221 research outputs found
Deep learning and wearable sensors for the diagnosis and monitoring of Parkinsonâs disease: A systematic review
Parkinsonâs disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL).
The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools.
Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method.
The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models.
The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment
The Phenomenology, Pathophysiology and Progression of the Core Features of Lewy Body Dementia
Lewy body dementias â Dementia with Lewy bodies (DLB) and Parkinsonâs disease dementia (PDD) - are disabling neurodegenerative conditions defined pathologically by the presence of intraneuronal α-synuclein rich aggregates (âLewy bodiesâ and âLewy neuritesâ). These disorders are characterized by a set of âcoreâ clinical features, namely cognitive fluctuations, visual hallucinations, motor parkinsonism, and most recently added, REM sleep behaviour disorder. These features are central to the diagnosis of Lewy bodies dementias (especially DLB) and discriminate them from other neurodegenerative disorders. Despite decades of research, the etiopathogenesis underlying Lewy body disorders is poorly understood. This accounts for the relative lack of objective biomarkers and both symptomatic and disease modifying therapies. The present thesis comprises a series of investigations that seeks to understand the phenomenology, pathophysiology, and clinical progression of Lewy body dementias through focus on each of the core clinical features. Systematic review and empiric studies are organized under the respective headings of cognitive fluctuations, visual hallucinations, REM sleep behaviour disorder, motor features, interrelationships, and clinical progression of the core features. Novel clinical and pathophysiological insights are obtained which have implications for the prediction and diagnosis of core features, the development of new objective biomarkers, and clinical endpoints of disease progression. From these studies, a shared pathophysiological basis for the core features is postulated and potential avenues for future directions are highlighted, focusing on replication and validation of new biomarkers and clinical measures, discovery of new biomarkers and mechanisms, and translation to prodromal and patient cohorts
Human Gait Analysis using Spatiotemporal Data Obtained from Gait Videos
Mit der Entwicklung von Deep-Learning-Techniken sind Deep-acNN-basierte Methoden
zum Standard fĂŒr Bildverarbeitungsaufgaben geworden, wie z. B. die Verfolgung menschlicher
Bewegungen und PosenschÀtzung, die Erkennung menschlicher AktivitÀten und
die Erkennung von Gesichtern. Deep-Learning-Techniken haben den Entwurf, die Implementierung
und den Einsatz komplexer und vielfÀltiger Anwendungen verbessert, die nun
in einer Vielzahl von Bereichen, einschlieĂlich der Biomedizintechnik, eingesetzt werden.
Die Anwendung von Computer-Vision-Techniken auf die medizinische Bild- und Videoanalyse
hat zu bemerkenswerten Ergebnissen bei der Erkennung von Ereignissen gefĂŒhrt. Die
eingebaute FĂ€higkeit von convolutional neural network (CNN), Merkmale aus komplexen
medizinischen Bildern zu extrahieren, hat in Verbindung mit der FĂ€higkeit von long short
term memory network (LSTM), die zeitlichen Informationen zwischen Ereignissen zu erhalten,
viele neue Horizonte fĂŒr die medizinische Forschung geschaffen. Der Gang ist einer der
kritischen physiologischen Bereiche, der viele Störungen im Zusammenhang mit Alterung
und Neurodegeneration widerspiegeln kann. Eine umfassende und genaue Ganganalyse
kann Einblicke in die physiologischen Bedingungen des Menschen geben. Bestehende
Ganganalyseverfahren erfordern eine spezielle Umgebung, komplexe medizinische GerÀte
und geschultes Personal fĂŒr die Erfassung der Gangdaten. Im Falle von tragbaren Systemen
kann ein solches System die kognitiven FĂ€higkeiten beeintrĂ€chtigen und fĂŒr die Patienten
unangenehm sein.
AuĂerdem wurde berichtet, dass die Patienten in der Regel versuchen, wĂ€hrend des
Labortests bessere Leistungen zu erbringen, was möglicherweise nicht ihrem tatsÀchlichen
Gang entspricht. Trotz technologischer Fortschritte stoĂen wir bei der Messung des menschlichen
Gehens in klinischen und Laborumgebungen nach wie vor an Grenzen. Der Einsatz
aktueller Ganganalyseverfahren ist nach wie vor teuer und zeitaufwÀndig und erschwert den
Zugang zu SpezialgerÀten und Fachwissen.
