Individual Prediction of Cognitive Decline in Mild Cognitive Impairment Using Support Vector Machine-Based Analysis of Diffusion Tensor Imaging Data

Abstract

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI

“…The individual SVM classification analysis is, in principle, identical to a previous study. 30 The individual classification was analyzed in the freely available WEKA software package (Version 3.6.1; http://www.cs.waikato. ac.nz/ml/weka/).…”

“…The second step consisted of the "actual" classification analyses for each comparison by using the SVM algorithm "sequential minimal optimization" 72 (distributed in the WEKA package) with a radial basis function kernel. 73 We chose the commonly used radial basis function kernel, which nonlinearly maps samples into a higher dimensional space, because this kernel provided slightly better classification accuracy in the present study and in a related previous study 30 than a linear kernel. Unlike linear kernels, radial basis function can handle the case when the relation between class labels and attributes is nonlinear.…”

“…Applying DTI analyses with voxelwise TBSS, 22 an increasing number of contributions described the damage of long interhemispheric and intrahemispheric white matter tracts with homogeneously oriented fibers (ie, genu or splenium of the corpus callosum, superior longitudinal fasciculus, cingulus) and, more rarely, in frontal, parietal, and temporal white matter in patients with MCI compared with healthy controls. [23][24][25][26][27][28][29][30][31] The biologic relevance of the description of MCI subtypes is still a challenging issue. In particular, it is unclear whether the neuropsychologic definition of these subtypes corresponds to distinct patterns of brain compromise.…”

“…We recently investigated FA patterns in prospectively documented patients with MCI compared with healthy controls and reported their use in the a priori identification of progressive MCI. 30 To explore the morphologic WM changes that characterize each subtype of MCI, we assessed all DTI parameters and developed models of automatic individual classification in a community-based series of cases of sd-aMCI, md-aMCI, and sdfMCI. First, a group-level analysis using TBSS, 22 an improved voxel-based technique with respect to spatial normalization, was performed to identify regions with altered white matter structure between groups.…”

“…Originating from machine learning, this technique provided individual risk scores for MCI conversion to AD on the basis of gray matter voxel-based morphometry 16,[42][43][44][45] and WM DTI data. 30 In contrast to these studies that focused on the discrimination between MCI versus controls, or stable versus progressive MCI, this work aims to explore the neuroradiologic background of the previously cited subgroups of MCI and to provide MR imaging tools for the individual classification of MCI subtypes.…”