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Numssen, O.

Publications and source records attributed to Numssen, O..

17 recordsLinked to original sources

Executive and language systems competitively shape predictive processing during reading

When we read, our brains constantly generate predictions about upcoming words, which is thought to make comprehension faster and more efficient. Yet it remains unclear whether this predictive mechanism relies on the same domain-general executive resources the brain uses for other demanding cognitive tasks, or operates largely independently within the language system. Here we tested this question using transcranial magnetic stimulation (TMS) in twenty-five healthy adults, who performed self-paced naturalistic reading, an n-back task, and a dual task of concurrent reading and n-back, allowing us to place competing demands on cognitive resources. In three sessions, we stimulated the left angular gyrus alone, the angular gyrus together with dorsolateral prefrontal cortex, a hub of executive control, or delivered sham stimulation, and used computational modeling of decision-making to isolate how each brain state altered the underlying cognitive processes. Combined stimulation of the angular gyrus and prefrontal cortex selectively weakened the reading-time costs of unexpected words, but only when reading competed with the working-memory task, revealing that linguistic prediction draws on executive resources specifically under cognitive load. Computational models showed that the relationship was bidirectional: Unpredictable words also disrupted concurrent working-memory decisions, slowing the accumulation of evidence and increasing response caution. Together, these findings show that predictive language processing is not a fixed, encapsulated function but is dynamically coupled to executive control when cognitive demands compete. Our results provide evidence that prediction during language comprehension is embedded within a broader, demand-sensitive cognitive architecture that flexibly coordinates linguistic and executive resources.

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One placement, many fields: robust voxel-wise mapping with multichannel TMS

Understanding the human brain at the individual level is central to understanding its function and a prerequisite for personalized therapy. Transcranial magnetic stimulation (TMS) can map structure-function relationships at high resolution by regressing trial-wise variation in the TMS-induced electric field (E-field) against a response such as motor evoked potentials (MEPs). This requires distinct E-fields across pulses at different cortical locations, which single-coil TMS (sTMS) achieves by physically repositioning the coil (typically supported by neuronavigation software such as InVesalius Navigator; Souza et al., 2018)--increasingly reliant on robotic guidance and capped by the coil geometry. Multichannel TMS (mTMS) instead steers the E-field electronically via independent currents in several coils, opening a design space of high variability without physical repositioning. We adapt determinant-based sequence optimization from discrete sTMS coil-placement sets to the continuous mTMS current space (D-optimal design), identifying pulse sequences whose induced cortical E-fields are as mutually dissimilar as biophysical constraints allow. We benchmarked optimized mTMS sequences against random baselines and optimized sTMS placements in virtual motor mapping across multiple head models, assessing three mTMS array geometries (5-channel planar, 6- and 12-channel spherical), two figure-of-eight sTMS coils, and varying pulse counts, response noise, and cortical depth. D-optimal mTMS currents substantially outperformed random currents and optimized sTMS at matched pulse counts. The gain was modest without noise but increased significantly with it: at the highest noise level, optimized sTMS failed to converge after 100 pulses (~20-22 mm error), whereas all mTMS arrays reached median errors at or below 6 mm. mTMS also recovered deep targets better than sTMS (up to 35 mm skin-cortex distance, versus 25 mm) and performed uniformly across the region of interest. A coil-space scheme based only on the magnetic vector potential, precomputable once per array without per-subject MRI, captured most of the benefit, matching subject-specific optimization at 100 pulses. mTMS thus makes voxel-wise mapping robust throughout a region of interest without coil repositioning.

