scholarly journals A novel approach to localize cortical TMS effects

2019 ◽  
Author(s):  
Konstantin Weise ◽  
Ole Numssen ◽  
Axel Thielscher ◽  
Gesa Hartwigsen ◽  
Thomas R. Knösche
Keyword(s):  
Electric Fields ◽  
Motor Evoked Potentials ◽  
Tangential Component ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Language Mapping ◽  
Individual Subject ◽  
Novel Approach ◽  
Critical Model ◽  
The Individual

ABSTRACTDespite 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.

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

2021 ◽  
Author(s):  
Ole Numssen ◽  
Anna-Leah Zier ◽  
Axel Thielscher ◽  
Gesa Hartwigsen ◽  
Thomas R. Knösche ◽  
...  
Keyword(s):  
Electric Field ◽  
Electric Fields ◽  
Motor Evoked Potentials ◽  
Random Coil ◽  
Individual Response ◽  
Precentral Gyrus ◽  
Individual Subject ◽  
Human Motor Cortex ◽  
Preoperative Mapping ◽  
The Individual

AbstractBackgroundThe precise cortical origins of the electrophysiological and behavioral effects of transcranial magnetic stimulation (TMS) remain largely unclear. Addressing this question is further impeded by substantial inter-individual response variability to TMS.ObjectiveWe present a novel method to reliably and user-independently determine the effectively stimulated cortical site at the individual subject level. This generic approach combines physiological measurements with electric field simulations and leverages information from random coil positions, electric field estimations, and electromyography.MethodsWe applied ~1000 single biphasic TMS pulses with standard TMS hardware to 13 subjects with random coil positions & orientations over the primary motor hand area. Motor evoked potentials (MEPs) of three finger muscles were recorded concurrently. We calculated the corresponding electric fields for all TMS pulses and regressed them against the elicited MEPs in each cortical element. This yields a cortical map of congruency between induced field strength and generated response.ResultsWe observed high congruence between the electric fields and the elicited MEPs in hotspots located primarily on the crowns and rims of the precentral gyrus. The three cortical digit representations could be distinguished at the individual subject level with a high spatial resolution. A post-hoc convergence analysis revealed a possible lower bound of only 180 pulses to obtain qualitatively identical results.ConclusionsLeveraging information from many different TMS pulses significantly reduces the number of necessary stimulations and mapping time. The protocol is easy to implement due to the realization of arbitrary coil positions & orientations and is suitable for practical and clinical use such as preoperative mapping.

scholarly journals Decoding individualized motor cortical excitability states from whole-brain electroencephalography

2021 ◽  
Author(s):  
Sara J Hussain ◽  
Romain Quentin
Keyword(s):  
Cortical Excitability ◽  
Motor Evoked Potentials ◽  
Brain State ◽  
Individual Subject ◽  
Whole Brain ◽  
State Dependent ◽  
Subject Specific ◽  
The Individual ◽  
Real Time Identification ◽  
The Right

OBJECTIVE: Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Here, we aimed to identify individualized, subject-specific motor cortex (M1) excitability states from whole-scalp electroencephalography (EEG) signals. METHODS: We analyzed a pre-existing dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings. Subject-specific multivariate pattern classification was used to discriminate between brain states during which TMS elicited small or large motor-evoked potentials (MEPs). RESULTS: Classifiers trained at the individual subject level successfully discriminated between low and high M1 excitability states. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states. Classifiers trained on subject-specific data obtained immediately before TMS delivery performed better than classifiers trained on data from earlier time points, and subject-specific classifiers generalized weakly but significantly across subjects. CONCLUSION: Decoding individualized M1 excitability states from whole-brain EEG activity is feasible and robust. SIGNIFICANCE: Deploying subject-specific classifiers during brain state-dependent TMS may enable effective, fully individualized neuromodulation in the future.

scholarly journals Functional modeling of the craniospinal system for in-vitro parameter studies on the pathogenesis of NPH

2017 ◽  
Vol 3 (2) ◽  
pp. 825-828 ◽  
Author(s):  
Anne Benninghaus ◽  
Christine Goffin ◽  
Steffen Leonhardt ◽  
Klaus Radermacher
Keyword(s):  
Vascular System ◽  
Dynamic Compliance ◽  
Normal Pressure ◽  
The Elderly ◽  
Sensitivity Analyses ◽  
Common Disease ◽  
Novel Approach ◽  
The Individual ◽  
The Impact

AbstractNormal Pressure Hydrocephalus (NPH) has become a common disease in the elderly coming along with typical symptoms of dementia, gait ataxia and urinary incontinence, which make the differential diagnosis with other forms of dementia difficult. Furthermore the pathogenesis of NPH is still not understood. About 10% of all demented patients might be suffering from NPH [1]. Many hypotheses suggest that modified biomechanical boundary conditions affect the craniospinal dynamics inducing the pathogenesis of NPH. We present a novel approach for an in-vitro model of the craniospinal system to investigate important hydrodynamic influences on the system such as (dynamic) compliance of the vascular system and especially the spinal subarachnoid space (SAS) as well as reabsorption and hydrostatics. The experimental set-up enables the individual adjustment of relevant parameters for sensitivity analyses regarding the impact of resulting CSF dynamics on the pathogenesis of NPH.

