Auditory Agnosia for Environmental Sounds in Alzheimer’s Disease: Not Hearing and Not Listening?

Abstract

Auditory agnosia for environmental sounds (AES) is an example of central auditory dysfunction. It is presumed to be independent of language deficits and in presence of normal hearing. We undertook a detailed neuropsychological assessment including environmental sound naming and recognition in 34 clinically mild Alzheimer’s disease (AD) patients and 29 age-matched healthy control subjects. In patients with AD, audiometry was performed to assess the impact on test performance, and in normal controls the Hearing Handicap Inventory for the Elderly – Screening Version to exclude more than mild hearing loss. We adapted a validated environmental sound battery and found near perfect scores in controls. We found that environmental sound agnosia is common in mild AD. We found a statistically significant difference in mean pure tone audiometry in the best ear between patients with and those patients without naming deficits of 11.3 dB (p = 0.010) and of 14.7 dB (p = 0.000) between those with and without recognition deficits. Statistical significance remained after correcting for age, aphasia, Mini-Mental State Examination score, and working memory. Slight and moderate peripheral hearing loss increases the odds ratio of recognition deficits by 13.75 (confidence interval 2.3–81.5) compared to normal hearing patients. We did not find evidence for different forms of AES. This work suggests that an interaction between peripheral hearing loss and AD pathology produces problems with environmental sound recognition. It confirms that the relationship between hearing and dementia is complex but also suggests that interventions to prevent and treat hearing loss could have an effect on AD in its clinical expression.

Keywords

Alzheimer’s disease central auditory dysfunction environmental sound agnosia hearing loss

INTRODUCTION

Auditory agnosia for environmental sounds (AES) is an example of cortical or central auditory dysfunction. Central auditory processing analyses patterns in time or frequency to localize and identify sound objects. Hearing in (Alzheimer’s) dementia has been studied extensively; AES less so. The relation between peripheral hearing and AES in Alzheimer’s disease (AD) has not been studied.

Auditory agnosia (AA) is defined as an inability to recognize auditorily presented sounds independent of spoken language in the presence of adequate hearing [1, 2]. It has been divided into AA for verbal (speech) and for non-verbal sounds. AA for non-verbal sounds can also be known as AES or AA for meaningful non-verbal sounds. Since the definition of AA for meaningful non-verbal sounds in theory could encompass disorders of music processing and listening, we prefer the term AES. An environmental sound can be defined as a sound that is produced by a real event; the sound takes on meaning because of the causal relationship with that event [3]. Environmental sounds are important in daily life; to warn of danger (siren), functioning of devices (water dripping), locating an event (explosion), monitoring change (clock), communicating information about emotional (scream) or physical (burp) state, and so on. They convey action and movement-related information—“news that something is happening” [4].

Complex sound processing is thought to be broadly hierarchically organized in the cortex. There are more or less distinct stages of early perceptual analysis, representation of the structural features of auditory objects (apperceptive level) and attribution of meaning (semantic level) [5]. An auditory object can be defined as a collection of acoustic data bound in a common perceptual representation and disambiguated from the auditory scene, also called auditory scene analysis (ASA), without necessarily uncovering the semantic links [6], which allows object identification [5]. Others have called this central auditory processing and resulting disorders (CAPD) of sound localization, auditory discrimination, and pattern and temporal recognition with competing and degraded acoustic signals [7]. Although complex sound processing is hierarchically organized, there is neuropsychological and neuroimaging evidence for parallel processing, local specializations, and interactions between apperceptive and semantic brain mechanisms [8 –10]. Environmental sound processing is dependent upon spectrotemporal analysis, and the interaction of ‘bottom-up’ (starting with signal analyses) and ‘top-down’ (executive processes; attention and working memory, and matching acoustic data with stored perceptual object representations, or ‘auditory templates’) processing [5 , 11–13].

AES has been presumed to be rare. AES has been described in Landau–Kleffner syndrome [14], schizophrenia [15], temporal lobe epilepsy before and after lobectomy [16], frontotemporal dementias [6 , 17–19], after herpes encephalitis [20], in post-lingual cochlear implant patients [21] and after stroke, often only temporarily present [22], and has been described in AD (see below). This work does suggest AES is a specific entity and not just an accumulation of deficits elsewhere in the sound processing pathway.

AD, auditory system, and function pathology

Abnormalities at all levels of sound processing in AD have been described, from peripheral hearing to ASA to semantic identification and recognition (for review, see [17, 23]). Neuropathological studies confirm a highly topographically specific and consistent pattern of degeneration of the central auditory system in AD with more neurofibrillary tangles in the secondary than the primary auditory association cortex [24, 25].

