Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging

Abstract

Purpose: To use large-scale network (LSN) analysis to classify subjects with Alzheimer disease (AD), those with amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) subjects.

Materials and methods: The study was conducted with institutional review board approval and was in compliance with HIPAA regulations. Written informed consent was obtained from each participant. Resting-state functional magnetic resonance (MR) imaging was used to acquire the voxelwise time series in 55 subjects with clinically diagnosed AD (n = 20), aMCI (n =15), and normal cognitive function (n = 20). The brains were divided into 116 regions of interest (ROIs). The Pearson product moment correlation coefficients of pairwise ROIs were used to classify these subjects. Error estimation of the classifications was performed with the leave-one-out cross-validation method. Linear regression analysis was performed to analyze the relationship between changes in network connectivity strengths and behavioral scores.

Results: The area under the receiver operating characteristic curve (AUC) yielded 87% classification power, 85% sensitivity, and 80% specificity between the AD group and the non-AD group (subjects with aMCI and CN subjects) in the first-step classification. For differentiation between subjects with aMCI and CN subjects, AUC was 95%; sensitivity, 93%; and specificity, 90%. The decreased network indexes were significantly correlated with the Mini-Mental State Examination score in all tested subjects. Similarly, changes in network indexes significantly correlated with Rey Auditory Verbal Leaning Test delayed recall scores in subjects with aMCI and CN subjects.

Conclusion: LSN analysis revealed that interconnectivity patterns of brain regions can be used to classify subjects with AD, those with aMCI, and CN subjects. In addition, the altered connectivity networks were significantly correlated with the results of cognitive tests.

© RSNA, 2011.

Figures

Figure 1a:

Images obtained with LSN classification. (a) W matrix obtained when AD and non-AD groups were compared. In the W matrix, the upper right 6670 z values are symmetrical to the lower left 6670 values along the diagonal line, similar to the r matrices in Figure E2 (online). Color bar shows z values. (b) Distribution of z values in the W matrix between AD and non-AD groups. In AD and non-AD classification, a threshold (z < −1.96, uncorrected for multiple comparison) was empirically used. (c) Distribution of z values in the W matrix between aMCI and CN groups. In aMCI and CN classification, four decreased connections and two increased connections were empirically used to enable better classification. (d) Result from LOO procedure during aMCI versus CN classification. In the LOO procedure, one subject with aMCI was left out; therefore, 14 subjects with aMCI and 20 CN subjects were used for training. Thus, classification criteria were determined by using all subjects except the one to be evaluated. As a result, Fisher linear discriminant function (diagonal line) was used to identify the subject with aMCI who was left out in the beginning. Ellipses represent 50% and 90% probability containment for the aMCI and CN groups, respectively. B.G. 1 Tha = basal ganglia and thalamus, DCI = decreased connectivity index, ICI = increased connectivity index.

Figure 1b:

Images obtained with LSN classification. (a) W matrix obtained when AD and non-AD groups were compared. In the W matrix, the upper right 6670 z values are symmetrical to the lower left 6670 values along the diagonal line, similar to the r matrices in Figure E2 (online). Color bar shows z values. (b) Distribution of z values in the W matrix between AD and non-AD groups. In AD and non-AD classification, a threshold (z < −1.96, uncorrected for multiple comparison) was empirically used. (c) Distribution of z values in the W matrix between aMCI and CN groups. In aMCI and CN classification, four decreased connections and two increased connections were empirically used to enable better classification. (d) Result from LOO procedure during aMCI versus CN classification. In the LOO procedure, one subject with aMCI was left out; therefore, 14 subjects with aMCI and 20 CN subjects were used for training. Thus, classification criteria were determined by using all subjects except the one to be evaluated. As a result, Fisher linear discriminant function (diagonal line) was used to identify the subject with aMCI who was left out in the beginning. Ellipses represent 50% and 90% probability containment for the aMCI and CN groups, respectively. B.G. 1 Tha = basal ganglia and thalamus, DCI = decreased connectivity index, ICI = increased connectivity index.

