Introduction: Alzheimer’s disease is an irreversible brain disorder characterized by distortion of memory and other mental functions. Although, several psychometric tests are available for diagnosis of Alzheimer’s, there is a great concern about the validity of these tests at recognizing the early onset of the disease. Currently, brain magnetic resonance imaging is not commonly utilized in the diagnosis of Alzheimer’s, because researchers are still puzzled by the association of brain regions with the disease status and its progress. Moreover, MRI data tend to be of high dimensional nature requiring advanced statistical methods to accurately analyze them. In the past decade, the application of Least Absolute Shrinkage and Selection Operator (LASSO) has become increasingly popular in the analysis of high dimensional data. With LASSO, only a small number of the regression coefficients are believed to have a non-zero value, and therefore allowed to enter the model; other coefficients are while others are shrunk to zero.
Aim: Determine the non-zero regression coefficients in models predicting patients’ classification (Normal, mild cognitive impairment (MCI), or Alzheimer’s) using both non-ordinal and ordinal LASSO.
Methods: Pre-processed high dimensional MRI data of the Alzheimer’s Disease Neuroimaging Initiative was analyzed. Predictors of the following model were differentiated: Alzheimer’s vs. normal, Alzheimer’s vs. normal and MCI, Alzheimer’s and MCI vs. Normal. Cross-validation followed by ordinal LASSO was executed on these same sets of models.
Results: Results were inconclusive. Two brain regions, frontal lobe and putamen, appeared more frequently in the models than any other region. Non-ordinal multinomial models performed better than ordinal multinomial models with higher accuracy, sensitivity, and specificity rates. It was determined that majority of the models were best suited to predict MCI status than the other two statues.
Discussion: In future research, the other stages of the disease, different statistical analysis methods, such as elastic net, and larger samples sizes should be explored when using brain MRI for Alzheimer’s disease classification.
| Identifer | oai:union.ndltd.org:GEORGIA/oai:scholarworks.gsu.edu:iph_theses-1593 |
| Date | 08 August 2017 |
| Creators | Salah, Zainab |
| Publisher | ScholarWorks @ Georgia State University |
| Source Sets | Georgia State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Public Health Theses |
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