Peripheral auditory function and AD

The literature of the relationship between hearing loss, cognitive function, and AD is complex. It was noted that those with hearing loss showed twice greater decline in cognitive functions than those without [26]. More recent studies [27, 28] confirmed that hearing loss (in the better ear) was independently associated with (AD) dementia which increases with severity of hearing loss (for meta-analysis, see [29]). Significant hearing loss was described (liberally defined as > 25 dB on pure tone audiometry (PTA) of 1, 2, or 4 kHz) in almost 90% of AD patients in a memory clinic, much more than expected for their age [30].

For individuals older than 60 years, more than one-third of the risk of incident all-cause dementia is associated with hearing loss [31] and more recent estimates for the population attributed risk was 9% [32]. In careful comparison of age-matched AD patients and controls, significantly worse hearing has been consistently found. For example, an increased (but small) hearing intensity threshold at 1 kHz (but not 3 and 4 kHz; only assessing right ear) [6], worse PTA at 0.5 kHz of 4.7 dB [33], and a 3.3 dB difference [26].

Apperceptive processing/ASA/CAPD in AD

In AD patients (mean Mini-Mental State Examination (MMSE) 22.1 and age 65), a deficit of auditory apperceptive processing (studied by forced choice between degraded sounds) has been described [34]. CAPD (as measured by Synthetic Sentence Identification test with Ipsilateral Competing Message (SSI-ICM), Dichotic Sentence Identification, and Dichotic Digits test) dysfunction is present in mild cognitive impairment (MCI) and early AD [35]. In CAPD studies, however, there is no clear cut-off in test performance between AD patients and normal controls.

Testing AES

Many sound properties influence the chance of correct recognition, such as duration, volume, direction, and familiarity. Characteristics of the observer including age, hearing loss, and personal and cultural background are important. Therefore culture-specific ‘sound events’ of ‘auditory objects’ need to be used [36]. A test that was developed by Spreen and Benton in 1963 (described in [37]), the Sound Recognition Test, gives the subject the choice of four pictures. Normal controls do not make any errors [38, 39]. A limitation is the lack of adequate normative data, no descriptions of the complexity, and the small numbers (2 x 13) of the sounds. Adults almost invariably obtained near-perfect scores on another acoustic recognition tests (scores of 38/39) [40]. Rapcsak and colleagues adapted Spreen and Benton’s test to include 20 sounds and four pictures [41]. Other groups played 30 non-verbal and non-musical sounds of 10 s at a minimal intensity of 75 dB [42] or used 16 environmental sounds; tested for recognizability in 10 healthy volunteers, but no further validation or description of sound presentation [43]. More extensively validated tests of environmental sounds have been developed [44 –46]. Marcell et al. [44] tested 120 natural sounds of animals, people, music instruments, tools, signals, and fluids with normative data for accuracy of naming and confidence in recognizing sounds. They assessed volunteers’ familiarity with these sounds as well and obtained ratings of their complexity. Marcell et al. [44] also determined how participants categorized the sounds and obtained average naming latencies for the various sounds tested. Bozeat et al. [18] studied 48 sounds of six categories (domestic animals, foreign animals, human sounds, household items, vehicles and musical instruments) in three conditions in semantic dementia patients. They tested matching of sounds to pictures, sounds to written words, and spoken words to pictures. The patients performed consistently between the different conditions. All semantic dementia patients showed a multimodal deficit of object knowledge, including environmental sound recognition. In this study, hearing was not assessed [18].

These AES studies use different groups of sounds (e.g., musical instruments, human noises, tools, (in-) animate objects), and methods from naming to recognition of pictures, written and spoken words. We asked the subject to name a sound without other clues, to increase task difficulty and investigate naming ability. To more closely resemble daily life, we provided cues consisting of written words below three pictures (the sound and two foils) for our multiple-choice sound recognition tests. Although all these studies use different tests for AES, they all find normal controls to perform nearly perfectly.

AES in AD and semantic identification (recognition and naming) of sounds

AES has been described previously in AD. Rapcsak et al. studied normal controls and 18 AD patients (mean age 76.3, MMSE 14, range 9–23) [41]. They found impaired recognition performance for (non-verbal) meaningful sounds correlated to the MMSE score on a sound-four pictures matching test. Nonaphasic (>95% score of Western Aphasia Battery) patients with AD made predominantly acoustic errors (confusing sounds of similar acoustic properties, e.g., the bellowing of a cow for a boat horn) and those with aphasia more semantic errors (a sound in a similar semantic context, e.g., a cow for a pig’s grunt). Consequently Rapcsak et al. [41] suggest that AES can be subdivided in perceptual-discriminative (making acoustic errors; a term applied by others to the ability to judge whether two consecutive sounds are identical [45]) and semantic-associative types (impaired at matching/naming). In this they follow Vignolo, whose studies were based on stroke patients [47]. The validity of the distinction between subtypes is unclear.

