The pathological processes leading to Alzheimer’s (AD) and Parkinson’s (PD) diseases start decades before the onset of the typical clinical symptoms. However, current diagnosis comes quite late in the course of the disease, while evidence underlines the multiple benefits that would be associated with earlier diagnosis. Notably, early care can delay the onset of dementia, therefore reducing the overall prevalence, and current therapeutics targets require early treatment to prove their efficacy. An outstanding challenge for clinical neurosciences is therefore to provide reliable, non-invasive, affordable and easy-to-track biomarkers able to improve both the early detection and the monitoring of neurodegenerative diseases that can be applied at an individual patient level. It is well acknowledged that AD and PD display a progressive multifactorial disruption of cerebral networks, all along the course of the diseases, which is highly related to the clinical phenotype. In the search for those biomarkers, the connectome neuroimaging technology has represented a helpful technique to characterize the brain changes, more specifically structural and functional brain networks. However, prior studies have largely focused on the comparison between patients suffering from AD or PD versus healthy subjects. As a result, the relevance of the reported alterations in brain network may be limited due to a lack of specificity. Indeed, the extracted features that are sensitive to AD or PD may well reflect common neurodegenerative processes, therefore lacking specificity for the disease-related physiopathology at the individual level. A recent framework called Graph signal Processing (GSP) is promising to shed new light on the complex interplay between brain function and structure, by jointly analyzing functional activity and the underlying structural connectome. For the first time, in sharp contrast with the traditional approach emphasizing single-modality data and clinical assessment, our proposal will extend GSP to the development of innovative and more sensitive features of AD and PD able not only to identify modifications associated with each disease but also with each disease stage, based on the cerebral functional-structural coupling. In the NODAL project, we will develop a new multimodal and multi-stage approach using innovative machine learning methods, adapted for GSP-based features, to provide non-invasive, reliable and easy-to-track candidate biomarkers for AD and PD. We will first apply this approach on two large patients’ cohorts from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Parkinson’s Progression Markers Initiative (PPMI). Then, we will assess the effectiveness of candidate’s disease-specific biomarkers on a new dedicated local multimodal cohort including patients with and without cognitive impairment, at various stages of the diseases. Based on these new methodological developments, we hypothesize that the NODAL project may yield the estimation of specific and prognostic biomarkers of AD and PD, suitable for both diagnosis and differentiation among diseases’ stages, i.e. disease monitoring.