Daher ist es zwingend erforderlich, ĂŒber Methoden zu verfĂŒgen, die langfristige Daten
ĂŒber den Gesundheitszustand des Patienten liefern, ohne doppelte kognitive Aufgaben oder
Unannehmlichkeiten bei der Verwendung tragbarer Sensoren. In dieser Arbeit wird daher eine einfache, leicht zu implementierende und kostengĂŒnstige Methode zur Erfassung von
Gangdaten vorgeschlagen. Diese Methode basiert auf der Aufnahme von Gehvideos mit
einer Smartphone-Kamera in einer hÀuslichen Umgebung unter freien Bedingungen. Deep
neural network (NN) verarbeitet dann diese Videos, um die Gangereignisse zu extrahieren.
Die erkannten Ereignisse werden dann weiter verwendet, um verschiedene rÀumlich-zeitliche
Parameter des Gangs zu quantifizieren, die fĂŒr jedes Ganganalysesystem wichtig sind.
In dieser Arbeit wurden Gangvideos verwendet, die mit einer Smartphone-Kamera mit
geringer Auflösung auĂerhalb der Laborumgebung aufgenommen wurden. Viele Deep-
Learning-basierte NNs wurden implementiert, um die grundlegenden Gangereignisse wie
die FuĂposition in Bezug auf den Boden aus diesen Videos zu erkennen. In der ersten
Studie wurde die Architektur von AlexNet verwendet, um das Modell anhand von Gehvideos
und öffentlich verfĂŒgbaren DatensĂ€tzen von Grund auf zu trainieren. Mit diesem Modell
wurde eine Gesamtgenauigkeit von 74% erreicht. Im nÀchsten Schritt wurde jedoch die
LSTM-Schicht in dieselbe Architektur integriert. Die eingebaute FĂ€higkeit von LSTM in
Bezug auf die zeitliche Information fĂŒhrte zu einer verbesserten Vorhersage der Etiketten
fĂŒr die FuĂposition, und es wurde eine Genauigkeit von 91% erreicht. Allerdings gibt es
Schwierigkeiten bei der Vorhersage der richtigen Bezeichnungen in der letzten Phase des
Schwungs und der Standphase jedes FuĂes.
Im nÀchsten Schritt wird das Transfer-Lernen eingesetzt, um die Vorteile von bereits
trainierten tiefen NNs zu nutzen, indem vortrainierte Gewichte verwendet werden. Zwei
bekannte Modelle, inceptionresnetv2 (IRNV-2) und densenet201 (DN-201), wurden mit
ihren gelernten Gewichten fĂŒr das erneute Training des NN auf neuen Daten verwendet. Das
auf Transfer-Lernen basierende vortrainierte NN verbesserte die Vorhersage von Kennzeichnungen
fĂŒr verschiedene FuĂpositionen. Es reduzierte insbesondere die Schwankungen
in den Vorhersagen in der letzten Phase des Gangschwungs und der Standphase. Bei der
Vorhersage der Klassenbezeichnungen der Testdaten wurde eine Genauigkeit von 94% erreicht.
Da die Abweichung bei der Vorhersage des wahren Labels hauptsÀchlich ein Bild
betrug, konnte sie bei einer Bildrate von 30 Bildern pro Sekunde ignoriert werden.
Die vorhergesagten Markierungen wurden verwendet, um verschiedene rÀumlich-zeitliche
Parameter des Gangs zu extrahieren, die fĂŒr jedes Ganganalysesystem entscheidend sind.
Insgesamt wurden 12 Gangparameter quantifiziert und mit der durch Beobachtungsmethoden
gewonnenen Grundwahrheit verglichen. Die NN-basierten rÀumlich-zeitlichen Parameter
zeigten eine hohe Korrelation mit der Grundwahrheit, und in einigen FĂ€llen wurde eine sehr
hohe Korrelation erzielt. Die Ergebnisse belegen die NĂŒtzlichkeit der vorgeschlagenen Methode.
DerWert des Parameters ĂŒber die Zeit ergab eine Zeitreihe, eine langfristige Darstellung des Ganges. Diese Zeitreihe konnte mit verschiedenen mathematischen Methoden weiter
analysiert werden.
