Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

KLASYFIKACJA CHOROBY PARKINSONA I INNYCH ZABURZEŃ NEUROLOGICZNYCH Z WYKORZYSTANIEM EKSTRAKCJI CECH GŁOSOWYCH I TECHNIK REDUKCJI

This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications.Przedstawione badanie miało na celu różnicowanie osób z chorobą Parkinsona (PD) od osób z innymi zaburzeniami neurologicznymi poprzez analizę próbek głosowych, biorąc pod uwagę związek między zaburzeniami głosu a PD. Próbki głosowe zostały zebrane od 76 uczestników przy użyciu różnych urządzeń i warunków nagrywania, a uczestnicy byli instruowani, aby wydłużyć samogłoskę /a/ w wygodnym tempie. Oprogramowanie PRAAT zostało zastosowane do ekstrakcji cech, takich jak autokorelacja (AC), krzyżowa korelacja (CC) i współczynniki cepstralne Mel (MFCC) z próbek głosowych. Analiza składowych głównych (PCA) została wykorzystana w celu zmniejszenia wymiarowości cech. Jako techniki nadzorowanego uczenia maszynowego wykorzystano drzewa decyzyjne (CT), regresję logistyczną, naiwny klasyfikator Bayesa (NB), maszyny wektorów nośnych (SVM) oraz metody zespołowe. Każda z tych metod posiadała swoje unikalne mocne strony i charakterystyki, umożliwiając kompleksową ocenę ich skuteczności w rozróżnianiu pacjentów z PD od osób z innymi zaburzeniami neurologicznymi. Naiwny klasyfikator Bayesa, wykorzystujący siedem składowych PCA, osiągnął najwyższy wskaźnik dokładności na poziomie 86,84% wśród przetestowanych metod klasyfikacji. Należy jednak zauważyć, że wydajność klasyfikatora może się różnić w zależności od zbioru danych i konkretnych cech próbek głosowych. Podsumowując, to badanie wykazało potencjał analizy głosu jako narzędzia diagnostycznego do rozróżniania pacjentów z PD od osób z innymi zaburzeniami neurologicznymi. Poprzez zastosowanie różnych technik analizy głosu i wykorzystanie różnych algorytmów uczenia maszynowego, takich jak drzewa decyzyjne, regresja logistyczna, naiwny klasyfikator Bayesa, maszyny wektorów nośnych i metody zespołowe, osiągnięto znaczący poziom dokładności. Niemniej jednak, konieczne są dalsze badania i walidacja na większych zbiorach danych w celu skonsolidowania i uogólnienia tych wyników dla przyszłych zastosowań klinicznych

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines

Social cognitive deficits and their neural correlates in progressive supranuclear palsy

Although progressive supranuclear palsy is defined by its akinetic rigidity, vertical supranuclear gaze palsy and falls, cognitive impairments are an important determinant of patients’ and carers’ quality of life. Here, we investigate whether there is a broad deficit of modality-independent social cognition in progressive supranuclear palsy and explore the neural correlates for these. We recruited 23 patients with progressive supranuclear palsy (using clinical diagnostic criteria, nine with subsequent pathological confirmation) and 22 age- and education-matched controls. Participants performed an auditory (voice) emotion recognition test, and a visual and auditory theory of mind test. Twenty-two patients and 20 controls underwent structural magnetic resonance imaging to analyse neural correlates of social cognition deficits using voxel-based morphometry. Patients were impaired on the voice emotion recognition and theory of mind tests but not auditory and visual control conditions. Grey matter atrophy in patients correlated with both voice emotion recognition and theory of mind deficits in the right inferior frontal gyrus, a region associated with prosodic auditory emotion recognition. Theory of mind deficits also correlated with atrophy of the anterior rostral medial frontal cortex, a region associated with theory of mind in health. We conclude that patients with progressive supranuclear palsy have a multimodal deficit in social cognition. This deficit is due, in part, to progressive atrophy in a network of frontal cortical regions linked to the integration of socially relevant stimuli and interpretation of their social meaning. This impairment of social cognition is important to consider for those managing and caring for patients with progressive supranuclear palsy

Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results

Objectives: To diagnose Parkinson disease (PD) at the individual level using pattern recognition of brain susceptibility-weighted imaging (SWI). Methods: We analysed brain SWI in 36 consecutive patients with Parkinsonism suggestive of PD who had (1) SWI at 3T, (2) brain 123I-ioflupane SPECT and (3) extensive neurological testing including follow-up (16 PD, 67.4 ± 6.2years, 11 female; 20 OTHER, a heterogeneous group of atypical Parkinsonism syndromes 65.2 ± 12.5years, 6 female). Analysis included group-level comparison of SWI values and individual-level support vector machine (SVM) analysis. Results: At the group level, simple visual analysis yielded no differences between groups. However, the group-level analyses demonstrated increased SWI in the bilateral thalamus and left substantia nigra in PD patients versus other Parkinsonism. The inverse comparison yielded no supra-threshold clusters. At the individual level, SVM correctly classified PD patients with an accuracy above 86%. Conclusions: SVM pattern recognition of SWI data provides accurate discrimination of PD among patients with various forms of Parkinsonism at an individual level, despite the absence of visually detectable alterations. This pilot study warrants further confirmation in a larger cohort of PD patients and with different MR machines and MR parameters. Key Points: • Magnetic resonance imaging data offers new insights into Parkinson's disease • Visual susceptibility-weighted imaging (SWI) analysis could not discriminate idiopathic from atypical PD • However, support vector machine (SVM) analysis provided highly accurate detection of idiopathic PD • SVM analysis may contribute to the clinical diagnosis of individual PD patients • Such information can be readily obtained from routine MR dat

