Processing of acoustically degraded emotional prosody in Alzheimer’s disease and primary progressive aphasia: a cohort study

doi:10.21203/rs.3.rs-4937332/v1

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Processing of acoustically degraded emotional prosody in Alzheimer’s disease and primary progressive aphasia: a cohort study

https://doi.org/10.21203/rs.3.rs-4937332/v1

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Background: Prosody refers to nonverbal speech features including pitch, intonation, stress, duration and intensity. These features are critical for successful communication, conveying emotions and distinguishing sentence structures. Previous research suggests that emotional prosody perception is impaired in neurodegenerative diseases like Alzheimer’s disease (AD) and primary progressive aphasia (PPA). However, no previous research has investigated how emotional prosody perception is affected in these patient groups under non-ideal listening conditions such as those encountered in everyday listening. Here, we aimed to examine the perception of emotional prosody in patients with AD and PPA syndromes under both clear and degraded (noise-vocoded) speech conditions.

Methods: We recruited 18 patients with AD, nine with logopenic variant PPA (lvPPA), 11 with non-fluent/agrammatic variant PPA (nfvPPA), and 11 with semantic variant PPA (svPPA), along with 24 healthy controls. Participants listened to numerical speech stimuli portraying three emotions (anger, surprise, sadness) in clear and noise-vocoded forms, and for each speech token were asked to identify the emotion being conveyed. We noise-vocoded the stimuli at three levels to generate easy, medium, and hard listening conditions. Performance was correlated with questionnaire measures of socio-emotional functioning.

Results: All patient groups showed significant impairments in identifying clear emotional prosody compared to healthy controls. These deficits were exacerbated under noise-vocoded speech conditions, with all patient groups performing significantly worse than healthy controls and patients with lvPPA performing significantly worse than those with svPPA. There was no interaction between level of noise-vocoding and participant group, but across groups, participants performed more poorly with more degradation. Significant correlations were observed both for clear and noise-vocoded emotional prosody identification with social cognition measures; however, these correlations were seen more consistently for performance on noise-vocoded emotional prosody than clear speech.

Conclusion: Our findings suggest that emotional prosody identification is impaired in AD and PPA patients, and that these deficits are particularly prominent under non-ideal (noise-vocoded) listening conditions. These findings open a window on a dimension of real-world emotional communication that has often been overlooked in dementia, with particular relevance to social cognition, and begin to suggest a novel candidate paradigm for investigating and quantifying this systematically.

degraded speech

emotional prosody

dementia

primary progressive aphasia

frontotemporal dementia

Alzheimer’s disease

social cognition

noise-vocoding

Despite verbal content in speech being the main component of human communication for hearing individuals, there is much more to successful communication than just the verbal message. To understand the contextual form of the word and gain full insights into the speaker’s communicative intent, nonverbal auditory information also needs to be comprehended (Liebenthal et al., 2016; Morningstar et al., 2022; Wilson & Sperber, 2002).

Prosody is a complex nonverbal speech attribute associated with acoustic properties of individual speech sounds, pitch, intonation, stress, duration, and intensity. It conveys multidimensional information and diverse functions, including disambiguating sentence structure, highlighting or emphasising elements in a sentence, and signalling emotion (Zatorre & Baum, 2012). Broadly, the functions of prosody can be considered linguistically (to distinguish whether a statement is declarative or interrogative), or applied in terms of decoding a speaker’s emotional state (Rohrer, Sauter, et al., 2010). Modulations in vocal pitch (fundamental frequency (F0)), syllable length, duration, intensity, and voice quality perceived by listeners to convey emotional states are collectively known as ‘emotional prosody’ (Everhardt et al., 2020; Horley et al., 2010). Different emotions tend to create different prosody ‘profiles’ in speech. For instance, joy is typically characterised by a faster speech rate, higher intensity, and increases in F0 mean and variability, resulting in more melodic and energetic speech; while sadness is typically characterised by slower speech, at a lower intensity, with decreases in F0 mean and variability (Banse & Scherer, 1996; Schirmer & Kotz, 2006).

Consideration of how processing of prosody is affected in neurodegenerative diseases is important: syndromes across the Alzheimer’s disease (AD) and frontotemporal lobar degeneration spectrum are characterised by impairments in speech processing (Crutch et al., 2013; Gorno-Tempini et al., 2011; Hardy et al., 2016; Rohrer, Ridgway, et al., 2010; Weiner et al., 2008) and/or social signal processing (Bertoux et al., 2016; Downey et al., 2015; Keane et al., 2002; Marshall et al., 2019; Sivasathiaseelan et al., 2021). Impaired prosodic processing has been shown to have implications for social interactions and interpersonal relationships (Everhardt et al., 2020). Previous research has identified impairments in nonverbal auditory perception in AD, non-fluent/agrammatic variant primary progressive aphasia (nfvPPA) and semantic variant primary progressive aphasia (svPPA) (Bozeat et al., 2000; Golden et al., 2015; Goll et al., 2010; Grube et al., 2016; Hsieh et al., 2011; Omar et al., 2010). Impaired perception of emotional prosody has been documented in AD (Amlerova et al., 2022; Arroyo-Anlló et al., 2019; Horley et al., 2010; Taler et al., 2008; Testa et al., 2001), svPPA, nfvPPA and logopenic variant primary progressive aphasia (lvPPA) (Macoir et al., 2019; Rankin et al., 2009; Rohrer, Sauter, et al., 2010; Shany-Ur & Rankin, 2011).

However, all of these studies have considered emotional prosodic processing using ‘clear’ speech stimuli under ideal, laboratory settings, which are unlikely to reflect the reality of communication in the real world where speech is often distorted (e.g. via a poor telephone or videoconferencing connection) or masked by other competing signals. One widely used technique for altering speech signals experimentally is noise-vocoding, a technique in which the speech signal is divided digitally into discrete frequency bands (‘channels’), each filled with white noise and modulated by the amplitude envelope of the original signal (Shannon et al., 1995). Among various alternative methods (Jiang et al., 2021; Mattys et al., 2012), noise-vocoding is an attractive paradigm to study the effects of neurodegenerative diseases on the processing of degraded speech generally for three reasons. Firstly, it allows for parameterisation of the amount of acoustic degradation of a speech signal. Secondly, spectral detail is important to many emotional prosodic cues, so noise-vocoding (as a technique that targets spectral detail) is a logical paradigm to assess the impact of acoustic degradation on comprehension of prosodic signals. Thirdly, noise-vocoding has previously been used successfully in the extraction of verbal message comprehension threshold in the patient groups targeted in the present study (Jiang et al., 2023).

Here, we explored perception of emotional prosody in major primary progressive aphasia syndromes and Alzheimer’s disease, both in ‘clear’ and ‘degraded’ speech forms. In line with previous research (Amlerova et al., 2022; Arroyo-Anlló et al., 2019; Horley et al., 2010; Macoir et al., 2019; Omar et al., 2011; Rankin et al., 2009; Rohrer, Sauter, et al., 2010; Shany-Ur & Rankin, 2011; Taler et al., 2008; Testa et al., 2001), we hypothesised that people with AD and primary progressive aphasia (PPA) (particularly nfvPPA) would perform worse than healthy individuals at identifying ‘clear’ emotional prosody. Secondly, we hypothesised that AD and PPA patients would have an additional cost from degrading emotional prosodic cues compared with healthy individuals, particularly in nfvPPA and lvPPA. Thirdly, we predicted that emotional prosody identification performance would correlate with measures of daily life socio-emotional functioning.

