Abstract
Machine learning approaches have been increasingly used in the neuroimaging field for the design of computer-aided diagnosis systems. In this paper, we focus on the ability of these methods to provide interpretable information about the brain regions that are the most informative about the disease or condition of interest. In particular, we investigate the benefit of group-based, instead of voxel-based, analyses in the context of Random Forests. Assuming a prior division of the voxels into non overlapping groups (defined by an atlas), we propose several procedures to derive group importances from individual voxel importances derived from Random Forests models. We then adapt several permutation schemes to turn group importance scores into more interpretable statistical scores that allow to determine the truly relevant groups in the importance rankings. The good behaviour of these methods is first assessed on artificial datasets. Then, they are applied on our own dataset of FDG-PET scans to identify the brain regions involved in the prognosis of Alzheimer's disease.
Highlights
Alzheimer’s disease is currently the neurodegenerative disease the most often encountered in aged population and, as the world’s population ages, the prevalence of the disease is expected to increase (Brookmeyer et al, 2007)
We first evaluate the quality of the group rankings obtained with the three aggregation functions: the average, the sum, and tFhigeumreasx1im, 2u,mre.spAeUctPivReslywwitihthtKhe=th1reaendfuKnc=tio√nsp,airne shown in both cases for an increasing number R of relevant groups and an increasing number of samples
The max function is competitive in large sample settings but it is clearly inferior with the smallest sample size
Summary
Alzheimer’s disease is currently the neurodegenerative disease the most often encountered in aged population and, as the world’s population ages, the prevalence of the disease is expected to increase (Brookmeyer et al, 2007). As current clinical trials testing amyloid-modifying therapies in demented individuals failed to show any effect, it is believed that interventions must start before the onset of clinical symptoms (Sperling et al, 2014). Before a definitive AD diagnosis has been established clinically with neuropsychological tests, individuals go through a stage of “mild cognitive impairment” (MCI) during which predicting the outcome, stabilisation or worsening of the cognitive deficit, is difficult. Many studies have focused on this prodromal stage of Alzheimer’s disease (Petersen et al, 1999, 2001)
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