Biology subjects

Hasan, N. I.

Publications and source records attributed to Hasan, N. I..

3 recordsLinked to original sources

bioRxiv2025

Modeling Pyramidal Neurons Using Bidomain BEM and Hierarchical Matrix Approximation

Electromagnetic brain stimulation uses electrodes or coils to induce electric fields (E-fields) in the brain and affect its activity. Our understanding of the precise effects of the device-induced E-fields on neural activity is limited. In this paper, we present a novel bidomain boundary integral equation-based method that enables the modeling of fully coupled E-fields from both neurons and stimulation devices. This boundary element approach is accelerated using fast direct solvers to allow for the analysis of realistic scenarios. We present examples, indicating the ability of our solver to analyze rat L2/3 pyramidal neurons derived from the Blue Brain Project. A comprehensive analysis shows that this method can be easily extended to model a group of neurons that were previously computationally intractable.

neuroscience↗

bioRxiv2023

Real-Time Computation of Brain E-Field for Enhanced Transcranial Magnetic Stimulation Neuronavigation and Optimization

Transcranial Magnetic Stimulation (TMS) coil placement and pulse wave-form current are often chosen to achieve a specified E-field dose on targeted brain regions. TMS neuronavigation could be improved by including real-time accurate distributions of the E-field dose on the cortex. We introduce a method and develop software for computing brain E-field distributions in real-time enabling easy integration into neuronavigation and with the same accuracy as 1st-order finite element method (FEM) solvers. Initially, a spanning basis set (< 400) of E-fields generated by white noise magnetic currents on a surface separating the head and permissible coil placements are orthogonalized to generate the modes. Subsequently, Reciprocity and Huygens principles are utilized to compute fields induced by the modes on a surface separating the head and coil by FEM, which are used in conjunction with online (real-time) computed primary fields on the separating surface to evaluate the mode expansion. We conducted a comparative analysis of E-fields computed by FEM and in real-time for eight subjects, utilizing two head model types (SimNIBSs headreco and mri2mesh pipeline), three coil types (circular, double-cone, and Figure-8), and 1000 coil placements (48,000 simulations). The real-time computation for any coil placement is within 4 milliseconds (ms), for 400 modes, and requires less than 4 GB of memory on a GPU. Our solver is capable of computing E-fields within 4 ms, making it a practical approach for integrating E-field information into the neuronavigation systems without imposing a significant overhead on frame generation (20 and 50 frames per second within 50 and 20 ms, respectively). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/564044v1_fig8.gif" ALT="Figure 8"> View larger version (27K): org.highwire.dtl.DTLVardef@1520141org.highwire.dtl.DTLVardef@d05835org.highwire.dtl.DTLVardef@4f1aa9org.highwire.dtl.DTLVardef@15f7b90_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 8:C_FLOATNO Mean computational time for pre-processing stage (mode and field generation stage) for mri2mesh models (A) and headreco models (B). At any rank (mode), the time is calculated across 8 head models from 8 subjects.) C_FIG

bioengineering↗

bioRxiv2023

Fast And Accurate Population Level Transcranial Magnetic Stimulation via Low-Rank Probabilistic Matrix Decomposition (PMD)

Transcranial magnetic stimulation (TMS) is used to study brain function and treat mental health disorders. During TMS, a coil placed on the scalp induces an E-field in the brain that modulates its activity. TMS is known to stimulate regions that are exposed to a large E-field. Clinical TMS protocols prescribe a coil placement based on scalp landmarks. There are inter-individual variations in brain anatomy that result in variations in the TMS-induced E-field at the targeted region and its outcome. These variations across individuals could in principle be minimized by developing a large database of head subjects and determining scalp landmarks that maximize E-field at the targeted brain region while minimizing its variation using computational methods. However, this approach requires repeated execution of a computational method to determine the E-field induced in the brain for a large number of subjects and coil placements. We developed a probabilistic matrix decomposition-based approach for rapidly evaluating the E-field induced during TMS for a large number of coil placements. Our approach can determine the E-field induced in over 1 Million coil placements in 9.5 hours, in contrast, to over 5 years using a bruteforce approach. After the initial set-up stage, the E-field can be predicted over the whole brain within 2-3 milliseconds and to 2% accuracy. We tested our approach in over 200 subjects and achieved an error of < 2% in most and < 3.5% in all subjects. We will present several examples of bench-marking analysis for our tool in terms of accuracy and speed across and its applicability for population level optimization of coil placement. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=125 HEIGHT=200 SRC="FIGDIR/small/527758v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1650e2dorg.highwire.dtl.DTLVardef@185e477org.highwire.dtl.DTLVardef@15a4376org.highwire.dtl.DTLVardef@10304fa_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA method for practical E-field informed population-level TMS coil placement strategies is developed. C_LIO_LIThis algorithm enables the determination of E-field informed optimal coil placement in seconds enabling its use for close-loop and on-the-fly reconfiguration of TMS. C_LIO_LIAfter the initial set-up stage of less than 10 hours, the E-field can be predicted for any coil placement across the whole brain within in 2-3 milliseconds. C_LI

neuroscience↗