Participants

Eighteen patients with typical amnestic AD, nine patients with lvPPA, 11 patients with nfvPPA, and 11 patients with svPPA were recruited via a specialist cognitive clinic. All patients fulfilled consensus clinical diagnostic criteria (Dubois et al., 2014; Gorno-Tempini et al., 2011). Twenty-four healthy older individuals with no history of neurological or psychiatric disorders were recruited from the Dementia Research Centre volunteer database.

All participants had a comprehensive general neuropsychological assessment (Table 1). None had a history of otological disease, other than presbycusis; participants assessed in person at the research centre had their peripheral hearing assessed using pure-tone audiometry, following a previously described procedure (details in Supplementary Material is available online).

Table 1

General demographic, clinical and neuropsychological characteristics of all participant groups
Characteristic	Healthy individuals	AD	lvPPA	nfvPPA	svPPA
Demographic and clinical
No. M:F	13:11	13:5	7:2	8:3	7:4
Age, years	68.7 (6.40)	70 (8.86)	70.4 (6.46)	68.7 (6.05)	62.5 (8.57)
Handedness (R:L:A)	21:1:1^a	17:1:0	9:0:0	11:0:0	10:1:0
Education (y)	16.1 (2.72)^a	15.4 (3.89)^a	14.6 (2.72)^a	14.6 (2.72)^a	15.7 (2.11)^a
Symptom duration (y)	NA	6 (3.10)^a	5.75 (4.53)^a	3.36 (1.57)	5.45 (2.62)
Best ear average*	17.9 (10.3)^b	28.1 (12.6)^h	17.5 (12.9)^j	30 (4.18)^k	22.7 (8.64)^l
Tested in-person/remote	20:4	9:9	5:9	6:5	4:7
MMSE (/30)	29.8 (0.62)^c	21.8 (7.52)^c	20 (7.51)^j	25.2 (0.71)^k	23.6 (5.13)^l
T-MMSE (/27)	26.1 (1.76)^d	17.4 (4.45)^h	21.5 (4.51)^j	22.8 (4.02)^1,j	24 (1.41)^1,m
General neuropsychology
Executive function
WASI Matrices (/32)	26.8 (2.74)^f	12.4 (8.58)ⁱ	21.9 (5.18)	22.1 (6.89)¹	25.5 (5.01)¹
Letter fluency (total)	15.9 (5.35)^f	10.9 (5.86)^a	8.22 (4.21)	9.11 (8.43)ⁱ	7.73 (6.62)
Category fluency (total)	24.1 (6.30)^f	11.4 (6.74)^a	10.6 (6.19)	16.8 (8.97)^2,i	7.27 (5.57)
Working memory
Digit span forward (max)	6.56 (1.03)^f	5.78 (1.44)	4.33 (1.32)²	4.91 (1.76)²	6.64 (1.03)
Digit span reverse (max)	5.19 (1.17)^f	3.28 (1.41)	3.22 (1.72)²	3.55 (1.69)^1,2	5.00 (1.61)¹
Auditory input processing
PALPA-3 (/36)	34.6 (1.66)^g	NA	33.5 (2.39)^a	32.9 (2.63)	33.6 (2.38)
Language skills
GNT (/30)	25.8 (2.46)^f	13.2 (7.39)	9.67 (7.43)	17.7 (10.8)¹	1.55 (4.50)¹
BPVS (/150)	148 (2.09)^f	139 (15.6)	141 (13.2)²	135 (19.1)²	79.8 (48.1)¹
PALPA-55 (/24)	23.5 (1.20)^g	NA	17.00 (7.01)^a	19.3 (3.58)	19.9 (5.05)
Modified Camel and Cactus (/32)	30.6 (1.09)^f	NA	26.4 (7.60)^a	28.5 (3.36)²	21.5 (7.92)^a
Episodic memory
RMT Faces (Short) (/25)	23.8 (2.5)^c	16.1 (3.26)^h	21.4 (3.65)^l	22.8 (3.49)^1,j	19.2 (3.69)^m
RMT Faces (Long) (/50)	41.7 3.70)^e	29.6 (5.57)^h	28.5 (5.74)ⁱ	36.8 (7.27)^m	31.1 (3.53)^l
Other skills
GDA calculation (/24)	14.8 (5.18)^f	5.06 (4.81)ⁱ	5.38 (4.63)^2,a	6.09 (5.41)	11.2 (5.67)^1,a
VOSP Object Decision(/20)	18.9 (1.48)^f	14.6 (2.94)	16.2 (2.49)^a	17.9 (1.85)^1,a	15.7 (3.95)^a
Mean (standard deviation) values and raw scores are presented (maximum value possible in parentheses), unless otherwise indicated; significant differences from healthy individuals (p < 0.05) are in bold; ¹significantly different to AD (p < 0.05); ²significantly different to svPPA (p < 0.05). *See Supplementary Material for details concerning the ‘best ear average’ measure. Participants assessed in-person did the MMSE while those assessed remotely did the T-MMSE. Similarly, the RMT Faces (Long) was administered to participants in-person, versus the RMT Faces (Short), which was administered to participants remotely (full details of our remote neuropsychological test battery are given in (Requena-Komuro et al., 2022). A, ambidextrous; AD, patient group with typical Alzheimer’s disease; BNT, Boston Naming Test; BPVS, British Picture Vocabulary Scale; Controls, healthy older individuals; Digit span forward/reverse, maximum digit span recorded; F, female; GDA, Graded Difficulty Arithmetic; GNT, Graded Naming Test; L, left; lvPPA, patient group with logopenic variant primary progressive aphasia; M, male; MMSE, Mini-Mental State Examination; NA, not available/applicable; nfvPPA, patient group with nonfluent/agrammatic variant primary progressive aphasia; PALPA, Psycholinguistic Assessments of Language Processing in Aphasia; R, right; RMT, Recognition Memory Test; svPPA, patient group with semantic variant primary progressive aphasia; Synonyms concrete/abstract, single-word comprehension of single words; T-MMSE, tele-MMSE; VOSP, Visual Object and Space Perception battery; WASI, Wechsler Abbreviated Scale of Intelligence.
^a missing data from one participant
^b missing data from 14 participants
^c missing data from 12 participants
^d missing data from 15 participants
^e missing data from 20 participants
^f missing data from 8 participants
^g missing data from 11 participants
^h missing data from 9 participants
ⁱ missing data from 2 participants
^j missing data from 5 participants
^k missing data from 6 participants
^l missing data from 4 participants
^m missing data from 7 participants

Due to the COVID-19 pandemic, some data for this study were collected remotely (see Supplementary Materials). We have described the design and implementation of our remote neuropsychological assessment protocol elsewhere (Requena-Komuro et al., 2022). Remote participants were administered the T-MMSE (Newkirk et al., 2004) which we have reported separately in Table 1.

