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dc.contributor.advisorNasseroleslami, Bahman
dc.contributor.advisorHardiman, Orla
dc.contributor.authorMetzger, Marjorie
dc.date.accessioned2024-03-06T13:16:51Z
dc.date.available2024-03-06T13:16:51Z
dc.date.issued2024en
dc.date.submitted2024
dc.identifier.citationMetzger, Marjorie, Resting-state electroencephalographic biomarkers for tracking cognitive network dysfunction in amyotrophiclateral sclerosis, Trinity College Dublin, School of Medicine, Clinical Medicine, 2024en
dc.identifier.otherYen
dc.identifier.urihttp://hdl.handle.net/2262/106620
dc.descriptionAPPROVEDen
dc.description.abstractAmyotrophic lateral sclerosis (ALS) stands as a multifaceted neurological disorder that primarily affects both upper and lower motor neurons. However, research in neuroimaging, neuropsychology and neurobiology has shown that ALS involves not only motor impairment but also exhibits broader dysfunction in sensory, cognitive and behavioural domains. Up to 50% of individuals with ALS experience cognitive or behavioural symptoms. In this context, high-density electroencephalography (EEG) was used to measure and quantify cognitive network dysfunctions in individuals with ALS as the disease progressed. The study involved four main aspects: 1. Longitudinal Assessment: Examining EEG measures longitudinally to understand how ALS-related network patterns change over time and their association with cognitive phenotypes. 2. Cluster Analysis: Identifying stable subgroups among ALS patients using EEG spectral power trajectories and linking these to clinical outcomes. 3. Microstate Analysis: Investigating transient brain states to uncover disruptions in neural activity associated with cognitive and behavioural impairments. 4. Exploration of brain network dynamics: Studying frequency-specific patterns of power and connectivity in resting-state EEG to detect specific network impairments. First, this project revealed significant changes in brain activity over time, particularly in the frontotemporal area, where a decrease in slower neural oscillatory activity (theta-band) and an increase in faster oscillatory activity (gamma-band) was observed. Different groups of individuals with ALS (subgrouped based on their cognitive and behavioural profiles using neuropsychological assessments) exhibited distinct patterns of brainwave changes linked to cognitive decline, behavioural symptoms, and worsening motor function. The study found an association between region-specific functional connectivity and the survival of individuals with ALS. Second, ALS subgroups were identified based on stable clustering of longitudinal patterns of brain activity. Importantly, these subgroups had varying survival rates and rates of functional decline. This further highlights the association between distinct longitudinal patterns of neural activity and clinical presentation. It emphasises the importance of studying longitudinal changes in brain activity in specific subgroups to better understand the variations in disease development, survival prospects, and functional decline. Third, this project employed EEG microstate analysis to examine the temporal dynamics of brain network activity, focusing on the global configuration of the electrical field at specific moments in time. In contrast to EEG functional connectivity, which investigates interactions between brain regions, EEG microstate analyses uncover spatial and temporal patterns in the overall electrical field. This approach showed that properties of the transient, recurring, quasi-stable brain states, called `microstates', can provide insights for showing how cognitive abilities are affected in ALS. There were differences in microstate properties between ALS and healthy-control groups, indicating alterations in specific brain networks related to sensory perception and attention. Last, this project analysed patterns of source-reconstructed resting-state EEG signals (estimated region-specific signals). A method that combines Hidden Markov Model and multivariate autoregression was applied to find disruptions in the timing of neural activity and functional connectivity within specific parts of the brain. By identifying distinct patterns in resting brain activity, we can pinpoint reliable signs of ALS-related alterations in specific brain networks. In particular, people with ALS showed changes in the dynamics of a specific brain state linked to the posterior default mode network. In summary, this thesis focuses on using resting-state EEG to detect cross-sectional and longitudinal spatio-temporal neural activity patterns associated with cognitive-behavioural impairments in ALS. These measures can provide valuable insights into the multifaceted nature of ALS beyond its motor symptoms. Integrating electrophysiological measurements into clinical practice for ALS has the potential to enhance patient care, predict individual prognoses, improve clinical trial design and objectively assess drug effectiveness.en
dc.language.isoenen
dc.publisherTrinity College Dublin. School of Medicine. Discipline of Clinical Medicineen
dc.rightsYen
dc.subjectALSen
dc.subjectsource reconstructionen
dc.subjectfrontotemporalen
dc.subjectspectral poweren
dc.subjectcognitionen
dc.subjectbehaviouren
dc.subjectco-modulationen
dc.subjectsynchronyen
dc.subjectclusteringen
dc.subjectmicrostateen
dc.subjectfunctional connectivityen
dc.subjectresting-state networken
dc.subjectbrain stateen
dc.subjectEEGen
dc.subjectlongitudinalen
dc.subjectneurodegenerationen
dc.titleResting-state electroencephalographic biomarkers for tracking cognitive network dysfunction in amyotrophiclateral sclerosisen
dc.typeThesisen
dc.contributor.sponsorThierry Latran foundationen
dc.type.supercollectionthesis_dissertationsen
dc.type.supercollectionrefereed_publicationsen
dc.type.qualificationlevelDoctoralen
dc.identifier.peoplefinderurlhttps://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:METZGERMen
dc.identifier.rssinternalid263263en
dc.rights.ecaccessrightsopenAccess


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