Développement de nouveaux biomarqueurs électrophysiologiques à visée diagnostique et pronostique dans la maladie à corps de Lewy et la maladie d'Alzheimer

Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are the two most common causes of neurodegenerative dementias, leading to significant cognitive decline and functional impairment. AD and DLB share overlapping yet distinct clinical and pathophysiological features. Both involve disruptions in synaptic function and network connectivity, key mechanisms that electrophysiology is uniquely positioned to capture. Developing accurate and accessible biomarkers for early diagnosis and prognosis is critical, as AD and DLB require distinct therapeutic approaches. This thesis leverages non-invasive electrophysiology, including electroencephalography (EEG) and magnetoencephalography (MEG), to address complementary challenges in AD and DLB: predicting cognitive decline and differentiating diagnoses. In Study 1, we explored the potential of MEG to predict cognitive decline from MCI (mild cognitive impairment) to AD dementia in the BioFIND dataset. MCI is a critical stage preceding dementia, but not all MCI patients progress to AD. Developing non-invasive and affordable biomarkers to detect AD at a prodromal stage is essential, particularly given recent disease-modifying therapies. We analyzed resting-state MEG data from 117 MCI patients, 64 of whom progressed to AD dementia, while 53 remained cognitively stable, using continuous spectral analysis. Logistic regression models estimated the additive contribution of selected clinical, MEG, and MRI variables for AD progression risk. A high-dimensional classification model combined these modalities and variables. We showed that parieto-occipital MEG 16-38Hz spectral power was significantly reduced in the AD progression group. In logistic regression models, decreased MEG 16-38 Hz spectral power and reduced hippocampal volume/total grey matter ratio on MRI were independently linked to higher AD progression risk. The classification model confirmed the complementary information of MEG covariance (AUC=0.74, SD=0.13) and MRI cortical volumes (AUC=0.74, SD=0.15), with combined inputs improving performances (AUC=0.81, SD=0.12). These findings highlight the potential of spectral power and covariance as robust non-invasive biomarkers to predict AD progression, complementing cognitive scores and MRI. Building on this framework, Study 2 extended these methods to investigate whether distinct EEG signatures could differentiate DLB from AD using high-density EEG data from the European-DLB consortium. Diagnostic challenges in DLB arise from its variable clinical expression, overlapping symptoms with AD and the lack of specific validated biomarkers, despite promising biomarkers currently in development (e.g., EEG, MRI, and biology). In our study, increased 1-8Hz spectral power in parieto-occipital regions emerged as a robust biomarker of DLB, correlating with visual hallucinations and rapid eye movement sleep behavior disorder. Power envelope correlation was decreased between 12Hz and 62Hz in centro-parieto-occipital regions in DLB, highlighting disruptions in long-range connectivity. Combining both spectral metrics achieved high diagnostic performance (AUC=0.90). Our findings suggest that these spatio-spectral EEG patterns may serve as biomarkers for the diagnosis of DLB versus AD, with potential clinical applications for early detection and improved therapeutic management. Together, these studies demonstrate how electrophysiological biomarkers can address distinct yet interconnected challenges in AD and DLB. By applying and refining a shared methodological framework, this thesis provides a solid foundation for integrating EEG and MEG into diagnostic and prognostic strategies for neurodegenerative diseases, with implications for broader clinical applications.