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1.
Front Neuroinform ; 16: 877139, 2022.
Article in English | MEDLINE | ID: mdl-35722168

ABSTRACT

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).

2.
Parkinsonism Relat Disord ; 84: 122-128, 2021 03.
Article in English | MEDLINE | ID: mdl-33609963

ABSTRACT

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.


Subject(s)
Connectome , Dysarthria , Gray Matter , Magnetic Resonance Imaging , Nerve Net , Parkinson Disease , Speech Acoustics , Temporal Lobe , Aged , Aged, 80 and over , Dysarthria/diagnosis , Dysarthria/etiology , Dysarthria/pathology , Dysarthria/physiopathology , Female , Gray Matter/diagnostic imaging , Gray Matter/pathology , Gray Matter/physiopathology , Humans , Male , Multimodal Imaging , Nerve Net/diagnostic imaging , Nerve Net/pathology , Nerve Net/physiopathology , Parkinson Disease/complications , Parkinson Disease/diagnostic imaging , Parkinson Disease/pathology , Parkinson Disease/physiopathology , Temporal Lobe/diagnostic imaging , Temporal Lobe/pathology , Temporal Lobe/physiopathology
3.
Parkinsonism Relat Disord ; 81: 96-102, 2020 12.
Article in English | MEDLINE | ID: mdl-33120076

ABSTRACT

BACKGROUND: Diffusion kurtosis imaging has been applied to evaluate white matter and basal ganglia microstructure in mixed Parkinson's disease (PD) groups with inconclusive results. OBJECTIVES: To evaluate specific patterns of kurtosis changes in PD and to assess the utility of diffusion imaging in differentiating between healthy subjects and cognitively normal PD, and between PD with and without mild cognitive impairment. METHODS: Diffusion scans were obtained in 92 participants using 3T MRI. Differences in white matter were tested by tract-based spatial statistics. Gray matter was evaluated in basal ganglia, thalamus, hippocampus, and motor and premotor cortices. Brain atrophy was also assessed. Multivariate logistic regression was used to identify a combination of diffusion parameters with the highest discrimination power between groups. RESULTS: Diffusion kurtosis metrics showed a significant increase in substantia nigra (p = 0.037, Hedges' g = 0.89), premotor (p = 0.009, Hedges' g = 0.85) and motor (p = 0.033, Hedges' g = 0.87) cortices in PD with normal cognition compared to healthy participants. Combined diffusion markers in gray matter reached 81% accuracy in differentiating between both groups. Significant white matter microstructural changes, and kurtosis decreases in the cortex were present in cognitively impaired versus cognitively normal PD. Diffusion parameters from white and gray matter differentiated between both PD phenotypes with 78% accuracy. CONCLUSIONS: Increased kurtosis in gray matter structures in cognitively normal PD reflects increased hindrance to water diffusion caused probably by alpha-synuclein-related microstructural changes. In cognitively impaired PD, the changes are mostly driven by decreased white matter integrity. Our results support the utility of diffusion kurtosis imaging for PD diagnostics.


Subject(s)
Brain/diagnostic imaging , Cognitive Dysfunction/diagnostic imaging , Parkinson Disease/diagnostic imaging , Aged , Atrophy , Basal Ganglia/diagnostic imaging , Brain/pathology , Cognitive Dysfunction/physiopathology , Cognitive Dysfunction/psychology , Diffusion Magnetic Resonance Imaging , Diffusion Tensor Imaging , Female , Gray Matter/diagnostic imaging , Hippocampus/diagnostic imaging , Humans , Logistic Models , Male , Middle Aged , Models, Statistical , Motor Cortex/diagnostic imaging , Multivariate Analysis , Parkinson Disease/physiopathology , Parkinson Disease/psychology , Thalamus/diagnostic imaging , White Matter/diagnostic imaging
4.
Front Psychol ; 10: 2937, 2019.
Article in English | MEDLINE | ID: mdl-32038361