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Real-time posterior probabilistic TMS motor mapping with robust individualized stopping criteria

Background. Electric-field-informed regression provides high-resolution transcranial magnetic stimulation (TMS) motor maps, but conventional nonlinear fitting is too slow to update the map during acquisition, so sessions rely on predefined stimulation budgets, despite substantial inter-individual variability in the data required for stable localization. Objective. To develop a real-time analysis updating a posterior probability map between pulses and compare its hotspots with finger-tapping functional MRI (fMRI). Methods. We analyzed two independent cohorts (12 and 13 participants, one newly collected), analyzing each hand muscle independently. We replaced the conventional per-element sigmoid fit with Spearman's rank correlation coefficient and transformed the resulting score map into a posterior probability map using marginal-likelihood evidence. Acquisition stopped once posterior mass within 5 mm of the hotspot passed a preset confidence level. Results. The Spearman and linear scores updated the region of interest within milliseconds, versus tens of seconds for sigmoid fitting. Spearman more closely matched the full-data sigmoid hotspot in recorded sessions and converged faster and more precisely to the true source in virtual subjects. Across both fast scores, at a confidence level of 0.85, the stopping criterion was met after a median of 74-159 pulses, with 26-454 across individual problems. FDI hotspots lay 8.3 {+/-} 4.3 mm from the fMRI center of gravity, in the precentral hand region. Conclusions. Rank correlation reduces computation time without loss of accuracy relative to sigmoid fitting. The resulting posterior probability map provides a session-specific stopping criterion in place of a fixed pulse budget.

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Adaptive Bayesian localization of motor representation areas

Transcranial magnetic stimulation (TMS) enables non-invasive localization of cortical motor representations, with important clinical applications in presurgical planning. Existing methods either disregard spatial information about the TMS-induced electric field (E-field) or use acquisition schemes that do not leverage previously elicited motor responses to guide subsequent stimulation. We present an adaptive Bayesian localization method that combines real-time E-field optimization with per-trial probabilistic inference. The cortical origin of the motor responses is represented as a spatial probability distribution that is updated after each stimulus. Subsequent stimuli are then optimized to maximize the expected localization improvement given previous responses. We validated the method experimentally, using multi-locus TMS for adaptive localization and single-coil TMS as a non-adaptive reference with randomized coil placements. Across eight subjects, the adaptive protocol at least halved the number of stimuli required for stable localization compared to the randomized protocol, converging in 60 stimuli on average, with 95% highest-density regions often below 10 mm2 by 150 stimuli.

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Optimal Pre-Experimental Coil Sequence Selection for TMS Motor Mapping

Transcranial magnetic stimulation (TMS) motor mapping increasingly relies on electric-field (E-field) modeling to localize cortical targets, but many candidate coil placements induce highly redundant cortical patterns. We frame prospective coil-sequence design as a subset-selection problem and compare farthest-point sampling, determinant-based objectives, and related controls in virtual mapping experiments across 12 realistic head models. Across convergence, matrix-diagnostic, and manifold-coverage analyses, the best-performing objectives combined low between-stimulation redundancy with preserved inter-element separability on the cortex; objectives that maximized one of these at the expense of the other fell below random sampling. An RBF-kernelized D-optimal objective matched FPS in mapping accuracy while using [~]15 fewer unique scalp positions per 100 pulses, suggesting reduced arm-reconfiguration cost on robotic TMS platforms. A singular-spectrum analysis of the candidate library provides a low-cost a-priori indicator that predicts when discretization choices fall below the diversity floor required for stable mapping. These results recast prospective TMS mapping as a limited manifold-coverage problem and offer concrete design rules for sample-efficient, robotically efficient sequence planning. Algorithm implementations are available as part of the open-source pynibs tool-box at https://gitlab.gwdg.de/tms-localization/pynibs/-/tree/dev/pynibs/optimization.

