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. 2009 Jun;46(2):394-410.
doi: 10.1016/j.neuroimage.2009.02.015. Epub 2009 Feb 21.

Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls

Yi-Yu Chou  1 , Natasha Leporé, Christina Avedissian, Sarah K Madsen, Neelroop Parikshak, Xue Hua, Leslie M Shaw, John Q Trojanowski, Michael W Weiner, Arthur W Toga, Paul M Thompson; Alzheimer's Disease Neuroimaging Initiative
Affiliations

Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls

Yi-Yu Chou et al. Neuroimage. 2009 Jun.

Abstract

We aimed to improve on the single-atlas ventricular segmentation method of (Carmichael, O.T., Thompson, P.M., Dutton, R.A., Lu, A., Lee, S.E., Lee, J.Y., Kuller, L.H., Lopez, O.L., Aizenstein, H.J., Meltzer, C.C., Liu, Y., Toga, A.W., Becker, J.T., 2006. Mapping ventricular changes related to dementia and mild cognitive impairment in a large community-based cohort. IEEE ISBI. 315-318) by using multi-atlas segmentation, which has been shown to lead to more accurate segmentations (Chou, Y., Leporé, N., de Zubicaray, G., Carmichael, O., Becker, J., Toga, A., Thompson, P., 2008. Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease, NeuroImage 40(2): 615-630); with this method, we calculated minimal numbers of subjects needed to detect correlations between clinical scores and ventricular maps. We also assessed correlations between emerging CSF biomarkers of Alzheimer's disease pathology and localizable deficits in the brain, in 80 AD, 80 mild cognitive impairment (MCI), and 80 healthy controls from the Alzheimer's Disease Neuroimaging Initiative. Six expertly segmented images and their embedded parametric mesh surfaces were fluidly registered to each brain; segmentations were averaged within subjects to reduce errors. Surface-based statistical maps revealed powerful correlations between surface morphology and 4 variables: (1) diagnosis, (2) depression severity, (3) cognitive function at baseline, and (4) future cognitive decline over the following year. Cognitive function was assessed using the mini-mental state exam (MMSE), global and sum-of-boxes clinical dementia rating (CDR) scores, at baseline and 1-year follow-up. Lower CSF Abeta(1-42) protein levels, a biomarker of AD pathology assessed in 138 of the 240 subjects, were correlated with lateral ventricular expansion. Using false discovery rate (FDR) methods, 40 and 120 subjects, respectively, were needed to discriminate AD and MCI from normal groups. 120 subjects were required to detect correlations between ventricular enlargement and MMSE, global CDR, sum-of-boxes CDR and clinical depression scores. Ventricular expansion maps correlate with pathological and cognitive measures in AD, and may be useful in future imaging-based clinical trials.

