P2-013 Superior Alzheimer's diagnosis by combination of partial-volume effects correction and multivariate analysis

Christian Habeck*, Ajna Borogovac§, Truman Brown§, Iris Asllani§, Yaakov Stern**Taub Institute and §Hatch MRI Research Center, Department of Radiology, Columbia University Medical Center, NY, NY 10032

Results

Conclusion and References- Both net CBF and GM-CBF images achieve very good diagnostic

classification with multivariate analysis

- More robust classification achieved by multivariate analysis than univariate analysis

- Future plans: Prospective application on MCI population for comparison of prognostic power

1)Asllani et al., JCBF & M, Oct24 (2007)

2)Donahue et al., MRM X (2006)  

3)Asllani et al. (Poster IC-P3-166, ICAD 2008, Chicago).

Introduction and Methods Multivariate analysis on Cerebral Blood Flow (CBF) measured by Arterial Spin

Labeling (ASL) has shown promise as an early marker of Alzheimer’s disease1 (AD). Further improvement on this preliminary result should be possible by adjusting for partial-volume effects in the ASL data. Since net total CBF at any brain voxel has different contributions from different tissue types according to the voxel’s tissue composition, it follows that (1) structural brain changes can impact net total CBF, even though CBF per unit volume of tissue type is unchanged. The converse might be true as well, i.e. (2) CBF per unit volume of tissue type can change, even when the overall structural composition of a voxel stays constant. Both effects could be utilized in the diagnosis of AD with respect to Gray Matter (GM): during the course of AD progressive atrophy results in drastic lowering of GM density in the brain, which will manifest as lower total net CBF (1). We want to investigate whether relative CBF, i.e. CBF per unit volume of GM is also affected (2). This may be particularly important in the prodromal disease stages, prior to any obvious structural changes and Gray Matter atrophy in the brain: if Gray Matter CBF is already lowered in this very early stage, this might provide valuable diagnostic and prognostic information.

1) Data acquisition:  ASL perfusion images were acquired on 10 patients with moderate-to-severe probable AD and 30 age-matched healthy controls. Pre-processing comprised of various stages described previously1, with the additional step of partial-volume correction inserted prior to the spatial normalization to the MNI template3. 2) Partial-volume effects correction: a method of computing pure-tissue contributions to regional CBF 2,3 was used prior to our group analysis to isolate the gray-matter contributions to CBF in particular. 3) Group-level data analysis: Principal components analysis (PCA) and linear discriminant analysis was performed on all scans to derive a pattern that distinguished patients from controls (AD=1/Controls=0), similar to our previous report1. Bootstrap resampling was performed to produce Z-maps to identify the most robust areas in the covariance pattern. This was done for both total CBF scans as well as pure Gray-Matter CBF scans. Several robustness computations were performed for both scan types for comparison purposes (described in Results section).

We derived a discriminant pattern in both net CBF scans as well as pure GM scans. Classification accuracy was very high for both with almost identical results.

The plots above show subject expression for the first Principal Components of both pure GM and net CBF analyses. Diagnostic classification was very good, the ROC area was 0.96 for both patterns (left panel) and the expression values highly correlated (right).

Pure GM vs net CBF. The panels above show significant negative voxel loadings (uncorrected p<0.001) indicating decreased CBF in the AD patients, superimposed on a probabilistic Gray-Matter mask. (No positive loadings were obtained.) On the left side, significant voxel loadings that were common to both covariance patterns are displayed. Large areas are participating in the covariance patterns, manifesting a large whole-brain flow difference between both groups. On the right side, an XOR plot shows voxels with significant loadings that are unique to each pattern (BLUE= pure GM pattern, RED= net CBF). The results are complex and do not permit easy characterization; however, one general observation is true: the unique voxels of the GM pattern are generally more anterior than the unique net-CBF pattern voxels, this is particularly obvious for ventromedial prefrontal areas that are only showing up in the GM pattern. On the other hand, there are no parietal areas in Brodmann Areas 7, 40 that are unique to the GM pattern, only the net-CBF pattern.

Next, we want to investigate further whether the pure GM flow can supply useful and robust information beyond net CBF. Adjusting for partial volume effects should pay off particularly in regions with mixed tissue types, since Gray Matter atrophy will have attenuated effects in the regions due to the noise of other tissue types, making these regions less useful as a diagnostic marker. To simulate this scenario realistically, we repeated our analysis on various voxel subsets: using the probabilistic Gray-Matter mask supplied by SPM5, we picked all voxels whose Gray Matter probabilities were less than a range of thresholds chosen from 1% to 99%. We expected that for high threshold both GM and net CBF pattern would give similar performance, while diverging for low thresholds.

This, of course, will only work out if the CBF per unit GM volume is really also decreased. The figure to the right shows the classification performance of net CBF and GM patterns derived from restricted set of mixed-tissue voxels: as anticipated, the worse the admixture of non-GM tissue, the more useful the partial-volume correction becomes in ensuring good diagnostic classification.

Multivariate versus univariate analysis. For the last part of the poster we would like to illustrate the differences between multivariate and univariate analysis with a simple split-half analysis. (The univariate results are discussed at length in detail in the adjacent poster IC-P3-166.) For the split-half analysis, both AD patients and healthy controls are divided into bipartitions (derivation and replication samples) 10,000 times. Each time, our analysis discussed before is performed in the derivation sample, i.e. a covariance pattern is derived with maximal discriminatory power, while the replicability is checked in the replication sample. T-statistics and ROC-areas are computed in both derivation and replication sample. For the univariate analog of this analysis, we just picked the voxel with the highest T-contrast in the derivation sample. T-statistic as well ROC-area were also recorded in both derivation and replication samples for this disease marker.

The panels above show the results of the split half analysis. On the left side, ROC-areas and T-statistics are plotted, for both markers (‘UV’ and ‘MV’) and both samples (‘DER’ and ‘REP’). One can appreciate the better replicability of the multivariate results for both statistics. Most striking are the high T-values achieved for the univariate marker in the derivation sample, which are not followed up with equal goodness in the replication sample. On the right side, we plotted a subset of 1,000 T-statistics from both derivation and replication samples against one another to convey the sample-to-sample variability in our performance metrics. Interestingly, for the multivariate marker (blue) there is a negative correlation between performance in both samples: better performance in the derivation sample implies worse performance in the replication sample. We see this as an example of bias-variance trade-off: better group discrimination in the derivation sample might be achieved through overfitting noise, which hampers the performance in the replication sample. For the univariate marker (red), this relationship is much weaker, but still present. The general cautionary tale illustrated here is that strong measures of association do not guarantee good replication of results.