Network-Guided Electric Field Optimization Improves tDCS Targeting Strategies
DOI: Pending DOI
Synopsis
Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that influences neuronal plasticity and has been applied in cognitive rehabilitation, including language recovery. However, conventional tDCS for language modulation involves a large parameter space and often does not account for underlying brain function, such as language network topology. Here, electric fields (EFs) induced by 25 previously used montages were systematically evaluated, and three novel montages were designed to optimally target the language network. The optimized two- (0.117 ± 0.032) and four-electrode (0.114 ± 0.023) configurations increased EF magnitude and improved focal concentration over language-network nodes compared to the eight-electrode configuration and most previously used montages.
Background
tDCS is applied through scalp electrodes to modulate neural activity and has been used in neurological and psychiatric disorders, including addiction, depression, and language impairments. Its effects are governed by the magnitude, direction, and spatial distribution of the induced EFs, which are shaped by individual anatomy and brain function, disorder characteristics, and electrode montage and stimulation parameters (e.g., number, size, location, and orientation of electrodes).¹,² However, prior literature indicates substantial variability in outcomes both within and between studies, likely due to heterogeneity in clinical populations and stimulation parameters. It has been estimated that fewer than half of participants respond to tDCS, suggesting limited clinical reproducibility for language modulation.³ Examining variations in electrode montages used in prior studies targeting the language network may help identify key sources of this variability. To address this, computational head modeling enables direct comparison of induced EFs across different montages.⁴ In addition, it allows for the assessment of how effectively these montages engage the underlying language network, as defined by functional MRI–based network organization, rather than focusing solely on the cortical regions beneath the stimulating electrodes.⁵
Methods
A systematic search was conducted through the end of 2024 to identify studies using tDCS for language modulation, yielding 25 distinct montages. Stimulation parameters, including electrode configuration and intensity, were extracted. Computational head models and EFs simulations were performed using SimNIBS 4.1, based on T1-weighted images from 30 healthy subjects selected from the Human Connectome Project. The functional distribution of the language network was identified through a meta-analytic synthesis of prior fMRI studies implemented in Neurosynth. EF strength within the language network was calculated for each subject and electrode configuration based on spatial overlap between the induced EF and the network mask. The auxiliary dipole method (ADM) was applied to design multi-electrode configurations that maximize EF delivery to the language network. Three optimized montages were generated with two, four, and eight electrodes. The two-electrode montage used 5 × 7 cm pads, while the four- and eight-electrode montages used 2 cm circular electrodes. Configurations were compared to assess how EF intensity varies with electrode number. Average EF within the network was used to index target engagement, and EF outside the network to assess focality. Differences were evaluated using repeated-measures ANOVA with Greenhouse–Geisser correction.
Results
The CP5–FP2 montage with large electrode pads was the most commonly employed configuration across the reviewed studies, with stimulation intensities ranging from 0.75 mA to 2 mA. Significant differences in EFs strength within the language network were observed across the 25 evaluated montages (F(2.91, 81.58) = 264.62, p < 0.001), indicating substantial variability in target engagement across configurations, which may contribute to the inconsistent outcomes reported in the literature. Among the conventional montages, the CP5–F10 configuration, delivered at 2 mA using 5 × 7 cm electrodes, produced the highest EF intensity within the language network and significantly outperformed all other montages (p < 0.05). However, this increased intensity was accompanied by less focal stimulation patterns. In contrast, the optimized montages demonstrated superior performance in both EF magnitude and spatial focality, reflecting an improved balance between intensity and target specificity. The two-electrode (0.117 ± 0.032) and four-electrode (0.114 ± 0.023) configurations yielded higher EF intensity and more concentrated stimulation over language-network nodes compared to the eight-electrode configuration and most previously used montages. These improvements were consistently observed across subjects, highlighting the advantage of network-guided optimization over conventional montage selection.
Discussion
These results highlight the importance of optimizing electrode montages for the language network by demonstrating substantial variability in EF distribution patterns across previously used configurations. Optimization of montages based on those identified as effective in this study may facilitate more consistent and focal stimulation across individuals, thereby improving targeting of the functional language network and ultimately supporting brain rehabilitation. Although network-based electrode montage design shows promise for maximizing EF strength within a specific network, several challenges remain. First, this study assessed stimulation effects only in the left hemisphere, potentially underestimating the effectiveness of bi-hemispheric montages that incorporate inhibitory stimulation of the right hemisphere. Second, simulations were conducted using healthy head models; as lesions can substantially alter EF distribution, these findings may not generalize to lesioned populations. An important next step is to evaluate montage performance using lesioned head models. Furthermore, integrating montages commonly used in stroke populations with those identified as effective in this study may provide a strong foundation for future clinical trials. Finally, as EF alone cannot determine behavioral outcomes, combining montage optimization with behavioral language assessments will be essential for translating these findings into clinical applications.
Disclosures
Nothing to disclose by any author.
References
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