Creation of experimental stimuli

Forty-five three-digit numbers (of the form: ‘three-hundred-and-seventy-three’), spoken by two adult male and two adult female speakers (all with Standard Southern British English accents), were taken from a previously normed set of vocal emotional stimuli (Sauter, 2006). The full stimuli set comprises six emotions (namely anger, surprise, sadness, fear, disgust and happiness). Here, to reduce response demands for patients, we selected numbers portraying three emotions: anger, surprise and sadness, chosen because previous work has shown that healthy older individuals are consistently able to identify these (Rohrer, Sauter, et al., 2010).

Speech recordings were noise-vocoded using Praat (https://www.fon.hum.uva.nl/praat/) to generate acoustically altered stimuli at either six, 12 or 18 channels (see Supplementary Fig. 1 for spectrograms). Details concerning the synthesis of noise-vocoded stimuli are provided in Supplementary Material online. Three levels of noise-vocoding were used due to considerations of the length of the task and concerns over participant fatigue, in comparison to a previous thresholding procedure which included 24 vocoding channels (Jiang et al., 2023). The three channels chosen were designated to signify hard (six channels), medium (12 channels) and easy (18 channels) perception. This was informed through previous work showing that vocoded speech at 10 channels is readily intelligible by healthy listeners, whereas four channels only becomes intelligible after hours of training (Davis et al., 2005). At each noise-vocoding level, 15 three-digit number stimuli were presented, with five numbers in each of the three emotions. Twenty-one three-digit number stimuli were kept in ‘clear’ form: six (two for each emotion) were used as practice items for familiarising the participant with the stimuli before the experimental test, and 15 (five per emotion) were used as a clear speech control condition. Thus, a total of 60 stimuli (20 for each emotion) were presented during the experimental test session, which typically lasted around 20 minutes.

Experimental procedure

The stimuli were administered either in-person in a quiet room via Audio-Technica ATH-M50x headphones at a comfortable fixed listening level (at least 70 dB), or remotely via Labvanced and shared through a video link (see Supplementary materials online).

To be familiarised with the experimental procedure, participants first heard the six practice clear three-digit number stimuli, and asked to identify which emotion each number was spoken with, using a cue card as a guide and response aid if necessary (Figure S2). Feedback was given for the practice trials and participants were told if they had answered correctly on each of the practice trials.

Participants then continued to the experimental task proper, where first, the 45 noise-vocoded speech trials were presented in randomised order to each participant, followed by the 15 clear speech trials, again in randomised order. On each trial, participants were tasked to indicate, either verbally or by pointing at the cue card (Figure S2), which emotion was portrayed (e.g., was it sadness, anger or surprise?). Responses were noted by the examiner for offline analysis, no feedback about the performance was given, and no time limits were imposed.

Assessment of social cognition

To assess patients’ social cognition, the modified Interpersonal Reactivity Index (mIRI) and the Revised Self-Monitoring Scale (RSMS) questionnaires were completed by the primary caregiver or another close informant on each patient’s behalf.

The mIRI, frequently used with people with dementia (Foster et al., 2022; Van't Hooft et al., 2024), is based on the Interpersonal Reactivity Index (Davis, 1983). It includes two seven-item subscales: the first subscale measures cognitive empathy in the form of perspective taking, and the second subscale assesses emotional empathy in the form of empathic concern. The questions are formatted in a series of statements and responders are asked how well each statement describes the participant on a Likert response scale.

The RSMS (Lennox & Wolfe, 1984), also frequently used with people with dementia (Franklin et al., 2021; Toller et al., 2020), is a 13-item questionnaire based on the Self-Monitoring Scale (Snyder, 1974). It is made up of two subscales: the first subscale measures participants’ sensitivity to expressive behaviour, and the second subscale measures the tendency to monitor self-presentation. The questions are formatted in a series of statements and responders are asked how well each statement describes the participant on a Likert response scale.

Data analysis

Data were analysed in R® (v4). For continuous demographic and background neuropsychological data, participant groups were compared using ANOVA or Kruskal Wallis tests, depending on whether assumptions of the general linear model were met. As some of our participants completed the T-MMSE and others completed the in-person MMSE, we used a scalar conversion to generate a combined MMSE score to be incorporated into analyses (Newkirk et al., 2004). Group categorical data were compared using Fisher’s exact test. Performance in seven healthy control participants who performed the experiment both in person and subsequently remotely were very similar, justifying combining participants tested in person and remotely in the main analyses (Figure S3) (Jiang et al., 2023).

For the main experimental and control (i.e. emotional prosody) tests, a binary score was given for each trial: one if the emotion was correctly identified, zero if incorrect. To reduce bias in subsequent analyses, participants who scored at or below chance on the clear emotion recognition control task (i.e., a score that could have been achieved by random guessing), were excluded. A threshold for chance performance was calculated using the cumulative probability function: 15 trials with probability 0.33’ suggested that a hit rate (k) of nine (out of 15) or above was unlikely to be achieved by chance (p = 0.029 [k = 8, p = 0.084]) (Berman, 2023).

For the control task (clear emotion recognition), due to ceiling effects, data were analysed using the Kruskal Wallis test. For the experimental task, data were analysed using a mixed ANCOVA, with vocoding channels as the repeated measure (three levels: six [hard], 12 [medium], 18 [easy]) and diagnosis as the between-subjects factor, adjusting for performance on the control task (performance on clear emotions), and WASI Matrix Reasoning (as a proxy for disease severity). Additionally, the model included an interaction term between diagnosis and vocoding channel number. Where the omnibus test was significant, post hoc analyses were conducted (pairwise t-tests for ANCOVA; Dunn test for Kruskal Wallis).

Spearman’s rank correlation was used to assess the relationship between accurate identification of degraded emotional prosody and disease severity (WASI matrix reasoning), auditory perception (PALPA-3, pure-tone audiometry), working memory (digit span), and measures of socio-emotional awareness (mIRI and RSMS). To assess whether any significant correlations between noise-vocoded emotional prosody identification and socio-emotional awareness could be explained by generic noise-vocoded speech perception ability rather than specific degraded emotional prosodic processing skills, we additionally conducted correlation analyses with performance on a neutral prosody noise-vocoded number repetition task (from Jiang et al. (2023)) as the majority of participants had also completed this (excluding three patients with nfvPPA)).

Considering the small cohort sizes and exploratory nature of the study, no corrections for multiple comparisons were conducted, to avoid inflating type II error. Effect sizes (epsilon squared for Kruskal Wallis models (ε²); partial eta squared for ANCOVA models (η²_p); Spearman’s rank correlation coefficient (rs) were generated in addition to p-values, and an alpha of 0.05 was adopted as the threshold for statistical significance on all tests.

General neuropsychology profiles were in keeping with the syndromic diagnosis for each patient group (Table 1).

Participants who scored at or below chance on the clear emotion identification control task were excluded from subsequent analyses (two patients with AD, one with nfvPPA, and one with svPPA). An additional AD patient was excluded as they were unable to correctly identify any of the angry stimuli in the clear speech condition.

General participant group characteristics

Participant groups did not differ significantly in age, sex, handedness, years of formal education or pure-tone audiometry (all p > 0.05). Patient groups did not differ in mean symptom duration (p = 0.136) or combined MMSE score (p = 0.069). Basic speech discrimination (assessed using the PALPA-3 (Kay et al., 1996)) performance also did not differ significantly across participant groups (p = 0.366).