Another study compared 15 AD patients (mean MMSE 21) and controls for matching a sound to one of four possible written sources. A significant impairment in the recognition of environmental sounds was found, but no relation to MMSE or visual naming scores [42]. Further studies also found poor performance in 28 AD patients compared to controls in an environmental sound naming (and picture recognition) test. This was strongly correlated to lower MMSE [48], something also found by others in 18 patients (mild MCI/AD/frontotemporal dementia) [43].

Hearing loss and dementia

Hearing loss could be causally related to dementia, possibly through exhaustion of cognitive reserve, social isolation, environmental deafferentation (loss of sensory auditory input), or a combination of these pathways [27]. Gallacher proposes a functional framework encompassing a common cause (both symptoms of widespread neuronal degradation), degradation (reduced information available for processing), deprivation and increased cognitive load (mostly working memory) [49].

Hearing loss and AES

People with hearing loss have been excluded from previous studies of AES in dementia. Testing for and definitions of hearing loss have varied widely. Hearing loss has been defined as more than 40–48 dB, sometimes without specification of frequency, average of ears or one/best/worst ear or on clinical grounds (suspected hearing problems [43 , 48], 40 dB (frequency/ear) [42], any frequency from 0.125–8 kHz 48 db hearing loss [7], clinical; on testing 10 patients 30–40 dB loss [41]. It has never been investigated whether hearing loss could contribute to AES. This study was therefore performed to explore the connection between peripheral hearing and AES in AD.

MATERIAL AND METHODS

Subjects

Patients with a diagnosis of probable AD (for demographics and patient characteristics, see Table 1) based on NINDS-ADRDA criteria [50(McKhann et al. 1984) were identified through our memory clinic. The diagnosis was made by a cognitive neurologist after clinical assessment, neuropsychological testing, magnetic resonance imaging (MRI) of the head (1.5 T unit; Philips, Best, The Netherlands), 20-min electroencephalogram (EEG) performed with NicoletOne (CareFusion) and analyzed with Studyroom, and routine laboratory testing. Lumbar puncture was performed on indication (n = 6, all consistent with AD). The study was approved by the Local Research Ethics Committee.

Table 1

Demographics and patient characteristics

	Patients	Controls
Total	34	29
Sex	63% ♀	47% ♀
Age Range	59–88	51–85
Mean Age (SD)	74 (±7)	69 (±8)
Mean MMSE (SD)	21.9 (±3.5)	–
MMSE Range	10–26	–
Hearing Aids	6	0
Fletcher>35 dB (best ear)	38% (n = 13)	–

Each patient received neuropsychological testing with (informant) history. Neuropsychological testing consisted of cognitive (K-SNAP), memory and language screening (Boston Naming Test, BNT), and tests of working memory, executive function, attention and concentration. The performed tests also included: California Verbal Learning Test, Adult intelligence test (KAIT), Word Fluency Test, Ruff’s Figure Fluency Test, Number series tests, Modified Card Sorting Test, Incomplete Letter test, Reverse Digit Span [51], and Sound recognition test (described below).

Patient consent

Patients with a caregiver were approached and written informed consent was provided. The study was conducted in accordance with the guidelines laid down in the declaration of Helsinki. Patients were then referred to an Ear, Nose, and Throat (ENT) specialist.

Controls

Subjects were healthy partners of patients visiting a neurologist or ENT specialist. They were asked about their ENT history and were screened using the Hearing Handicap Inventory for the Elderly–Screening Version (HHIE-S) [52]. Controls who reported more than mild hearing loss were excluded. None of the controls used a hearing aid, psychotropic medication, or had self-reported cognitive problems. There were also no cognitive problems obviously present to the neuropsychologist who performed the test. Controls had not been present while their partner underwent the test.

AES test (see Supplementary Material)

For the sound recognition test, 24 sounds appropriate for Dutch culture (like bicycle bells and baby cry, all described in the Supplementary Material and available on request) and with high accuracy and familiarity p-values were selected (from [44]). Errors in the AES test were coded as being of an acoustic nature, a semantic nature, or both [47]. Two independent raters (JC and SM) performed coding. Disagreement was resolved through consensus. On the recognition test, there were 18 acoustic foils and 29 semantic foils. Sounds are played in a quiet environment at a volume of 75 dB. In case of hearing loss, sounds are amplified and/or the hearing aid is adjusted to a level where the patient felt they were able to hear the sound fully. No patients reported that a sound was too quiet to hear.

What is AES on the test?

Norms of naming are derived from controls. Norms of recognition are also based on unpublished data from a prior study of 55 healthy controls between 32 and 85 years of age (mean = 57.7 years). The average level of education was 12 years.