Als dritter Beitrag in dieser Dissertation wurden Verbesserungen an den bestehenden
mathematischen Methoden der Zeitreihenanalyse von zeitlichen Gangdaten vorgeschlagen.
Zu diesem Zweck werden zwei Verfeinerungen bestehender entropiebasierter Methoden
zur Analyse von Schrittintervall-Zeitreihen vorgeschlagen. Diese Verfeinerungen wurden
an Schrittintervall-Zeitseriendaten von normalen und neurodegenerativen Erkrankungen
validiert, die aus der öffentlich zugÀnglichen Datenbank PhysioNet heruntergeladen wurden.
Die Ergebnisse zeigten, dass die von uns vorgeschlagene Methode eine klare Trennung
zwischen gesunden und kranken Gruppen ermöglicht.
In Zukunft könnten fortschrittliche medizinische UnterstĂŒtzungssysteme, die kĂŒnstliche
Intelligenz nutzen und von den hier vorgestellten Methoden abgeleitet sind, Ărzte bei der
Diagnose und langfristigen Ăberwachung des Gangs von Patienten unterstĂŒtzen und so die
klinische Arbeitsbelastung verringern und die Patientensicherheit verbessern
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brainâmachine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brainâmachine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study
Mild traumatic brain injury (mTBI) is a significant public health concern, specially characterized by a complex pattern of abnormal neural activity and functional connectivity. It is often associated with a broad spectrum of short-term and long-term cognitive and behavioral symptoms including memory dysfunction, headache, and balance difficulties. Furthermore, there is evidence that oxidative stress significantly contributes to these symptoms and neurophysiological changes. The purpose of this study was to assess the effect of N-acetylcysteine (NAC) on brain function and chronic symptoms in mTBI patients. Fifty patients diagnosed with chronic mTBI participated in this study. They were categorized into two groups including controls (CN, nâ=â25), and patients receiving treatment with N-acetyl cysteine (NAC, nâ=â25). NAC group received 50âmg/kg intravenous (IV) medication once a day per week. In the rest of the week, they took one 500âmg NAC tablet twice per day. Each patient underwent rs-fMRI scanning at two timepoints including the baseline and 3âmonths later at follow-up, while the NAC group received a combination of oral and IV NAC over that time. Three rs-fMRI metrics were measured including fractional amplitude of low frequency fluctuations (fALFF), degree centrality (DC), and functional connectivity strength (FCS). Neuropsychological tests were also assessed at the same day of scanning for each patient. The alteration of rs-fMRI metrics and cognitive scores were measured over 3âmonths treatment with NAC. Then, the correlation analysis was executed to estimate the association of rs-fMRI measurements and cognitive performance over 3âmonths (pâ<â0.05). Two significant group-by-time effects demonstrated the changes of rs-fMRI metrics particularly in the regions located in the default mode network (DMN), sensorimotor network, and emotional circuits that were significantly correlated with cognitive function recovery over 3âmonths treatment with NAC (pâ<â0.05). NAC appears to modulate neural activity and functional connectivity in specific brain networks, and these changes could account for clinical improvement. This study confirmed the short-term therapeutic efficacy of NAC in chronic mTBI patients that may contribute to understanding of neurophysiological effects of NAC in mTBI. These findings encourage further research on long-term neurobehavioral assessment of NAC assisting development of therapeutic plans in mTBI
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brainâmachine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Trajectory Data Mining in Mouse Models of Stroke
Contains fulltext :
273912.pdf (Publisherâs version ) (Open Access)Radboud University, 04 oktober 2022Promotor : Kiliaan, A.J. Co-promotor : Wiesmann, M.167 p
Novel functional imaging approaches for investigating brain plasticity in multiple sclerosis and Parkinson\u2019s disease: from research to clinical applications
Neuronal plasticity, as the capacity of the brain to respond to external demands or to injury, has emerged as a crucial mechanism to preserve, at least in part, an adequate behavioral functioning after an injury and as the process underlying improvements in disability during rehabilitation. Brain plasticity can be detected with both structural and functional magnetic resonance imaging and more and more processing techniques have been developed to better capture the occurring changes and to better define the potential plasticity. Gait and balance are affected in patients with multiple sclerosis since the early stages of the disease with sensory deficits playing a major role in determining both balance and gait impairment. Moreover, gait disorders are one of the major causes of disability in patients with Parkinson\u2019s disease, in particular if suffering from freezing of gait. With this work we aimed at i) investigating the functional reorganization occurring in multiple sclerosis at both early and late stages of