CLASSIFICATION OF PARKINSON’S DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES

This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications

The role of optical coherence tomography (OCT) and skin biopsy in the diagnosis and follow-up of Parkinson's disease and multiple system atrophy: looking for possible early biomarkers

Presupposti: Come dimostrato dal lungo ritardo diagnostico (in media 10 anni), le prime fasi della malattia di Parkinson (PD) sono difficili da identificare. Anche la diagnosi differenziale di tale malattia, soprattutto con altre α-sinucleinopatie, può risultare complessa, soprattutto in fase precoce. Nell’atrofia multisistemica (MSA), la presentazione clinica con sintomi prevalentemente non-motori all’esordio della malattia può essere indistinguibile da quella di altre α-sinucleinopatie. Inoltre, con l’avvento della medicina personalizzata, emerge la necessità di trovare dei predittori prognostici per poter valutare la progressione della malattia in ogni paziente. Scopo dello studio: Definire il possibile ruolo della tomografia a coerenza ottica (OCT) e della biopsia di cute come biomarkers nella diagnosi precoce e nel follow-up di PD e MSA. Validare OCT e biopsia di cute come strumenti nella diagnosi differenziale delle α-sinucleinopatie. Materiali e metodi: Pazienti con PD, MSA e controlli sani (HC) sono stati sottoposti a OCT. I pazienti con PD e MSA, inoltre, sono stati valutati clinicamente usando le scale, rispettivamente, MDS-UPDRS e UMSARS per determinare il grado di malattia e con la scala COMPASS 31 per la stima della disautonomia e sono stati sopposti a biopsia di cute analizzata sia con immunoistochimica (IHC) che con real-time quaking-induced conversion (RT-QuIC). Risultati: Abbiamo analizzato le scansioni OCT di 24 pazienti PD, 12 pazienti MSA e 10 HC. Il numero di foci iperriflettenti retinici (HRF), un potenziale marker di attivazione della microglia, è risultato significativamente maggiore nei sottogruppi patologici (PD>MSA) rispetto agli HC. Il rilevamento tramite IHC di α-syn nelle biopsie di cute è stato in grado di identificare i soggetti malati con buona sensibilità (71% in PD e 67% in MSA). I punteggi RT-QuIC dei pazienti con PD hanno mostrato una correlazione lineare sia con la gravità della malattia (UPDRS II, UPDRS III, punteggio totale UPDRS) che con i sintomi autonomici (domini relativi a ipotensione ortostatica e sintomi genitourinari della scala validata COMPASS 31). Conclusioni: Gli HRF sono significativamente aumentati nei sottogruppi patologici (PD>MSA). La biopsia di cute analizzata tramite IHC mostra buona sensibilità. Gli score RT-QuIC correlano con grado di malattia e sintomi autonomici. Si può, dunque, concludere che sia l'OCT che la biopsia cutanea possono essere considerati potenziali biomarkers nella diagnosi precoce e nel follow-up delle α-sinucleinopatie.Background: The identification of Parkinson’s Disease (PD) in the early stages might be challenging, as demonstrated by the long diagnostic delay (on average 10 years) in the majority of patients. The differential diagnosis, especially with other α-synucleinopathies, may also be difficult. In multiple system atrophy (MSA), non-motor features, predominant at disease onset, may be indistinguishable from other α-synucleinopathies. Moreover, with the rise of personalized medicine, new prognostic and predictive markers are needed in order to assess disease progression. Aim of the study: To define the possible role of optical coherence tomography (OCT) and skin biopsy as biomarkers in the early diagnosis and follow-up of PD and MSA. To validate OCT and skin biopsy as possible tools in the differential diagnosis of α-synucleinopathies. Methods: OCT was performed in patients with PD, MSA and in healthy controls (HC). PD and MSA patients were clinically evaluated with, respectively, MDS-UPDRS and UMSARS scores in order to assess disease severity and with COMPASS 31 to estimate autonomic dysfunction. Pathological subgroups (PD and MSA) also underwent skin biopsy, analyzed both via immunohistochemistry (IHC) and real-time quaking-induced conversion (RT-QuIC). Results: We analyzed OCT scans from 24 PD patients, 12 MSA patients and 10 HCs. The number of hyperreflective foci (HRF), a potential marker for microglial activation, was significantly greater in the pathological subgroups (PD>MSA) than in the HCs. IHC detection of α-syn in skin punch biopsies was able to identify diseased subjects with good sensitivity (71% in PD and 67% in MSA). RT-QuIC scores of PD patients showed a linear correlation with both disease severity (UPDRS II, UPDRS III, UPDRS total score) and autonomic symptoms (orthostatic hypotension and genitourinary domains of the validated COMPASS 31 scale). Conclusion: HRF are significantly increased in pathological subgroups (PD>MSA). IHC detection of α-syn in skin punch biopsies showed good sensitivity. RT-QuIC scores correlate with both disease severity and autonomic symptoms. It can be concluded that both OCT and skin biopsy can be considered potential biomarkers in the early diagnosis and follow-up of α-synucleinopathies

