Project description
Identifying the earliest motor fingerprint of Parkinson’s disease
Parkinson’s disease is often diagnosed late, long after its neurodegenerative processes have begun, limiting opportunities for effective intervention. Early detection of motor changes and underlying α-synuclein pathology is crucial to understanding disease onset and developing neuroprotective therapies. With this in mind, the ERC-funded Re-Start PD project tracks speech, facial expression, hand movements, gait, and sleep patterns for early clinical identification and uses advanced biological assays to detect pathological α-synuclein in identified individuals. Using machine learning, researchers will define the earliest universal motor fingerprint of Parkinson’s, enabling large-scale mobile screening. By mapping the initial spread of α-synuclein in the body and brain, the project aims to identify subtypes, accelerate biomarker development and provide the tools for timely therapeutic intervention.
Objective
Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, but current diagnostic criteria identify individuals late, impeding the development of neuroprotective therapies: There is the critical yet unmet need to identify the disease onset timely and to define the inception of pathology. Advancing recognition of early motor deterioration and coupling it with biological evidence of pathological α-synuclein will overcome present barriers and tackle the questions when and where PD starts.
Isolated REM sleep behavior disorder, confidently detectable by polysomnography, signals an early α-synucleinopathy. Overseeing a large cohort, I will integrate multimodal motor data on speech, facial expression, hand movements, gait, and long-term actigraphy within a machine learning framework, permitting a tablet-based application for highly mobile uses. This earliest PD motor-fingerprint, common to PD in general, will enable prospective large-scale screening from an unselected population. Motor phenotype detection will be paired with biological confirmation of pathological α-synuclein species in the cerebrospinal fluid using precise seed aggregation assays (SAA), capturing imminent PD earlier than ever before. This universal identification of the earliest phase of PD will allow to explore the initial spread of α-synuclein pathology, elucidating variations of pathology initiation: I will employ α-synuclein SAA on biosamples from the olfactory epithelium (brain-first) and the peripheral autonomic nervous system (body-first) to decode fundamental subtypes.
Re-Start PD pairs general PD characteristics - its primal motor phenotype and its biological α-synuclein signatures - outlining the earliest phase of PD prospectively and being readily transferable for broad application to kickstart timely therapeutic innovations. Complementary biobanking, brain-imaging, and follow-up of this unique cohort will foster further biomarker development and subtype determination.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences basic medicine pathology
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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(opens in new window) ERC-2024-COG
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53127 Bonn
Germany
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