Stasis Anxiety

2017 ◽  
Author(s):  
Andrew van der Vlies
Keyword(s):  
South Africa ◽  
Civil War ◽  
Black Writers ◽  
Political Rights ◽  
Ancient Greek ◽  
Individual Subject ◽  
Starting Point ◽  
The Individual ◽  
Past Experiences ◽  
Contemporary Writing

Two recent debut novels, Songeziwe Mahlangu’s Penumbra (2013) and Masande Ntshanga’s The Reactive (2014), reflect the experience of impasse, stasis, and arrested development experienced by many in South Africa. This chapter uses these novels as the starting point for a discussion of writing by young black writers in general, and as representative examples of the treatment of ‘waithood’ in contemporary writing. It considers (spatial and temporal) theorisations of anxiety, discerns recursive investments in past experiences of hope (invoking Jennifer Wenzel’s work to consider the afterlives of anti-colonial prophecy), assesses the usefulness of Giorgio Agamben’s elaboration of the ancient Greek understanding of stasis as civil war, and asks how these works’ elaboration of stasis might be understood in relation to Wendy Brown’s discussion of the eclipsing of the individual subject of political rights by the neoliberal subject whose very life is framed by its potential to be understood as capital.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

scholarly journals Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1280
Author(s):  
Hyeonseok Lee ◽  
Sungchan Kim
Keyword(s):  
Neural Networks ◽  
Model Prediction ◽  
Saliency Map ◽  
Model Parameters ◽  
Learning Capability ◽  
Fine Grained ◽  
Network Layers ◽  
Saliency Maps ◽  
Novel Approach ◽  
Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

scholarly journals Physical Activity and Redox Balance in the Elderly: Signal Transduction Mechanisms

2021 ◽  
Vol 11 (5) ◽  
pp. 2228
Author(s):  
Daniela Galli ◽  
Cecilia Carubbi ◽  
Elena Masselli ◽  
Mauro Vaccarezza ◽  
Valentina Presta ◽  
...  
Keyword(s):  
Physical Activity ◽  
Molecular Mechanisms ◽  
Vascular Tissue ◽  
The Elderly ◽  
Redox Balance ◽  
Antioxidant Systems ◽  
Targeted Prevention ◽  
Individual Subject ◽  
Aged People ◽  
The Individual

Reactive Oxygen Species (ROS) are molecules naturally produced by cells. If their levels are too high, the cellular antioxidant machinery intervenes to bring back their quantity to physiological conditions. Since aging often induces malfunctioning in this machinery, ROS are considered an effective cause of age-associated diseases. Exercise stimulates ROS production on one side, and the antioxidant systems on the other side. The effects of exercise on oxidative stress markers have been shown in blood, vascular tissue, brain, cardiac and skeletal muscle, both in young and aged people. However, the intensity and volume of exercise and the individual subject characteristics are important to envisage future strategies to adequately personalize the balance of the oxidant/antioxidant environment. Here, we reviewed the literature that deals with the effects of physical activity on redox balance in young and aged people, with insights into the molecular mechanisms involved. Although many molecular pathways are involved, we are still far from a comprehensive view of the mechanisms that stand behind the effects of physical activity during aging. Although we believe that future precision medicine will be able to transform exercise administration from wellness to targeted prevention, as yet we admit that the topic is still in its infancy.

scholarly journals Optimal Investment and Proportional Reinsurance in a Regime-Switching Market Model under Forward Preferences

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1610
Author(s):  
Katia Colaneri ◽  
Alessandra Cretarola ◽  
Benedetta Salterini
Keyword(s):  
Stock Prices ◽  
Regime Switching ◽  
Common Factor ◽  
Optimal Investment ◽  
Investment Strategy ◽  
Market Model ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Insurance Company ◽  
Optimal Investment Strategy

In this paper, we study the optimal investment and reinsurance problem of an insurance company whose investment preferences are described via a forward dynamic exponential utility in a regime-switching market model. Financial and actuarial frameworks are dependent since stock prices and insurance claims vary according to a common factor given by a continuous time finite state Markov chain. We construct the value function and we prove that it is a forward dynamic utility. Then, we characterize the optimal investment strategy and the optimal proportional level of reinsurance. We also perform numerical experiments and provide sensitivity analyses with respect to some model parameters.

scholarly journals HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Sonia Setia ◽  
Verma Jyoti ◽  
Neelam Duhan
Keyword(s):  
Web Mining ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Web Pages ◽  
Continuous Growth ◽  
Novel Approach ◽  
Content Mining ◽  
Long Access ◽  
N Gram ◽  
The Individual

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

scholarly journals Considering multiple process observables to determine material model parameters for FE-cutting simulations

2021 ◽  
Author(s):  
Marvin Hardt ◽  
Thomas Bergs
Keyword(s):  
Chip Thickness ◽  
Material Model ◽  
Experimental Investigations ◽  
Model Parameters ◽  
Inverse Approach ◽  
Local Material ◽  
Novel Approach ◽  
Validation Parameters ◽  
Multiple Process ◽  
Cutting Simulations

AbstractAnalyzing the chip formation process by means of the finite element method (FEM) is an established procedure to understand the cutting process. For a realistic simulation, different input models are required, among which the material model is crucial. To determine the underlying material model parameters, inverse methods have found an increasing acceptance within the last decade. The calculated model parameters exhibit good validity within the domain of investigation, but suffer from their non-uniqueness. To overcome the drawback of the non-uniqueness, the literature suggests either to enlarge the domain of experimental investigations or to use more process observables as validation parameters. This paper presents a novel approach merging both suggestions: a fully automatized procedure in conjunction with the use of multiple process observables is utilized to investigate the non-uniqueness of material model parameters for the domain of cutting simulations. The underlying approach is two-fold: Firstly, the accuracy of the evaluated process observables from FE simulations is enhanced by establishing an automatized routine. Secondly, the number of process observables that are considered in the inverse approach is increased. For this purpose, the cutting force, cutting normal force, chip temperature, chip thickness, and chip radius are taken into account. It was shown that multiple parameter sets of the material model can result in almost identical simulation results in terms of the simulated process observables and the local material loads.

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