Role of Functional Neuroimaging with 123I-MIBG and 123I-FP-CIT in De Novo Parkinson's Disease: A Multicenter Study

Background: Parkinson's disease is a progressive neurodegenerative disorder, with incidence and prevalence rates of 8-18 per 100,000 people per year and 0.3-1%, respectively. As parkinsonian symptoms do not appear until approximately 50-60% of the nigral DA-releasing neurons have been lost, the impact of routine structural imaging findings is minimal at early stages, making Parkinson's disease an ideal condition for the application of functional imaging techniques. The aim of this multicenter study is to assess whether 123I-FP-CIT (DAT-SPECT), 123I-MIBG (mIBG-scintigraphy) or an association of both exams presents the highest diagnostic accuracy in de novo PD patients. Methods: 288 consecutive patients with suspected diagnoses of Parkinson's disease or non- Parkinson's disease syndromes were analyzed in the present Italian multicenter retrospective study. All subjects were de novo, drug-naive patients and met the inclusion criteria of having undergone both DAT-SPECT and mIBG-scintigraphy within one month of each other. Results: The univariate analysis including age and both mIBG-SPECT and DAT-SPECT parameters showed that the only significant values for predicting Parkinson's disease in our population were eH/M, lH/M, ESS and LSS obtained from mIBG-scintigraphy (p < 0.001). Conclusions: mIBG-scintigraphy shows higher diagnostic accuracy in de novo Parkinson's disease patients than DAT-SPECT, so given the superiority of the MIBG study, the combined use of both exams does not appear to be mandatory in the early phase of Parkinson's disease

Phoniatricians and otorhinolaryngologists approaching oropharyngeal dysphagia : an update on FEES

Purpose Oropharyngeal dysphagia (OD) is a common phenomenon in otorhinolaryngology and phoniatrics. As both sub-disciplines have a strong tradition and clinical experience in endoscopic assessment of the upper aerodigestive tract, the implementation of fiberoptic endoscopic evaluation of swallowing (FEES) was an almost self-evident evolution. This review aims to provide an update on FEES and the role of phoniatricians and otorhinolaryngologists using FEES in Europe. Methods A narrative review of the literature was performed by experts in the field of FEES both in the clinical context and in the field of scientific research. Results FEES is the first-choice OD assessment technique for both phoniatricians and otorhinolaryngologists. FEES is becoming increasingly popular because of its usefulness, safety, low costs, wide applicability, and feasibility in different clinical settings. FEES can be performed by health professionals of varying disciplines, once adequate knowledge and skills are acquired. FEES aims to determine OD nature and severity and can provide diagnostic information regarding the underlying etiology. The direct effect of therapeutic interventions can be evaluated using FEES, contributing to design the OD management plan. Standardization of FEES protocols and metrics is still lacking. Technological innovation regarding image resolution, frame rate frequency, endoscopic light source specifications, and endoscopic rotation range has contributed to an increased diagnostic accuracy. Conclusion The rising number of phoniatricians and otorhinolaryngologists performing FEES contributes to the early detection and treatment of OD in an aging European population. Nevertheless, a multidisciplinary approach together with other disciplines is crucial for the success of OD management.Peer reviewe

The clinical and electrophysiological investigation of tremor

The various forms of tremor are now classified in two axes: clinical characteristics (axis 1) and etiology (axis 2). Electrophysiology is an extension of the clinical exam. Electrophysiologic tests are diagnostic of physiologic tremor, primary orthostatic tremor, and functional tremor, but they are valuable in the clinical characterization of all forms of tremor. Electrophysiology will likely play an increasing role in axis 1 tremor classification because many features of tremor are not reliably assessed by clinical examination alone. In particular, electrophysiology may be needed to distinguish tremor from tremor mimics, assess tremor frequency, assess tremor rhythmicity or regularity, distinguish mechanical-reflex oscillation from central neurogenic oscillation, determine if tremors in different body parts, muscles, or brain regions are strongly correlated, document tremor suppression or entrainment by voluntary movements of contralateral body parts, and document the effects of voluntary movement on rest tremor. In addition, electrophysiologic brain mapping has been crucial in our understanding of tremor pathophysiology. The electrophysiologic methods of tremor analysis are reviewed in the context of physiologic tremor and pathologic tremors, with a focus on clinical characterization and pathophysiology. Electrophysiology is instrumental in elucidating tremor mechanisms, and the pathophysiology of the different forms of tremor is summarized in this review