Dorsolateral Prefrontal Cortex: A Possible Target for Modulating Dyskinesias in Parkinson's Disease by Repetitive Transcranial Magnetic Stimulation

We studied whether five sessions of 10 Hz repetitive transcranial magnetic stimulation (rTMS treatment) applied over the dorsolateral prefrontal cortex (DLPFC) or the primary motor cortex (MC) in advanced Parkinson's disease (PD) patients would have any effect on L-dopa-induced dyskinesias and cortical excitability. We aimed at a randomised, controlled study. Single-pulse transcranial magnetic stimulation (TMS), paired-pulse transcranial magnetic stimulation, and the Unified Parkinson's Disease Rating Scale (UPDRS parts III and IV) were performed prior to, immediately after, and one week after an appropriate rTMS treatment. Stimulation of the left DLPFC induced a significant motor cortex depression and a trend towards the improvement of L-dopa-induced dyskinesias

Unexpected Complications of Novel Deep Brain Stimulation Treatments: Ethical Issues and Clinical Recommendations.

BACKGROUND: Innovative neurosurgical treatments present a number of known risks, the natures and probabilities of which can be adequately communicated to patients via the standard procedures governing obtaining informed consent. However, due to their novelty, these treatments also come with unknown risks, which require an augmented approach to obtaining informed consent. OBJECTIVE: This paper aims to discuss and provide concrete procedural guidance on the ethical issues raised by serious unexpected complications of novel deep brain stimulation treatments. APPROACH: We illustrate our analysis using a case study of the unexpected development of recurrent stereotyped events in patients following the use of deep brain stimulation (DBS) to treat severe chronic pain. Examining these unexpected complications in light of medical ethical principles, we argue that serious complications of novel DBS treatments do not necessarily make it unethical to offer the intervention to eligible patients. However, the difficulty the clinician faces in determining whether the intervention is in the patient's best interests generates reasons to take extra steps to promote the autonomous decision making of these patients. CONCLUSION AND RECOMMENDATIONS: We conclude with clinical recommendations, including details of an augmented consent process for novel DBS treatment

Articulatory network reorganization in Parkinson's disease as assessed by multimodal MRI and acoustic measures

Introduction: Hypokinetic dysarthria (HD) is common in Parkinson's disease (PD). Our objective was to evaluate articulatory networks and their reorganization due to PD pathology in individuals without overt speech impairment using a multimodal MRI protocol and acoustic analysis of speech. Methods: A total of 34 PD patients with no subjective HD complaints and 25 age-matched healthy controls (HC) underwent speech task recordings, structural MRI, and reading task-induced and resting-state fMRI. Grey matter probability maps, task-induced activations, and resting-state functional connectivity within the regions engaged in speech production (ROIs) were assessed and compared between groups. Correlation with acoustic parameters was also performed. Results: PD patients as compared Tto HC displayed temporal decreases in speech loudness which were related to BOLD signal increases in the right-sided regions of the dorsal language pathway/articulatory network. Among those regions, activation of the right anterior cingulate was increased in PD as compared to HC. We also found bilateral posterior superior temporal gyrus (STG) GM loss in PD as compared to HC that was strongly associated with diadochokinetic (DDK) irregularity in the PD group. Task-induced activations of the left STG were increased in PD as compared to HC and were related to the DDK rate control. Conclusions: The results provide insight into the neural correlates of speech production control and distinct articulatory network reorganization in PD apparent already in patients without subjective speech impairment

Exploration of Various Fractional Order Derivatives in Parkinson's Disease Dysgraphia Analysis

Parkinson's disease (PD) is a common neurodegenerative disorder with a prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor symptoms of PD such as rigidity and bradykinesia affect the muscles involved in the handwriting process resulting in handwriting abnormalities called PD dysgraphia. Nowadays, online handwritten signal (signal with temporal information) acquired by the digitizing tablets is the most advanced approach of graphomotor difficulties analysis. Although the basic kinematic features were proved to effectively quantify the symptoms of PD dysgraphia, a recent research identified that the theory of fractional calculus can be used to improve the graphomotor difficulties analysis. Therefore, in this study, we follow up on our previous research, and we aim to explore the utilization of various approaches of fractional order derivative (FD) in the analysis of PD dysgraphia. For this purpose, we used the repetitive loops task from the Parkinson's disease handwriting database (PaHaW). Handwritten signals were parametrized by the kinematic features employing three FD approximations: Gr\"unwald-Letnikov's, Riemann-Liouville's, and Caputo's. Results of the correlation analysis revealed a significant relationship between the clinical state and the handwriting features based on the velocity. The extracted features by Caputo's FD approximation outperformed the rest of the analyzed FD approaches. This was also confirmed by the results of the classification analysis, where the best model trained by Caputo's handwriting features resulted in a balanced accuracy of 79.73% with a sensitivity of 83.78% and a specificity of 75.68%.Comment: Print ISBN 978-3-031-19744-

Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of Graphomotor and Handwriting Difficulties

To this date, studies focusing on the prodromal diagnosis of Lewy body diseases (LBDs) based on quantitative analysis of graphomotor and handwriting difficulties are missing. In this work, we enrolled 18 subjects diagnosed with possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7 subjects having more than 50% probability of developing Parkinson's disease (PD), 21 subjects with both possible/probable MCI-LB and probability of PD > 50%, and 37 age- and gender-matched healthy controls (HC). Each participant performed three tasks: Archimedean spiral drawing (to quantify graphomotor difficulties), sentence writing task (to quantify handwriting difficulties), and pentagon copying test (to quantify cognitive decline). Next, we parameterized the acquired data by various temporal, kinematic, dynamic, spatial, and task-specific features. And finally, we trained classification models for each task separately as well as a model for their combination to estimate the predictive power of the features for the identification of LBDs. Using this approach we were able to identify prodromal LBDs with 74% accuracy and showed the promising potential of computerized objective and non-invasive diagnosis of LBDs based on the assessment of graphomotor and handwriting difficulties.Comment: Print ISBN 978-3-031-19744-

Non-invasive brain stimulation for speech in Parkinson’s disease: A randomized controlled trial.

Background: Hypokinetic dysarthria is a common but difficult-to-treat symptom of Parkinson's disease (PD). Objectives: We evaluated the long-term effects of multiple-session repetitive transcranial magnetic stimulation on hypokinetic dysarthria in PD. Neural mechanisms of stimulation were assessed by functional MRI. Methods: A randomized parallel-group sham stimulation-controlled design was used. Patients were randomly assigned to ten sessions (2 weeks) of real (1 Hz) or sham stimulation over the right superior temporal gyrus. Stimulation effects were evaluated at weeks 2, 6, and 10 after the baseline assessment. Articulation, prosody, and speech intelligibility were quantified by speech therapist using a validated tool (Phonetics score of the Dysarthric Profile). Activations of the speech network regions and intrinsic connectivity were assessed using 3T MRI. Linear mixed models and post-hoc tests were utilized for data analyses. Results: Altogether 33 PD patients completed the study (20 in the real stimulation group and 13 in the sham stimulation group). Linear mixed models revealed significant effects of time (F(3, 88.1) = 22.7, p < 0.001) and time-by-group interactions: F(3, 88.0) = 2.8, p = 0.040) for the Phonetics score. Real as compared to sham stimulation led to activation increases in the orofacial sensorimotor cortex and caudate nucleus and to increased intrinsic connectivity of these regions with the stimulated area. Conclusions: This is the first study to show the long-term treatment effects of non-invasive brain stimulation for hypokinetic dysarthria in PD. Neural mechanisms of the changes are discussed. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants

Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson's Disease.This work is being funded by Grants TEC2016-77791-C4-4-R from the Ministry of Economic Affairs and Competitiveness of Spain, Teka-Park 55 02 CENIE-0348_CIE_6_E POCTEP (InterReg Programme) and 16-30805A, SIX Research Center (CZ.1.05/2.1.00/03.0072), and LO1401 from the Czech Republic Government

Diffusion kurtosis imaging detects the time-dependent progress of pathological changes in the oral rotenone mouse model of Parkinson's disease

Clinical diagnosis of Parkinson's disease (PD) occurs typically when a substantial proportion of dopaminergic neurons in the substantia nigra (SN) already died, and the first motor symptoms appear. Therefore, tools enabling the early diagnosis of PD are essential to identify early-stage PD patients in which neuroprotective treatments could have a significant impact. Here, we test the utility and sensitivity of the diffusion kurtosis imaging (DKI) in detecting progressive microstructural changes in several brain regions of mice exposed to chronic intragastric administration of rotenone, a mouse model that mimics the spatiotemporal progression of PD-like pathology from the ENS to the SN as described by Braak's staging. Our results show that DKI, especially kurtosis, can detect the progression of pathology-associated changes throughout the CNS. Increases in mean kurtosis were first observed in the dorsal motor nucleus of the vagus (DMV) after 2 months of exposure to rotenone and before the loss of dopaminergic neurons in the SN occurred. Remarkably, we also show that limited exposure to rotenone for 2 months is enough to trigger the progression of the disease in the absence of the environmental toxin, thus suggesting that once the first pathological changes in one region appear, they can self-perpetuate and progress within the CNS. Overall, our results show that DKI can be a useful radiological marker for the early detection and monitoring of PD pathology progression in patients with the potential to improve the clinical diagnosis and the development of neuroprotective treatments. (Figure presented.). © 2021 International Society for Neurochemistr

Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN). Copyright © 2022 Galaz, Drotar, Mekyska, Gazda, Mucha, Zvoncak, Smekal, Faundez-Zanuy, Castrillon, Orozco-Arroyave, Rapcsak, Kincses, Brabenec and Rektorova