Application of principal component analysis to study topography of hypoxic-ischemic brain injury

The regions at risk of ischemia following cardio-respiratory arrest have not been systematically analysed. This knowledge may be of use in determining the mechanism of ischemic injury at vulnerable sites. The aim of this study is to evaluate the use of principal component analysis to analyse the covariance patterns of hypoxic ischemic injury. The inclusion criteria were: age ≥ 17 years, cardio-respiratory arrest and coma on admission (2003-2011). Regions of ischemic injury were manually segmented on fluid attenuated inversion recovery (FLAIR) and diffusion weighted (DWI) sequences and linearly registered into common stereotaxic coordinate space. Topography of ischemic injury was assessed using principal component analysis (covariance data) and compared qualitatively against current method of topography analysis, the probabilistic method (frequency data). For the probabilistic data, subgroup analyses were performed using t-statistics while for the covariance data, subgroup analyses were performed by calculating the angle between the principle components. To account for bias due to a higher frequency of coma survivors in the studied group, we performed sensitivity analysis by sequentially removing coma survivors such that the final data set contained higher rate of death. Quantitative analysis between these methods could not be performed as they have different units of measurement. Forty one patients were included in this series (mean age ± SD=51.5 ± 18.9 years). In our probabilistic map, the highest frequency of ischemic injury on the DWI and FLAIR sequences was putamen (0.250), caudate (0.225), temporal lobes (0.175), occipital (0.150) and hippocampus (0.125). The first 6 principal components contained 77.7% of the variance of the data. The first component showed covariance between the deep grey matter nuclei and posterior cortical structures (contains 50.2% of the variance of the data). There was similarity in the findings of the subgroup analyses by the downtime whether it was assessed by t-statistics for probabilistic data or angle between the principal components for the covariance data. The sensitivity analysis showed that the pattern of ischemic injury did not change when the analysis was restricted to patients who died. In conclusion, PCA method has many advantages over probabilistic method. In the context of this dataset, PCA showed covariance between deep grey matter nuclei and the posterior cortical structures whereas the probabilistic map provided complementary information on the frequency of occurrence at these locations.