The mean sound naming score in our current control group was 22.9/24 (SD 1.6; range 17–24) and therefore 97% of controls scored > 20. After excluding the outlier who scored 17, the mean was 23.1 (SD 1.2; range 20–24). We conclude that four or more errors (score≤20,>2SD) point to an underlying disorder (from here on denoted as AESn+) and less than four errors do not (AESn-).

In the first control group, the mean recognition score was 23.4 out of 24 (SD = 0.79; range: 21–24) and in the second control group, 23.97 (range 23–24). Therefore, we conclude that three or more recognition errors (score≤21,>2SD) point to an underlying disorder (AESr+) and less than three errors does not (AESr-) (as in [44]).

Imaging

The MRI of the brain was scored for global cortical atrophy [53], medial temporal lobe atrophy [54], and Fazekas scale for white matter lesions [55] by a single experienced neuroradiologist.

ENT

An ENT specialist examined all patients clinically, and speech and tone audiometry was performed with an AC40200 audiometer (Emid, Doesburg, the Netherlands) by headphone (Telephonics C69313) and bone conduction (RadioEar B-71) in a soundproof room (dB Hearing level (HL) was recorded). For analysis, the worst score of bone or air conduction was used. The main classification of hearing loss used was the PTA4 (0.5, 1, 2, and 4 kHz) but also analyzed were the PTA3 (0.5, 1, and 2 kHz) and the Fletcher index (average of 1, 2, and 4 kHz). Hearing loss was classified according to the WHO as: None (0–25 dB), Slight (26–40 dB), Moderate (41–60 dB), Severe (61–80 dB), or Profound (≥81 dB). Unexpected differences between speech and tone audiometry were determined to be fully, partly, or not at all explained by peripheral ENT pathology. Furthermore left-right differences in speech audiometry were noted and at what volume (dB) 100% of speech sounds were understood. Six patients had peripheral ENT pathology (previous mastoidectomy, Meniere’s, unilateral congenital deafness, unilateral idiopathic sensorineural hearing loss, eardrum perforation, and oval window rupture).

Statistical methods

The t-test for independent samples was used to compare characteristics between patients with and without AES. Analyses were repeated taking into account several covariates, using ANOVA and ANCOVA. The data was relatively normally distributed. Upon inspection of a histogram of the unadjusted residuals, no profound deviations from normality were seen. The Mann Whitney U test was performed for data not following the normal distribution when transformed; a significance level of 0.05 was selected. It was deemed inappropriate to correct p-values for multiple comparisons (for example, by Bonferroni correction) since many factors were highly correlated and this would increase type II errors. All performed analyses have been described. SPSS (Version 17.0, 2008) for Windows was used.

RESULTS

Demographic data (Table 1)

There were 29 controls. 23 reported ‘no’ and six ‘mild hearing problems’. Five had HHIE-S scores of two (of which two self-reported no and three slight hearing problems; three of whom made one mistake in naming); one had a HHIE-S score of ten, who made no errors on the test and had a hearing aid that he did not use.

AES neuropsychological test results (Table 2)

Table 2

AES naming and recognition scores (total score maximum 24)

	Patients	Controls
Sound Naming≤83.3% (<20 points)	22 subjects (67%)	1 subject (3%)
Sound Naming Mean Score (% and SD)	17.7 (73.8% ±16.7%)	22.9 (95.4% ±6.7%)
Sound Naming Range	37.5 to 100.0% (9–24 points)	70.8 to 100.0% (17–24 points)
Sound Recognition≤87.5% (21 points)	12 subjects (35%)	None
Sound Recognition Mean Score (SD)	21.9 (91.3% (±9.2%)	23.97 (99.9% ±0.8%))
Sound recognition Range	54.2 to 100.0% (13–24)	95.8 to 100.0% (23–24)

The results of the Sound Naming test were missing for four patients: for one patient, only the cumulative score was noted and not the individual responses; for three patients, the test was not administered due to the severity of the aphasia. Their 30-item BNT scores were 4, 7, and 16.5. We had total naming scores in 31 and in 30 patients individual answers. Two of the patients with a missing Naming Test had AESr. Because all of the patients who had AESr also had AESn, we have assumed that these two patients would also have had AESn. These two patients were included in analyses comparing AESn+to AESn- as a group, but were excluded from analyses of individual results.

Sound naming

For individual sound naming and recognition performance, qualitative impressions, sound group analysis, and sound identification error subtypes, see the Supplementary Material.

There were no significant differences in age, MMSE, Reverse Digit Span, and Visual Recognition score between patients with and without AESr/n. Mean BNT score did differ significantly both for whether AESr and AESn were present or not; the mean difference is of equal magnitude. The BNT score for the AESr group is lower than AESn due to the inclusion of the three patients who struggled to name sounds at all. Excluding the bottom quartile of patients based on BNT performance did not affect the difference in PTA4 between AESr+and AESr- (analysis not shown; mean PTA 4 AESr+36.7 dB and AESr- 21.5 dB) (Table 3).