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Cortical Stimulation Strength and sgACC Connectivity Shape Neuro-Cardiac Responses to Prefrontal TMS

Autonomic dysregulation is a core feature of major depressive disorder and a promising physiological readout for early signs of clinical effectiveness of therapeutic TMS targeting prefrontal regions. Yet the stimulation dose and network properties required to optimally engage autonomic circuits remain elusive. Here, we applied a cortex- and circuit-centered dosing framework combining resting-state fMRI, simulations of the TMS-induced electric field, and electrocardiography acquired during repetitive TMS of six left dorsolateral prefrontal cortex (DLPFC) targets in healthy adults. Heart-brain coupling (HBC), defined as spectral power in the cardiac response locked to the TMS onset frequency, was modeled as a function of either conventional motor-threshold-based dose or cortical electric-field strength, with and without DLPFC connectivity to the subgenual anterior cingulate cortex (sgACC). Across linear and nonlinear models, cortical electric-field dose explained substantially more variance in HBC, indicating that prefrontal TMS dosing is best assessed at the cortical level. Incorporating sgACC connectivity further improved model fit and revealed site-specific electric-field-by-connectivity interactions, with the strongest and most reliable HBC modulation at DLPFC regions anticorrelated with sgACC. Voxel-wise TMS mapping identified individualized cortical hotspots spatially overlapping with sgACC-anticorrelated tissue. Notably, whereas negative connectivity showed the most robust and monotonic effects, nonlinear dose-response analyses suggested a confined stimulation range over which positively connected sites also exhibited HBC modulation. Together, these findings position HBC as a network-sensitive physiological readout of DLPFC-sgACC-vagal engagement and offer a principled answer to where and how much to stimulate, supporting an electric-field- and connectivity-informed dosing framework for refining prefrontal TMS in depression-related autonomic dysregulation.

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Optimizing and assessing multichannel TMS focality

BackgroundMultichannel transcranial magnetic stimulation (mTMS) enables electronic steering of induced electric fields across multiple cortical targets without physical coil repositioning, addressing key limitations of conventional single-channel TMS (sTMS). However, determining optimal input currents for focal stimulation remains challenging, and different mTMS systems have not been systematically compared under realistic hardware constraints. ObjectiveTo develop an user-centric framework for optimizing and assessing mTMS focality by introducing a generic optimization algorithm, establishing meaningful quality metrics, and comparing mTMS coil arrays with traditional single-channel TMS across cortical targets. MethodsWe developed a fast optimization framework incorporating target E-field constraints via parametrization of degenerated hyperellipsoids, explicitly integrating current-rate limits, for example from stimulator electronics and coil heating. Using high-resolution finite-element models of nine individual brains, we compared two mTMS designs (5-channel planar and 6/12-channel spherical systems) with standard sTMS figure-of-eight coils through E-field simulations. Three complementary metrics quantified stimulation quality: Focality, Target2Max, and OverstimulatedArea. ResultsDespite using a single optimized placement for all region-of-interest targets, mTMS achieved focality comparable to repositioned single-channel TMS in this in-silico study. For superficial targets, single-channel TMS showed slightly better stimulation, but for deeper cortical targets (>25 mm skin-cortex distance), mTMS performed similarly. More mTMS channels improved focality but required stronger current-rate constraints. The planar design performed better for deeper targets, while spherical designs improved with additional channels. ConclusionmTMS systems demonstrate remarkable performance comparable to standard TMS, enabling efficient multi-target stimulation without repositioning. Our open-source framework provides practical tools for designing and evaluating mTMS systems, supporting goal-directed mTMS development and effective application.

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Target-Specificity and Repeatability in Neuro-Cardiac-Guided TMS for Heart-Brain Coupling

