The course of Parkinson’s disease
The symptoms of Parkinson’s disease are not caused directly by the loss of dopaminergic neurons, but by the accumulation of α-synuclein protein in different areas of the brain.
Other clinical signs include depression, genitourinary problems, pain, memory problems, object recognition problems and even cardiac problems.
There are also problems with walking and balance, which 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 best-known and most incapacitating symptom of Parkinson’s disease is “freezing”, an inability to initiate walking or a sudden stop in walking due to the loss of automaticity. The patient is then obliged to think about how to walk, and has the impression that his feet are nailed to the ground. This phenomenon occurs unexpectedly and is very often the cause of falls.
These late symptoms are hardly improved by dopaminergic treatments. Parkinson’s patients also suffer from speech disorders, with impairment of the timbre of speech, speech rhythm and loss of intonation. The swallowing problems also observed in these patients can be the cause of recurrent ‘false routes’.
At the Paris Brain Institute
In 2018, the living lab, a collaborative Paris Brain Institute-APHP structure, initiated a participative innovation approach targeting PARKINSON’S 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 Parkinson’s patient to facilitate the initiation of walking. The device is open-access and comes in kit form, enabling interaction between the patient and his carers during assembly according to the tutorial provided.
An innovative project combining biology and artificial intelligence: the Sémaphore project.
This collaborative project between two research teams at the Paris Brain Institute, Marie VIDAILHET and Stéphane LEHERICY’s team and Stanley DURRLEMAN and Olivier COLLIOT’s team, aims to collect and analyse 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 phases of the disease’s development in individuals at risk of developing the condition, and to monitor its progression. Ultimately, the researchers hope to develop a personalised model of disease progression so that therapy can be tailored to each patient profile.