Experimental behavioural data

There was a significant difference across diagnostic groups on performance of correctly identifying the clear emotional stimuli (Table 2) (χ2(4) = 20.22, p = 0.001, ε² = 0.24, 95% CI [0.13, 0.46]), with all patient groups performing worse than healthy individuals (AD: t=-3.23, p = 0.001; lvPPA: t=-2.86, p = 0.004; nfvPPA: t=-3.40, p < 0.001; svPPA t=-2.93, p = 0.003). There were no differences between patient groups (p > 0.05).

Table 2

Average correct raw responses for identification of emotional prosody in clear and noise-vocoded speech, in each participant group
	Healthy individuals	AD	lvPPA	nfvPPA	svPPA
Correct Raw Responses
Clear (/15)	14.71 (0.46)	13.53 (1.36)	13.67 (1.36)	13.10 (1)	13.80 (0.63)
18 Channels (/15)	12.83 (1.40)	10.87 (2.50)	10.44 (2.56)	10.90 (2.56)	11.80 (1.75)
12 Channels (/15)	12.50 (1.53)	10.40 (2.64)	9.78 (1.72)	10.90 (3.18)	10.60 (2.88)
6 Channels (/15)	11.13 (1.85)	9.13 (2.72)	8.33 (3.24)	10.10 (2.28)	10.50 (2.27)
Mean (standard deviation) values are shown. Raw scores are presented (maximum value possible in parentheses). Values significantly different from healthy individuals (p < 0.05) are in bold. AD, patient group with Alzheimer’s disease; lvPPA, patient group with logopenic variant primary progressive aphasia; nfvPPA, patient group with nonfluent variant primary progressive aphasia; svPPA, patient group with semantic variant primary progressive aphasia.

After adjusting for performance on identifying clear emotions and WASI matrix reasoning (as an index of disease severity), there was a significant main effect of diagnosis on vocoded emotional stimuli identification (F(4,58) = 5.47, p < 0.001, η²_p = 0.27, 95% CI [0.09, 1.00]). There were also significant effects of clear emotion identification performance (F(1,58) = 8.45, p = 0.005, η²_p = 0.13, 95% CI [0.02, 1.00]), vocoding channels (F(2,120) = 18.03, p < 0.001, η²_p = 0.23, 95% CI [0.12, 1.00]), and WASI matrix reasoning (F(1,58) = 4.46, p = 0.039, η²_p = 0.07, 95% CI [0.00, 1.00]). Post-hoc analyses showed that all patient groups performed worse than the healthy individuals (all p < 0.03), and the lvPPA patient group performed significantly worse than the svPPA group (t=-2.35, p = 0.020); but no other between-group comparisons were significant (all p > 0.05) (Fig. 1).

Across groups, participants performed significantly worse at six channels compared with 12 (t = 2.63, p = 0.009) and 18 (t = 3.72, p < 0.001) channels. There was no significant difference between performance at 12 and 18 channels (t = 1.08, p = 0.280). The interaction between diagnosis and vocoding channels was also not significant (F(8,120) = 0.60, p = 0.774).

Correlational analyses

In the combined patient cohort, performance on noise-vocoded emotional prosody identification was not significantly correlated with peripheral hearing (as measured with pure-tone audiometry; rs(21)=-0.09, p = 0.669) or speech discrimination (as measured with the PALPA-3; rs(26) = 0.23, p = 0.240)). Noise-vocoded emotional prosody was significantly correlated with WASI matrix reasoning score (rs(40) = 0.34, p = 0.029), forward digit span (rs(42) = 0.44, p = 0.003) and reverse digit span (rs(42) = 0.36, p = 0.018).

Total scores on the clear and noise-vocoded emotional prosody tasks were also significantly correlated with relevant scores on the mIRI and RSMS across the combined patient cohort (Fig. 2 and Fig. 3, respectively). Accurate identification of clear emotional prosody was significantly correlated with the mIRI subscale: cognitive empathy (perspective taking) (p = 0.038) and at threshold for significance with total mIRI score (p = 0.050) (Fig. 2). Accurate identification of noise-vocoded emotional prosody was significantly correlated with the full mIRI (p = 0.016), mIRI subscale: emotional empathy (p = 0.020), mIRI subscale: cognitive empathy (p = 0.031), the full RSMS (p = 0.011), and the RSMS subscale: sensitivity to expressive behaviour (p = 0.001; Fig. 3).

No significant correlations were observed between noise-vocoded number repetition (i.e. a non-prosodic degraded speech control) and any of the social cognition (mIRI or RSMS) measures (all p > 0.05) (Figure S4).

Here, we have shown that comprehension of acoustically degraded emotional prosody is impaired in patients with AD and PPA syndromes relative to healthy individuals. Deficits in clear prosody perception have been previously identified (Amlerova et al., 2022; Macoir et al., 2019; Rohrer, Sauter, et al., 2010), however, the deficit for emotional prosody identification in noise-vocoded speech remained after adjusting for performance on the ‘clear’ (i.e. natural, undistorted’) speech task in the present study, suggesting that deficits in emotional prosody perception are exacerbated in non-ideal listening conditions in patients with PPA and AD. This additional ‘cost’ to noise-vocoding in the patient groups studied here could reflect previously identified problems with perceiving noise-vocoded auditory stimuli (Jiang et al., 2023), and a core deficit in AD and lvPPA for apperceptive processing (e.g., the representation and decoding of auditory objects). There are likely to be stored neural ‘templates’ corresponding to the perceptual characteristics of the prosodic signatures of particular emotions, and under acoustic degradation, the neural template matching is stressed, analogous with previous work in AD and lvPPA suggesting impaired ‘template activation’ for phonemes (Hardy et al., 2017; Jiang et al., 2022; Johnson et al., 2020).

We also identified significant associations between emotional prosody identification performance and scores on two social cognition questionnaires (mIRI and RSMS) and their subscales, more consistently for vocoded than clear stimuli. In clear emotional prosody, there was a significant correlation with the mIRI cognitive empathy subscale and a borderline significant correlation with the full mIRI (Fig. 2). For noise-vocoded emotional prosody, we identified the same significant correlations as with clear emotional prosody identification performance and found additional significant correlations with the mIRI emotional empathy subscale, the full RSMS and the RSMS sensitivity to expressive behaviour subscale (Fig. 3). Importantly, no significant correlations were identified between social cognition questionnaire scores and a noise-vocoded number repetition control task (Figure S4), nor between noise-vocoded emotional prosody identification and pure-tone audiometry or speech discrimination task performance, implying that dissociable, central auditory mechanisms process verbal and nonverbal dimensions of speech signals. We did observe significant correlations between noise-vocoded nonverbal emotional prosody identification and measures of executive functioning and working memory (i.e. WASI Matrix Reasoning and digit span tests), consistent with previous research implicating the involvement of a fronto-parietal brain network (Breitenstein et al., 2001; Darki & Klingberg, 2014; Sauseng et al., 2005)(Breitenstein et al., 2001; Darki & Klingberg, 2014; Sauseng et al., 2005) in these processes. The significant correlation here implies that our noise-vocoded emotional prosody identification task could hold promise as a tool for tracking disease evolution.

