Joel Schaerer, Mehul Sampat, Florent Roche, Joonmi Oh, Luc Bracoud, Joyce Suhy and the Alzheimer's Disease Neuroimaging Initiative
Bioclinica, Newark, CA, USA and Lyon, France
Brain cortical thickness is gaining popularity as an endpoint in AD clinical trials. While a global approach provides valuable insights, a regional analysis may be more sensitive. The aim of this work is to determine if a regional approach is preferable and potentially which aggregation of regions is the most sensitive for prediction of conversion to AD.
Cortical thickness measurements were performed on the Baseline 3DT1-weighted sequences of 232 MCI subjects (116 who did not convert to AD within 36 months, 116 who did) from the ADNI-1 database (http://adni.loni.ucla.edu).
FreeSurfer v5.3 (http://surfer.nmr.mgh.harvard.edu/) [1, 2] was used for the determination of the white/gray boundary (in blue below), pial/CSF boundary (in green below) and subcortical parcellation (see Fig. 1). Cortical thickness was assessed by solving Laplace’s equation , building the set of paths between the inner and outer cortical surfaces and deriving the average thickness from them, for each subcortical region provided by FreeSurfer, as well as overall.
Performance of the region(s) of interest in discriminating between converters and non-converters was tested via ROC analyses using the Area Under the ROC Curve (AUC).
The Lasso method  was used in order to optimally combine regions. This consists in optimally weighting each region in order to best predict conversion to AD using a composite cortical thickness score. The function to minimize consists of a data term reflecting prediction accuracy which aims at rewarding good predictivity, and a model term which aims at controlling for the number of regions used and potentially penalizing models with too many regions. This model term is controlled by a regularization coefficient λ. A leave-one-out strategy was used in order to estimate the optimal λ value.
Using this optimal λ, the model was fitted over the whole database for determination of the corresponding list of regions and coefficients, thus defining an optimal model.
A threefold cross-validation strategy run 100 times with the corresponding λ was used in order to estimate the AUC for this model.
First looking at the average cortical thickness over the whole cortex, analyses of ROC curves yielded an AUC of 0.664 to distinguish between MCI converters and non-converters.
Moving to a more regional analysis, we then looked at each region individually. Of the available regions from the FreeSurfer atlas, the Right Inferior Temporal was the most sensitive (AUC = 0.698). Results for the next best other individual regions are shown in Table 1.
In order to look for the most optimal combination of regions for the prediction of conversion, the Lasso method determined an optimal λ value (see Figure 2). When applied to the whole database, this led to a composite region made of 15 subregions (see Table 2).
Cross-validation estimated the AUC for this model at 0.700, which was only marginally higher in sensitivity than the best individual region.
While a regional analysis of cortical thickness best predicts conversion to AD, compared to whole cortex analysis, looking for an optimal aggregation did not provide significantly better results. Nevertheless, such approach may be more robust to image quality issues, which is to be further confirmed on other datasets.
This methodology could be used for other applications, such as discriminating different patient subgroups using other endpoints. In particular, a similar analysis will be performed on longitudinal data in order to determine which single or composite region would provide the best effect size when measuring change in cortical thickness over time.
 Fischl et al., Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain, Neuron 2002
 Fischl et al., Automatically Parcellating the Human Cerebral Cortex, Cerebral Cortex 2004
 Jones et al., Three-Dimensional Mapping of Cortical Thickness Using Laplaces Equation, Human Brain Mapping 2000.
 Tibshirani et al., Regression Shrinkage and Selection via the Lasso, J. Royal. Statist. Soc B. 1996