The dorsolateral prefrontal cortex (DLPFC) is a principal target for transcranial magnetic stimulation (TMS) in treating major depressive disorder, with therapeutic effects thought to be mediated by its connectivity with the subgenual anterior cingulate cortex. As both regions are involved in autonomic regulation, short-term heart rate changes following DLPFC stimulation may serve as physiological markers to identify stimulation targets. We employed neuro-cardiac guided TMS in a cohort of healthy participants to examine the effects of stimulation intensity and DLPFC target specificity on heart-brain coupling (HBC). We used generalized additive models to assess nonlinear effects of stimulation intensity and target location on HBC, while accounting for pain ratings and other side effects. Intra-subject repeatability across three sessions was evaluated using intraclass correlation coefficients. We observed a non-linear modulation of HBC depending on stimulation intensity and target location, with greater effects at the F3 lateral and F3 posterior targets compared to sham. By evaluating these effects across sessions within participants, we demonstrate the robustness of our results beyond the influence of pain and other side effects on HBC modulation. Exploratory analyses of the directionality show a consistent decrease in HR only at the F3 lateral target with suprathreshold stimulation. These results demonstrate that HBC is modulated in a target- and intensity-specific manner, with particularly consistent effects at F3 lateral sites within the DLPFC. The findings enhance the understanding of TMS-modulated heart-brain interactions, offering a potential framework for optimizing individualized rTMS treatment protocols for depression.

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Hindering memory suppression by perturbing the right dorsolateral prefrontal cortex

A reminder of the past can trigger the involuntary retrieval of an unwanted memory. Yet, we can intentionally stop this process and thus prevent the memory from entering awareness. Such suppression not only transiently hinders the retrieval of the memory, it can also induce forgetting. Neuroimaging has implicated the right dorsolateral prefrontal cortex (dlPFC) in initiating this process. Specifically, this region seems to downregulate activity in brain systems that would otherwise support memory reinstatement. Here, we probed the causal contribution of the right dlPFC to suppression by combining the Think/No-Think task with repetitive transcranial magnetic stimulation (rTMS). Participants first learned pairs of cue and target words, and then repeatedly recalled some of the targets (think condition) and suppressed others (no-think condition). We applied 10 Hz rTMS bursts to the right dlPFC during the suppression of half the no-think items and to the contra-lateral primary motor area (M1) as an active control site during the other half. As hypothesized, participants experienced less success at keeping the memories out of awareness with concurrent dlPFC than M1 stimulation. Similarly, a memory test yielded evidence for suppressioninduced forgetting (SIF) following M1 but not dlPFC stimulation. However, the difference in forgetting between the stimulation conditions was not significant. The study thus provides causal evidence for the role of the dlPFC in preventing retrieval. Future work will need to conclusively establish the relationship between this transient effect and suppression-induced forgetting.

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Effects of Electric Field Direction on TMS-based Motor Cortex Mapping

BackgroundTranscranial magnetic stimulation (TMS) modulates brain activity by inducing electric fields (E-fields) that can elicit action potentials in cortical neurons. Neuronal responses to TMS depend not only on the magnitude of the induced E-field but also on various physiological factors. In this study, we incorporated a novel average response model that efficiently estimates the firing threshold of neurons based on their orientation relative to the applied E-field, thereby advancing TMS mapping for motor function. MethodsWe conducted a regression-based TMS mapping experiment with fourteen subjects to localize cortical origins of motor evoked potential (MEP) on the first dorsal interosseous (FDI) muscle. Firing thresholds were estimated for excitatory neurons in cortical layers 2/3 and 5 via an average response model. Regression was performed between MEPs and three E-field quantities: the magnitude (magnitude model), the normal component (cosine model), and the effective E-field, which scales the E-field magnitude based on the firing thresholds specific to the neuronal orientation (neuron model). To validate, we applied TMS to ten subjects with optimized coil placements based on these three models to determine which model could yield the highest MEPs. ResultsThe magnitude and neuron models performed similarly, while the cosine model showed lower explained variance in regression results, required more TMS trials for stable mapping, and yielded the lowest MEP in the validation. ConclusionThis study is the first to advance TMS modeling by incorporating neuron-specific factors at the individual level. Results show that on the motor cortex, the magnitude model is-as expected-a good approximation of cortical TMS effects as it shows similar results as the neuron model. In contrast, the classic cosine model exhibited lower performance and required more TMS trials for stable results, and is not recommended for future studies.