Table 3

AES naming and recognition with other neuropsychological correlations

	AESn+	AESn–	Mean Difference (95% CI)	p
Mean Age	74.6 (±7.2)	72.8 (±7.6)	–1.8 (–7.4 to 3.9)	0.524
Mean BNT	16.2 (±4.3)	22.1 (±3.8)	5.9 (2.8 to 9.1)	0.001
Mean MMSE	21.8 (±2.7)	23.5 (±2.9)	1.6 (–0.5 to 3.8)	0.134
Mean VisRec	16.9 (±3.7)	18.4 (±0.8)	1.5 (–1.0 to 4.0)	0.070
Mean RDS	2.7 (±1.0)	3.3 (±1.3)	n/a	n/a
	AESr+	AESr–	Mean Difference (95% CI)	p
Mean Age	76.5 (±7.3)	73.2 (±7.1)	–3.3 (–8.4 to 1.9)	0.205
Mean BNT	13.9 (±5.9)	19.7 (±4.3)	5.8 (2.2 to 9.3)	0.002
Mean MMSE	21.1 (±4.6)	22.3 (±2.7)	1.2 (–2.0 to 4.3)	0.439
Mean VisRec	17.3 (±3.0)	17.5 (±3.0)	0.2 (–2.1 to 2.5)	0.862
Mean RDS	2.8 (±1.2)	2.9 (±1.2)	n/a	n/a

ENT data

A statistically significant difference in mean PTA4 in the best ear of 11.3 dB (p = 0.010) was found between patients with and without AESn, and 14.7 dB (p = 0.000) between those with and without AESr. These results remained significant when correcting for age, sex, MMSE, Reverse Digit Span, and Visual Recognition Test. These differences remained significant when excluding those with moderate hearing loss, peripheral ENT pathology or more than 48 dB in either ear (see Table 5). No significant difference was found when comparing mean hearing loss at 8 kHz in the best ear between patients with and without AESn and AESr (data not shown). Analysis using Fletcher index and PTA3 scores gave similar results (see Supplementary Material). Slight and moderate peripheral hearing loss increases the odds ratio of recognition deficits by 13.75 (confidence interval (CI) 2.3–81.5) and by 4.7 (CI 1.0–22.8) for naming compared to normal hearing patients (Table 4).

Table 4

Hearing loss and agnosia for environmental sound (AES) naming and recognition

	AESn+	AESn–	Mean Difference (95% CI)	p
Mean (SD) PTA4 (dB)	31.5 (±11.8)	20.2 (±9.1)	11.3 (2.9 to 19.7)	0.010
Excl. PTA4 > 40 (dB)	27.9 (±8.1)	21.2 (±9.1)	7.8 (1.0 to 14.5)	0.025
Excl. peripheral ENT pathology (dB)	32.9 (±11.6)	19.1 (±8.7)	13.8 (4.6 to 23.0)	0.005
Excl. 48 dB loss in either ear	28.2 (±7.1)	19.2 (±8.9)	9.0 (2.6 to 15.4)	0.008
Mean Speech Audiometry (dB)	28.6 (14.6)	19.0 (7.9)	9.6 (0.2 to 19.4)	0.054
Excl. PTA4 > 40 (dB)	23.7 (±8.5)	19.0 (±7.9)	4.7 (–2.0 to 11.3)	0.159
Excl. peripheral ENT pathology(dB)	28.1 (±15.1)	17.7 (±7.8)	10.4 (–0.9 to 21.6)	0.068
	AESr+	AESr–	Mean Difference (95% CI)	p
Mean PTA4 (dB)	36.2 (±10.9)	21.5 (±8.3)	14.7 (8.0 to 21.4)	0.000
Excl. PTA4 > 40	31.6 (±5.9)	21.5 (±8.3)	10.1 (4.1 to 16.0)	0.002
Excl. peripheral ENT pathology	36.3 (±11.7)	21.8 (±8.3)	14.4 (6.7 to 22.2)	0.001
Excl. 48 dB loss in either ear	31.2 (±5.7)	21.4 (±8.4)	9.9 (3.5 to 16.2)	0.004
Mean Speech Audiometry (dB)	32.6 (±14.8)	19.6 (±7.9)	13.1 (5.0 to 21.1)	0.010
Excl. PTA4 > 40	25.9 (±6.7)	19.6 (±1.8)	6.4 (0.4 to 12.3)	0.038
Excl. peripheral ENT pathology	31.5 (±15.7)	19.0 (±8.1)	12.5 (3.3 to 21.8)	0.010

Table 5

Hearing loss severity (best ear) and agnosia for environmental sound (AES) naming and recognition

