Imaging the Neural Network Connectivity on Patients with Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) referred to a decline of cognition in elder adults that are not of sufficient magnitude to meet the criteria for dementia. It is usually regarded as a transition state between patients of Alzheimer’s Disease and the age matched healthy adults. It is a heterogeneous syndrome which can cover a wide spectrum with different outcomes. This four year project proposed to investigate the difference in neural connectivity in two subtypes of patients with Mild Cognitive Impairment: amnestic and dysexecutive. The purposes are three. First, it is to provide an image based diagnosis such that early intervention is possible. Secondly it is to address the underlying changes in pathophysiology between two diseases. Thirdly in a longitudinal follow-up process, the conversion rate to Alzheimer’s disease will be compared. The subjects will be divided into 3 groups: 30 patients with amnestic MCI, 30 with dysexecutive MCI and 30 healthy age-matched normal controls. Comprehensive neuropsychological examinations will be performed after detailed clinical history and physical screening, including Mini-Mental Status Examination, Clinical Dementia Rating and the Cognitive Abilities Screening Instrument. Successful candidate will be examined by 3T MRI, including diffusion imaging, resting state fMRI and high resolution T1 weighted anatomical images. The changes in anatomical connectivity in the neural network will be assessed using diffusion imaging. The functional connectivity will be examined by resting state fMRI. Conventional morphometry measurement will be used as control. The difference between both subtypes will be evaluated. Efforts will be made to develop a consensus for differential diagnosis. During the 4 years follow-up process, the evolution in neural network integrity will be associated with clinical scores such as conversion rate to Alzheimer’s Disease, tissue atrophy rate and death rate. It is expected that changes in diffusion can be used an image based surrogate marker during the neurodegenerative process, which can be associated with that as in default mode network. Comprehensive interpretation of the difference in functional and effective connectivity can help to understand the underlying etiology and pathophysiology. The difference between the amnestic and dysexecutive types of MCI can contribute to an early intervention strategy which might ultimately lead to an effective treatment.

Project ID:PC10007-1185
External Project ID:NSC100-2314-B182-024