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).

• Publication type
• Journal Article MeSH

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.

• MeSH
• Speech Acoustics * MeSH
• Dysarthria * diagnosis   etiology   pathology   physiopathology   MeSH
• Connectome * MeSH
• Humans MeSH
• Magnetic Resonance Imaging * MeSH
• Multimodal Imaging MeSH
• Nerve Net * diagnostic imaging   pathology   physiopathology   MeSH
• Parkinson Disease * complications   diagnostic imaging   pathology   physiopathology   MeSH
• Gray Matter * diagnostic imaging   pathology   physiopathology   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Temporal Lobe * diagnostic imaging   pathology   physiopathology   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

INTRODUCTION: Hypokinetic dysarthria (HD) is a common symptom of Parkinson's disease (PD) which does not respond well to PD treatments. We investigated acute effects of repetitive transcranial magnetic stimulation (rTMS) of the motor and auditory feedback area on HD in PD using acoustic analysis of speech. METHODS: We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the left orofacial primary motor area, the right superior temporal gyrus (STG), and over the vertex (a control stimulation site) in 16 PD patients with HD. A cross-over design was used. Stimulation sites and protocols were randomised across subjects and sessions. Acoustic analysis of a sentence reading task performed inside the MR scanner was used to evaluate rTMS-induced effects on motor speech. Acute fMRI changes due to rTMS were also analysed. RESULTS: The 1 Hz STG stimulation produced significant increases of the relative standard deviation of the 2nd formant (p = 0.019), i.e. an acoustic parameter describing the tongue and jaw movements. The effects were superior to the control site stimulation and were accompanied by increased resting state functional connectivity between the stimulated region and the right parahippocampal gyrus. The rTMS-induced acoustic changes were correlated with the reading task-related BOLD signal increases of the stimulated area (R = 0.654, p = 0.029). CONCLUSION: Our results demonstrate for the first time that low-frequency stimulation of the temporal auditory feedback area may improve articulation in PD and enhance functional connectivity between the STG and the cortical region involved in an overt speech control.

• MeSH
• Speech Acoustics MeSH
• Dysarthria diagnostic imaging   etiology   physiopathology   MeSH
• Parahippocampal Gyrus diagnostic imaging   physiopathology   MeSH
• Connectome * MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Motor Cortex diagnostic imaging   physiopathology   MeSH
• Nerve Net diagnostic imaging   physiopathology   MeSH
• Parkinson Disease complications   diagnostic imaging   physiopathology   MeSH
• Aged MeSH
• Feedback, Sensory physiology   MeSH
• Temporal Lobe diagnostic imaging   physiopathology   MeSH
• Transcranial Magnetic Stimulation * MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson's disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a mathematical model that would predict FOG deficits using acoustic analysis at baseline. We enrolled 75 PD patients who were assessed by 6 clinical scales including the Freezing of Gait Questionnaire (FOG-Q). We subsequently extracted 19 acoustic measures quantifying speech disorders in the fields of phonation, articulation and prosody. To identify the relationship between HD and FOG, we performed a partial correlation analysis. Finally, based on the selected acoustic measures, we trained regression models to predict the change in FOG during a 2-year follow-up. We identified significant correlations between FOG-Q scores and the acoustic measures based on formant frequencies (quantifying the movement of the tongue and jaw) and speech rate. Using the regression models, we were able to predict a change in particular FOG-Q scores with an error of between 7.4 and 17.0 %. This study is suggesting that FOG in patients with PD is mainly linked to improper articulation, a disturbed speech rate and to intelligibility. We have also proved that the acoustic analysis of HD at the baseline can be used as a predictor of the FOG deficit during 2 years of follow-up. This knowledge enables researchers to introduce new cognitive systems that predict gait difficulties in PD patients.

• Publication type
• Journal Article MeSH