Network biomarkers for the diagnosis and treatment of movement disorders

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Abstract

Functional brain networks provide a set of useful biomarkers for the assessment of movement disorders such as Parkinson's disease (PD). Spatial covariance analysis of imaging data from PD patients has led to the identification of abnormal metabolic patterns associated with the motor and cognitive features of this disease. Measurements of pattern expression have been used for diagnosis, assessment of rates of disease progression, and objective evaluation of the efficacy of therapeutic interventions. For instance, the recent identification of new disease-specific patterns for Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) has improved diagnostic accuracy in patients with parkinsonian syndromes. Further, disease-related networks have been found to be modulated by novel treatment strategies such as gene therapy. Finally, the application of network analysis to the study of inherited movement disorders such as Huntington's disease can aid in the assessment of disease-modifying therapies in pre-symptomatic gene mutation carriers.

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