Taken together, these correlation analyses therefore suggest that noise-vocoding of emotional prosody could represent a ‘stress test’ for predicting daily-life vocal emotion processing in patients with dementia. One possible explanation is that patients with neurodegenerative diseases targeting core auditory processing networks experience difficulties processing emotional cues within our daily hearing environments (i.e. an intrinsic auditory deficit), meaning that they are therefore less likely to engage with emotional cues being spoken and utilised towards them, manifesting as perceived socio-emotional difficulties which are actually secondary to the more basic auditory deficit (Goll et al., 2012; Hardy, Marshall, et al., 2018; Jiang et al., 2023). An alternative explanation is that patients with these conditions have a ‘double-hit’ of a top-down impairment in social cognition (Fittipaldi et al., 2019; Goldberg et al., 2021; Kessels et al., 2021) coupled with the bottom-up auditory processing deficit. Either way, the significant correlation here implies that our noise-vocoded emotional prosody identification task could hold promise as a tool for tracking impairment.

There are several limitations in this experiment and more work needs to be conducted to not only refine the paradigm used here but to also understand the underlying mechanisms, alongside how they are impacted (and likely differently) in different dementias and implications for daily life function for patients. Firstly, across groups, participants in the present study performed significantly worse at six than 12 channels, and at six than 18 channels, but there were no significant differences between 12 and 18 channels. This ‘flattening’ between 12 and 18 channels is perhaps unsurprising as it is consistent with the nonlinear noise-vocoding scale (e.g., the difference between 10 and 11 channels is negligible, whereas the difference between one and two channels is drastic), but future work should aim to refine channel selection. Secondly, the group sizes reported here were relatively small. Considering the rarity of PPA, the collection of substantially larger datasets would require multi-centre collaboration.

Future studies, particularly in the case of emotional prosody, should extend to investigate which emotional nonverbal vocal signals are more easily transferred transculturally and cross-linguistically. It should also be extended to patients with behavioural variant and right temporal variant FTD, in whom impaired emotional prosody perception has previously been identified (Perry et al., 2001). It would additionally be interesting to see whether comprehension of noise-vocoded emotional prosody can be modulated pharmacologically, by dopaminergic or cholinergic stimulation (Breitenstein et al., 2001; Kee et al., 1998) and/or perceptual learning (Hardy, Bond, et al., 2018). This paradigm should also be extended to establish the brain basis and neural mechanisms for processing degraded prosodic and other more complex socio-emotional signals in dementia, such as sarcasm (Downey et al., 2015). Finally, Conversation Analysis methods could be incorporated to explore how these deficits of emotional prosody perception under degraded conditions impact real-world interactions between people with dementia and their communication partners (Volkmer et al., 2023).

The findings presented here open a window on a dimension of real-world emotional communication that has often been overlooked in dementia, with particular relevance to social cognition, and begin to suggest a novel candidate paradigm for investigating and quantifying this systematically. Brief, easy-to-administer measures of social cognition within neurodegenerative dementias generally and frontotemporal dementias specifically, that are available for deployment in clinical settings, are currently lacking. Our work suggests that the new noise-vocoded emotional prosody identification task introduced here has potential utility as a clinical marker of social cognition impairment in these conditions.

AD = Alzheimer’s disease; lvPPA = logopenic variant primary progressive aphasia; mIRI = modified Interpersonal Reactivity Index; MMSE = Mini Mental State Examination; nfvPPA = nonfluent variant primary progressive aphasia; NHNN = National Hospital for Neurology and Neurosurgery; PALPA = Psycholinguistic Assessment of Language Processing in Aphasia; PPA = primary progressive aphasia; RSMS = Revised Self-Monitoring Scale; svPPA = semantic variant primary progressive aphasia; T-MMSE = telephone Mini Mental State Examination; WASI = Wechsler Abbreviated Scale of Intelligence

Author Contribution

JJ, JCSJ, MCRK, EB, HS, AC, and AN collected data associated with this manuscript. Analysis was conducted by JJ with guidance from RSW, DB, JDW and CJDH. JJ, JDW and CJDH wrote the main manuscript text. All authors reviewed the manuscript.

Funding Declaration

The Dementia Research Centre is supported by Alzheimer’s Research UK, Brain Research Trust, and The Wolfson Foundation. The work was supported by the Alzheimer’s Society (grant AS-PG-16-007 to JDW), the Royal National Institute for Deaf People (G105 to JDW), Alzheimer’s Research UK and the National Institute for Health Research University College London Hospitals Biomedical Research Centre. JJ was supported by a Frontotemporal Dementia Research Studentship in Memory of David Blechner (funded through The National Brain Appeal). JCSJ was supported by an Association of British Neurologists Clinical Research Training Fellowship. MCRK was supported by a Wellcome Trust PhD studentship (102129/B/13/z). EB was supported by a Brain Research UK PhD Studentship. HS was funded by a Clinical Research Fellowship from the Leonard Wolfson Experimental Neurology Centre. RSW is supported by a Wellcome Clinical Research Career Development Fellowship (205167/Z/16/Z). AV is supported by a NIHR Advanced Fellowship (NIHR302240). CRM is supported by a grant from Bart’s Charity and the National Institute for Health Research (NIHR204280). DEB is supported by the Royal National Institute for Deaf People. CJDH was supported by Fellowship awards from the Royal National Institute for Deaf people and Dunhill Medical Trust (grant number PA23) and the Alzheimer’s Society, UK (grant number 627).

Ethics Declaration

All participants gave informed consent to take part in the study, in accordance with Declaration of Helsinki guidelines. Ethical approval was granted by the University College London-National Hospital for Neurology and Neurosurgery Joint Research Ethics Committees.