the disease, ii) characterizing the functional pattern underlying sensory impairment in patients with early multiple sclerosis and iii) verifying the neural correlates of action observation of gait in patients with Parkinson\u2019s disease. These different studies fit into a larger framework where neuroimaging techniques, in particular functional imaging, would support the clinicians in identifying tailored rehabilitation treatments and the patients who would better benefit from them. We found that patients with early multiple sclerosis showed a higher brain functional flexibility, expressed in terms of blood oxygen level dependent signal variability, which correlated to clinical disability, representing a possible compensatory mechanism. In patients with early multiple sclerosis we also observed subtle position sense deficits, not detectable with a standard neurological examination, and which affected still standing balance. Moreover, these deficits were related to a structural damage at the level of the corpus callosum and to functional activity patterns mainly involving the frontoparietal regions. On the contrary, patients with multiple sclerosis at the progressive stages presented with more subtle changes in the resting state functional connectivity which, nonetheless, were related to clinical disability. Lastly, the presence of freezing of gait in patients with Parkinson disease influenced the neural activation underpinning the action observation of walking. Altogether, these results offer an better insight into the pathophysiological mechanisms underlying disability in patients with multiple sclerosis and constitute a groundwork for the enhancement of rehabilitation protocols to improve gait and balance in both multiple sclerosis and Parkinson\u2019s disease, supporting the embracing of new strategies such as sensory integration and action observation training
Neuroimaging biomarkers associated with clinical dysfunction in Parkinson disease
Parkinson disease (PD) is the second most common neurodegenerative disorder in the world, directly affecting 2-3% of the population over the age of 65. People diagnosed with the disorder can experience motor, autonomic, cognitive, sensory and neuropsychiatric symptoms that can significantly impact quality of life. Uncertainty still exists about the pathophysiological mechanisms that underlie a range of clinical features of the disorder, linked to structural as well as functional brain changes.
This thesis thus aimed to uncover neuroimaging biomarkers associated with clinical dysfunction in PD. A 'hubs-and-spokes' neural circuit-based approach can contribute to this aim, by analysing the component elements and also the interconnections of important brain networks. This thesis focusses on structures within basal ganglia-thalamocortical neuronal circuits that are linked to a range functions impacted in the disorder, and that are vulnerable to the consequences of PD pathology. This thesis investigated neuronal 'hubs' by studying the morphology of the caudate nucleus, putamen, thalamus and neocortex. The caudate nucleus, putamen and thalamus are all vital subcortical 'hubs' that play important roles in a number of functional domains that are compromised in PD. The neocortex, on the other hand, has a range of 'hubs' spread across it, regions of the brain that are crucial for neuronal signalling and communication. The interconnections, or 'spokes', between these hubs and other brain regions were investigated using seed-based resting-state functional connectivity analyses. Finally, a morphological analysis was used to investigate possible structural changes to the corpus callosum, the major inter-hemispheric white matter tract of the brain, crucial to effective higher-order brain processes.
This thesis demonstrates that the caudate nucleus, putamen, thalamus, corpus callosum and neocortex are all atrophied in PD participants with dementia. PD participants also demonstrated a significant correlation between volumes of the caudate nuclei and general cognitive functioning and speed, while putamina volumes were correlated with general motor function. Cognitively unimpaired PD participants demonstrated minimal morphological alterations compared to control participants, however they demonstrated significant increases in functional connectivity of the caudate nucleus, putamen and thalamus with areas across the frontal lobe, and decreases in functional connectivity with parietal and cerebellar regions. PD participants with mild cognitive impairment and dementia show decreased functional connectivity of the thalamus with paracingulate and posterior cingulate cortices, respectively.
This thesis contributes a deeper understanding of the relationship between structures of basal ganglia-thalamocortical neuronal circuits, corpus callosal and neocortical morphology, and the clinical dysfunction associated with PD. This thesis suggests that functional connectivity changes are more common in early stages of the disorder, while morphological alterations are more pronounced in advanced disease stages
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