Comparing All Possible Combinations of Structural, Diffusion, and Resting State Functional MRI to Classify Alzheimer’s Disease

Introduction: Structural MRI has been widely used for Alzheimer’s disease (AD) diagnosis, but resting state functional MRI (RS-fMRI) and diffuse tensor imaging (DTI) can also be used for AD classification. Various studies compared several combinations of MRI types to assess whether combinations would increase classification accuracy, yet no study that researched these 3 MRI types compared all possible combinations for AD classification within one dataset. Methods: This thesis compares 7 models of MRI combinations from 1 dataset to classify AD patients (N = 76) and controls (N = 173). From the sMRI scans, 14 subcortical volumes, 68 regional cortical thickness values, and 48 regional grey matter density values were calculated, the RS-fMRI scans were used to calculate pairwise functional connectivity (FC) between 70 brain regions, and from the DTI scans fractional anisotropy (FA) values were calculated for 20 white matter regions. The Least Absolute Shrinkage and Selection Operator ...Show moreIntroduction: Structural MRI has been widely used for Alzheimer’s disease (AD) diagnosis, but resting state functional MRI (RS-fMRI) and diffuse tensor imaging (DTI) can also be used for AD classification. Various studies compared several combinations of MRI types to assess whether combinations would increase classification accuracy, yet no study that researched these 3 MRI types compared all possible combinations for AD classification within one dataset. Methods: This thesis compares 7 models of MRI combinations from 1 dataset to classify AD patients (N = 76) and controls (N = 173). From the sMRI scans, 14 subcortical volumes, 68 regional cortical thickness values, and 48 regional grey matter density values were calculated, the RS-fMRI scans were used to calculate pairwise functional connectivity (FC) between 70 brain regions, and from the DTI scans fractional anisotropy (FA) values were calculated for 20 white matter regions. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to set a penalty, with the goal to reduce overfitting by reducing the number of variables. With cross-validation, the models were trained and tested to estimate the classification accuracy of AD. Results: sMRI had an AUC of .931, sMRI + DTI resulted in an AUC of .929. The sMRI + RS-fMRI + DTI model had an AUC of .902. Conclusion: sMRI is the best single MRI type for classifying AD, and is better than any combination of MRI types. RS-fMRI and DTI were not helpful for increasing classification accuracy.Show less

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Comparing All Possible Combinations of Structural, Diffusion, and Resting State Functional MRI to Classify Alzheimer’s Disease