ABSTRACT

Dysgraphia (D) is a complex specific learning disorder with a prevalence of up to 30%, which is linked with handwriting issues. The factors recognized for assessing these issues are legibility and performance time. Two questionnaires, the Handwriting Proficiency Screening Questionnaire (HPSQ) for teachers and its modification for children (HPSQ-C), were established as quick and valid screening tools along with a third factor - emotional and physical well-being. Until now, in the Czechia, there has been no validated screening tool for D diagnosis. A study was conducted on a set of 294 children from 3rd and 4th year of primary school (132 girls/162 boys; M age 8.96 ± 0.73) and 21 teachers who spent most of their time with them. Confirmatory factor analysis based on the theoretical background showed poor fit for HPSQ [χ2(32) = 115.07, p < 0.001; comparative fit index (CFI) = 0.95; Tucker-Lewis index (TLI) = 0.93; root mean square error of approximation (RMSEA) = 0.09; standard root mean square residual (SRMR) = 0.05] and excellent fit for HPSQ-C [χ2(32) = 31.12, p = 0.51; CFI = 1.0; TLI = 1.0; RMSEA = 0.0; SRMR = 0.04]. For the HPSQ-C models, there were no differences between boys and girls [Δχ2(7) = 12.55, p = 0.08]. Values of McDonalds's ω indicate excellent (HPSQ, ω = 0.9) and acceptable (HPSQ-C, ω = 0.7) reliability. Boys were assessed as worse writers than girls based on the results of both questionnaires. The grades positively correlate with the total scores of both HPSQ (r = 0.54, p < 0.01) and HPSQ-C (r = 0.28, p < 0.01). Based on the results, for the assessment of handwriting difficulties experienced by Czech children, we recommend using the HPSQ-C questionnaire for research purposes.

5.
Int J Neural Syst ; 29(2): 1850037, 2019 Mar.
Article in English | MEDLINE | ID: mdl-30336711

ABSTRACT

Neurodegenerative pathologies as Parkinson's Disease (PD) show important distortions in speech, affecting fluency, prosody, articulation and phonation. Classically, measurements based on articulation gestures altering formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been proposed to measure speech distortion, but these markers are based mainly on static positions of sustained vowels. The present study introduces a measurement based on the mutual information distance among probability density functions of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the jaw and tongue articulation gestures is estimated and modeled statistically. The distribution of this feature may differentiate PD patients from normative speakers during sustained vowel emission. The study is based on a limited database of 53 male PD patients, contrasted to a very selected and stable set of eight normative speakers. In this sense, distances based on Kullback-Leibler divergence seem to be sensitive to PD articulation instability. Correlation studies show statistically relevant relationship between information contents based on articulation instability to certain motor and nonmotor clinical scores, such as freezing of gait, or sleep disorders. Remarkably, one of the statistically relevant correlations point out to the time interval passed since the first diagnostic. These results stress the need of defining scoring scales specifically designed for speech disability estimation and monitoring methodologies in degenerative diseases of neuromotor origin.


Subject(s)
Articulation Disorders/physiopathology , Biomechanical Phenomena/physiology , Parkinson Disease/diagnosis , Aged , Articulation Disorders/etiology , Datasets as Topic , Dysarthria/etiology , Dysarthria/physiopathology , Humans , Jaw/physiopathology , Male , Middle Aged , Parkinson Disease/complications , Severity of Illness Index , Tongue/physiopathology
6.
Parkinsonism Relat Disord ; 61: 187-192, 2019 04.
Article in English | MEDLINE | ID: mdl-30337204

ABSTRACT

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.


Subject(s)
Connectome , Dysarthria/physiopathology , Feedback, Sensory/physiology , Motor Cortex/physiopathology , Nerve Net/physiopathology , Parahippocampal Gyrus/physiopathology , Parkinson Disease/physiopathology , Temporal Lobe/physiopathology , Transcranial Magnetic Stimulation , Aged , Dysarthria/diagnostic imaging , Dysarthria/etiology , Female , Humans , Magnetic Resonance Imaging , Male , Middle Aged , Motor Cortex/diagnostic imaging , Nerve Net/diagnostic imaging , Parahippocampal Gyrus/diagnostic imaging , Parkinson Disease/complications , Parkinson Disease/diagnostic imaging , Speech Acoustics , Temporal Lobe/diagnostic imaging
7.
Cognit Comput ; 10(6): 1006-1018, 2018.
Article in English | MEDLINE | ID: mdl-30595758

ABSTRACT

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.