Figure 1c:

Images obtained with LSN classification. (a) W matrix obtained when AD and non-AD groups were compared. In the W matrix, the upper right 6670 z values are symmetrical to the lower left 6670 values along the diagonal line, similar to the r matrices in Figure E2 (online). Color bar shows z values. (b) Distribution of z values in the W matrix between AD and non-AD groups. In AD and non-AD classification, a threshold (z < −1.96, uncorrected for multiple comparison) was empirically used. (c) Distribution of z values in the W matrix between aMCI and CN groups. In aMCI and CN classification, four decreased connections and two increased connections were empirically used to enable better classification. (d) Result from LOO procedure during aMCI versus CN classification. In the LOO procedure, one subject with aMCI was left out; therefore, 14 subjects with aMCI and 20 CN subjects were used for training. Thus, classification criteria were determined by using all subjects except the one to be evaluated. As a result, Fisher linear discriminant function (diagonal line) was used to identify the subject with aMCI who was left out in the beginning. Ellipses represent 50% and 90% probability containment for the aMCI and CN groups, respectively. B.G. 1 Tha = basal ganglia and thalamus, DCI = decreased connectivity index, ICI = increased connectivity index.

Figure 1d:

Images obtained with LSN classification. (a) W matrix obtained when AD and non-AD groups were compared. In the W matrix, the upper right 6670 z values are symmetrical to the lower left 6670 values along the diagonal line, similar to the r matrices in Figure E2 (online). Color bar shows z values. (b) Distribution of z values in the W matrix between AD and non-AD groups. In AD and non-AD classification, a threshold (z < −1.96, uncorrected for multiple comparison) was empirically used. (c) Distribution of z values in the W matrix between aMCI and CN groups. In aMCI and CN classification, four decreased connections and two increased connections were empirically used to enable better classification. (d) Result from LOO procedure during aMCI versus CN classification. In the LOO procedure, one subject with aMCI was left out; therefore, 14 subjects with aMCI and 20 CN subjects were used for training. Thus, classification criteria were determined by using all subjects except the one to be evaluated. As a result, Fisher linear discriminant function (diagonal line) was used to identify the subject with aMCI who was left out in the beginning. Ellipses represent 50% and 90% probability containment for the aMCI and CN groups, respectively. B.G. 1 Tha = basal ganglia and thalamus, DCI = decreased connectivity index, ICI = increased connectivity index.

Figure 2a:

ROC curve and behavioral importance of altered network connectivity strengths. (a) ROC curve shows that use of the LSN method resulted in accurate classification of subjects with AD and those without AD (area under the ROC curve, 0.87). (b) Relationship between Mini-Mental State Examination (MMSE) scores and decreased connectivity index (DCI). MMSE = 30/[1 + exp(−8.1·DCI − 2.7)]. F = 61.26; df = 1, 53; P < .001.

Figure 2b:

ROC curve and behavioral importance of altered network connectivity strengths. (a) ROC curve shows that use of the LSN method resulted in accurate classification of subjects with AD and those without AD (area under the ROC curve, 0.87). (b) Relationship between Mini-Mental State Examination (MMSE) scores and decreased connectivity index (DCI). MMSE = 30/[1 + exp(−8.1·DCI − 2.7)]. F = 61.26; df = 1, 53; P < .001.

Figure 3a:

ROC curve and behavioral importance of altered network connectivity strengths. (a) ROC curve shows that use of the LSN method resulted in accurate classification of subjects with aMCI and CN subjects (area under the ROC curve, 0.95). (b) Relationship between Rey Auditory Verbal Leaning Test (RAVLT) score and altered connectivity indexes. DCI = decreased connectivity index, ICI = increased connectivity index. (RAVLT = 15/[1 + exp(−1.2·DCI + 3.2·ICI−1.4)]; F = 6.70; df = 2, 32; P < .004.

Figure 3b:

ROC curve and behavioral importance of altered network connectivity strengths. (a) ROC curve shows that use of the LSN method resulted in accurate classification of subjects with aMCI and CN subjects (area under the ROC curve, 0.95). (b) Relationship between Rey Auditory Verbal Leaning Test (RAVLT) score and altered connectivity indexes. DCI = decreased connectivity index, ICI = increased connectivity index. (RAVLT = 15/[1 + exp(−1.2·DCI + 3.2·ICI−1.4)]; F = 6.70; df = 2, 32; P < .004.