Functional connectivity of spoken language processing in early-stage Parkinson's disease: An MEG study

A bibliometric analysis of speech and language impairments in Parkinson's disease based on Web of Science

Many individuals with Parkinson's disease suffer from speech and language impairments that significantly impact their quality of life. Despite several studies on these disorders, there is a lack of relevant bibliometric analyses. This paper conducted a bibliometric analysis of 3,610 papers on speech and language impairments in Parkinson's disease patients from January 1961 to November 2023, based on the Web of Science Core Collection database. Using Citespace software, the analysis focused on annual publication volume, cooperation among countries and institutions, author collaborations, journals, co-citation references, and keywords, aiming to explore the current research status, hotspots, and frontiers in this field. The number of annual publications related to speech and language impairment in Parkinson's disease have been increasing over the years. The USA leads in the number of publications. Research hotspots include the mechanism underlying speech and language impairments, clinical symptoms, automated diagnosis and classification of patients with PD using linguistic makers, and rehabilitation interventions.

Systematic Review and Meta-Analyses of Word Production Abilities in Dysfunction of the Basal Ganglia: Stroke, Small Vessel Disease, Parkinson's Disease, and Huntington's Disease

Clinical populations with basal ganglia pathologies may present with language production impairments, which are often described in combination with comprehension measures or attributed to motor, memory, or processing-speed problems. In this systematic review and meta-analysis, we studied word production in four (vascular and non-vascular) pathologies of the basal ganglia: stroke affecting the basal ganglia, small vessel disease, Parkinson's disease, and Huntington's disease. We compared scores of these clinical populations with those of matched cognitively unimpaired adults on four well-established production tasks, namely picture naming, category fluency, letter fluency, and past-tense verb inflection. We conducted a systematic search in PubMed and PsycINFO with terms for basal ganglia structures, basal ganglia disorders and language production tasks. A total of 114 studies were included, containing results for one or more of the tasks of interest. For each pathology and task combination, effect sizes (Hedges' g) were extracted comparing patient versus control groups. For all four populations, performance was consistently worse than that of cognitively unimpaired adults across the four language production tasks (p-values < 0.010). Given that performance in picture naming and verb inflection across all pathologies was quantified in terms of accuracy, our results suggest that production impairments cannot be fully explained by motor or processing-speed deficits. Our review shows that while language production difficulties in these clinical populations are not negligible, more evidence is necessary to determine the exact mechanism that leads to these deficits and whether this mechanism is the same across different pathologies.

Retinal Alterations Predict Early Prodromal Signs of Neurodegenerative Disease

Neurodegenerative diseases are an increasingly common group of diseases that occur late in life with a significant impact on personal, family, and economic life. Among these, Alzheimer's disease (AD) and Parkinson's disease (PD) are the major disorders that lead to mild to severe cognitive and physical impairment and dementia. Interestingly, those diseases may show onset of prodromal symptoms early after middle age. Commonly, the evaluation of these neurodegenerative diseases is based on the detection of biomarkers, where functional and structural magnetic resonance imaging (MRI) have shown a central role in revealing early or prodromal phases, although it can be expensive, time-consuming, and not always available. The aforementioned diseases have a common impact on the visual system due to the pathophysiological mechanisms shared between the eye and the brain. In Parkinson's disease, α-synuclein deposition in the retinal cells, as well as in dopaminergic neurons of the substantia nigra, alters the visual cortex and retinal function, resulting in modifications to the visual field. Similarly, the visual cortex is modified by the neurofibrillary tangles and neuritic amyloid β plaques typically seen in the Alzheimer's disease brain, and this may reflect the accumulation of these biomarkers in the retina during the early stages of the disease, as seen in postmortem retinas of AD patients. In this light, the ophthalmic evaluation of retinal neurodegeneration could become a cost-effective method for the early diagnosis of those diseases, overcoming the limitations of functional and structural imaging of the deep brain. This analysis is commonly used in ophthalmic practice, and interest in it has risen in recent years. This review will discuss the relationship between Alzheimer's disease and Parkinson's disease with retinal degeneration, highlighting how retinal analysis may represent a noninvasive and straightforward method for the early diagnosis of these neurodegenerative diseases.

