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Computer aided Methods for Diagnosis and Early Risk Assessment for Parkinson`s Disease Dementia

Titel Englisch Computer aided Methods for Diagnosis and Early Risk Assessment for Parkinson`s Disease Dementia
Gesuchsteller/in Roth Volker
Nummer 159682
Förderungsinstrument Interdisziplinäre Projekte
Forschungseinrichtung Fachbereich Informatik Departement Mathematik und Informatik Universität Basel
Hochschule Universität Basel – BS
Hauptdisziplin Informatik
Beginn/Ende 01.01.2016 - 31.12.2018
Bewilligter Betrag 629'994.00
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Alle Disziplinen (2)

Disziplin
Informatik
Nervenheilkunde, Psychiatrie

Keywords (9)

Biomedical Data Analysis; Machine Learning; neuropsychology; genetics; quantitative EEG; ultra high risk state; dementia; cognitive domains; Parkinson's disease

Lay Summary (Deutsch)

Lead
Neurodegenerative Krankheiten sind langsam fortschreitende Erkrankungen des Nervensystems und führen zu einem Verlust von Nervenzellen. Symptome sind typischerweise ein Abbau der geistigen Leistungsfähigkeit bis hin zur Demenz sowie – insbesondere beim Parkinson-Syndrom – eine zunehmende Beeinträchtigung der Motorik. Die Parkinson Erkrankung verläuft individuell sehr unterschiedlich, dementsprechend sind zuverlässige Vorhersagemethoden über die zu erwartende Krankheitsentwicklung bisher kaum möglich. Hier setzt dieses Forschungsprojekt an, mit dem Ziel solche Vorhersagen durch die gezielte Anwendung Computergestützter statistischer Verfahren zu ermöglichen und dadurch die Voraussetzung für individuelle Behandlungsmethoden zu schaffen.
Lay summary
Inhalt und Ziel des Forschungsprojekts

Ziel dieses Forschungsprojektes ist die Identifikation von genetischen, neurophysiologischen und neuropsychologischen Merkmalen zur verbesserten Diagnosestellung und zur Vorhersage des weiteren Krankheitsverlaufs. Ein wesentlicher Bestandteil unseres Forschungsplans besteht in der Erforschung des Zusammenhangs zwischen krankheitstypischen Ausprägungen von elektroenzephalographischen Signalen aus dem Gehirn (EEG) und genetischen Merkmalen der einzelnen Patienten. Mit Hilfe von lernfähigen Algorithmen zur Analyse grosser Datenbestände wollen wir objektive Modelle für die Beurteilung des Krankheitsrisikos und die Vorhersage der Krankeitsprogression entwickeln.   

Wissenschaftlicher und gesellschaftlicher Kontext des Forschungsprojekts

Unsere Forschung zielt nicht nur darauf ab, die bisherigen statistischen Vorhersagemodelle weiter zu entwickeln, um somit das Verständnis von komplexen neurodegenerativen Erkrankungen zu verbessern, sondern diese Modelle auch in klinische Anwendungen zu überführen, letzteres z.B. im Zusammenhang mit einer verbesserten Indikationsstellung für die sogenannte tiefe Hirnstimulation.
Da in unserer alternden Bevölkerung die Häufigkeit von neurodegenerativen Erkrankungen  stark zunimmt und der Schlüssel für eine erfolgreiche Behandlung eindeutig in der Früherkennung liegt, erwarten wir von diesem Forschungsprojekt eine erhebliche gesellschaftliche Ausstrahlung.  

Direktlink auf Lay Summary Letzte Aktualisierung: 20.05.2015

Verantw. Gesuchsteller/in und weitere Gesuchstellende

Mitarbeitende

Verbundene Projekte

Nummer Titel Start Förderungsinstrument
146178 Copula Distributions in Machine Learning: Models, Inference and Applications 01.07.2013 Projektförderung (Abt. I-III)
140338 Improved prediction and monitoring of central nervous system disorders with advanced neurophysiological and genetic assessment. 01.04.2012 SPUM