Amlerova, J., Laczó, J., Nedelska, Z., Laczó, M., Vyhnálek, M., Zhang, B., Sheardova, K., Angelucci, F., Andel, R., & Hort, J. (2022, 2022/04/05). Emotional prosody recognition is impaired in Alzheimer’s disease. Alzheimer's Research & Therapy, 14(1), 50. https://doi.org/10.1186/s13195-022-00989-7
Arroyo-Anlló, E. M., Dauphin, S., Fargeau, M. N., Ingrand, P., & Gil, R. (2019, 2019/08/07). Music and emotion in Alzheimer’s disease. Alzheimer's Research & Therapy, 11(1), 69. https://doi.org/10.1186/s13195-019-0523-y
Banse, R., & Scherer, K. R. (1996). Acoustic Profiles in Vocal Emotion Expression. Journal of Personality and Social Psychology, 70(3), 614-636.
Berman, H. B. (2023). Binomial Probability Distribution. Retrieved 14/08/2023 from https://stattrek.com/probability-distributions/Binomial
Bertoux, M., de Souza, L. C., O'Callaghan, C., Greve, A., Sarazin, M., Dubois, B., & Hornberger, M. (2016). Social Cognition Deficits: The Key to Discriminate Behavioral Variant Frontotemporal Dementia from Alzheimer’s Disease Regardless of Amnesia? Journal of Alzheimer's Disease, 49(4), 1065-1074. https://doi.org/10.3233/JAD-150686
Bozeat, S., Lambon Ralph, M. A., Patterson, K., Garrard, P., & Hodges, J. R. (2000). Non-verbal semantic impairment in semantic dementia. Neuropsychologia, 38(9), 8. https://doi.org/10.1016/S0028-3932(00)00034-8
Breitenstein, C., Lancker, D. V., Daum, I., & Waters, C. H. (2001). Impaired Perception of Vocal Emotions in Parkinson's Disease: Influence of Speech Time Processing and Executive Functioning. Brain and Cognition, 45, 277-314. https://doi.org/doi:10.1006/brcg.2000.1246
Crutch, S. J., Lehmann, M., Warren, J. D., & Rohrer, J. D. (2013, Apr). The language profile of posterior cortical atrophy. J Neurol Neurosurg Psychiatry, 84(4), 460-466. https://doi.org/10.1136/jnnp-2012-303309
Darki, F., & Klingberg, T. (2014). The Role of Fronto-Parietal and Fronto-Striatal Networks in the Development of Working Memory: A Longitudinal Study. Cerebral Cortex, 25(6), 1587-1595. https://doi.org/10.1093/cercor/bht352
Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional approach. Journal of Personality and Social Psychology, 44, 113-126.
Davis, M. H., Johnsrude, I. S., Hervais-Adelman, A., Taylor, K., & McGettigan, C. (2005, May). Lexical information drives perceptual learning of distorted speech: evidence from the comprehension of noise-vocoded sentences. J Exp Psychol Gen, 134(2), 222-241. https://doi.org/10.1037/0096-3445.134.2.222
Downey, L. E., Mahoney, C. J., Buckley, A. H., Golden, H. L., Henley, S. M., Schmitz, N., Schott, J. M., Simpson, I. J., Ourselin, S., Fox, N. C., Crutch, S. J., & Warren, J. D. (2015). White matter tract signatures of impaired social cognition in frontotemporal lobar degeneration. NeuroImage: Clinical, 8, 11. https://doi.org/10.1016/j.nicl.2015.06.005
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., Dekosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., Cappa, S., Crutch, S., Engelborghs, S., Frisoni, G. B., Fox, N. C., Galasko, D., Habert, M.-O., Jicha, G. A., Nordberg, A., Pasquier, F., Rabinovici, G., Robert, P., Rowe, C., Salloway, S., Sarazin, M., Epelbaum, S., De Souza, L. C., Vellas, B., Visser, P. J., Schneider, L., Stern, Y., Scheltens, P., & Cummings, J. L. (2014). Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. The Lancet Neurology, 13(6), 614-629. https://doi.org/10.1016/s1474-4422(14)70090-0
Everhardt, M. K., Sarampalis, A., Coler, M., Baskent, D., & Lowie, W. (2020). Meta-Analysis on the Identification of Linguistic and Emotional Prosody in Cochlear Implant Users and Vocoder Simulations. Ear and Hearing, 41(5), 1092-1102. https://doi.org/10.1097/aud.0000000000000863
Fittipaldi, S., Ibanez, A., Baez, S., Manes, F., Sedeno, L., & Garcia, A. M. (2019, May). More than words: Social cognition across variants of primary progressive aphasia. Neurosci Biobehav Rev, 100, 263-284. https://doi.org/10.1016/j.neubiorev.2019.02.020
Foster, P. H., Russell, L. L., Peakman, G., Convery, R. S., Bouzigues, A., Greaves, C. V., Bocchetta, M., Cash, D. M., van Swieten, J. C., Jiskoot, L. C., Moreno, F., Sanchez-Valle, R., Laforce, R., Graff, C., Masellis, M., Tartaglia, C., Rowe, J. B., Borroni, B., Finger, E., Synofzik, M., Galimberti, D., Vandenberghe, R., de Mendonça, A., Butler, C. R., Gerhard, A., Ducharme, S., Le Ber, I., Tagliavini, F., Santana, I., Pasquier, F., Levin, J., Danek, A., Otto, M., Sorbi, S., & Rohrer, J. D. (2022, May). Examining empathy deficits across familial forms of frontotemporal dementia within the GENFI cohort. Cortex, 150, 12-28. https://doi.org/10.1016/j.cortex.2022.01.012
Franklin, H. D., Russell, L. L., Peakman, G., Greaves, C. V., Bocchetta, M., Nicholas, J., Poos, J., Convery, R. S., Cash, D. M., van Swieten, J., Jiskoot, L., Moreno, F., Sanchez-Valle, R., Borroni, B., Laforce, R., Jr., Masellis, M., Tartaglia, M. C., Graff, C., Galimberti, D., Rowe, J. B., Finger, E., Synofzik, M., Vandenberghe, R., de Mendonça, A., Tagliavini, F., Santana, I., Ducharme, S., Butler, C., Gerhard, A., Levin, J., Danek, A., Otto, M., Sorbi, S., Le Ber, I., Pasquier, F., & Rohrer, J. D. (2021, Jul 12). The Revised Self-Monitoring Scale detects early impairment of social cognition in genetic frontotemporal dementia within the GENFI cohort. Alzheimers Res Ther, 13(1), 127. https://doi.org/10.1186/s13195-021-00865-w
Goldberg, Z. L., El-Omar, H., Foxe, D., Leyton, C. E., Ahmed, R. M., Piguet, O., & Irish, M. (2021, Dec 1). Cognitive and Neural Mechanisms of Social Communication Dysfunction in Primary Progressive Aphasia. Brain Sci, 11(12). https://doi.org/10.3390/brainsci11121600
Golden, H. L., Downey, L. E., Fletcher, P. D., Mahoney, C. J., Schott, J. M., Mummery, C. J., Crutch, S. J., & Warren, J. D. (2015). Identification of environmental sounds and melodies in syndromes of anterior temporal lobe degeneration. Journal of the Neurological Sciences, 352, 4. https://doi.org/10.1016/j.jns.2015.03.007
Goll, J. C., Crutch, S. J., Loo, J. H., Rohrer, J. D., Frost, C., Bamiou, D. E., & Warren, J. D. (2010, Jan). Non-verbal sound processing in the primary progressive aphasias. Brain, 133(Pt 1), 272-285. https://doi.org/10.1093/brain/awp235
Goll, J. C., Kim, L. G., Ridgway, G. R., Hailstone, J. C., Lehmann, M., Buckley, A. H., Crutch, S. J., & Warren, J. D. (2012, Jan). Impairments of auditory scene analysis in Alzheimer's disease. Brain, 135(Pt 1), 190-200. https://doi.org/10.1093/brain/awr260
