An official website of the United States government
Abstract Objective.To develop a coil placement optimization pipeline for transcranial magnetic stimulation (TMS) that improves over existing solutions by guaranteeing the feasibility of the solution when double-cone coils are used and/or targets are placed over nonconvex scalp areas like the occipital region.Approach.Our proposed pipeline estimates feasible candidate coil locations by projecting the coil’s geometry over the scalp around the target site and optimizing the coil’s orientation to maximize scalp exposure to coil while avoiding coil-scalp collision. Then, the reciprocity principle is used to select the best position/orientation among candidates and maximize the average electric field (E-field) intensity at the target site. Our pipeline was tested on five magnetic resonance imaging-derived human head models for three different targets (motor cortex, lateral cerebellum, and cerebellar inion) and four coil models (planar coil: MagStim D70; double-cone coils: MagStim DCC, MagVenture Cool-D-B80, and Deymed 120BFV).Main results.Our pipeline returned several feasible solutions for any combination of anatomical target and coil, calculated and screened over 2000 candidates in minutes, and resulted in optimal locations that satisfy the minimum coil-scalp distance, whereas the direct method returned feasible candidates for just one combination of target and coil, i.e. planar coil and convex target over the motor cortex. We also found that, when the objective is to maximize the E-field magnitude, the target-to-scalp extension line is a better axis for coil translation compared to the normal vector at the scalp’s surface, which is commonly used in existing approaches.Significance.We expand the use of numerical optimization for coil placement to double-cone coils, which are rapidly diffusing in research and clinical settings, and novel application domains, e.g. cerebellar TMS and ataxia treatment.
more »
« less
This content will become publicly available on March 4, 2026
Computational Methods for Optimal Coil Placement and Maximization of Lobule-Focused Cortical Activation in Cerebellar TMS
Background: Coil placement on the cerebellum lacks accuracy in targeting the intended lobules and limits the efficacy of cerebellar transcranial magnetic stimulation (TMS) in treating movement disorders. Objective: Develop a multiscale computational pipeline and method to rapidly predict the cellular response to cerebellar TMS and optimize the coil placement accordingly for lobule-specific activation. Methods: The pipeline integrates 3T T1/T2-weighted MRI scans of the human cerebellum, lobule parcellation, and finite element models of the TMS-induced electric (E-) fields for figure-of-eight coils (MagStim D70) and double-cone coils (Deymed 120BFV). A constrained optimization method is developed to estimate the fiber bundles from cerebellar cortices to deep nuclei and, for both coil types, find the coil placement and orientation that maximize the E-field intensity in a user-selected lobule. Multicompartmental Purkinje cell models with realistic axon geometries and Gaussian process regression are added to predict the recruitment in the Purkinje layer. Results: Our pipeline was tested in five individuals to target the left lobule VIII and resulted in normalized E-field intensities at the target 49.6±25.6% (D70) and 29.3±17.7% (120BFV) higher compared to standard coil positions (i.e., 3 cm left, 1 cm below the inion), mean±S.D. The minimum pulse intensity to recruit Purkinje cells on a 4 mm2-surface in the target decreased by 21.6% (range: 4.7-55.0%) and 10.7% (range: 7.9-18.2%), and the spillover to adjacent lobules decreased by 70.6±16.3% and 71.7±20.8% compared to standard positions (D70 and 120BFV, respectively). Conclusion: Our tools are effective at targeting specific lobules and pave the way toward patient-specific setups.
