Biology subjects

Drumm, D. A.

Publications and source records attributed to Drumm, D. A..

5 recordsLinked to original sources

bioRxiv2026

Self-powered electronics-free Wearable Disposable Electrotherapy (WDE) platform for accelerated wound healing

Electrical stimulation accelerates wound repair by modulating endogenous bioelectric signals that regulate inflammation, angiogenesis, extracellular matrix remodeling, and cellular responses within the wound microenvironment. However, clinical translation has been hindered by cumbersome devices with procedures that disrupt standard wound-care workflows, direct electrode contact with the wound bed, and/or limited stimulation output. Wearable Disposable Electrotherapy (WDE) integrates an electronics-free printed electrochemical architecture into an mm-thick patch that looks and is applied like a conventional bandage. The device is self- powered and delivers a single electrotherapy dose simply by application to the skin. Device dose-control (electrochemical performance) and efficacy were evaluated in a full-thickness excisional wound model in rats, compared with a sham device and a conventional Constant Current (CC) stimulator. WDE or control treatments were applied daily from day 1 through day 13, with endpoint evaluation on day 14. WDE delivered electrical stimulation comparable to CC while reducing the time required to achieve 50% wound closure by 2.08 days ([~]29%) relative to sham treatment. Histological and immunofluorescence analyses at day 14 demonstrated enhanced tissue remodeling, including increased collagen deposition ([~]25%), tissue cellularity ([~]73%), myofibroblast-associated SMA expression ([~]2.6-fold), angiogenesis-associated CD31 expression ([~]2.0-fold), and increased expression of both M2- (CD206, [~]2.0-fold) and M1- associated (iNOS, [~]1.7-fold) markers compared with sham. A novel cellular-resolution dosimetry model, leveraging charge-based boundary element method accelerated with the fast multipole method (BEM-FMM), provides a biophysical framework linking electrical stimulation with wound microenvironment and tissue repair mechanisms. Together, these findings establish WDE as a practical bioelectric wound dressing that accelerates wound healing and tissue remodeling, with the simplicity and scalability of disposable bandages.

bioengineering↗

bioRxiv2026

Charge Based Boundary Element Method with Residual Driven Adaptive Mesh Refinement for High Resolution Electrical Simulation Modeling

Accurate transcranial electrical stimulation (TES), electroconvulsive therapy (ECT), and electroencephalography (EEG) forward modeling requires resolving numerical singularities in the charge density near electrodes and tissue interfaces. We present an adaptive mesh refinement (AMR) strategy for the charge based boundary element method (BEM) accelerated by the fast multiple method (BEM-FMM) including electrode and interface singularities. We derive a new error estimator which considers both local and nonlocal contributions of the single-layer potential operator and construct a refinement criterion based on the difference in charge solution across AMR iterations. We evaluate this approach on a 5-layer sphere model and on multiple subject-specific head models derived from the 7-tissue SimNIBS (headreco) and 40-tissue Sim4Life (head40) segmentations, using both voltage-controlled and current-controlled electrode formulations. Through convergence analysis on the white matter and deep hippocampal targets, we find electric fields with relative residual errors below 0.1% and 1% for SimNIBS and Sim4Life models, respectively. Our results indicate that the residual based AMR applied to BEM-FMM leads to numerically stable TES and EEG forward solutions in realistic head models.

neuroscience↗

bioRxiv2025

High-Resolution EEG Source Reconstruction from PCA-Corrected BEM-FMM Reciprocal Basis Funcions: A Study with Visual Evoked Potentials from Intermittent Photic Stimulation

Modern automated human head segmentations can generate high-resolution computational meshes involving many non-nested tissues. However, most source reconstruction software is limited to 3 -4 nested layers of low resolution and a small number of dipolar sources[~] 10, 000. Recently, we introduced modeling techniques for source reconstruction of magnetoencephalographic (MEG) signals using the reciprocal approach and the boundary element fast multipole method (BEM-FMM). The technique of BEM-FMM can process both nested and non-nested models with as many as 4 million surface elements. In this paper, we present an analogue technique for source reconstruction of electroencephalographic (EEG) signals based on cortical global basis functions. The present work uses Helmholtz reciprocity to relate the reciprocally-generated lead-field matrices to their direct counterpart, while resolving the issue of possible biases toward the reference electrode. Our methodology is tested with experimental EEG data collected from a cohort of 12, young and healthy, volunteers subjected to intermittent photic stimulation (IPS). Our novel high-resolution source reconstruction models can have impact on mental health screening as well as brain-computer inter-faces.

neuroscience↗

bioRxiv2025

Improved Source Localization of Auditory Evoked Fields using Reciprocal BEM-FMM

The precise localization of auditory evoked fields (AEFs) from magnetoencephalography (MEG) data is very important for the functional understanding of the auditory cortex in medicine and cognitive neuroscience. The numerical solution of the field equations in the human head using the boundary element method (BEM) is a powerful tool for achieving this. We hypothesized that the spatial resolution of the BEM is crucial for the achievable accuracy. However, in classical BEM (as implemented, e.g., in MNE-Python), very high resolutions are impractical due to the associated prohibitive computational effort. In contrast, our recently introduced reciprocal boundary element fast multipole method (reciprocal BEM-FMM) allows for hitherto unprecedented spatial resolution. In this work, we apply our reciprocal BEM-FMM technique for source estimation to localize AEFs, and we compare our results with the source estimates produced using a 3-layer BEM model (standard BEM) via MNE-Python. We first validate our methodology through comparison of source estimates of simulated N1m components of AEFs using a receiver operating characteristic (ROC) measure. While we obtain ROC measures of about 80% for the standard BEM, reciprocal BEM-FMM reaches about 90%, a significant statistical improvement. We then apply this methodology to analyze the source estimates of experimental data obtained from a cohort of 7 participants subjected to binaural auditory stimulation. Using a dispersion measurement to quantify the focality of localized sources, we find improvements upwards of 30% using reciprocal BEM-FMM over the standard BEM. Analyses from both simulated and experimental data show localization of AEFs using high-resolution reciprocal BEM-FMM is significantly better in terms of accuracy and focality than those estimates of the low-resolution standard BEM. We therefore recommend using the high-resolution reciprocal BEM-FMM to utilize high spatial anatomical precision for the modeling of neural activity.

neuroscience↗

bioRxiv2025

High-Definition MEG Source Estimation using the Reciprocal Boundary Element Fast Multipole Method

Magnetoencephalographic (MEG) source estimation relies on the computation of the gain (lead-field) matrix, which embodies the linear relationship between the amplitudes of the sources and the recorded signals. However, with a realistic forward model, the calculation of the gain matrix in a "direct" fashion is a computationally expensive task because the number of dipolar sources in standard MEG pipelines is often limited to [~]10,000. We propose a fast approach based on the reciprocal relationship between MEG and transcranial magnetic stimulation (TMS). This approach couples naturally with the charge-based boundary element fast multipole method (BEM-FMM), which allows us to efficiently generate gain matrices for high-resolution multi-layer non-nested meshes involving source spaces of up to a [~]1 million dipoles. We evaluate our approach by performing MEG source reconstruction against simulated data (at varying noise levels) obtained from the direct computation of MEG readings from 2000 different dipole positions over the cortical surface of 5 healthy subjects. Additionally, we test our methods with real MEG data from evoked somatosensory fields by right-hand median nerve stimulation in these same 5 subjects. We compare our experimental source reconstruction results against the standard MNE-Python source reconstruction pipeline.

neuroscience↗