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Statistical method accounts for microscopic electric field distortions around neurons when simulating activation thresholds

IntroductionNotwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (175-350 V/m). MethodsWe hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach. ResultsWe show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical methods match TMS experimental thresholds. ConclusionsOur approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be computationally tractable.

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Electrical field based dosing improves non-invasive brain stimulation

AbstractTranscranial magnetic stimulation (TMS) is an invaluable non-invasive brain stimulation (NIBS) technique to modulate cortical activity and behavior, but high within- and between-participant variability limits its efficacy and reliability. Here, we explore the potential of electric field (e-field) based TMS dosing to reduce its variability and discuss current challenges as well as future pathways. In contrast to previous dosing approaches, e-field dosing better matches the stimulation strength across cortical areas, both within and across individuals. Challenges include methodological uncertainties of the e-field simulation, target definitions, and comparability of different stimulation thresholds across cortical areas and NIBS protocols. Despite these challenges, e-field dosing promises to substantially improve NIBS applications in neuroscientific research and personalized medicine. Outstanding Questions Box O_TEXTBOXOutstanding Questions O_LIDoes the cortical threshold for effective stimulation differ between primary regions and higher-level association areas? How large is the impact of cytoarchitectonic differences between regions on a stimulation threshold? C_LIO_LIDo cortical stimulation thresholds differ across individuals? Are thresholds stable within an individual across the lifespan? What are the physiological factors influencing these thresholds? C_LIO_LICan a cortical stimulation threshold measured with single-pulse TMS be transferred to repetitive TMS protocols for the study of cognition? C_LIO_LIHow does the cortical stimulation threshold interact with the current brain state? C_LI C_TEXTBOX Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/551253v2_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@16d6e44org.highwire.dtl.DTLVardef@1626d90org.highwire.dtl.DTLVardef@10713e7org.highwire.dtl.DTLVardef@1abdcaa_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Modeling the Effects of Transcranial Magnetic Stimulation on Spatial Attention

ObjectivesTranscranial magnetic stimulation (TMS) has been widely used to modulate brain activity in healthy and diseased brains, but the underlying mechanisms are not fully understood. Previous research leveraged biophysical modeling of the induced electric field (E-field) to map causal structure-function relationships in the primary motor cortex. This study aims at transferring this localization approach to spatial attention, which helps to understand the TMS effects on cognitive functions, and may ultimately optimize stimulation schemes. ApproachThirty right-handed healthy participants underwent a functional magnetic imaging (fMRI) experiment, and seventeen of them participated in a TMS experiment. The individual fMRI activation peak within the right inferior parietal lobule (rIPL) during a Posner-like attention task defined the center target for TMS. Thereafter, participants underwent 500 Posner task trials. During each trial, a 5-pulse burst of 10 Hz repetitive TMS (rTMS) was given over the rIPL to modulate attentional processing. The TMS-induced E-fields for every cortical target were correlated with the behavioral modulation to identify relevant cortical regions for attentional orientation and reorientation. Main resultsWe did not observe a robust correlation between E-field strength and behavioral outcomes, highlighting the challenges of transferring the localization method to cognitive functions with high neural response variability and complex network interactions. Nevertheless, TMS selectively inhibited attentional reorienting in five out of seventeen subjects, resulting in task-specific behavioral impairments. The BOLD-measured neuronal activity and TMS-evoked neuronal effects showed different patterns, which emphasizes the principal distinction between the neural activity being correlated with (or maybe even caused by) particular paradigms, and the activity of neural populations exerting a causal influence on the behavioral outcome. SignificanceThis study is the first to explore the mechanisms of TMS-induced attentional modulation through electrical field modeling. Our findings highlight the complexity of cognitive functions and provide a basis for optimizing attentional stimulation protocols.