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Figures

Fig. 1
Fig. 1
CSF levels of Tau, Aβ1–42, pTau181p and ratios of Tau/Aβ1–42, pTau181p/Aβ1–42 in the three diagnostic groups. Error bars denote standard deviations. There were significant differences between groups (AD>MCI>normal) for the pTau181p and pTau181p/Aβ1–42 measures (p < 0.05). The differences between AD and MCI were not significant for CSF levels of Tau, Aβ1–42 and ratios of Tau/Aβ1–42.
Fig. 2
Fig. 2
Methods flowchart. (a) Multiple surface meshes are mapped into new subjects’ scans via fluid registration. N images (subsequently called atlases) were randomly selected from the sample and the lateral ventricles were manually traced and converted into surface mesh models. N new ventricular models were then produced by fluid registration of each image to a different atlas. The N surface meshes per subject were integrated by simple mesh averaging for each individual subject (see Chou et al., 2008, for details). (b) Medial curves (red) are extracted, and the radial distance of each ventricular boundary point to a medial curve may be interpreted as a local thickness. These distance measures are then averaged across subjects at each boundary point and plotted in color to produce a regional measure of radial expansion or contraction of the ventricles. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
Mean ventricular volumes in the control, MCI, and AD groups. As expected, there is greater ventricular expansion in MCI than controls, and greater expansion in AD than MCI and controls. There is also a well known ventricular asymmetry (left larger than right) in all groups (Grossman et al., 1990). Error bars denote standard deviations.
Fig. 4
Fig. 4
Significance maps for correlations between local ventricular enlargement and (1) diagnosis (MCI vs. normal, AD vs. normal and AD vs. MCI); (2) cognitive scores (MMSE, global CDR, and sum-of-boxes CDR); (3) ApoE genotype, (4) educational level and (5) clinical depression scores. Figs. 5 and 6 show the corrected significance and correlation coefficients of these maps respectively. One MCI subject was missing data on educational level, so they were excluded from the maps assessing that covariate.
Fig. 5
Fig. 5
Cumulative Distribution Functions (CDFs) of significance maps associating ventricular enlargement with diagnosis and clinical measures. Based on FDR q-values, the AD vs. control and MCI vs. control contrast are significant, as is the link between ventricular dilation and (1) MMSE, global CDR, and sum-of-boxes CDR scores, and (2) depression severity. This type of plot means that all covariates examined, apart from educational level and the AD-MCI comparison, were significantly associated with ventricular expansion. The suprathreshold area in the correlation maps was higher than would be expected by chance (red line) for all statistical thresholds ranging from 0 to 0.05 (and even as high as 0.7 in some cases). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6
Fig. 6
Correlation coefficients (r-maps) and proportion of variance explained (r2) for the 3 diagnostic comparisons, showing the strength of association between radial ventricular size and diagnosis, as well as with cognitive and clinical scores. The correlations in the MMSE map are negative (red colors) because a higher MMSE score is associated with less degeneration (opposite to all the other ones). It is of interest that the correlations with the MMSE scores, across the full sample, are higher than those with the CDR ratings, including the sum-of-boxes CDR scores. A correlation with an absolute value of around 0.2–0.3 for MMSE suggests that around 10% of the variation in the MMSE scores is accounted for by the ventricular enlargement. It is likely that atrophy (and the resulting ventricular enlargement) caused cognitive decline, hence changes in MMSE score. This would be regarded as a moderate to weak correlation, but is highly significant in a sample of this size. These maps are visually in very strong agreement with the corresponding p-maps, and so they are not shown for the other covariates. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 7
Fig. 7
Significance maps revealing the profile of correlations between local ventricular enlargement and CSF biomarkers, including levels of Aβ1–42, pTau181p, Tau, and ratios of Tau/Aβ1–42 and pTau/Aβ1–42. Fig. 8 shows the corrected significance of these maps.
Fig. 8
Fig. 8
Corrected significance for correlation between lateral ventricular expansion with CSF biomarkers by FDR analysis. Of all the plasma measures, levels of Aβ1–42 correlated best with the morphometric differences.
Fig. 9
Fig. 9
Significance maps correlate baseline ventricular shape with subsequent decline, over the following year, in 3 commonly used clinical scores.
Fig. 10
Fig. 10
FDR analysis of correlations with future cognitive changes. Correlations were significant between baseline ventricular enlargement and future 1-year changes in MMSE, global CDR and sum-of-boxes scores. The baseline measures are therefore good predictors of future cognitive decline, at least at the group level.
Fig. 11
Fig. 11
Comparison of parametric versus non-parametric tests. Here we show the p-values for the comparison of ventricular surface anatomy in AD versus normal subjects (left panel) based on a non-parametric test. This test permutes the assignment of subjects to groups and computes a non-parametric null distribution for the resulting Student’s t statistics, rather than assuming that the underlying distributions are Gaussian. As the cumulative plots of p-values show, parametric and non-parametric tests give almost identical results for both the cumulative p-value plot and the q-value derived from the plot. The q-value is the highest statistical threshold at which the expected false discovery rate is kept below the conventional 5% rate.
Fig. 12
Fig. 12
Predicting later cognitive decline from baseline measures of MMSE, ventricular volumes and the Aβ1–42 biomarker using least-squares regression models. MMSE_12Mo, MMSE_base, Vol and Aβ1–42 denote MMSE scores after a one-year follow-up interval, the baseline MMSE score, ventricular volumes and Aβ1–42 protein levels, respectively. The values shown in the legend represent the mean square of the deviations of the data from the predictive models, showing that prediction errors were successively reduced (but only by about 5%) as each more invasive measure was added to the predictive model.
Fig. 13
Fig. 13
ROC curves for regression models predicting clinical diagnosis (here treated as the gold standard) based on ventricular volume and various CSF-derived biomarkers. The line of no discrimination (based on random guessing) is diagonal, and all the ROC curves lie above it, suggesting that all measures have some discriminative power. The curves that lie above the others represent the best classifiers. The area under the ROC curve represents the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. In this sample, all of the CSF-derived biomarkers discriminate AD from normality better than ventricular volumes do. Ventricular volumes and CSF-derived biomarkers perform about equally well in distinguishing MCI subjects from controls, and, as expected, MCI is harder to differentiate from normality than AD is.
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