In press, Motivation Science Author note OCS and AGR designed the studies; MF coordinated and supervised all data collection and initial data processing; SL and LM conducted most digit length measurements; DÖ, AO, and MS coded most PSE stories; OCS processed and integrated all data files and ran all statistical analyses; and OCS wrote the manuscript, with contributions by all co-authors during editing and revision phases. Partial results from study 2 were presented at the 47 th annual conference of the

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Background: Comprehensive knowledge bases as NeuroMMSig, a knowledge base of multimodal mechanistic signatures, catalog knowledge about Alzheimer's Disease (AD) and its underlying candidate mechanisms. Besides, digital atlases provide information about cytoarchitectonics and receptor densities, e.g. JuBrain at the EBRAINS platform