Connectome-guided personalization of optimal tDCS intervention selection in Alzheimers disease

Abstract

Transcranial direct current stimulation (tDCS) is being investigated as a clinical intervention in Alzheimers disease (AD) with the goal of reducing its neurophysiological effects (i.e. oscillatory slowing and loss of functional connectivity). However, progress is hampered by variable outcomes across studies, likely related to both methodological and individual differences. We recently described a virtual brain network simulation method for optimizing tDCS interventions and now propose a novel methodology for further personalizing this approach. While the general model of the AD brain we used for our previous study was based on an average human connectome and brain anatomy, we now created new personalized models for 10 biomarker-confirmed AD patients. We used individual, amplitude envelope correlation (AEC)-based connectivity matrices extracted from magnetoencephalography (MEG) scans and implemented individual structural MRI data for current flow modeling of the tDCS effects. We then assessed a set of previously established stimulation strategies based on their ability to restore relevant neurophysiological outcome parameters in each personalized model, while undergoing AD damage. Personalized tDCS strategies were able to delay neurophysiological deterioration, but in dissimilar ways compared to our previous results. While the general model favored posterior anodal stimulation targeting the precuneus region, the personalized models favored frontal anodal stimulation targeting the dorsolateral prefrontal cortex (dlPFC) region in 90% of the cases. This may be explained by higher connectivity levels of frontal regions in the personalized connectivity matrices, as anodal stimulation of highly connected regions produced more beneficial effects. In this methodological study we propose several ways to improve personalized computational tDCS stimulation prediction modeling. We conclude that connectome-guided personalization of tDCS effects lead to different strategies with potentially better intervention outcomes. For external validation of this model-guided tDCS approach, personalized and general model predictions are currently being tested and compared in a clinical tDCS-MEG trial in AD patients.