Otolaryngologic symptoms in multiple sclerosis: a review

Many symptoms of multiple sclerosis may affect the ear, nose and throat. The most common otolaryngologic symptoms of multiple sclerosis are speech disorders, followed by sleep disorders, vertigo and disequilibrium, dysphagia, smell alterations, and hearing loss. Less common symptoms include sialorrhea, facial palsy, taste alterations, trigeminal neuralgia and tinnitus. The origin of otolaryngologic symptoms in multiple sclerosis is mainly central, although increasing evidence also suggests a peripheral involvement. Otolaryngologic symptoms in multiple sclerosis may have different clinical presentations; they can appear in different stages of the pathology, in some cases they can be the presenting symptoms and their worsening may be correlated with reactivation of the disease. Many of these symptoms significantly affect the quality of life or patients and lead to increased morbidity and mortality. Otolaryngologic symptoms are common in multiple sclerosis; however, they are often overlooked. In many cases, they follow the relapsing-remitting phases of the disease, and may spontaneously disappear, leading to a delay in multiple sclerosis diagnosis. Clinicians should be aware of otolaryngologic symptoms of multiple sclerosis, especially when they are associated to neurologic symptoms, as they may be early signs of a still undiagnosed multiple sclerosis or could help monitor disease progression in already diagnosed patients

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

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Hearing in dementia: defining deficits and assessing impact

The association between hearing impairment and dementia has emerged as a major public health challenge, with significant opportunities for earlier diagnosis, treatment and prevention. However, the nature of this association has not been defined. We hear with our brains, particularly within the complex soundscapes of everyday life: neurodegenerative pathologies target the auditory brain and are therefore predicted to damage hearing function early and profoundly. Here I present evidence for this proposition, based on structural and functional features of auditory brain organisation that confer vulnerability to neurodegeneration, the extensive, reciprocal interplay between ‘peripheral’ and ‘central’ hearing dysfunction, and recently characterised auditory signatures of canonical neurodegenerative dementias (Alzheimer’s disease and frontotemporal dementia). In chapter 3, I examine pure tone audiometric thresholds in AD and FTD syndromes and explore the functional interplay between the auditory brain and auditory periphery by assessing the contribution of auditory cognitive factors on pure tone detection. In chapter 4, I develop this further by examining the processing of degraded speech signals, leveraging the increased importance of top-down integrative and predictive mechanisms on resolving impoverished bottom-up sensory encoding. In chapter 5, I use a more discrete test of phonological processing to focus in on a specific brain region that is an early target in logopenic aphasia, to explore the potential of auditory cognitive tests as disease specific functional biomarkers. Finally, in chapter 6, I use auditory symptom questionnaires to capture real-world hearing in daily life amongst patients with dementia as well as their carers and measure how this correlates with audiometric performance and degraded speech processing. I call for a clinical assessment of real-world hearing in these diseases that moves beyond pure tone perception to the development of novel auditory ‘cognitive stress tests’ and proximity markers for the early diagnosis of dementia and management strategies that harness retained auditory plasticity

Occasional essay: upper motor neuron syndrome in amyotrophic lateral sclerosis

The diagnosis of amyotrophic lateral sclerosis (ALS) requires recognition of both lower (LMN) and upper motor neuron (UMN) dysfunction.1 However, classical UMN signs are frequently difficult to identify in ALS.2 LMN involvement is sensitively detected by electromyography (EMG)3 but, as yet, there are no generally accepted markers for monitoring UMN abnormalities,4 the neurobiology of ALS itself, and disease spread through the brain and spinal cord,.5 Full clinical assessment is therefore necessary to exclude other diagnoses and to monitor disease progression. In part, this difficulty regarding detection of UMN involvement in ALS derives from the definition of ‘the UMN syndrome’. Abnormalities of motor control in ALS require reformulation within an expanded concept of the UMN, together with the neuropathological, neuro-imaging and neurophysiological abnormalities in ALS. We review these issues here

Exploring the Usage of Text-Entry as a Digital Endpoint in Parkinson’s Disease

Tese de mestrado, Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasNeurodegenerative diseases are a group of diseases characterised by the loss of neurons and tend to be fatal. The most researched being Parkinson’s disease, some connections have been established between this disease and the use of text-entry towards its diagnosis and monitoring. With such scattered information regarding neurodegenerative diseases and text-entry, a systematic review was carried out to show which diseases have been researched in that direction, being mainly PD but also MCI and MS. The main metrics collected were flight time, hold time and pressure. As previous research did not include clinicians participation towards the design of diagnosing and monitoring tools, this dissertation went a step further and worked together with clinicians to understand their expectations on data and its visualisations. Clinicians believe that text-entry does have potential towards the diagnosis and monitoring of neurodegenerative diseases. Clinicians also provided concepts of interest against recently suggested metrics, such as apraxia, bradykinesia and dyskinesia. Finally, it was possible to understand how clinicians would deem to be the best way to view the data for the patients’ assessments