8.
Front Neuroinform ; 11: 56, 2017.
Article in English | MEDLINE | ID: mdl-28970792

ABSTRACT

Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease. Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech. Methods: A dataset of sustained vowels recorded from Parkinson Disease patients is contrasted with similar recordings from normative subjects. The probability distribution of the absolute kinematic velocity of the jaw-tongue system is extracted from each utterance. A Random Least Squares Feed-Forward Network (RLSFN) has been used as a binary classifier working on the pathological and normative datasets in a leave-one-out strategy. Monte Carlo simulations have been conducted to estimate the influence of the stochastic nature of the classifier. Two datasets for each gender were tested (males and females) including 26 normative and 53 pathological subjects in the male set, and 25 normative and 38 pathological in the female set. Results: Male and female data subsets were tested in single runs, yielding equal error rates under 0.6% (Accuracy over 99.4%). Due to the stochastic nature of each experiment, Monte Carlo runs were conducted to test the reliability of the methodology. The average detection results after 200 Montecarlo runs of a 200 hyperplane hidden layer RLSFN are given in terms of Sensitivity (males: 0.9946, females: 0.9942), Specificity (males: 0.9944, females: 0.9941) and Accuracy (males: 0.9945, females: 0.9942). The area under the ROC curve is 0.9947 (males) and 0.9945 (females). The equal error rate is 0.0054 (males) and 0.0057 (females). Conclusions: The proposed methodology avails that the use of highly normalized descriptors as the probability distribution of kinematic variables of vowel articulation stability, which has some interesting properties in terms of information theory, boosts the potential of simple yet powerful classifiers in producing quite acceptable detection results in Parkinson Disease.

9.
Comput Methods Programs Biomed ; 127: 301-17, 2016 Apr.
Article in English | MEDLINE | ID: mdl-26826900

ABSTRACT

BACKGROUND AND OBJECTIVE: Hypokinetic dysarthria (HD) is a frequent speech disorder associated with idiopathic Parkinson's disease (PD). It affects all dimensions of speech production. One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate, flat speech melody, and impairment of speech intensity control. Dysprosody has a detrimental impact on speech naturalness and intelligibility. METHODS: This paper deals with quantitative prosodic analysis of neutral, stress-modified and rhymed speech in patients with PD. The analysis of prosody is based on quantification of monopitch, monoloudness, and speech rate abnormalities. Experimental dataset consists of 98 patients with PD and 51 healthy speakers. For the purpose of HD identification, sequential floating feature selection algorithm and random forests classifier is used. In this paper, we also introduce a concept of permutation test applied in the field of acoustic analysis of dysarthric speech. RESULTS: Prosodic features obtained from stress-modified reading task provided higher classification accuracies compared to the ones extracted from reading task with neutral emotion demonstrating the importance of stress in speech prosody. Features calculated from poem recitation task outperformed both reading tasks in the case of gender-undifferentiated analysis showing that rhythmical demands can in general lead to more precise identification of HD. Additionally, some gender-related patterns of dysprosody has been observed. CONCLUSIONS: This paper confirms reduced variation of fundamental frequency in PD patients with HD. Interestingly, increased variability of speech intensity compared to healthy speakers has been detected. Regarding speech rate disturbances, our results does not report any particular pattern. We conclude further development of prosodic features quantifying the relationship between monopitch, monoloudness and speech rate disruptions in HD can have a great potential in future PD analysis.


Subject(s)
Parkinson Disease/physiopathology , Speech Disorders/physiopathology , Algorithms , Case-Control Studies , Humans , Stress, Physiological
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