Abstract
Parkinson’s disease, the second most common neurodegenerative disease after Alzheimer’s disease, is the main cause of Parkinsonism, which is defined by a combination of resting tremor, rigidity, bradykinesia, and postural instability. Atypical Parkinsonism refers to other rare neurodegenerative disorders characterized by a poorer diagnosis, including progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal degeneration. Degeneration of the substantia nigra pars compacta is the pathological hallmark of neurodegenerative Parkinsonian disorders. These diseases present significant challenges regarding early diagnosis, differentiation, and understanding of pathophysiology.
Neuroimaging has profoundly changed the landscape of research and clinical management of Parkinsonism over the past twenty-five years. Magnetic resonance imaging (MRI) provides several in vivo quantitative biomarkers sensitive to different tissue properties that inform about the underlying pathological processes. Regional brain atrophy, detected using T1-weighted three-dimensional (3D) sequences, reflects neuronal loss. Diffusion anomalies indicate the presence of microstructural alterations in the tissues related to neuroinflammation, while iron-sensitive imaging detects iron deposits. Neuromelanin-sensitive MRI serves as a proxy for the degeneration of the substantia nigra. More recently, positron emission tomography (PET) has specifically allowed for detecting misfolded protein aggregates, such as Tau and alpha-synuclein, observed in these diseases, giving access to an ante-mortem pathological diagnosis.
In the first part of my talk, I will illustrate how neuroimaging biomarkers contribute to a better understanding of the pathophysiology of Parkinson’s disease and how they can be used to provide an early diagnosis, quantify disease severity and progression, and monitor treatment effects. The second part will highlight the contribution of neuroimaging to the differential diagnosis of Parkinsonian disorders, focusing on the recent applications of machine learning algorithms trained with MRI features.