Vocal emotion processing in Parkinson's disease: Reduced sensitivity to negative emotions

Dara, Chinar; Monetta, Laura; Pell, Marc D.

doi:10.1016/j.brainres.2007.10.034

Cited by 123 publications

(83 citation statements)

References 71 publications

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“…In the auditory modality, this impairment in emotional processing prevents patients from correctly identifying the affective quality of vocal utterances (emotional prosody) (P eron et al, 2010). Compared to healthy controls, Parkinson's disease patients exhibit deficit in the ability to decipher the emotional quality of speech, both at the level of the type of emotion (Scott et al, 1984;P eron et al, 2014), and at the level of its overall valence (Dara et al, 2008). These impairments could reflect a more general adverse effect of Parkinson's disease on working memory and the executive function (Breitenstein et al, 2001), to which the BG are known to contribute (Brown et al, 1997).…”

Section: Basal Gangliamentioning

confidence: 99%

Subcortical processing in auditory communication

2015

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a b s t r a c tThe voice is a rich source of information, which the human brain has evolved to decode and interpret. Empirical observations have shown that the human auditory system is especially sensitive to the human voice, and that activity within the voice-sensitive regions of the primary and secondary auditory cortex is modulated by the emotional quality of the vocal signal, and may therefore subserve, with frontal regions, the cognitive ability to correctly identify the speaker's affective state. So far, the network involved in the processing of vocal affect has been mainly characterised at the cortical level. However, anatomical and functional evidence suggests that acoustic information relevant to the affective quality of the auditory signal might be processed prior to the auditory cortex.Here we review the animal and human literature on the main subcortical structures along the auditory pathway, and propose a model whereby the distinction between different types of vocal affect in auditory communication begins at very early stages of auditory processing, and relies on the analysis of individual acoustic features of the sound signal. We further suggest that this early feature-based decoding occurs at a subcortical level along the ascending auditory pathway, and provides a preliminary coarse (but fast) characterisation of the affective quality of the auditory signal before the more refined (but slower) cortical processing is completed.

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Section: Basal Gangliamentioning

confidence: 99%

Subcortical processing in auditory communication

2015

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“…Each native listener judged the emotional meaning of all the pseudo-utterances produced in the same language in a forced-choice response format involving seven response alternatives (anger, disgust, fear, sadness, happiness, pleasant surprise, neutral). Seven emotional response categories were included in our stimulus validation studies because the goal of this work was to construct a stimulus inventory that could be used in a broad range of investigations about emotion and affective communication in healthy and brain-damaged individuals (e.g., Dara et al 2008;Pell and Skorup 2008). Stimuli representing the seven emotions were fully randomized for presentation to native listeners in each language condition.…”

Section: Emotion Validation Proceduresmentioning

confidence: 99%

Recognizing Emotions in a Foreign Language

et al. 2009

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Expressions of basic emotions (joy, sadness, anger, fear, disgust) can be recognized pan-culturally from the face and it is assumed that these emotions can be recognized from a speaker's voice, regardless of an individual's culture or linguistic ability. Here, we compared how monolingual speakers of Argentine Spanish recognize basic emotions from pseudo-utterances (''nonsense speech'') produced in their native language and in three foreign languages (English, German, Arabic). Results indicated that vocal expressions of basic emotions could be decoded in each language condition at accuracy levels exceeding chance, although Spanish listeners performed significantly better overall in their native language (''in-group advantage''). Our findings argue that the ability to understand vocally-expressed emotions in speech is partly independent of linguistic ability and involves universal principles, although this ability is also shaped by linguistic and cultural variables.

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“…Evidence indicates the individuals with PD have deficits in recognizing emotions from prosody (Dara et al, 2008;Pell and Leonard, 2003;Yip et al, 2003) and facial expressions (Ariatti et al, 2008;Clark et al, 2008;Dujardin et al, 2004), although not all findings have been consistent. Several studies have failed to find impaired performance in the recognition of facial expressions related to emotion in their PD samples (Adolphs et al, 1998;Pell and Leonard, 2005), whereas others have failed to find deficits in recognition from prosody (Clark et al, 2008;Kan et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

Yuvaraj

Murugappan

Ibrahim

et al. 2014

International Journal of Psychophysiology

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In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity.This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded.Multi-modal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both the groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

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Vocal emotion processing in Parkinson's disease: Reduced sensitivity to negative emotions

Cited by 123 publications

References 71 publications

Subcortical processing in auditory communication

Subcortical processing in auditory communication

Recognizing Emotions in a Foreign Language

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

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