Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment

Abstract

Purpose: To use structural magnetic resonance (MR) images to identify a pattern of regional atrophy characteristic of mild Alzheimer disease (AD) and to investigate whether presence of this pattern prospectively can aid prediction of 1-year clinical decline and increased structural loss in mild cognitive impairment (MCI).

Materials and methods: The study was conducted with institutional review board approval and compliance with HIPAA regulations. Written informed consent was obtained from each participant. High-throughput volumetric segmentation and cortical surface reconstruction methods were applied to MR images from 84 subjects with mild AD, 175 with MCI, and 139 healthy control (HC) subjects. Stepwise linear discriminant analysis was used to identify regions that best can aid discrimination of HC subjects from subjects with AD. A classifier trained on data from HC subjects and those with AD was applied to data from subjects with MCI to determine whether presence of phenotypic AD atrophy at baseline was predictive of clinical decline and structural loss.

Results: Atrophy in mesial and lateral temporal, isthmus cingulate, and orbitofrontal areas aided discrimination of HC subjects from subjects with AD, with fully cross-validated sensitivity of 83% and specificity of 93%. Subjects with MCI who had phenotypic AD atrophy showed significantly greater 1-year clinical decline and structural loss than those who did not and were more likely to have progression to probable AD (annual progression rate of 29% for subjects with MCI who had AD atrophy vs 8% for those who did not).

Conclusion: Semiautomated, individually specific quantitative MR imaging methods can be used to identify a pattern of regional atrophy in MCI that is predictive of clinical decline. Such information may aid in prediction of patient prognosis and increase the efficiency of clinical trials.

Figure 1:

Pial representations of ROIs included as candidate input variables in the classifier. ROIs from both hemispheres were included; only left-hemisphere ROIs are shown here for convenience. Only labeled ROIs were included as candidate input variables in the classifier. In addition to ROIs visible on image, thickness of bank of superior temporal sulcus and volumes of hippocampus, amygdala, and lateral and inferior lateral ventricles were also included as candidate input variables. ROIs in yellow were selected as features in model. * = ROI in right hemisphere was included in model, C = cingulate, F = frontal gyrus, Occ = occipital gyrus, P = parietal gyrus, T = temporal gyrus.

Figure 2:

Graph shows separation of HC (NC) subjects and subjects with AD on basis of LDA score as function of age. Results of fully cross-validated discriminant model are shown. Discriminant model assumed equal prior group probabilities. Individuals were classified as HC subjects if their scores were above −0.10. This cutoff score was chosen on basis of receiver operating characteristic curve to maximize overall classification accuracy.

Figure 3:

Receiver operating characteristic curve for fully cross-validated discriminant model.

Figure 4:

Graph shows distribution of atrophy scores used to classify subjects with MCI. MCI atrophy score was derived from LDA trained on data from all HC subjects and subjects with AD. Discriminant model assumed equal prior group probabilities. Individuals were classified as having HC phenotype if their scores were above −0.33. Cutoff score was chosen to maximize overall accuracy of classifying HC subjects and subjects with AD on whom this model was trained. Average atrophy score for subjects with MCI was −0.50. Atrophy score is not normally distributed (Kolmogorov-Smirnov test = 0.73, df = 175, P = .025) but shows evidence of bimodal distribution.

Figure 5:

Average differences in thickness for subjects with AD and MCI relative to HC (NC). Top: HC subjects versus subjects with AD. Middle: HC subjects versus subjects with MCI who had AD imaging phenotype. Bottom: HC subjects versus subjects with MCI who had HC imaging phenotype. Right: Lateral views. Left: Mesial views. Blue areas indicate regions of thinning with disease. Scale reflects thickness ranging from −0.3-mm thickness (bright blue or cyan) to +0.3-mm thickness (yellow).

Figure 6:

Graph shows MMSE score at baseline and at 6- and 12-month follow-up as function of neuroimaging phenotype in participants with MCI; 1-year clinical follow-up data were available for 160 participants with MCI. MCI group with AD phenotype (MCI_AD) (n = 72) had significant decline over time; MCI group with HC phenotype (MCI_NC) (n = 88) did not.

Figure 7:

Percentage change in volume for HC subjects or thickness at 6- and 12-month follow-up sessions for subjects with MCI who had HC and AD imaging phenotypes; 1-year follow-up MR imaging data were available for 129 participants with MCI. Percentage changes are shown for eight ROIs used to compute atrophy score. Red bars indicate subjects with MCI who had AD phenotype (MCI_AD) (n = 66); significantly greater structural loss was observed in these subjects than in those who had HC phenotype (MCI_NC), signified by teal bars (n = 63), particularly in mesial and lateral middle temporal areas. Group differences in structural loss for superior temporal gyrus, isthmus cingulate, and frontal ROIs (bottom row) were not significant. LH = left hemisphere, RH = right hemisphere, STS = superior temporal sulcus.