Source activity during emotion processing and its relationship to cognitive impairment in Parkinson's disease

Parkinson's disease speech production network as determined by graph-theoretical network analysis

Parkinson's disease (PD) can affect speech as well as emotion processing. We employ whole-brain graph-theoretical network analysis to determine how the speech-processing network (SPN) changes in PD, and assess its susceptibility to emotional distraction. Functional magnetic resonance images of 14 patients (aged 59.6 ± 10.1 years, 5 female) and 23 healthy controls (aged 64.1 ± 6.5 years, 12 female) were obtained during a picture-naming task. Pictures were supraliminally primed by face pictures showing either a neutral or an emotional expression. PD network metrics were significantly decreased (mean nodal degree, p < 0.0001; mean nodal strength, p < 0.0001; global network efficiency, p < 0.002; mean clustering coefficient, p < 0.0001), indicating an impairment of network integration and segregation. There was an absence of connector hubs in PD. Controls exhibited key network hubs located in the associative cortices, of which most were insusceptible to emotional distraction. The PD SPN had more key network hubs, which were more disorganized and shifted into auditory, sensory, and motor cortices after emotional distraction. The whole-brain SPN in PD undergoes changes that result in (a) decreased network integration and segregation, (b) a modularization of information flow within the network, and (c) the inclusion of primary and secondary cortical areas after emotional distraction.

Emotional Sentence Processing in Parkinson's Disease

PURPOSE

Emotional processing allows us to predict our own and others' behavior, communicate our wants and needs, and understand those of others. Thus, deficits in emotional processing can negatively impact one's quality of life. While changes in emotional processing across several domains (e.g., prosody, faces) in Parkinson's disease (PD) are widely accepted, there is a dearth of literature, with equivocal results, regarding how emotional language processing is affected by PD. This study investigated emotional sentence processing in this population.

METHOD

Eighteen persons with PD and 22 healthy adults (HAs) completed a language task in which they rated sentences on their pleasantness (valence), and a battery of cognitive tasks and mood measures that were examined as factors influencing performance. As an interaction between emotionality and concreteness during processing has been indicated in prior research, concreteness of sentence stimuli was also manipulated.

RESULTS

Individuals with PD rated negatively valenced sentences as less negative and positively-valenced sentences as less positive than HAs. The PD group also demonstrated a reduced overall range of valence rating scores. Sentence concreteness did not influence ratings. Results for positive sentences could be explained by individual differences in working memory (WM), whereas individual differences in WM, depression, and group explained differences in ratings to negative sentences.

CONCLUSIONS

Our study provides one of few accounts of emotional language processing deficits in PD, particularly beyond the word level. Individuals with PD may experience difficulty perceiving and assessing the intensity of the emotional content of language, and deficits may disproportionately impact processing of sentences about negative situations.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.21313713.

Atremorine in Parkinson's disease: From dopaminergic neuroprotection to pharmacogenomics

Atremorine is a novel bioproduct obtained by nondenaturing biotechnological processes from a genetic species of Vicia faba. Atremorine is a potent dopamine (DA) enhancer with powerful effects on the neuronal dopaminergic system, acting as a neuroprotective agent in Parkinson's disease (PD). Over 97% of PD patients respond to a single dose of Atremorine (5 g, p.o.) 1 h after administration. This response is gender-, time-, dose-, and genotype-dependent, with optimal doses ranging from 5 to 20 g/day, depending upon disease severity and concomitant medication. Drug-free patients show an increase in DA levels from 12.14 ± 0.34 pg/ml to 6463.21 ± 1306.90 pg/ml; and patients chronically treated with anti-PD drugs show an increase in DA levels from 1321.53 ± 389.94 pg/ml to 16,028.54 ± 4783.98 pg/ml, indicating that Atremorine potentiates the dopaminergic effects of conventional anti-PD drugs. Atremorine also influences the levels of other neurotransmitters (adrenaline, noradrenaline) and hormones which are regulated by DA (e.g., prolactin, PRL), with no effect on serotonin or histamine. The variability in Atremorine-induced DA response is highly attributable to pharmacogenetic factors. Polymorphic variants in pathogenic (SNCA, NUCKS1, ITGA8, GPNMB, GCH1, BCKDK, APOE, LRRK2, ACMSD), mechanistic (DRD2), metabolic (CYP2D6, CYP2C9, CYP2C19, CYP3A4/5, NAT2), transporter (ABCB1, SLC6A2, SLC6A3, SLC6A4) and pleiotropic genes (APOE) influence the DA response to Atremorine and its psychomotor and brain effects. Atremorine enhances DNA methylation and displays epigenetic activity via modulation of the pharmacoepigenetic network. Atremorine is a novel neuroprotective agent for dopaminergic neurons with potential prophylactic and therapeutic activity in PD.

Lightweight Building of an Electroencephalogram-Based Emotion Detection System

Brain–computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection. These studies have also attempted to apply BCI research findings in varied contexts. Interestingly, advances in BCI technologies have increased the interest of scientists because such technologies’ practical applications in human–machine relationships seem promising. This emphasizes the need for a building process for an EEG-based emotion detection system that is lightweight, in terms of a smaller EEG dataset size and no involvement of feature extraction methods. In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction methods while maintaining decent accuracy. The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that of previous studies.