Gorno-Tempini, M. L., Hillis, A. E., Weintraub, S., Kertesz, A., Mendez, M., Cappa, S. F., Ogar, J. M., Rohrer, J. D., Black, S., Boeve, B. F., Manes, F., Dronkers, N. F., Vandenberghe, R., Rascovsky, K., Patterson, K., Miller, B. L., Knopman, D. S., Hodges, J. R., Mesulam, M. M., & Grossman, M. (2011). Classification of primary progressive aphasia and its variants. Neurology, 76(11), 1006-1014. https://doi.org/10.1212/wnl.0b013e31821103e6
Grube, M., Bruffaerts, R., Schaeverbeke, J., Neyens, V., De Weer, A. S., Seghers, A., Bergmans, B., Dries, E., Griffiths, T. D., & Vandenberghe, R. (2016, Jun). Core auditory processing deficits in primary progressive aphasia. Brain, 139(Pt 6), 1817-1829. https://doi.org/10.1093/brain/aww067
Hardy, C. J., Buckley, A. H., Downey, L. E., Lehmann, M., Zimmerer, V. C., Varley, R. A., Crutch, S. J., Rohrer, J. D., Warrington, E. K., & Warren, J. D. (2016). The Language Profile of Behavioral Variant Frontotemporal Dementia. J Alzheimers Dis, 50(2), 359-371. https://doi.org/10.3233/jad-150806
Hardy, C. J. D., Agustus, J. L., Marshall, C. R., Clark, C. N., Russell, L. L., Brotherhood, E. V., Bond, R. L., Fiford, C. M., Ondobaka, S., Thomas, D. L., Crutch, S. J., Rohrer, J. D., & Warren, J. D. (2017, Aug). Functional neuroanatomy of speech signal decoding in primary progressive aphasias. Neurobiol Aging, 56, 190-201. https://doi.org/10.1016/j.neurobiolaging.2017.04.026
Hardy, C. J. D., Bond, R. L., Jaisin, K., Marshall, C. R., Russell, L. L., Dick, K., Crutch, S. J., Rohrer, J. D., & Warren, J. D. (2018). Sensitivity of Speech Output to Delayed Auditory Feedback in Primary Progressive Aphasias. Front Neurol, 9, 894. https://doi.org/10.3389/fneur.2018.00894
Hardy, C. J. D., Marshall, C. R., Bond, R. L., Russell, L. L., Dick, K., Ariti, C., Thomas, D. L., Ross, S. J., Agustus, J. L., Crutch, S. J., Rohrer, J. D., Bamiou, D. E., & Warren, J. D. (2018, Jul 25). Retained capacity for perceptual learning of degraded speech in primary progressive aphasia and Alzheimer's disease. Alzheimers Res Ther, 10(1), 70. https://doi.org/10.1186/s13195-018-0399-2
Horley, K., Reid, A., & Burnham, D. (2010, Oct). Emotional prosody perception and production in dementia of the Alzheimer's type. J Speech Lang Hear Res, 53(5), 1132-1146. https://doi.org/10.1044/1092-4388(2010/09-0030)
Hsieh, S., Hornberger, M., Piguet, O., & Hodges, J. R. (2011). Neural basis of music knowledge: evidence from the dementias. Brain, 134(9), 2523-2534. https://doi.org/10.1093/brain/awr190
Jiang, J., Benhamou, E., Waters, S., Johnson, J. C. S., Volkmer, A., Weil, R. S., Marshall, C. R., Warren, J. D., & Hardy, C. J. D. (2021). Processing of Degraded Speech in Brain Disorders. Brain Sciences, 11(394), 28. https://doi.org/10.3390/brainsci11030394
Jiang, J., Johnson, J. C. S., Requena-Komuro, M.-C., Benhamou, E., Sivasathiaseelan, H., Sheppard, D. L., Volkmer, A., Crutch, S. J., Hardy, C. J. D., & Warren, J. D. (2022). Phonemic restoration in Alzheimer’s disease and semantic dementia: a preliminary investigation. Brain Communications, 4(3). https://doi.org/10.1093/braincomms/fcac118
Jiang, J., Johnson, J. C. S., Requena-Komuro, M. C., Benhamou, E., Sivasathiaseelan, H., Chokesuwattanaskul, A., Nelson, A., Nortley, R., Weil, R. S., Volkmer, A. P., Marshall, C. R., Bamiou, D. E., Warren, J., & Hardy, C. J. (2023). Comprehension of acoustically degraded speech in Alzheimer's disease and primary progressive aphasia. Brain.
Johnson, J. C. S., Jiang, J., Bond, R. L., Benhamou, E., Requena-Komuro, M. C., Russell, L. L., Greaves, C., Nelson, A., Sivasathiaseelan, H., Marshall, C. R., Volkmer, A. P., Rohrer, J. D., Warren, J. D., & Hardy, C. J. D. (2020, Jul). Impaired phonemic discrimination in logopenic variant primary progressive aphasia. Ann Clin Transl Neurol, 7(7), 1252-1257. https://doi.org/10.1002/acn3.51101
Kay, J., Lesser, R., & Coltheart, M. (1996). Psycholinguistic assessments of language processing in aphasia (PALPA): An introduction. Aphasiology, 10(2), 159-180. https://doi.org/https://doi.org/10.1080/02687039608248403
Keane, J., Calder, A. J., Hodges, J. R., & Young, A. W. (2002). Face and emotion processing in frontal variant frontotemporal dementia. Neuropsychologia, 40(6), 655-665. https://doi.org/https://doi.org/10.1016/S0028-3932(01)00156-7
Kee, K. S., Kern, R. S., Marshall, B. D., & Green, M. F. (1998, 1998/05/25/). Risperidone versus haloperidol for perception of emotion in treatment-resistant schizophrenia: preliminary findings. Schizophrenia Research, 31(2), 159-165. https://doi.org/https://doi.org/10.1016/S0920-9964(98)00026-7
Kessels, R. P. C., Waanders-Oude Elferink, M., & van Tilborg, I. (2021, Jun). Social cognition and social functioning in patients with amnestic mild cognitive impairment or Alzheimer's dementia. J Neuropsychol, 15(2), 186-203. https://doi.org/10.1111/jnp.12223
Liebenthal, E., Silbersweig, D. A., & Stern, E. (2016). The Language, Tone and Prosody of Emotions: Neural Substrates and Dynamics of Spoken-Word Emotion Perception. Frontiers in Neuroscience, 10(506). https://doi.org/10.3389/fnins.2016.00506
Lin, F. R. & Reed, N. S. (2020). The Pure-Tone Average as a Universtal Metric-Knowing Your Hearing. JAMA Otolaryngol Head Neck Surg. doi:10.1001/jamaoto.2020.4862.
Macoir, J., Hudon, C., Tremblay, M.-P., Laforce, R., Jr., & Wilson, M. A. (2019, 2019/11/02). The contribution of semantic memory to the recognition of basic emotions and emotional valence: Evidence from the semantic variant of primary progressive aphasia. Social Neuroscience, 14(6), 705-716. https://doi.org/10.1080/17470919.2019.1577295
Marshall, C. R., Hardy, C. J. D., Russell, L. L., Bond, R. L., Sivasathiaseelan, H., Greaves, C., Moore, K. M., Agustus, J. L., van Leeuwen, J. E. P., Wastling, S. J., Rohrer, J. D., Kilner, J. M., & Warren, J. D. (2019). The functional neuroanatomy of emotion processing in frontotemporal dementias. Brain, 142(9), 2873-2887. https://doi.org/10.1093/brain/awz204
Mattys, S. L., Davis, M. H., Bradlow, A. R., & Scott, S. K. (2012). Speech recognition in adverse conditions: A review. Language and Cognitive Processes, 27(7-8), 953-978. https://doi.org/10.1080/01690965.2012.705006
Morningstar, M., Mattson, W. I., & Nelson, E. E. (2022). Longitudinal change in neural response to vocal emotion in adolescence. Social Cognitive and Affective Neuroscience, 17(10), 890-903. https://doi.org/10.1093/scan/nsac021
