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).

• Publikační typ
• časopisecké články MeSH

Vysokoškolská učebnice, která se zaměřuje na anorganickou chemii. Obsahuje praktické laboratorní úkoly.

• MeSH
• chemické techniky analytické MeSH
• chemie anorganická MeSH

• Konspekt
• Chemie. Mineralogické vědy
• Učební osnovy. Vyučovací předměty. Učebnice
• NLK Obory
• chemie, klinická chemie
• NLK Publikační typ
• učebnice vysokých škol
• laboratorní cvičení

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; Mage 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.

• Publikační typ
• časopisecké články 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
• akustika řeči MeSH
• dysartrie diagnostické zobrazování   etiologie   patofyziologie   MeSH
• gyrus parahippocampalis diagnostické zobrazování   patofyziologie   MeSH
• konektom * MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• motorické korové centrum diagnostické zobrazování   patofyziologie   MeSH
• nervová síť diagnostické zobrazování   patofyziologie   MeSH
• Parkinsonova nemoc komplikace   diagnostické zobrazování   patofyziologie   MeSH
• senioři MeSH
• senzorická zpětná vazba fyziologie   MeSH
• spánkový lalok diagnostické zobrazování   patofyziologie   MeSH
• transkraniální magnetická stimulace * MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

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.

• MeSH
• biomechanika fyziologie   MeSH
• čelisti patofyziologie   MeSH
• datové soubory jako téma MeSH
• dysartrie etiologie   patofyziologie   MeSH
• jazyk patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• Parkinsonova nemoc komplikace   diagnóza   MeSH
• poruchy artikulace etiologie   patofyziologie   MeSH
• senioři MeSH
• stupeň závažnosti nemoci MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• Publikační typ
• časopisecké články 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.

• Publikační typ
• časopisecké články MeSH

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.

• Publikační typ
• časopisecké články MeSH

Hypokinetic dysarthria (HD) belongs to the most common early symptoms of PD, which are difficult to treat by antiparkinsonian medication and/or surgical treatment. Impaired ability of communication significantly reduces the quality of life of PD subjects. Although some other axial symptoms of PD (e.g. postural instability/gait difficulty) are predictors of the development of dementia in PD, the relationship between HD and cognitive impairment is not clear. The aims of the project are firstly to identify the specific disorders of speech which are characteristic of PD depending on various demographic and clinical variables and secondly to identify the relationship between specific parameters of HD and cognitive impairment. Partial aims are to explore effects of PD treatment, its pathophysiological mechanisms, and the effect of rTMS on HD. The results will make the speech therapy in PD patients more efficient and may help to find suitable biomarkers for cognitive impairment and dementia in PD.

Hypokinetická dysartrie (HD) patří mezi nejčastější a již časné příznaky PN, které jsou obtížně ovlivnitelné běžnou medikací a/nebo chirurgií PN. Přitom snížená schopnost komunikace prokazatelně snižuje kvalitu života lidí s PN. Ačkoliv některé další axiální symptomy PN (např. posturální instabilita a poruchy chůze) predikují rozvoj demence u PN, není jasné, jaký je vztah mezi HD a kognitivní deteriorací. Projekt má za cíl jednak zjistit, jaké specifické motorické poruchy řeči jsou charakteristické pro PN v závislosti na různých demografických a klinických parametrech (ve srovnání s věkově vázanými kontrolami) a jednak zjistit, jaký je vztah mezi klinickými a fyzikálními parametry HD a kognitivní deteriorací. Dílčí cíle jsou zaměřeny na výzkum efektu terapie PN a jeho patofyziologických mechanismů a na efekt rTMS na specifické parametry HD. Výsledky výzkumu umožní zefektivnit logopedickou péči pacientů s PN a mohou pomoci v hledání vhodných markerů pro rozvoj kognitivní poruchy a demence u PN.

• MeSH
• demence MeSH
• dopaminové látky terapeutické užití   MeSH
• dysartrie MeSH
• epidemiologické faktory MeSH
• hluboká mozková stimulace MeSH
• kognitivní poruchy MeSH
• komunikace MeSH
• kvalita života MeSH
• Parkinsonova nemoc patofyziologie   MeSH
• poruchy artikulace MeSH
• transkraniální magnetická stimulace MeSH

• Konspekt
• Patologie. Klinická medicína
• NLK Obory
• neurologie
• NLK Publikační typ
• závěrečné zprávy o řešení grantu IGA MZ ČR

BACKGROUND: Impairment of speech prosody is characteristic for Parkinson's disease (PD) and does not respond well to dopaminergic treatment. OBJECTIVES: We assessed whether baseline acoustic parameters, alone or in combination with other predominantly non-dopaminergic symptoms may predict global cognitive decline as measured by the Addenbrooke's cognitive examination (ACE-R) and/or worsening of cognitive status as assessed by a detailed neuropsychological examination. METHODS: Forty-four consecutive non-depressed PD patients underwent clinical and cognitive testing, and acoustic voice analysis at baseline and at the two-year follow-up. Influence of speech and other clinical parameters on worsening of the ACE-R and of the cognitive status was analyzed using linear and logistic regression. RESULTS: The cognitive status (classified as normal cognition, mild cognitive impairment and dementia) deteriorated in 25% of patients during the follow-up. The multivariate linear regression model consisted of the variation in range of the fundamental voice frequency (F0VR) and the REM Sleep Behavioral Disorder Screening Questionnaire (RBDSQ). These parameters explained 37.2% of the variability of the change in ACE-R. The most significant predictors in the univariate logistic regression were the speech index of rhythmicity (SPIR; p = 0.012), disease duration (p = 0.019), and the RBDSQ (p = 0.032). The multivariate regression analysis revealed that SPIR alone led to 73.2% accuracy in predicting a change in cognitive status. Combining SPIR with RBDSQ improved the prediction accuracy of SPIR alone by 7.3%. CONCLUSIONS: Impairment of speech prosody together with symptoms of RBD predicted rapid cognitive decline and worsening of PD cognitive status during a two-year period.

• MeSH
• kognitivní poruchy diagnóza   etiologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• neparametrická statistika MeSH
• neuropsychologické testy MeSH
• Parkinsonova nemoc komplikace   MeSH
• poruchy řeči etiologie   MeSH
• řeč fyziologie   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• statistika jako téma MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

OBJECTIVE: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. METHODS AND MATERIAL: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). RESULTS: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. CONCLUSION: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.

• MeSH
• biomechanika * MeSH
• diferenciální diagnóza MeSH
• lidé středního věku MeSH
• lidé MeSH
• Parkinsonova nemoc diagnóza   MeSH
• psaní rukou * MeSH
• senioři MeSH
• studie případů a kontrol MeSH
• support vector machine MeSH
• tlak MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH