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Buschermöhle Y, Höltershinken MB, Erdbrügger T, Radecke JO, Sprenger A, Schneider TR, Lencer R, Gross J, Wolters CH. Comparing the performance of beamformer algorithms in estimating orientations of neural sources. iScience 2024; 27:109150. [PMID: 38420593 PMCID: PMC10901088 DOI: 10.1016/j.isci.2024.109150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/12/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
The efficacy of transcranial electric stimulation (tES) to effectively modulate neuronal activity depends critically on the spatial orientation of the targeted neuronal population. Therefore, precise estimation of target orientation is of utmost importance. Different beamforming algorithms provide orientation estimates; however, a systematic analysis of their performance is still lacking. For fixed brain locations, EEG and MEG data from sources with randomized orientations were simulated. The orientation was then estimated (1) with an EEG and (2) with a combined EEG-MEG approach. Three commonly used beamformer algorithms were evaluated with respect to their abilities to estimate the correct orientation: Unit-Gain (UG), Unit-Noise-Gain (UNG), and Array-Gain (AG) beamformer. Performance depends on the signal-to-noise ratios for the modalities and on the chosen beamformer. Overall, the UNG and AG beamformers appear as the most reliable. With increasing noise, the UG estimate converges to a vector determined by the leadfield, thus leading to insufficient orientation estimates.
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Affiliation(s)
- Yvonne Buschermöhle
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Malte B Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Institute for Analysis and Numerics, University of Münster, 48149 Münster, Germany
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Institute for Analysis and Numerics, University of Münster, 48149 Münster, Germany
| | - Jan-Ole Radecke
- Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Andreas Sprenger
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Germany
- Institute of Psychology II, University of Lübeck, 23562 Lübeck, Germany
| | - Till R Schneider
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Rebekka Lencer
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
- Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Institute of Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
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Kroker T, Rehbein MA, Wyczesany M, Bölte J, Roesmann K, Wessing I, Junghöfer M. Higher-order comparative reward processing is affected by noninvasive stimulation of the ventromedial prefrontal cortex. J Neurosci Res 2024; 102:e25248. [PMID: 37815024 DOI: 10.1002/jnr.25248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/17/2023] [Accepted: 09/10/2023] [Indexed: 10/11/2023]
Abstract
A crucial skill, especially in rapidly changing environments, is to be able to learn efficiently from prior rewards or losses and apply this acquired knowledge in upcoming situations. Often, we must weigh the risks of different options and decide whether an option is worth the risk or whether we should choose a safer option. The ventromedial prefrontal cortex (vmPFC) is suggested as a major hub for basic but also higher-order reward processing. Dysfunction in this region has been linked to cognitive risk factors for depression and behavioral addictions, including reduced optimism and feedback learning. Here, we test whether modulations of vmPFC excitability via noninvasive transcranial direct current stimulation (tDCS) can alter reward anticipation and reward processing. In a financial gambling task, participants chose between a higher and a lower monetary risk option and eventually received feedback whether they won or lost. Simultaneously feedback on the unchosen option was presented as well. Behavioral and magnetoencephalographic correlates of reward processing were evaluated in direct succession of either excitatory or inhibitory tDCS of the vmPFC. We were able to show modulated reward approach behavior (expectancy of greater reward magnitudes) as well as altered reevaluation of received feedback by vmPFC tDCS as indicated by modified choice behavior following the feedback. Thereby, tDCS not only influenced early, rather basic reward processing, but it also modulated higher-order comparative feedback evaluation of gains and losses relative to alternative outcomes. The neural results underline this idea, as stimulation-driven modulations of the basic reward-related effect occurred at rather early time intervals and were followed by stimulation effects related to comparative reward processing. Importantly, behavioral ratings were correlated with neural activity in left frontal areas. Our results imply a dual function of the vmPFC consisting of approaching reward (as indicated by more risky choices) and elaborately evaluating outcomes. In addition, our data suggest that vmPFC activity is associated with adaptive decision-making in the future via modulated behavioral adaptation or reinforcement learning.
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Affiliation(s)
- Thomas Kroker
- Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Maimu Alissa Rehbein
- Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | | | - Jens Bölte
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Kati Roesmann
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Institute for Clinical Psychology and Psychotherapy, University of Siegen, Siegen, Germany
| | - Ida Wessing
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Department of Child and Adolescent Psychiatry, University Hospital Muenster, Muenster, Germany
| | - Markus Junghöfer
- Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
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Radecke JO, Sprenger A, Stöckler H, Espeter L, Reichhardt MJ, Thomann LS, Erdbrügger T, Buschermöhle Y, Borgwardt S, Schneider TR, Gross J, Wolters CH, Lencer R. Normative tDCS over V5 and FEF reveals practice-induced modulation of extraretinal smooth pursuit mechanisms, but no specific stimulation effect. Sci Rep 2023; 13:21380. [PMID: 38049419 PMCID: PMC10695990 DOI: 10.1038/s41598-023-48313-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/24/2023] [Indexed: 12/06/2023] Open
Abstract
The neural networks subserving smooth pursuit eye movements (SPEM) provide an ideal model for investigating the interaction of sensory processing and motor control during ongoing movements. To better understand core plasticity aspects of sensorimotor processing for SPEM, normative sham, anodal or cathodal transcranial direct current stimulation (tDCS) was applied over visual area V5 and frontal eye fields (FEF) in sixty healthy participants. The identical within-subject paradigm was used to assess SPEM modulations by practice. While no specific tDCS effects were revealed, within- and between-session practice effects indicate plasticity of top-down extraretinal mechanisms that mainly affect SPEM in the absence of visual input and during SPEM initiation. To explore the potential of tDCS effects, individual electric field simulations were computed based on calibrated finite element head models and individual functional localization of V5 and FEF location (using functional MRI) and orientation (using combined EEG/MEG) was conducted. Simulations revealed only limited electric field target intensities induced by the applied normative tDCS montages but indicate the potential efficacy of personalized tDCS for the modulation of SPEM. In sum, results indicate the potential susceptibility of extraretinal SPEM control to targeted external neuromodulation (e.g., personalized tDCS) and intrinsic learning protocols.
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Affiliation(s)
- Jan-Ole Radecke
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany.
| | - Andreas Sprenger
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany
- Department of Neurology, University of Lübeck, 23562, Lübeck, Germany
- Institute of Psychology II, University of Lübeck, 23562, Lübeck, Germany
| | - Hannah Stöckler
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany
| | - Lisa Espeter
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany
| | - Mandy-Josephine Reichhardt
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany
- Institute of Psychology II, University of Lübeck, 23562, Lübeck, Germany
| | - Lara S Thomann
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149, Münster, Germany
| | - Yvonne Buschermöhle
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149, Münster, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany
| | - Till R Schneider
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149, Münster, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149, Münster, Germany
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562, Lübeck, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149, Münster, Germany
- Institute for Translational Psychiatry, University of Münster, 48149, Münster, Germany
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Galaz Prieto F, Lahtinen J, Samavaki M, Pursiainen S. Multi-compartment head modeling in EEG: Unstructured boundary-fitted tetra meshing with subcortical structures. PLoS One 2023; 18:e0290715. [PMID: 37729152 PMCID: PMC10511141 DOI: 10.1371/journal.pone.0290715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/12/2023] [Indexed: 09/22/2023] Open
Abstract
This paper introduces an automated approach for generating a finite element (FE) discretization of a multi-compartment human head model for electroencephalographic (EEG) source localization. We aim to provide an adaptable FE mesh generation tool for EEG studies. Our technique relies on recursive solid angle labeling of a surface segmentation coupled with smoothing, refinement, inflation, and optimization procedures to enhance the mesh quality. In this study, we performed numerical meshing experiments with the three-layer Ary sphere and a magnetic resonance imaging (MRI)-based multi-compartment head segmentation which incorporates a comprehensive set of subcortical brain structures. These experiments are motivated, on one hand, by the sensitivity of non-invasive subcortical source localization to modeling errors and, on the other hand, by the present lack of open EEG software pipelines to discretize all these structures. Our approach was found to successfully produce an unstructured and boundary-fitted tetrahedral mesh with a sub-one-millimeter fitting error, providing the desired accuracy for the three-dimensional anatomical details, EEG lead field matrix, and source localization. The mesh generator applied in this study has been implemented in the open MATLAB-based Zeffiro Interface toolbox for forward and inverse processing in EEG and it allows for graphics processing unit acceleration.
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Affiliation(s)
- Fernando Galaz Prieto
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
| | - Joonas Lahtinen
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
| | - Maryam Samavaki
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
| | - Sampsa Pursiainen
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
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Medani T, Garcia-Prieto J, Tadel F, Antonakakis M, Erdbrügger T, Höltershinken M, Mead W, Schrader S, Joshi A, Engwer C, Wolters CH, Mosher JC, Leahy RM. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. Neuroimage 2023; 267:119851. [PMID: 36599389 PMCID: PMC9904282 DOI: 10.1016/j.neuroimage.2022.119851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 11/28/2022] [Accepted: 12/31/2022] [Indexed: 01/02/2023] Open
Abstract
Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.
