Rilevazione automatica di microtubuli astrali in immagini di microscopia a fluorescenza

Questa tesi si colloca nel contesto dell'elaborazione automatica di immagini di microscopia a fluorescenza, e si propone di studiare algoritmi utili per identificare e seguire automaticamente l'evoluzione dei microtubuli astrali. L'uso di metodiche di elaborazione e analisi di immagini sta assumendo un ruolo sempre più importante nelle scienze biomediche, in quanto è possibile generare enormi masse di dati che contengono informazioni dinamiche su strutture subcellulari in vivo. Lo studio dell'evoluzione dinamica di tali strutture (di cui i microtubuli rappresentano un significativo esempio) permette di ottenere informazioni di fondamentale importanza nei campi della biologia molecolare e della medicina. Per esempio, è noto come il corretto orientamento del fuso mitotico sia un fattore importante che regola la differenziazione cellulare durante l'embriogenesi, e che quindi mutazioni in geni che interessano tale processo (ASPM - abnormal spindle-like microcephaly associated, CIT - citron kinase) siano responsabili di casi di microcefalia. Entrambi questi geni sono coinvolti nell'organizzazione dei microtubuli astrali, e interagiscono tra loro in maniera non completamente compresa. Il presente lavoro si colloca quin- di nell'ambito di un'attività di ricerca volta a chiarire aspetti legati ad anormale nucleazione e instabilità dei microtubuli astrali in cellule in cui questi geni siano stati soppressi tramite specifici siRNA. Tale attività è sponsorizzata dall'associazio- ne Telethon (grant n. 12095) e dall'Associazione Italiana per la Ricerca sul Cancro (AIRC - grant n. IG 17527), il che testimonia le grandi ricadute mediche, attuali e potenziali, di questo tipo di ricerca

Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson's Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient's needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user's engagement during an exergame. As a first step, healthy subjects were recruited to assess the system's capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play(accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system

Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning

Parkinson's disease (PD) is characterised by a progressive worsening of motor functionalities. In particular, limited hand dexterity strongly correlates with PD diagnosis and staging. Objective detection of alterations in hand motor skills would allow, for example, prompt identification of the disease, its symptoms and the definition of adequate medical treatments. Among the clinical assessment tasks to diagnose and stage PD from hand impairment, the Finger Tapping (FT) task is a well-established tool. This preliminary study exploits a single RGB-Depth camera (Azure Kinect) and Google MediaPipe Hands to track and assess the Finger Tapping task. The system includes several stages. First, hand movements are tracked from FT video recordings and used to extract a series of clinically-relevant features. Then, the most significant features are selected and used to train and test several Machine Learning (ML) models, to distinguish subjects with PD from healthy controls. To test the proposed system, 35 PD subjects and 60 healthy volunteers were recruited. The best-performing ML model achieved a 94.4% Accuracy and 98.4% Fl score in a Leave-One-Subject-Out validation. Moreover, different clusters with respect to spatial and temporal variability in the FT trials among PD subjects were identified. This result suggests the possibility of exploiting the proposed system to perform an even finer identification of subgroups among the PD population

Detection of freezing of gait in people withParkinson’s disease using smartphones

Freezing of Gait (FOG) is one of the most trouble-some motor symptoms associated with Parkinson’s disease (PD),characterised by brief episodes of inability to step. It involvesincreased risk of falls and reduced quality of life, and correlateswith motor fluctuations and progression of the disease. Hence, theknowledge of FOG event frequency, duration, daily distributionand response to drug therapy is fundamental for a reliablepatient’s assessment. In this study, we propose a FOG detectionalgorithm that takes as input inertial data from a single waist-mounted smartphone, and provides information about presenceand duration of FOG episodes. Data acquisition was carried on38 PD patients and 21 elderly subjects executing a standard6-minute walking test. More than 3.5 hours of accelerationdata have been collected. A combination of Support VectorMachine and k-Nearest Neighbour classifiers has been designed.Sensitivity of 95.4%, specificity of 98.8%, precision of 92.8%and accuracy of 98.3% in the 10-fold cross validation, and adetection rate of 84% in Leave-one-Subject-Out validation were obtained. These results, along with a good time resolution in theFOG duration identification and very efficient processing times,make the algorithm a promising tool for reliable FOG assessmentduring activities of daily livin

Electrodermal Activity in the Evaluation of Engagement for Telemedicine Applications

Electrodermal Activity (EDA) is a broadly-investigated physiological signal, whose behaviour is connected to nervous system arousal. Such system, indeed, influences the properties of the skin, producing a measurable electrical signal. Among the possible applications of such measurements, several studies have correlated the signal behaviour to engagement during mental and physical tasks, and the subjects' response to specific multimodal stimuli. Also due to the possibility of performing remote assessment and rehabilitation, telemedicine applications are gaining ground in the healthcare system. However, acceptance and engagement, hence continuity of usage, still remain significant obstacles. Therefore, it would be highly beneficial to verify, through objective measures, if these solutions are actually providing a sufficient stimulation to properly engage subjects while playing. This study investigates the possibility of employing EDA in the automatic recognition of different levels of user engagement, while playing a motor-cognitive exergame specifically designed for this purpose. Preliminary results, obtained on a cohort of 25 healthy subjects, seem to confirm that features extracted from EDA analysis are significant and able to train supervised classifiers, achieving high accuracy and precision in the engagement recognition problem

