Shannon entropy: A novel parameter for quantifying pentagon copying performance in non-demented Parkinson's disease patients

Introduction: Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI). Methods: We enrolled 27 patients with PD without major cognitive impairment and 25 age-matched healthy controls (HC). We focused on drawing parameters using a digitizing tablet. Parameters with between-group differences were correlated with cognitive outcomes and were used as covariates in the whole-brain voxel-wise analysis using voxel-based morphometry; familywise error (FWE) threshold p < 0.001. Results: PD patients differed from HC in attention domain z-scores (p < 0.0001). In terms of tablet parameters, the groups differed in Shannon entropy (horizontal in-air, p = 0.003), which quantifies the movements between two strokes. In PD, a correlation was found between the median of Shannon entropy (horizontal in-air) and attention z-scores (R = -0.55, p = 0.006). The VBM revealed an association between our drawing parameter of interest and gray matter (GM) volume variability in the right superior parietal lobe (SPL). Conclusion: Using a digitizing tablet during the PCT, we identified a novel entropy-based parameter that differed between the nondemented PD and HC groups. This in-air parameter correlated with the level of attention and was linked to GM volume variability of the region engaged in spatial attention

Shannon entropy: A novel parameter for quantifying pentagon copying performance in non-demented Parkinson's disease patients

Introduction Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI). Methods We enrolled 27 patients with PD without major cognitive impairment and 25 age-matched healthy controls (HC). We focused on drawing parameters using a digitizing tablet. Parameters with between-group differences were correlated with cognitive outcomes and were used as covariates in the whole-brain voxel-wise analysis using voxel-based morphometry; familywise error (FWE) threshold p < 0.001. Results PD patients differed from HC in attention domain z-scores (p < 0.0001). In terms of tablet parameters, the groups differed in Shannon entropy (horizontal in-air, p = 0.003), which quantifies the movements between two strokes. In PD, a correlation was found between the median of Shannon entropy (horizontal in-air) and attention z-scores (R = 0.55, p = 0.006). The VBM revealed an association between our drawing parameter of interest and gray matter (GM) volume variability in the right superior parietal lobe (SPL). Conclusion Using a digitizing tablet during the PCT, we identified a novel entropy-based parameter that differed between the nondemented PD and HC groups. This in-air parameter correlated with the level of attention and was linked to GM volume variability of the region engaged in spatial attention

Vliv probiotik ve výživě telat na hmotnostní přírůstky živé hmotnosti a zdravotní stav

This paper aims to monitor the impact of Lactobacillus sporogenes (LS), Saccharomyces cerevisiae (SC), the combination thereof Lactobacillus sporogenes and Saccharomyces cerevisiae (CLS) on the health status and the live weight gain in calves compared to a control group (C). The experiment took place in the period from March 2022 to March 2023. 100 Holstein heifers in the age from 1 to 56 days were included in the experiment. The differences in live weight gain were significant when the live weight gains were compared in the first 14 days after birth between the CLS vs C group (63,36.72 ± 4.81 vs 59.55 ± 4.55, P 0.05. The impact on decrease and duration of diarrhea was not proved statistically P = 0.0634. However, a tendency to decrease the occurrence and duration thereof was proved. The impact of feed additives on the transmission of passive immunity in calves in their first week of life was not proved as statistically significant.Cílem této studie bylo sledovat vliv Lactobacillus sporogenes (LS), Saccharomyces cerevisiae (SC) a jejich kombinaci Lactobacillus sporogenes and Saccharomyces cerevisiae (CLS) na zdravotní stav a přírůstek živé hmotnosti telat oproti skupině kontrolní (C). Pokus se uskutečnil v období březen 2022 až březen 2023. Do pokusu bylo zařazeno celkem 100 holštýnských jaloviček ve stáří 1 až 56 dní. Rozdíly v přírůstku živé hmotnosti byly významné, pokud byly porovnány hmotnostní přírůstky ve 14. dech po narození mezi skupinou CLS vs C (63,36.72 ± 4.81 vs 59.55 ± 4.55, P < 0.05) a v 56 dnech po narození mezi skupinu CLS vs C, LS vs C a SC vs C (87.34 ± 4.95 kg vs 83.15 ± 5.32 kg, P < 0.01; 86.41 ± 5.34 kg vs 83.15 ± 5.32 kg, P < 0.05 a 85.92 ± 5.86 kg vs 83.15 ± 5.32 kg, P < 0.05). Rozdíly v přírůstku živé hmotnosti mezi pokusnými skupinami nebyly statisticky prokázány P > 0.05. Vliv na snížení výskytu a trvání průjmových onemocnění nebyl statisticky prokázán P = 0.0634, ovšem byla zde prokázána tendence ke snížení jejich výskytů a době trvání. Statisticky významný nebyl prokázán vliv krmných aditiv na přenos pasivní imunity u telat v prvním týdnu života

Non-invasive stimulation of the auditory feedback area for improved articulation in Parkinson's disease

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

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessmen

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)