Hearing Loss	AESn+	AESn–	Missing	AESr+	AESr–	Total
None (0–25 dB)	8	8	–	2 (12%)	15	17
Slight (26–40 dB)	11 (75%)	3	–	8 (57%)	6	14
Moderate (41–60 dB)	3	–	–	3	–	3
Total	22	11	1	13	21	34
Odds Ratio (95% CI)			AESr+		AESn+
None: Slight Hearing Loss			10 (1.6 to 61.5)		3.7 (0.7 to 18.3)
None: Slight &Moderate Hearing Loss			13.75 (2.3 to 81.5)		4.7 (1.0 to 22.8)

Hearing loss (Table 5)

No patient had severe or profound hearing loss. No significant difference in MMSE score was found between patients with varying degrees of hearing loss (p = 0.54), also when excluding patients with peripheral ENT pathology (p = 0.48).

Hearing loss and age

Our patients with AES were older than those without. There are frequency, sex, and age group differences in age-related hearing threshold decline. It is suggested there is an average increase of approximately 1 dB per year for subjects age 60 and over [56](Lee et al. 2005) or up to 1.5 dB a year [57](Wiley et al. 2008). Adjusting the mean PTA4 for the age-related difference between AESr+ and AESr- (3.3 years x 1.5 dB/year=–5 dB) still gave a significant difference of 9.7 dB (CI: 3–16, p = 0.006), also for AESn+ versus AESn- (1.8 years x 1.5/years=2.7 dB), mean difference 8.6 dB (CI 0.22–17.0, p = 0.045).

For interaural differences, MRI data, and medication use, see the Supplementary Material.

DISCUSSION

AES

Our findings confirm previous studies which found AES is common in AD [41 , 48]. According to our criteria, 37% of patients had AES for recognition (91% mean accuracy) and 57% AES for naming (74% mean accuracy). Rapczak and colleagues [41] reported accuracy scores of 82% in non-aphasic and 65% in aphasic patients. Eustache et al. [42] found a 22% error rate in patients and 8% in controls, but did not provide cut-off values for normal or abnormal. It is important to note that methods of AES testing between studies vary widely and could influence results (see [10, 44] for discussion). In contrast to Eustache and Rapczak, we established the diagnosis with support of MRI, EEG, cerebrospinal fluid, and neuropsychological testing. All our patients lived at home and likely had less severe AD than those studied by Rapczak (average MMSE 14).

AES test

We used a validated dataset [44] and tested recognition using three pictures (with written description of picture). Therefore, our test probably has higher accuracy scores than those with just picture recognition (Rapczak (four); Saygin (two pictures)), recognition of a written name (Eustache), five written and spoken options (Brandt), or only naming (Diehl-Schmid).

Hearing loss

This study confirms the presence of unrecognized hearing loss in AD. This study for the first time links peripheral hearing loss to environmental sound naming and recognition performance in AD. The mean tone audiometry hearing loss difference (14.7 dB for recognition and 11.3 dB for naming) for best ear PTA4 frequencies remained significant when using the Fletcher index or PTA3, but was not replicated at 8 kHz. Environmental sounds have most of their spectral envelope in PTA4 frequencies and are easiest to identify between 1 and 2 kHz [58]. We do not believe that not hearing the sound explains this finding. Gates et al. have defined adequate peripheral function for their CAPD studies as PTA of 48 dB or better in both ears (after adjusting for any conductive loss) [7]. 85% (n = 29) of our patients had this and the relationship between AES and hearing loss remained when corrected for this. There was also a significantly elevated odds ratio of AESr in moderate versus mild versus no hearing loss. This suggests a dose-response effect of peripheral hearing loss on AES in AD. The effect remained after excluding those with peripheral ENT pathology or moderate or worse hearing loss. Future studies could present sounds at a set (e.g., 50 dB) volume above PTA4 hearing threshold. Auditory learning does occur with hearing aid use, at least for sounds inaudible before hearing aid implantation [59]. In our group, AES performance (and hearing) between those eligible for hearing aid and actual hearing aid users was similar. We found no correlation between MMSE and hearing loss, or MMSE and AA and neither did others [42], but some have [41 , 48].

Previous studies of cortical auditory processing have not linked individual or group performance (deficit present or not, deficit≤2SD compared to controls) to peripheral hearing loss. Gates et al. describe that the 16 hearing aid users had poorer scores on the SSI-ICM regardless of their Clinical Dementia Rating category [60]. Others found a low frequency hearing loss in subjects with reduced SSI performance compared to matched controls [61]. Hearing loss was correlated to naming failures (but not accuracy) in older subjects in a test using brief environmental sounds (akin to picture fragments). In mild AD patients, in contrast, there was a significant correlation between hearing loss and naming accuracy [62].

A major limitation is that we did not study whether in healthy age-matched controls AES performance is correlated to hearing loss. The HHIE-S has good sensitivity of 89.1% and a specificity of 75.0% to rule out hearing loss when compared to audiometry [63], but not 100% so therefore there could have been controls with hearing loss. We find it unlikely that controls who can hear the sounds despite mild to moderate hearing loss (like possibly the one control with high HHIE-S score that performed well) would perform similarly to the AD patients, but a degree of hearing loss severity related performance is not fully excluded in controls. Fabiani’s finding—that accuracy was not correlated to hearing impairment in older controls in a sound naming experiment—also supports the suggestion that the degree of correlation between AES and hearing loss in AD is different to what would be seen in controls [62].

Possible explanations for the relationship between hearing loss, cortical auditory processing, and AD fall into three groups: functional, structural, and as a result of confounding or artifacts. Other theories linking hearing loss to dementia will be discussed below and suggest a possible interaction between cognitive reserve depletion, social isolation, and environmental deafferentation (see [31, 49]).

Artifact or confounding?

Aging affects peripheral hearing. Our AES group is older than our non-AES group by 3.3 years for recognition and 1.8 years for naming (which is non-significant). Covariate analysis with age did not influence our findings. After correcting for expected age-related hearing loss, the correlation between PTA4 and AES remained significant. Our older age control group performed as well as a younger one, so impact of aging on test performance is unlikely.

Hearing loss can cause poor social networks [64]. Poor social networks have been associated with dementia [65]. We do not think major social isolation explains AES in our population since they lived with their partners.

MMSE administration is affected by hearing deficits, which normalizes with hearing amplification [66]. Although some patients wore hearing aids and where possible the examiner amplified his/her voice, it is unlikely but cannot be excluded that in some patients peripheral hearing loss will have affected MMSE score. The correlation between hearing and AES remained after correcting for MMSE score. Although the MMSE is not an ideal substitute for dementia severity, others have also not found a strong correlation between dementia severity and complex sound processing deficits [7 , 68].

Aphasia

Words and environmental sounds share overlapping neural networks (as studied by ERP) [69]. There are strong correlations between accuracy and reaction time performance for verbal and nonverbal (environmental) sounds trials in patients with aphasia. It is therefore no surprise that our AESr+/AESn+ groups had worse BNT (test of aphasia) scores than the AESr-/AESn- groups. Rapcsak also found a relationship between Auditory Verbal Comprehension score on a Western Aphasia Battery and AES in their AD patients [41]. The significant difference in PTA4 between AES+ and AES- was not affected by excluding the patients in the lowest quintile of BNT performance. The patients with aphasia still answered most AES questions correctly, and therefore understood the test questions. This possible interrelationship raises the question whether hearing loss could be a risk factor for developing language deficits in AD. To our knowledge this has not been studied.

Functional/cognitive reserve depletion

There was no significant difference between AESr+/n+ and AESr-/n- groups in reverse digit span performance (test of verbal working memory). Working memory is evidently affected in AD [70]. The role of modality specific working memory processes is not fully elucidated [71] but does seem important [72, 73]. Auditory working memory precision does vary with memory load, cueing and serial order which are consistent with a resource model account rather than it being fixed and capacity limited [74]. Reverse visuospatial span (non-verbal working memory) influenced performance on ASA tasks in AD in one study [6] but less in another [75]. Cognitive reserve reflects interindividual differences in neurocognitive processing that allows some individuals to cope better with neuropathology than others [76]. The potential effect of hearing loss on cognitive reserve is suggested by studies demonstrating that, under conditions in which auditory perception is difficult (i.e., hearing loss), greater cognitive resources are dedicated to auditory perceptual processing to the detriment of other cognitive processes such as working memory [77]. Tun found that mild hearing loss has negative consequences on downstream recall, which supports the so-called ‘effortfullness hypothesis’ [78]. This reallocation of neural resources to auditory processing could deplete the cognitive reserve available to other cognitive processes and possibly lead to the earlier clinical expression of dementia [79].

Gates et al. found a significant relation between executive functioning and central auditory function in elderly people with and without memory impairment or dementia. Executive functioning was associated with PTA3 in the worst ear, after controlling for sex, age, and education and excluding people with 48 dB loss in either ear, or > 40 dB in both ears but it is not clear if this was in both subject groups [80]. Impairments of ASA in AD were not attributable to disease duration or executive performance, consistent with a relatively specific disorder, but as noted above, were affected by working memory [68].

