Before revision:  To quantify the latter, we compute size-corrected metrics of the summed evidence per brain area, e.g. the 'evidence density ' or 'evidence gain' . Although these metrics produce very individual 'fingerprints' of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including hippocampus and amygdala. We conclude that LRP provides a powerful tool for assisting clinicians in finding evidence for AD (and potentially other diseases) in structural MRI data.
After revision:  To quantify the latter, we compute size-corrected metrics of the summed evidence per brain area, e.g. the 'evidence density ' or 'evidence gain' . Although these metrics produce very individual 'fingerprints' of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP provides a powerful tool for assisting clinicians in finding evidence for AD (and potentially other diseases) in structural MRI data.