An algorithm for Parkinson's disease speech classification based on isolated words analysis

Introduction Automatic assessment of speech impairment is a cutting edge topic in Parkinson's disease (PD). Language disorders are known to occur several years earlier than typical motor symptoms, thus speech analysis may contribute to the early diagnosis of the disease. Moreover, the remote monitoring of dysphonia could allow achieving an effective follow-up of PD clinical condition, possibly performed in the home environment. Methods In this work, we performed a multi-level analysis, progressively combining features extracted from the entire signal, the voiced segments, and the on-set/off-set regions, leading to a total number of 126 features. Furthermore, we compared the performance of early and late feature fusion schemes, aiming to identify the best model configuration and taking advantage of having 25 isolated words pronounced by each subject. We employed data from the PC-GITA database (50 healthy controls and 50 PD patients) for validation and testing. Results We implemented an optimized k-Nearest Neighbours model for the binary classification of PD patients versus healthy controls. We achieved an accuracy of 99.4% in 10-fold cross-validation and 94.3% in testing on the PC-GITA database (average value of male and female subjects). Conclusion The promising performance yielded by our model confirms the feasibility of automatic assessment of PD using voice recordings. Moreover, a post-hoc analysis of the most relevant features discloses the option of voice processing using a simple smartphone application

Assessing REM sleep behaviour disorder: from machine learning classification to the definition of a continuous dissociation index

Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a pro-drome of neurodegeneration, with a high conversion rate to α–synucleinopathies such as Parkinson’s Disease (PD). The clinical diagnosis of RBD co–exists with evidence of REM Sleep Without Atonia (RSWA), a parasomnia that features loss of physiological muscular atonia during REM sleep. The objectives of this study are to implement an automatic detection of RSWA from polysomnographic traces, and to propose a continuous index (the Dissociation Index) to assess the level of dissociation between REM sleep stage and atonia. This is performed using Euclidean distance in proper vector spaces. Each subject is assigned a dissociation degree based on their distance from a reference, encompassing healthy subjects and clinically diagnosed RBD patients at the two extremes. Methods: Machine Learning models were employed to perform automatic identification of patients with RSWA through clinical polysomnographic scores, together with variables derived from electromyography. Proper distance metrics are proposed and tested to achieve a dissociation measure. Results: The method proved efficient in classifying RSWA vs. not-RSWA subjects, achieving an overall accuracy, sensitivity and precision of 87%, 93% and 87.5%, respectively. On its part, the Dissociation Index proved to be promising in measuring the impairment level of patients. Conclusions: The proposed method moves a step forward in the direction of automatically identifying REM sleep disorders and evaluating the impairment degree. We believe that this index may be correlated with the patients’ neurodegeneration process; this assumption will undergo a robust clinical validation process involving healthy, RSWA, RBD and PD subjects

A Preliminary Comparison between Traditional and Gamified Leg Agility Assessment in Parkinsonian Subjects

Parkinson's disease (PD) severity is assessed through a set of standardised tasks defined by clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). In particular, Leg Agility is a well-established test among the motor tasks included in UPDRS, which consists in repeated cycles of knee lifting and lowering, while sitting on a chair. Leg Agility objective evaluation through optical devices is often investigated for telemedicine applications. Moreover, remote rehabilitation for PD subjects through virtual exergaming is becoming a popular approach thanks to its versatility, increased user engagement and the possibility of coupling it with remote monitoring tools. This work investigates if lower-limb exergaming may also be exploited for assessment purposes similar to traditional evaluation. In particular, if there exists a statistical difference between the kinematic description of Leg Agility versus the one of a Bouncing Ball exergame, as provided by an optical (RGB-D) acquisition system suitable for remote monitoring. Preliminary results obtained by the comparison of the two types of assessment in a small group of parkinsonian subjects are presented and discussed

Hallmarks of Parkinson’s disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space

Parkinson’s disease (PD) is one of the most widespread neurodegenerative diseases worldwide, affected by a number of alterations, among which speech impairments that, interestingly, manifests up to 10 years before other major evidences (e.g. motor impairments). In this regard, we investigated the feasibility of a model based on the temporal evolution of speech attractors in the reconstructed phase space to identify hallmarks of PD identification and progression. To this end, the adopted dataset was made of vocal emissions of 46 de-novo and 54 mid-advanced People with PD, plus 113 healthy counterpart. A statistical analysis was applied to test the identified hallmarks effectiveness for diagnostic support, monitoring, and staging of the disease. According to the obtained results, the adopted approach of considering the temporal evolution of speech attractors in the reconstructed phase-space results effective to discriminate among the three groups of pathological or healthy voice