The Evolution of Parkinson’s Disease
These parkinson symptoms are not directly caused by the loss of dopaminergic neurons but are due to the accumulation of α-synuclein protein in different brain regions.
These clinical signs may also include depressive syndrome, genitourinary disorders, pain, memory or object recognition disorders, and even cardiac disorders.
There are also walking and balance disorders that appear later in the course of the disease. These symptoms occur in 20-80% of patients 10-15 years after the onset of the disease.
The most well-known symptom of the highly disabling parkinson’s disease is “freezing”, an inability to initiate or abrupt cessation of the march related to the loss of automatism. The patient is then forced to think about how to walk, feeling like his feet are nailed to the ground. This happens unexpectedly and is very often the cause of falls.
These late-onset symptoms are little improved by dopaminergic treatments. Patients parkinsoniens also experience speech impairment with impaired timbre, speech rhythm and loss of intonation. Disorders of swallowing also seen in these patients can lead to repeated “false routes.”
At Paris Brain Institute
The living lab, a collaborative structure Paris Brain Institute-APHP, has launched in 2018 a participatory innovation approach targeted at PARKINSON disease.
This initiative resulted in the development of an “anti-freezing” device based on the principle of creating a virtual obstacle (a laser line) in front of the Parkinsonian patient to facilitate the initiation of walking. This device is in open access and has the characteristic of being in a kit, thus promoting interaction between the patient and his/her caregivers during the assembly according to the proposed tutorial.
An innovative project combining biology and artificial intelligence: the Semaphore project.
This collaborative project between two Paris Brain Institute research teams, Marie VIDAILHET and Stéphane LEHERICY and Stanley DURRLEMAN and Olivier COLLIOT, aims to collect and analyze together clinical, behavioural, genetic, metabolomic and brain imaging data from a large cohort of patients using a mathematical model. The model generated will help to identify specific biomarkers of the early stages of disease development in individuals at risk of developing the disease and to monitor its progression. Eventually, the researchers hope to develop a personalized model of disease progression in order to tailor the therapy to each patient profile.
