Biology subjects

de Haan, W.

Publications and source records attributed to de Haan, W..

3 recordsLinked to original sources

bioRxiv2025

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

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.

neuroscience↗

bioRxiv2023

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

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.

neuroscience↗

bioRxiv2022

A mechanistic model explains oscillatory slowing and neuronal hyperactivity in Alzheimer's disease

Alzheimers disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-{beta} and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-{beta} and tau affect neurons at the cellular scale. On a larger scale, Alzheimers patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency-slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-{beta} and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-{beta} and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation, and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency-slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-{beta} and tau protein.

neuroscience↗