A whole-brain modeling framework for tDCS montage optimization in drug-resistant epilepsy

Objective

Transcranial direct current stimulation (tDCS) is a promising non-invasive therapy for drug-resistant epilepsy, but the montage optimization pipelines used to personalize it often rely on biophysical head models that capture only the induced electric field and ignore the network dynamics through which stimulation influences epileptogenic activity. We aimed to develop and evaluate a montage optimization framework that incorporates individualized network dynamics.

Approach

We introduce a framework based on a digital twin, or neurotwin , a personalized replica of a patient’s brain that couples a biophysical head model of the stimulation-induced field with a personalized whole-brain model (WBM) of seizure dynamics constrained by structural connectivity and intracranial recordings. We applied it to produce optimal montages in 12 patients with drug-resistant epilepsy, compared them with conventional biophysics-based optimization, and assessed the framework retrospectively against the clinical outcomes of six treated patients.

Main results

Because the neurotwin optimization suppresses simulated spread by construction, the informative result is the divergence between pipelines: the network-informed and field-based optimizations produced substantially different montages despite delivering comparable inhibitory fields at the epileptogenic zone, and montages with nearly identical field distributions could yield markedly different network outcomes within the model. A minimum-replacement-set analysis traced these differences to field changes at a few propagation-zone and off-target parcels rather than to the overall field. The parcels whose inhibition most reduced spread tended to occupy highly connected (hub) positions in the structural connectome, a robust but partial association indicating that network structure shapes, but does not fully determine, where stimulation is most effective. In the retrospective analysis, model-predicted spread reduction tracked clinical seizure-frequency change in the expected direction, though not significantly ( n = 6).

Significance

We hypothesize that accounting for individualized network dynamics may improve seizure control, a hypothesis now being tested prospectively ( NCT06334952 ).

Highlights

• A neurotwin couples a biophysical head model with a personalized whole-brain model (WBM) for tDCS optimization.

• neurotwin and field-based optimization yield divergent montages despite comparable EZ fields.

• Field differences at a few target and off-target parcels, not overall field magnitude, drive the simulated network outcomes.

• Parcels controlling simulated seizure spread tend to be structural network hubs (high nodal strength and centrality), an association that persists after adjusting for local dynamics.

• WBM-predicted spread reduction was directionally consistent with clinical seizure-frequency change (non-significant, n = 6).