more »
« less
- Award ID(s):
- 1845348
- PAR ID:
- 10576898
- Publisher / Repository:
- bioRxiv
- Date Published:
- Subject(s) / Keyword(s):
- Transcranial Magnetic Stimulation Cerebellum Finite Element Method Purkinje Cells Neuron Models Gaussian Process Regression
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Objective.Cerebellar transcranial magnetic stimulation (TMS) has been proposed to suppress limb tremors in essential tremor (ET), but mixed results have been reported so far, both when pulses are applied repetitively TMS (rTMS) and in bursts. We aim to investigate the cellular effects of TMS on the cerebellum under ET through numerical simulations.Approach.A computational model of the olivo-cerebello-thalamocortical pathways exhibiting the main neural biomarkers of ET (i.e. circuit-wide tremor-locked neural oscillations) was expanded to incorporate the effects of TMS-induced electric field (E-field) on Purkinje cells. TMS pulse amplitude, frequency, and temporal pattern were varied, and the resultant effects on ET biomarkers were assessed. Four levels of cellular response to TMS were considered, ranging from low to high cell recruitment underneath the coil, and three stimulation patterns were tested, i.e. rTMS, irregular TMS (ir-TMS, pulses were arranged according to Sobol sequences with average frequency matching rTMS), and phase-locked TMS (PL-TMS).Main results.rTMS can suppress ET oscillations, but its efficacy depends on tremor frequency and recruitment level, with these factors shaping a narrow range of effective settings. The ratio between tremor and rTMS frequencies also affects the neural response and further narrows the span of viable settings, while ir-TMS is ineffective. PL-TMS is highly effective and robust against changes to cell recruitment level and tremor frequency. Across all scenarios, PL-TMS provides a rapid (i.e. within seconds) suppression of tremor oscillations and, when both PL-TMS and rTMS are effective, the time to tremor suppression decreases by 50% or more in PL-TMS versus rTMS. At the cellular level, PL-TMS operates by disrupting the synchronization along the olivo-cerebellar loop, and the preferred phases map onto the mid-region of the silent period between complex spikes of the Purkinje cells.Significance.Cerebellar PL-TMS can provide robust suppression of ET oscillations while operating within safety boundaries.more » « less
-
Abstract The cerebellum regulates nonmotor behavior, but the routes of influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task through a network of diencephalic and neocortical structures, and in flexibility of free behavior. After chemogenetic inhibition of lobule VI vermis or hemispheric crus I Purkinje cells, mice could learn a water Y-maze but were impaired in ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of whole-body movement revealed deficiencies in across-day behavioral habituation to an open-field environment. Taken together, these experiments reveal brainwide systems for cerebellar influence that affect multiple flexible responses.more » « less
-
Blackwell, Kim T. (Ed.)Transcranial direct current stimulation (tDCS) of the cerebellum has rapidly raised interest but the effects of tDCS on cerebellar neurons remain unclear. Assessing the cellular response to tDCS is challenging because of the uneven, highly stratified cytoarchitecture of the cerebellum, within which cellular morphologies, physiological properties, and function vary largely across several types of neurons. In this study, we combine MRI-based segmentation of the cerebellum and a finite element model of the tDCS-induced electric field (EF) inside the cerebellum to determine the field imposed on the cerebellar neurons throughout the region. We then pair the EF with multicompartment models of the Purkinje cell (PC), deep cerebellar neuron (DCN), and granule cell (GrC) and quantify the acute response of these neurons under various orientations, physiological conditions, and sequences of presynaptic stimuli. We show that cerebellar tDCS significantly modulates the postsynaptic spiking precision of the PC, which is expressed as a change in the spike count and timing in response to presynaptic stimuli. tDCS has modest effects, instead, on the PC tonic firing at rest and on the postsynaptic activity of DCN and GrC. In Purkinje cells, anodal tDCS shortens the repolarization phase following complex spikes (-14.7 ± 6.5% of baseline value, mean ± S.D.; max: -22.7%) and promotes burstiness with longer bursts compared to resting conditions. Cathodal tDCS, instead, promotes irregular spiking by enhancing somatic excitability and significantly prolongs the repolarization after complex spikes compared to baseline (+37.0 ± 28.9%, mean ± S.D.; max: +84.3%). tDCS-induced changes to the repolarization phase and firing pattern exceed 10% of the baseline values in Purkinje cells covering up to 20% of the cerebellar cortex, with the effects being distributed along the EF direction and concentrated in the area under the electrode over the cerebellum. Altogether, the acute effects of tDCS on cerebellum mainly focus on Purkinje cells and modulate the precision of the response to synaptic stimuli, thus having the largest impact when the cerebellar cortex is active. Since the spatiotemporal precision of the PC spiking is critical to learning and coordination, our results suggest cerebellar tDCS as a viable therapeutic option for disorders involving cerebellar hyperactivity such as ataxia.more » « less
-
The cerebellum is considered a “learning machine” essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum’s input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning. NEW & NOTEWORTHY During movement, mossy fiber inputs to the cerebellum relay time-varying information with strong intrinsic relationships to ongoing movement. Are such mossy fibers signals sufficient to support Purkinje signals and learning? In a model, we show how the GCL greatly improves Purkinje learning of complex, temporally dynamic signals relative to mossy fibers alone. Learning-optimized GCL population activity was moderately dense, which retained intrinsic input variance while also performing pattern separation.more » « less
