A Multimodal Meta-Analysis Of Alzheimer’s Disease And Based MRI Image To Classify AD And MCI

Alzheimer’s disease (AD) is a progressive neurodegenerative disease, which is clinically characterized by the decline of emotional, memory and cognitive functions. It is also the most common cause of dementia, accounting for over 50-60% of all dementia with incidence rates doubling every 5 years after the age of 65. We know that function and structure in the human brain are closely related. In Alzheimer’s disease (AD) patients, brain imaging studies have identified robust changes in brain structure and brain activity, but no multimodal meta-analysis are available relating these two domains. In order to better understand the system level of neurobiology of AD, it is of great significance to explore the relationship between structure and function in elderly controls and patients with AD. In addition, with the development of imaging technology, research on disease becomes more and more in-depth. In the past, the point of research on MR images of AD patients is focused on the change of brain gray matter. However, the research on the damage of white matter has become the top topic recently. In order to explore the clinical diagnosis for Alzheimer’s disease and discover illness earlier, using an algorithm to classify the MR imaging of AD patients has become the trend of research scholars.Firstly, the PubMed database was searched to identify published articles about structural or functional imaging studies in patients with AD. The search was conducted from January 1995 to December 2013.A total of 41 articles met our requirements. The structural database comprised 688 AD patients matched with 984 controls, and the cognitive task-based functional cohort included 120 AD patients matched with 146 controls. The main findings of the present study were that patients with AD showed decreases in GMV and altered brain response in the left parahippocampal gyrus, hippocampus, right parahippocampal gyrus, and left precuneus. Both the subparts of MTL and left precuneus showed hypoactivation in AD patients. However, compared with healthy controls, greater activity appeared in the right parahippocampal gyrus of the AD patients. Secondly, from the ADNI database, download the MR brain imaging of AD patients, including healthy control group of 20 patients (10 males,10 females), MCI group of 20 patients (7 males,13 females), AD group of 20 patients (6 males,14 females), mean age was 74.5,76.4,78.5 years old, no significantly different, mean MMSE score was 29.3,23.7,19.4.Then extracting the data of the corpus callosum that we are interested, use the PCA algorithm which is combined with the LDA and SVM classifier to classify and identify the AD patients and patients with mild cognitive impairment. Furthermore, the statistical results which based on the corpus callosum of LDA and SVM classification were 88.89%,93% 06. This result is helpful for Clinical diagnosis of AD.