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Towards a causal role of Broca's area in language: A TMS-EEG study on syntactic prediction

Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in human language. Accordingly, grammar-based expectations facilitate the analysis of incoming syntactic information--e.g., hearing the determiner "the" enhances the prediction of a noun--which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Brocas area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during on-line language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Brocas area is functionally relevant in predictive mechanisms for language. Specifically, we transiently perturbed Brocas area during the categorical prediction phase in two-word constructions, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Brocas area is involved in predictive mechanisms for syntax, disruptive TMS during the processing of the first word (determiner/pronoun) would mitigate the difference in ERP responses for predicted and unpredicted categories when composing basic phrases and sentences. Contrary to our hypothesis, perturbation of Brocas area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the magnitude of the EEG response on the scalp further confirmed this pattern. We discuss the present results in light of an alternative account of the role of Brocas area in syntactic composition, namely the bottom-up integration of words into constituents.

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Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression

Transcranial magnetic stimulation (TMS) is a powerful tool to investigate causal structure-function relationships in the human brain. However, a precise delineation of the effectively stimulated neuronal populations is notoriously impeded by the widespread and complex distribution of the induced electric field. Here, we propose a method that allows rapid and feasible cortical localization at the individual subject level. The functional relationship between electric field and behavioral effect is quantified by combining experimental data with numerically modelled fields to identify the cortical origin of the modulated effect. Motor evoked potentials (MEPs) from three finger muscles were recorded for a set of random stimulations around the primary motor area. All induced electric fields were nonlinearly regressed against the elicited MEPs to identify their cortical origin. We could distinguish cortical muscle representation with high spatial resolution and localized them primarily on the crowns and rims of the precentral gyrus. A post-hoc analysis revealed exponential convergence of the method with the number of stimulations, yielding a minimum of about 180 random stimulations to obtain stable results. Establishing a functional link between the modulated effect and the underlying mode of action, the induced electric field, is a fundamental step to fully exploit the potential of TMS. In contrast to previous approaches, the presented protocol is particularly easy to implement, fast to apply, and very robust due to the random coil positioning and therefore is suitable for practical and clinical applications.

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Hemispheric specialization within the inferior parietal lobe across cognitive domains

The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social cognition, that define human interactions. Its putative domain-global role appears to tie into poorly understood differences between cognitive domains in both hemispheres. Across attentional, semantic, and social cognitive tasks, our study explored functional specialization within the IPL. The task specificity of IPL subregion activity was substantiated by distinct predictive signatures identified by multivariate pattern-learning algorithms. Moreover, the left and right IPL exerted domain-specific modulation of effective connectivity among their subregions. Task-evoked functional interactions of the anterior and posterior IPL subregions involved recruitment of distributed cortical partners. While anterior IPL subregions were engaged in strongly lateralized coupling links, both posterior subregions showed more symmetric coupling patterns across hemispheres. Our collective results shed light on how under-appreciated functional specialization in the IPL supports some of the most distinctive human mental capacities.

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A novel approach to localize cortical TMS effects

Despite the widespread use of transcranial magnetic stimulation (TMS), the precise cortical location underlying the physiological and behavioral stimulation effects are still only coarsely known. So far, mapping strategies rely on center of gravity approaches and therefore localize the stimulated cortical site only approximately and indirectly. Focusing on the motor cortex, we present a novel method to reliably determine the effectively stimulated cortical site at the individual subject level. The approach combines measurements of motor evoked potentials (MEPs) at different coil positions and orientations with numerical modeling of induced electric fields. We identify sharply bounded cortical areas around the gyral crowns and rims of the motor hand area as the origin of MEPs and show that the tangential component and the magnitude of the electric field is most relevant for the observed effect. To validate our approach, we determined motor thresholds for coil positions and orientations for the predicted cortical target. Our methods allows for the identification of optimal coil positions and orientations. Moreover, we used extensive uncertainty and sensitivity analyses to verify the robustness of the method and identify the most critical model parameters. Our generic approach improves the localization of the cortex area stimulated by TMS and may be transferred to other modalities such as language mapping.

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