Processing emotional tone from speech in Parkinson’s disease: A role for the basal ganglia

Pell, Marc D.; Léonard, Carol

doi:10.3758/cabn.3.4.275

Cited by 164 publications

(141 citation statements)

References 89 publications

Supporting

Mentioning

126

Contrasting

Unclassified

Order By: Relevance

“…Pilgrim et al, 2002;Copland, 2003) and prosodic components (e.g. Brådvik et al, 1991;Breitenstein et al, 1998Breitenstein et al, , 2001Pell and Leonard, 2003;P eron et al, 2013). The notion that the BG play a role in the processing of emotional prosody is supported by the presence of incoming projections from the superior temporal gyrus (i.e.…”

Section: Basal Gangliamentioning

confidence: 86%

Subcortical processing in auditory communication

2015

View full text Add to dashboard Cite

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.

show abstract

Section: Basal Gangliamentioning

confidence: 86%

Subcortical processing in auditory communication

2015

View full text Add to dashboard Cite

show abstract

“…A commonly drawn inference is that the emotion recognition deficit experienced by individuals with PD is likely to be cross-modal (Peron et al, 2010), yet only a small number of studies have examined emotion recognition performance in both facial and prosodic modalities with same Emotional state classification in PD 4 participants. A number of these report found deficits in both modalities (Ariatti et al, 2008;Yip et al, 2003), whereas others found problems in only one modality (facial, Pell and Leonard, 2003); prosody, Pell and Leonard, 2005), and at least one failed to find deficits in recognition in either modality (Caekebeke et al, 1991).…”

Section: Introductionmentioning

confidence: 96%

“…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

View full text Add to dashboard Cite

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.

show abstract

“…On the other hand, the stability for happiness might be related with the relative preservation of the basal ganglia with increasing age. The basal ganglia are known to subserve the processing of happiness and it is also known that they undergo little decline with age (e.g., for musical stimuli, see Mitterschiffthaler, Fu, Dalton, Andrew, & Williams, 2007;for other modalities: Calder et al, 2003;Pell & Leonard, 2003;Phan et al, 2002;Williams et al, 2006). Regarding the stability for peacefulness,…”

Section: Explanatory Hypothesesmentioning

confidence: 99%

Emotion recognition in music changes across the adult life span

Lima

Castro

2011

Cognition & Emotion

View full text Add to dashboard Cite

In comparison with other modalities, the recognition of emotion in music has received little attention. An unexplored question is whether and how emotion recognition in music changes as a function of aging. In the present study, healthy adults aged between 17 and 84 years (N = 114) judged the magnitude to which a set of musical excerpts (Vieillard et al., 2008) expressed happiness, peacefulness, sadness and fear/threat. The results revealed emotion specific age-related changes: advancing age was associated with a gradual decrease in responsiveness to sad and scary music from middle age onwards, whereas the recognition of happiness and peacefulness, both positive emotional qualities, remained stable from young adulthood to older age. Additionally, the number of years of music training was associated with more accurate categorization of the musical emotions examined here. We argue that these findings are consistent with two accounts on how aging might influence the recognition of emotions: motivational changes towards positivity and, to a lesser extent, selective neuropsychological decline.

show abstract

Processing emotional tone from speech in Parkinson’s disease: A role for the basal ganglia

Cited by 164 publications

References 89 publications

Subcortical processing in auditory communication

Subcortical processing in auditory communication

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

Emotion recognition in music changes across the adult life span

Contact Info

Product

Resources

About