Completed Brain & Nervous System Psychology & Behaviour

Defining and predicting variability in early Parkinson's disease using quantitative MRI

In plain English

AI plain-English summary

Parkinson’s disease is diagnosed only after half the nerve cells in a key brain region have already died, and no one can predict how quickly the condition will worsen once it appears. This variability is a major obstacle. About half of people diagnosed develop significant problems within four years, while others decline more slowly. The disease may begin up to 20 years before diagnosis, starting in a different part of the brain and causing subtle early signs such as loss of smell or sleep disturbances. But there is currently no way to identify who among those individuals has pre-clinical Parkinson’s, or to forecast their future progression. The researchers will use non-invasive brain scanning to map the structural differences that drive this variability. If successful, the work could allow clinicians to predict an individual’s disease course from their brain structure alone, and to diagnose Parkinson’s during its pre-clinical phase. That would make it possible to test treatments aimed at slowing the disease before major damage occurs, and to tailor existing therapies to a person’s present and future needs. The project is applied rather than purely fundamental, but it addresses a gap in basic understanding of how the disease unfolds in the brain.

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One of the biggest challenges facing developed countries in the 21st centaury is the increasingly aged population and rising life expectancy. In the UK nearly a quarter of the population will be aged 65 or over within 20 years. As a consequence, more people will be living with chronic, long-standing health problems. This poses significant challenges, particularly in healthcare, and will require a shift towards pre-emptive treatments designed to prevent or slow the progression of chronic diseases in order to cope with this changing demographic. Parkinson's disease is a common degenerative disorder of the brain that becomes more likely with age. A combination of muscle stiffness, tremor and slow movements alert the clinician to its presence. These symptoms begin to be detectable once half of the nerve cells within a brain region called the substantia nigra have already died. Once diagnosed, disease progression is highly variable. About half the people diagnosed will develop significant problems within four years. There is evidence that Parkinson's disease actually begins up to 20 years before it is diagnosed; it starts in a different part of the brain and progresses slowly causing more subtle problems. This intervening period is called "pre-clinical Parkinson's". During this period there are certain problems, such as loss of smell or certain sleep disturbances, which are more likely to be experienced by individuals with the condition. However, it is not currently possible to identify who amongst these individuals have pre-clinical Parkinson's or how quickly the disease will progress once diagnosed. This work seeks to use non-invasive brain-scanning techniques to understand why Parkinson's disease is so variable, develop ways to predict how quickly the disease will progress based on an individual's brain structure and diagnose the condition during the pre-clinical phase. By achieving these aims, strategies aimed at slowing the condition can be researched more accurately, and the disease can be better characterized within an individual, allowing current treatments to be tailored to their present and future needs.

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Researchers

Christian Lambert (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

MRC TS Award: Defining and predicting variability in early Parkinson's disease using quantitative MRI
Improving Diagnosis And Measurement Of Progression In Dementia: Longitudinal Clinical And MRI Studies
Understanding and predicting Parkinson's progression
Defining the microstructural basis of tremor in vivo to identify novel therapeutic targets
Quantification of vascular and neuronal pathology in dementia using PET and MRI

Original classification

Fellowship

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