Statistical Approach towards Parkinson’s Disease Progression

Parkinson's disease (PD) is a neurodegenerative disorder of the central nervous system and is the second most commonly found movement disorder in elders. It is mainly caused by progressive loss of dopaminergic neurons in the substantia nigra (SN) region of the midbrain. PD has affected millions of people worldwide. Since the exact understanding of brain functioning seems to be mammoth task, scores of research has been carried out to identify the biomarkersresponsible for Parkinson's disease. Now a day's several models are being used to understand the behaviour of the complex systems like brain. In this regard, one of the most important tools is “modelling”. Mathematical modelling not only helps in better understanding of the disease, but also in early diagnosis thus minimizing the trialand error attempts. This type of quantitative analysis can enhance understanding of the disorders, behaviour and symptoms. Further, it is cost-effective and efficient as it correlates different biomarkers that help neurologists in taking better decisions. However, we must note that its practical implementation requires human intelligence to authenticate the predicted results.