Abstract

Neurodegenerative disorders begin insidiously in midlife and are relentlessly progressive. Currently, there exists no established curative or protective treatment, and they constitute a major and increasing health problem and, in consequence, an economic burden in aging populations globally. Parkinson’s disease (PD), following Alzheimer’s disease (AD), is the second most common neurodegenerative disorder worldwide, estimated to occur in approximately 1% of population above 60 and at least in 3% in individuals above 80 years of age. In Switzerland, about 15’000 persons are diagnosed with PD. In addition to motor signs, which due to recent medical progress can be treated satisfactorily in most cases, non-motor symptoms and signs severely affect the well-being of patients. They include mood disorders, psychosis, cognitive decline, disorders of circadian rhythms, as well as vegetative and cardiovascular dysregulation. Neurodegeneration in PD progresses for years before clinical diagnosis is possible, at which time e.g. 80% of dopaminergic neurons in the Substantia nigra are lost already. Therefore, any clinical targeting disease modification, prognosis and personalized treatment including guiding the indication for deep brain stimulation (DBS) requires reliable and valid biomarkers.The main goal of this research project is the identification of a pertinent set of genetic and neurophysiological markers for diagnosis and early risk assessment of PD-dementia. Our approach has a distinct interdisciplinary basis, in that it fosters close collaborations between physicians, neuroscientists, psychiatrists, psychologists, computer scientists and statisticians. Based on current research findings we postulate that a combination of (1) quantitative electroencephalographic measures (QEEG, e.g. frequency power and connectivity patterns and network analysis), (2) genetic biomarkers (e.g. MAPT, COMT, GBA, APOE) and (3) neuropsychological assessment improves early recognition and monitoring of cognitive decline in PD. To test this hypothesis, this project proposes an interdisciplinary long-term study of patients diagnosed with PD without signs of dementia, among them a subgroup of patients undergoing DBS. The workup of the proposed study includes collection of clinical, neuropsychological, neurophysiological and genotyping data at the baseline, as well as at 3, 4 and 5 years follow-ups. Sophisticated statistical models that can deal with noisy measurements, missing values and heterogeneous data types will be used to extract the best combination of biomarkers and neuropsychological variables for diagnosis and prediction of prognosis of PD-dementia. Besides this clinical perspective, this project further aims at deciphering the unknown disease mechanisms in PD both on a genetic and neurophysiological level, with particular emphasis of the interplay of genetic markers and temporal changes in the functional connectivity of the brain over time. Neurodegenerative disorders begin insidiously in midlife and are relentlessly progressive. Currently, there exists no established curative or protective treatment, and they constitute a major and increasing health problem and, in consequence, an economic burden in aging populations globally. Parkinson’s disease (PD), following Alzheimer’s disease (AD), is the second most common neurodegenerative disorder worldwide, estimated to occur in approximately 1% of population above 60 and at least in 3% in individuals above 80 years of age. In Switzerland, about 15’000 persons are diagnosed with PD. In addition to motor signs, which due to recent medical progress can be treated satisfactorily in most cases, non-motor symptoms and signs severely affect the well-being of patients. They include mood disorders, psychosis, cognitive decline, disorders of circadian rhythms, as well as vegetative and cardiovascular dysregulation. Neurodegeneration in PD progresses for years before clinical diagnosis is possible, at which time e.g. 80% of dopaminergic neurons in the Substantia nigra are lost already. Therefore, any clinical targeting disease modification, prognosis and personalized treatment including guiding the indication for deep brain stimulation (DBS) requires reliable and valid biomarkers.The main goal of this research project is the identification of a pertinent set of genetic and neurophysiological markers for diagnosis and early risk assessment of PD-dementia. Our approach has a distinct interdisciplinary basis, in that it fosters close collaborations between physicians, neuroscientists, psychiatrists, psychologists, computer scientists and statisticians. Based on current research findings we postulate that a combination of (1) quantitative electroencephalographic measures (QEEG, e.g. frequency power and connectivity patterns and network analysis), (2) genetic biomarkers (e.g. MAPT, COMT, GBA, APOE) and (3) neuropsychological assessment improves early recognition and monitoring of cognitive decline in PD. To test this hypothesis, this project proposes an interdisciplinary long-term study of patients diagnosed with PD without signs of dementia, among them a subgroup of patients undergoing DBS. The workup of the proposed study includes collection of clinical, neuropsychological, neurophysiological and genotyping data at the baseline, as well as at 3, 4 and 5 years follow-ups. Sophisticated statistical models that can deal with noisy measurements, missing values and heterogeneous data types will be used to extract the best combination of biomarkers and neuropsychological variables for diagnosis and prediction of prognosis of PD-dementia. Besides this clinical perspective, this project further aims at deciphering the unknown disease mechanisms in PD both on a genetic and neurophysiological level, with particular emphasis of the interplay of genetic markers and temporal changes in the functional connectivity of the brain over time.
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