Newkirk, L. A., Kim, J. M., Thompson, J. M., Tinklenberg, J. R., Yesavage, J. A., & Taylor, J. L. (2004). Validation of a 26-Point Telephone Version of the Mini-Mental State Examination. Journal of Geriatric Psychiatry and Neurology, 17(2), 81-87. https://doi.org/10.1177/0891988704264534
Omar, R., Hailstone, J. C., Warren, J. E., Crutch, S. J., & Warren, J. D. (2010). The cognitive organization of music knowledge: a clinical analysis. Brain, 133(4), 1200-1213. https://doi.org/10.1093/brain/awp345
Omar, R., Henley, S. M., Bartlett, J. W., Hailstone, J. C., Gordon, E., Sauter, D. A., Frost, C., Scott, S. K., & Warren, J. D. (2011). The structural neuroanatomy of music emotion recognition: evidence from frontotemporal lobar degeneration. NeuroImage, 56(3), 7. https://doi.org/10.1016/j.neuroimage.2011.03.002
Perry, R. J., Rosen, H. R., Kramer, J. H., Beer, J. S., Levenson, R. L., & Miller, B. L. (2001, 2001/01/01). Hemispheric Dominance for Emotions, Empathy and Social Behaviour: Evidence from Right and Left Handers with Frontotemporal Dementia. Neurocase, 7(2), 145-160. https://doi.org/10.1093/neucas/7.2.145
Rankin, K. P., Salazar, A., Gorno-Tempini, M. L., Sollberger, M., Wilson, S. M., Pavlic, D., & Miller, B. L. (2009). Detecting sarcasm from paralinguistic cues: anatomic and cognitive correlates in neurodegenerative disease. NeuroImage, 47(4), 10.
Requena-Komuro, M.-C., Jiang, J., Dobson, L., Benhamou, E., Russell, L., Bond, R. L., Brotherhood, E. V., Greaves, C., Barker, S., Rohrer, J. D., Crutch, S. J., Warren, J. D., & Hardy, C. J. (2022). Remote versus face-to-face neuropsychological testing for dementia research: a comparative study in patients with Alzheimer’s disease, patients with frontotemporal dementia, and healthy older individuals. BMJ Open.
Rohrer, J. D., Ridgway, G. R., Crutch, S. J., Hailstone, J., Goll, J. C., Clarkson, M. J., Mead, S., Beck, J., Mummery, C., Ourselin, S., Warrington, E. K., Rossor, M. N., & Warren, J. D. (2010, 2010/01/01/). Progressive logopenic/phonological aphasia: Erosion of the language network. NeuroImage, 49(1), 984-993. https://doi.org/https://doi.org/10.1016/j.neuroimage.2009.08.002
Rohrer, J. D., Sauter, D., Scott, S., Rossor, M. N., & Warren, J. D. (2010). Receptive prosody in nonfluent primary progressive aphasias. Cortex, 48(3), 8. https://doi.org/10.1016/j.cortex.2010.09.004
Sauseng, P., Klimesch, W., Schabus, M., & Doppelmayr, M. (2005, 2005/08/01/). Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. International Journal of Psychophysiology, 57(2), 97-103. https://doi.org/https://doi.org/10.1016/j.ijpsycho.2005.03.018
Sauter, D. (2006). An Investigation into Vocal Expressions of Emotions: The Roles of Valence, Culture, and Acoustic Factors University College London].
Schirmer, A., & Kotz, S. A. (2006, 2006/01/01/). Beyond the right hemisphere: brain mechanisms mediating vocal emotional processing. Trends in Cognitive Sciences, 10(1), 24-30. https://doi.org/https://doi.org/10.1016/j.tics.2005.11.009
Shannon, R. V., Zeng, F. G., Kamath, V., Wygonski, J., & Ekelid, M. (1995). Speech Recognition with Primarily Temporal Cues. Science, 270(5234), 303-304. https://doi.org/10.1126/science.270.5234.303
Shany-Ur, T., & Rankin, K. P. (2011, Dec). Personality and social cognition in neurodegenerative disease. Curr Opin Neurol, 24(6), 550-555. https://doi.org/10.1097/WCO.0b013e32834cd42a
Sivasathiaseelan, H., Marshall, C. R., E., B., van Leeuwen, J. E. P., Bond, R. L., Russell, L. L., Greaves, C., Moore, K. M., Hardy, C. J. D., Frost, C., Rohrer, J. D., Scott, S. K., & Warren, J. D. (2021). Laughter as a paradigm of socio-emotional signal processing in dementia. Cortex.
Snyder, M. (1974). Self-monitoring of expressive behavior. Journal of Personality and Social Psychology, 30, 526-537.
Taler, V., Baum, S. R., Chertkow, H., & Saumier, D. (2008, Mar). Comprehension of grammatical and emotional prosody is impaired in Alzheimer's disease. Neuropsychology, 22(2), 188-195. https://doi.org/10.1037/0894-4105.22.2.188
Testa, J. A., Beatty, W. W., Gleason, A. C., Orbelo, D. M., & Ross, E. D. (2001, Oct 23). Impaired affective prosody in AD: relationship to aphasic deficits and emotional behaviors. Neurology, 57(8), 1474-1481. https://doi.org/10.1212/wnl.57.8.1474
Toller, G., Ranasinghe, K., Cobigo, Y., Staffaroni, A., Appleby, B., Brushaber, D., Coppola, G., Dickerson, B., Domoto-Reilly, K., Fields, J., Fong, J., Forsberg, L., Ghoshal, N., Graff-Radford, N., Grossman, M., Heuer, H., Hsiung, G. Y., Huey, E., Irwin, D., Kantarci, K., Kaufer, D., Kerwin, D., Knopman, D., Kornak, J., Kramer, J., Litvan, I., Mackenzie, I., Mendez, M., Miller, B., Rademakers, R., Ramos, E., Rascovsky, K., Roberson, E., Syrjanen, J., Tartaglia, C., Weintraub, S., Boeve, B., Boxer, A., Rosen, H., & Rankin, K. (2020, Jun 2). Revised Self-Monitoring Scale: A potential endpoint for frontotemporal dementia clinical trials. Neurology, 94(22), e2384-e2395. https://doi.org/10.1212/wnl.0000000000009451
Van't Hooft, J. J., Benhamou, E., Albero Herreros, C., Jiang, J., Levett, B., Core, L. B., Requena-Komuro, M. C., Hardy, C. J. D., Tijms, B. M., Pijnenburg, Y. A. L., & Warren, J. D. (2024). Musical experience influences socio-emotional functioning in behavioural variant frontotemporal dementia. Front Neurol, 15, 1341661. https://doi.org/10.3389/fneur.2024.1341661
Volkmer, A., Mistry, S., Thompson, D., Warren, J.D., & Bekke, S. (2023). Collaborative turn-construction practices of people with primary progressive aphasia and their family conversation partners. Journal of Interactional Research in Communication Disorders, 14(3).
Weiner, M. F., Neubecker, K. E., Bret, M. E., & Hynan, L. S. (2008, Aug). Language in Alzheimer's disease. J Clin Psychiatry, 69(8), 1223-1227. https://doi.org/10.4088/jcp.v69n0804
Wilson, D., & Sperber, D. (2002). Relevance Theory. In G. Ward & L. Horn (Eds.), Handbook of Pragmatics. Blackwell.
Zatorre, R. J., & Baum, S. R. (2012). Musical Melody and Speech Intonation: Singing a Different Tune? PLOS Biology, 10(7).

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Processing of acoustically degraded emotional prosody in Alzheimer’s disease and primary progressive aphasia: a cohort study

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