Neurocognitive correlates of semantic memory navigation in Parkinson's disease

Cognitive studies on Parkinson's disease (PD) reveal abnormal semantic processing. Most research, however, fails to indicate which conceptual properties are most affected and capture patients' neurocognitive profiles. Here, we asked persons with PD, healthy controls, and individuals with behavioral variant frontotemporal dementia (bvFTD, as a disease control group) to read concepts (e.g., 'sun') and list their features (e.g., hot). Responses were analyzed in terms of ten word properties (including concreteness, imageability, and semantic variability), used for group-level comparisons, subject-level classification, and brain-behavior correlations. PD (but not bvFTD) patients produced more concrete and imageable words than controls, both patterns being associated with overall cognitive status. PD and bvFTD patients showed reduced semantic variability, an anomaly which predicted semantic inhibition outcomes. Word-property patterns robustly classified PD (but not bvFTD) patients and correlated with disease-specific hypoconnectivity along the sensorimotor and salience networks. Fine-grained semantic assessments, then, can reveal distinct neurocognitive signatures of PD.

EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network

Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical and mental wellness of elderly individuals which makes it problematic to diagnose in its initial stages. Electroencephalogram (EEG) promises to be an efficient and cost-effective method for promptly detecting cognitive impairment in PD. Nevertheless, prevailing diagnostic practices utilizing EEG features have failed to examine the functional connectivity among EEG channels and the response of associated brain areas causing an unsatisfactory level of precision. Here, we construct an attention-based sparse graph convolutional neural network (ASGCNN) for diagnosing PD. Our ASGCNN model uses a graph structure to represent channel relationships, the attention mechanism for selecting channels, and the L1 norm to capture channel sparsity. We conduct extensive experiments on the publicly available PD auditory oddball dataset, which consists of 24 PD patients (under ON/OFF drug status) and 24 matched controls, to validate the effectiveness of our method. Our results show that the proposed method provides better results compared to the publicly available baselines. The achieved scores for Recall, Precision, F1-score, Accuracy and Kappa measures are 90.36%, 88.43%, 88.41%, 87.67%, and 75.24%, respectively. Our study reveals that the frontal and temporal lobes show significant differences between PD patients and healthy individuals. In addition, EEG features extracted by ASGCNN demonstrate significant asymmetry in the frontal lobe among PD patients. These findings can offer a basis for the establishment of a clinical system for intelligent diagnosis of PD by using auditory cognitive impairment features.

Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study

Background

It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking.

Objectives

We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy.

Methods

Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts.

Results

We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (pθ = 0.048) and decrease in WB node degree (pα = 0.039) in PSEs versus PNSEs at the uncorrected level.

Conclusion

Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.

Analysis of spontaneous speech in Parkinson's disease by natural language processing

INTRODUCTION

Patients with Parkinson's disease (PD) encounter a variety of speech-related problems, including dysarthria and language disorders. To elucidate the pathophysiological mechanisms for linguistic alteration in PD, we compared the utterance of patients and that of healthy controls (HC) using automated morphological analysis tools.

METHODS

We enrolled 53 PD patients with normal cognitive function and 53 HC, and assessed their spontaneous speech using natural language processing. Machine learning algorithms were used to identify the characteristics of spontaneous conversation in each group. Thirty-seven features focused on part-of-speech and syntactic complexity were used in this analysis. A support-vector machine (SVM) model was trained with ten-fold cross-validation.

RESULTS

PD patients were found to speak less morphemes on one sentence than the HC group. Compared to HC, the speech of PD patients had a higher rate of verbs, case particles (dispersion), and verb utterances, and a lower rate of common noun utterances, proper noun utterances, and filler utterances. Using these conversational changes, the respective discrimination rates for PD or HC were more than 80%.

CONCLUSIONS

Our results demonstrate the potential of natural language processing for linguistic analysis and diagnosis of PD.