We found a significant correlation between AES recognition (p = 0.01), but not AES naming (p = 0.054), and speech audiometry performance. It is possible that cognitive load/working memory is a confounder by decreasing both AES naming and speech audiometry performance, but adjusting for this co-variable did not fully explain it. The task difficulty of both AES naming and speech audiometry is higher than for PTA and AES recognition respectively. As noted above, environmental and speech sounds share similar spectrotemporal characteristics. If cognitive reserve depletion contributed to AES in AD, one could expect a difference (which we did) in performance between naming and recognition. Controls also find naming more difficult.

In future AES/CAPD/ASA studies, it can be studied whether the test performances and their relationship to PTA are mediated through (auditory modality specific) working memory and cognitive reserve using degraded sounds with similar spectrotemporal characteristics and reducing sound familiarity. It is important that validated tests of auditory working memory are used since the RDS and reverse visuospatial span might not capture this (see [74] for alternative methods). EEG/MEG/fMRI/ERD studies could also shed light on auditory working memory in CAPD/ASA/environmental sound recognition and co-existent hearing loss.

Some of our patients were started on acetylcholinesterase inhibitors (AChEIs) before the ENT examination. AChEIs do alter auditory cortical somatosensory potentials (N20, P50) in treated versus untreated MCI subjects [81]. Acetylcholine modulates communication between primary and higher order auditory cortices in an animal model [82]. In our small sample, patients on AChEIs did not have better speech recognition compared to PTA. Our study was not powered to detect a small difference.

Structural/environmental deafferentation

We did not study specific MRI markers of AES in AD. It could be studied whether AES AD patients have greater (relative) atrophy/reduced grey matter in areas known to be involved in ES processing. Goll et al. did find a correlation between ASA performance in AD and volume (voxel-based morphometry) in posterior superior temporal lobes and posterior cingulate [68]. An association between voice discrimination and grey matter in the right inferior parietal lobe in AD has also been demonstrated [33]. Two studies have linked hearing loss to increased atrophy in primary auditory cortex [83] and whole brain and right temporal lobe [84]. Peripheral deafferentation in animal models disrupts hippocampal function [85]. Environmental deafferentation leading to structural changes is therefore an important theory to consider.

It could be studied whether hearing aid use would induce structural changes in AD. Lin et al. found self-reported hearing aid use not associated with reduction in dementia risk. They stressed that other key variables of successful hearing aid use were unknown to them (e.g., type of hearing aid, hours per day, number of years, user characteristics, other communicative strategies, and adequacy of rehabilitation) [27]. Hearing aid use has been linked to better cognitive function in those age 40–69 [86] and reduction in cognitive decline [87]. Animal studies demonstrate that environmental enrichment can reduce amyloid levels in transgenic mouse models [88]. A multicenter double-blind randomized placebo-controlled trial in patients with AD above 65 showed no significant effect on ADAS-COG after six months, but declines in both groups were smaller than expected [89]. There was no significant effect either on behavioral symptoms, functional status, or quality of life of hearing-impaired AD patients and their caregivers [90]. This raises the question whether earlier and longer intervention would be beneficial.

Acoustic and semantic subtypes of AES

We did not find a clear pattern distinguishing acoustic and semantic errors in AD as suggested by others [41, 42]. The distinction between acoustic and semantic mistakes and extrapolating to acoustic/discriminative and semantic/associative subtypes of AES is maybe not as valid as previously thought. A clearer identification of what acoustic and semantic errors are would be needed to study this further (see [10] for review). We also raise the question whether more recently acquired (personally) meaningful sounds (e.g., mobile phone ringtone as an ‘auditory templates’ [5]) are lost before sounds acquired earlier in life (e.g., baby crying).

Conclusion

This study tried to determine the prevalence and properties of AES in AD and its relation to hearing loss. AES is common in AD, as measured by a validated test. Even a small increase of peripheral hearing loss increased the presence of AES which increased further with hearing loss severity.

Our data possibly supports a “use or lose it” view of the auditory brain in AD; but perhaps more importantly: if you lose it (hearing loss), it is difficult to use it (environmental sound identification). Peripheral hearing loss affects higher order cortical auditory processing ability in the form of environmental sound recognition, probably partly through increased cognitive load and exhausting reduced working memory capacity in AD, as well as loss of auditory templates and disruption of auditory sounds analysis, thereby causing problems in top-down and bottom-up processes and their interaction, as well as possibly structural changes.

This and previous studies linking hearing and dementia suggest longitudinal intervention studies of hearing screening, hearing aids and rehabilitation are indicated both in cognitively normal elderly, MCI and early AD patients. Its impact on general measures of quality of life, cognition, mood, ADL and AD specific biomarkers like volumetric MRI should be studied. It is of paramount importance to establish whether hearing loss is a modifiable risk factor for AD.

Footnotes

ACKNOWLEDGMENTS

We are grateful to the patients and their caregivers for their participation. We like to thank Dr. A. Van Gils for reviewing the MRI data.

This work was supported by HagaHospital.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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