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Affiliation(s)
- Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States.
| | - Juan Garcia-Prieto
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States.
| | - Francois Tadel
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Marios Antonakakis
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; School of Electrical and Computer Engineering, Technical University of Crete, Greece
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Malte Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Wayne Mead
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Sophie Schrader
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Department of Applied Mathematics, University of Münster, Germany
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Christian Engwer
- Department of Applied Mathematics, University of Münster, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - John C Mosher
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
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Unnwongse K, Rampp S, Wehner T, Kowoll A, Parpaley Y, von Lehe M, Lanfer B, Rusiniak M, Wolters C, Wellmer J. Validating EEG source imaging using intracranial electrical stimulation. Brain Commun 2023; 5:fcad023. [PMID: 36824389 PMCID: PMC9942548 DOI: 10.1093/braincomms/fcad023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 08/31/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023] Open
Abstract
Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual four-compartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (r(3617) = [0.19], P = 0.000) and decreased with an increase in skull conductivity (r(3617) = [-0.26], P = 0.000). High skull conductivities (0.0413-0.0118 S/m) yielded significantly lower localization errors for all source depths. For superficial sources (<20 mm from the inner skull), all skull conductivities yielded insignificantly different localization errors. However, for deeper sources, in particular >40 mm, high skull conductivities of 0.0413 and 0.0206 S/m yielded significantly lower localization errors. In relation to stimulation locations, the majority of estimated dipoles moved outward-forward-downward to inward-forward-downward with a decrease in source depth and an increase in skull conductivity. Multivariate analysis revealed that an increase in source depth, number of skull holes and white matter volume, while a decrease in skull conductivity independently led to higher localization error. This evaluation of electrical source imaging accuracy using artificial patterns with a high signal-to-noise ratio supports its application in presurgical epilepsy evaluation and cognitive neurosciences. In our artificial potential model, optimizing the selected skull conductivity minimized the localization error. Future studies should examine if this accounts for true neural signals.
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Affiliation(s)
- Kanjana Unnwongse
- Correspondence to: Kanjana Unnwongse, MD Ruhr Epileptologie, Neurologische Klinik Universitätsklinikum Knappschaftskrankenhaus Bochum In der Schornau 23-25, 44892 Bochum, Germany E-mail:
| | | | - Tim Wehner
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | - Annika Kowoll
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | - Yaroslav Parpaley
- Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany
| | - Marec von Lehe
- Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany
| | - Benjamin Lanfer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | | | - Carsten Wolters
- Institute for Biomagnetism und Biosignalanalysis, University of Münster, 48149 Münster, Germany,Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Jörg Wellmer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
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Kroker T, Wyczesany M, Rehbein MA, Roesmann K, Wessing I, Junghöfer M. Noninvasive stimulation of the ventromedial prefrontal cortex modulates rationality of human decision-making. Sci Rep 2022; 12:20213. [PMID: 36418381 DOI: 10.1038/s41598-022-24526-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 11/16/2022] [Indexed: 11/25/2022] Open
Abstract
The framing-effect is a bias that affects decision-making depending on whether the available options are presented with positive or negative connotations. Even when the outcome of two choices is equivalent, people have a strong tendency to avoid the negatively framed option. The ventromedial prefrontal cortex (vmPFC) is crucial for rational decision-making, and dysfunctions in this region have been linked to cognitive biases, impulsive behavior and gambling addiction. Using a financial decision-making task in combination with magnetoencephalographic neuroimaging, we show that excitatory compared to inhibitory non-invasive transcranial direct current stimulation (tDCS) of the vmPFC reduces framing-effects while improving the assessment of loss-probabilities, ultimately leading to increased overall gains. Behavioral and neural data consistently suggest that this improvement in rational decision-making is predominately due to an attenuation of biases towards negative affect (loss-aversion and risk-aversion). These findings recommend further research towards clinical applications of vmPFC-tDCS as in addictive disorders.
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Raj A, Verma P, Nagarajan S. Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging. Front Neurosci 2022; 16:959557. [PMID: 36110093 PMCID: PMC9468900 DOI: 10.3389/fnins.2022.959557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022] Open
Abstract
We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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