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bioRxiv · 10.1101/2023.05.09.539945

Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer's disease

Luppi, J.·Stam, C.·Scheltens, P.·de Haan, W.
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Abstract

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with potential for counteracting disrupted brain network activity in Alzheimers disease (AD) to improve cognition. However, the results of tDCS studies in AD have been variable due to different methodological choices such as electrode placement. To address this, a virtual brain network model of AD was used to explore tDCS optimization. We compared a large, representative set of virtual tDCS intervention setups, to identify the theoretically optimized tDCS electrode positions for restoring functional network features disrupted in AD. We simulated 20 tDCS setups using a computational dynamic network model of 78 neural masses coupled according to human structural topology. AD network damage was simulated using an activity-dependent degeneration algorithm. Current flow modeling was used to estimate tDCS-targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density (alpha bands, 8-10 Hz and 10-13 Hz), total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC). Virtual tDCS performance varied, with optimized strategies improving all outcome measures, while others caused further deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of stimulated regions and tDCS success was not observed. This modeling-informed approach can guide and perhaps accelerate tDCS therapy development and enhance our understanding of tDCS effects. Follow-up studies will compare the general predictions to personalized virtual models and validate them with tDCS-magnetoencephalography (MEG) in a clinical AD patient cohort. Author summaryPatient-friendly and non-invasive forms of brain stimulation are being investigated as alternative or additional treatments to medication in Alzheimers disease, but there is still no general agreement on how to best perform them. Transcranial direct current stimulation (tDCS) is one of these techniques, in which a low electrical current is passed between electrodes placed on the scalp in order to regulate brain activity. In this study, we used a computer model of the Alzheimers disease brain to simulate the effects that tDCS would have on brain activity, with the aim of predicting where the electrodes should be placed to see the most beneficial changes in brain activity. We compared 20 different electrode placements, and discovered placing the positive electrode at the back of the head resulted in the best improvement. For example, we saw a general increase in the speed of brain activity and increase in connectivity between brain regions, both of which are reduced in Alzheimers disease. We believe that our approach can help guide non-invasive brain stimulation treatments in Alzheimers disease and potentially other disorders, while helping keep the burden on patients to a minimum.

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Luppi, J., Stam, C., Scheltens, P., de Haan, W.. 2023-05-09. Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer's disease. https://doi.org/10.1101/2023.05.09.539945

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