Biology subjects

Manor, B.

Publications and source records attributed to Manor, B..

3 recordsLinked to original sources

bioRxiv2025

Group optimization methods for dose planning in transcranial electrical current stimulation (tES)

ObjectiveOptimizing transcranial electrical stimulation (tES) parameters--including stimulator settings and electrode placements--using magnetic resonance imaging-derived head models is essential for achieving precise electric field (E-field) distributions, enhancing therapeutic efficacy, and reducing inter-individual variability. However, the dependence on individually personalized MRI-based models limits their scalability in some clinical and research contexts. To overcome this limitation, we propose a novel group-level optimization framework employing multiple representative head models. ApproachThe proposed optimization approach utilizes computational modeling based on multiple representative head models selected to minimize group-level error compared to baseline (no stimulation). This method effectively balances focal stimulation intensity within targeted brain regions while minimizing off-target effects. We evaluated our method through computational modeling and leave-one-out cross-validation using data from 54 subjects and analyzed the effectiveness, generalizability, and predictive utility of anatomical characteristics. Main resultsOur approach demonstrated that group optimization significantly outperformed protocols derived from standard templates or randomly selected individual models, notably reducing variability in outcomes across participants. Additionally, correlations between anatomical features (e.g., head perimeter and tissue volumes) and E-field parameters revealed predictive relationships. This insight enables further optimization improvements through the strategic selection of representative head models that are electro-anatomically similar to the target subjects. SignificanceThe proposed group optimization framework provides a scalable and robust alternative to personalized approaches, substantially enhancing the feasibility and accessibility of model-driven tES protocols in diverse clinical and research environments. Data Access StatementThe data that support the findings of this study are available from the corresponding author, R.S., upon reasonable request.

biophysics↗

bioRxiv2022

Towards the identification and optimization of the "dose-response" relationship of transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic tool in several neuropsychiatric disorders, but its mechanisms of action remain incompletely understood and its effectiveness is highly variable from subject to subject. Some of this inter-subject variance is thought to stem from individual differences in head anatomy, which result in different electric field distributions across individuals for the same stimulation settings. In a recent block-randomized, sham-controlled pilot study, 18 older adults with mild-to-moderate motor and cognitive impairments underwent a 2-week, 10-session intervention of 2 mA bipolar tDCS with the anode over the left dorsolateral prefrontal cortex (DLPFC) and cathode over the right supraorbital region (anodal F3 vs. FP2 using sponge electrodes in the 10-20 EEG system). tDCS, compared to sham stimulation, improved average cognitive-motor performance over a two-week follow-up. For a subset of 12 participants from this cohort who underwent structural brain MRIs at baseline, we derived tDCS-induced electric field properties using MRI-derived realistic, individualized finite element modeling. We hypothesized that the electric field component orthogonal (En) to the cortex over the left DLPFC would correlate with observed cognitive-motor improvements. Across the 12 subjects the average tDCS-induced electric field over the target area was highly variable with a standard deviation of the surface average En over the left DLPFC of 21% of the mean. For those subjects who were randomized to tDCS intervention (n=6), those who exhibited greater improvement in cognitive-motor performance were exposed to greater normal component of the tDCS electric field averaged over the left DLPFC (r95 % [isin] [-0.99,-0.59]). In contrast, averaged over the right DLPFC, En was a poor predictor of outcome (r95 % [isin] [-0.08,0.97]), as was the amplitude of the current injected (a widely adopted dosing parameter). In light of these results, we conducted a retrospective modeling experiment using personalized montage optimization algorithms to achieve a desired En amplitude over the left DLPFC. The optimized montages induced an average En on the left DLPFC comparable to the bipolar montages, but with half the variability (standard deviation was 11% compared to 21% with bipolar montages). In addition, these results suggest that optimized multichannel montages afford more focal targeting, with lower "spill-over" of electric field onto other cortical areas. These proof-of-concept results, though based upon a small sample size, indicate that assessment of the E-field distribution is critical when evaluating the effects of tDCS intervention. They also reinforce the importance of controlled cortical electric field dosing in tDCS and the potential advantages of optimized multichannel stimulation. Main findings: O_LIThe lDPFC averaged normal component of the electric field correlated with cognitive-motor improvements following tDCS intervention. C_LIO_LIIn older adults, head and brain anatomical differences resulted in high inter-subject variability of tDCS-induced electric fields. C_LIO_LItDCS montages optimized from modeling and delivered via multichannel electrode arrays can control variance and maximize electric field on target, while minimizing effects elsewhere. C_LI

neuroscience↗

bioRxiv2020

Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain

The segmentation of structural MRI data is an essential step for deriving geometrical information about brain tissues. One important application is in transcranial electrical stimulation (e.g., tDCS), a non-invasive neuromodulatory technique where head modeling is required to determine the electric field (E-field) generated in the cortex to predict and optimize its effects. Here we propose a deep learning-based model (StarNEt) to automatize white matter (WM) and gray matter (GM) segmentation and compare its performance with FreeSurfer, an established tool. Since good definition of sulci and gyri in the cortical surface is an important requirement for E-field calculation, StarNEt is specifically designed to output masks at a higher resolution than that of the original input T1w-MRI. StarNEt uses a residual network as the encoder (ResNet) and a fully convolutional neural network with U-net skip connections as the decoder to segment an MRI slice by slice. Slice vertical location is provided as an extra input. The model was trained on scans from 425 patients in the open-access ADNI+IXI datasets, and using FreeSurfer segmentation as ground truth. Model performance was evaluated using the Dice Coefficient (DC) in a separate subset (N=105) of ADNI+IXI and in two extra testing sets not involved in training. In addition, FreeSurfer and StarNEt were compared to manual segmentations of the MRBrainS18 dataset, also unseen by the model. To study performance in real use cases, first, we created electrical head models derived from the FreeSurfer and StarNEt segmentations and used them for montage optimization with a common target region using a standard algorithm (Stimweaver) and second, we used StarNEt to successfully segment the brains of minimally conscious state (MCS) patients having suffered from brain trauma, a scenario where FreeSurfer typically fails. Our results indicate that StarNEt matches FreeSurfer performance on the trained tasks while reducing computation time from several hours to a few seconds, and with the potential to evolve into an effective technique even when patients present large brain abnormalities.

bioinformatics↗