Aberrant neurophysiological signaling associated with speech impairments in Parkinson's disease

Difficulty producing intelligible speech is a debilitating symptom of Parkinson's disease (PD). Yet, both the robust evaluation of speech impairments and the identification of the affected brain systems are challenging. Using task-free magnetoencephalography, we examine the spectral and spatial definitions of the functional neuropathology underlying reduced speech quality in patients with PD using a new approach to characterize speech impairments and a novel brain-imaging marker. We found that the interactive scoring of speech impairments in PD (N = 59) is reliable across non-expert raters, and better related to the hallmark motor and cognitive impairments of PD than automatically-extracted acoustical features. By relating these speech impairment ratings to neurophysiological deviations from healthy adults (N = 65), we show that articulation impairments in patients with PD are associated with aberrant activity in the left inferior frontal cortex, and that functional connectivity of this region with somatomotor cortices mediates the influence of cognitive decline on speech deficits.

Top-down and bottom-up propagation of disease in the neuronal ceroid lipofuscinoses

Background

The Neuronal Ceroid Lipofuscinoses (NCLs) may be considered distinct neurodegenerative disorders with separate underlying molecular causes resulting from monogenetic mutations. An alternative hypothesis is to consider the NCLs as related diseases that share lipofuscin pathobiology as the common core feature, but otherwise distinguished by different a) initial anatomic location, and b) disease propagation.

Methods

We have tested this hypothesis by comparing known differences in symptomatology and pathology of the CLN1 phenotype caused by complete loss of PPT1 function (i.e., the classical infantile form) and of the classical juvenile CLN3 phenotype. These two forms of NCL represent early onset and rapidly progressing vs. late onset and slowly progressing disease modalities respectively.

Results

Despite displaying similar pathological endpoints, the clinical phenotypes and the evidence of imaging and postmortem studies reveal strikingly different time courses and distributions of disease propagation. Data from CLN1 disease are indicative of disease propagation from the body, with early effects within the spinal cord and subsequently within the brainstem, the cerebral hemispheres, cerebellum and retina. In contrast, the retina appears to be the most vulnerable organ in CLN3, and the site where pathology is first present. Pathology subsequently is present in the occipital connectome of the CLN3 brain, followed by a top-down propagation in which cerebral and cerebellar atrophy in early adolescence is followed by involvement of the peripheral nerves in later adolescence/early twenties, with the extrapyramidal system also affected during this time course.

Discussion

The propagation of disease in these two NCLs therefore has much in common with the “Brain-first” vs. “Body-first” models of alpha-synuclein propagation in Parkinson's disease. CLN1 disease represents a “Body-first” or bottom-up disease propagation and CLN3 disease having a “Brain-first” and top-down propagation. It is noteworthy that the varied phenotypes of CLN1 disease, whether it starts in infancy (infantile form) or later in childhood (juvenile form), still fit with our proposed hypothesis of a bottom-up disease propagation in CLN1. Likewise, in protracted CLN3 disease, where both cognitive and motor declines are delayed, the initial manifestations of disease are also seen in the outer retinal layers, i.e., identical to classical Juvenile NCL disease.

Solving neurodegeneration: common mechanisms and strategies for new treatments

Across neurodegenerative diseases, common mechanisms may reveal novel therapeutic targets based on neuronal protection, repair, or regeneration, independent of etiology or site of disease pathology. To address these mechanisms and discuss emerging treatments, in April, 2021, Glaucoma Research Foundation, BrightFocus Foundation, and the Melza M. and Frank Theodore Barr Foundation collaborated to bring together key opinion leaders and experts in the field of neurodegenerative disease for a virtual meeting titled "Solving Neurodegeneration". This "think-tank" style meeting focused on uncovering common mechanistic roots of neurodegenerative disease and promising targets for new treatments, catalyzed by the goal of finding new treatments for glaucoma, the world's leading cause of irreversible blindness and the common interest of the three hosting foundations. Glaucoma, which causes vision loss through degeneration of the optic nerve, likely shares early cellular and molecular events with other neurodegenerative diseases of the central nervous system. Here we discuss major areas of mechanistic overlap between neurodegenerative diseases of the central nervous system: neuroinflammation, bioenergetics and metabolism, genetic contributions, and neurovascular interactions. We summarize important discussion points with emphasis on the research areas that are most innovative and promising in the treatment of neurodegeneration yet require further development. The research that is highlighted provides unique opportunities for collaboration that will lead to efforts in preventing neurodegeneration and ultimately vision loss.