with hearing outcomes in one cohort will significantly correlate with the magnitudes in the other cohort (with medium effect size, r ~ 0.5). We do not have a specific hypothesis about specific sub cortical structures, as unfortunately the imaging data we have will not let us analyse deeper white matter connections in subcortical regions (e.g. smaller tracts within and around the brainstem). Therefore, we are running this as an exploratory analysis, but still hypothesise that the magnitudes of associations across the subcortex will replicate (spatial agreement, as above) between cohorts. Sex-specific effects: We also conduct secondary analyses to ascertain sex moderation of the observed effects, including a sex × hearing interaction term for all brain regional analyses. Hypothesis: women will show weaker associations between hearing loss and brain structural measures, consistent with the hypothesised protective effects of estrogen on brain structure – hearing loss changes (Hultcrantz et al., 2006), though this remains to be tested in populations that have gone through the menopause, such as in LBC1936, and older participants in UKB. Throughout, we will subscribe to the categorisation of effect sizes according to (Cohen, 1989) when reporting and interpreting association magnitudes. Asymmetrical hearing loss Hemispheric effects. Finally, right vs left lobe differences in the magnitude of associations between regional brain-wide measures (cortical ROIs, WM tracts, subcortical ROIs) and hearing acuity will be investigated by including an hearing x asymmetry (difference between left and right acuity scores) interaction term to the models above. Hypothesis: Similar to Tarabichi et al., (2018) findings among patients with sensorineural hearing loss, we expect that FA and MD values in WM and GM volumes on the ipsilateral side of greater hearing loss will be significantly ‘poorer’ (lower FA, higher MD, lower volume). We also expect to see enhanced connectivity (using AutoPtx to indicate tractography) between visual and auditory networks in hearing impaired lobe with unilateral hearing loss in the UK Biobank data. List of Exclusion Criteria: Dementia or Alzheimer’s disease, Parkinson’s disease, Chronic degenerative neurological problem, Guillain-Barré syndrome, Multiple Sclerosis, Other demyelinating disease, Stroke or ischaemic stroke, Brain cancer, Brain haemorrhage, Brain/intracranial abscess, Cerebral aneurysm, Cerebral palsy, Encephalitis Epilepsy Head injury or neurological injury/trauma Infections of the nervous system, Meningeal cancer Meningioma (benign), Meningitis, Motor Neuron Disease, Spina bifida, Subdural haematoma, Subarachnoid haemorrhage or Transient ischemic attack. Current Data Access Status Whereas the authors have worked extensively with the Lothian Birth Cohort and UK Biobank, analytic datasets that contain any combination of the hearing measures and brain MRI data identified here have not been accessed or created for either cohort by any of the authors. dMRI data processing to derive AutoPtx white matter tracts in the LBC1936 has not yet been conducted. 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