Predictive model of spread of Parkinson's pathology using network diffusion

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PROJECT SUMMARY / ABSTRACT Project Summary Parkinson’s disease (PD) is a debilitating neurodegenerative disease characterized by progressive bradykinesia, rigidity, tremor and postural instability. The etiology, mechanism and progression of pathology and its relationship to clinical manifestations is not fully understood. These factors, coupled with its insidious onset, clinical heterogeneity, overlap with dementias, and the variability in speed and pattern of symptom progression, make a rigorous characterization and prognosis of PD difficult. Recent bench research on the trans-neuronal “prion-like” transmission of misfolded proteins is at last filling the gaps in the pathological context of PD, whereby misfolded alpha-synuclein protein can trigger misfolding in adjacent cells. If this spread mechanisms could be quantitatively modeled, it could enable accurate prediction of PD progression. This is the aim of our proposal. We will turn hitherto qualitative neuropathological insights into a rigorous “network-diffusion” model of disease spread. The model will be fed baseline in vivo MRI of PD patients, and will produce a deterministic and testable prediction for PD progression and conversion to dementia. By explicitly incorporating the brain’s connectivity network, our model will quantify the role of the brain’s anatomic connectivity network in disease transmission. We are targeting various applications, including diagnostic imaging biomarker, prognostic tool for assessing likely future patterns of disease and future neurocognitive status including likelihood of conversion to dementia. Relevance Parkinson’s Disease is a debilitating and common age-related degenerative disorder. The proposed network model will yield a validated deterministic and predictive model for PD progression, with applications in prediction of a patient’s future atrophy patterns, neurocognitive and motor scores.