Neuroelectromagnetic Forward Head Modeling Toolbox

Acar, Zeynep Akalin; Makeig, Scott

doi:10.1016/j.jneumeth.2010.04.031

Cited by 114 publications

(99 citation statements)

References 40 publications

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“…Individualized modeling implies the need to consider the anatomy of individual subjects , in particular for patient populations that may have abnormal brain anatomy Dmochowski et al, 2013). Various teams have also worked on automating segmentation and modeling for this purpose (Acar and Makeig, 2010;Windhoff et al, 2013;Dannhauer et al, 2012;Huang et al, 2013;Huang and Parra, 2015;Thielscher et al, 2015). We confirmed on the present data that modeling individualized anatomy indeed is beneficial in correctly predicting electric field distribution across space.…”

Section: Guidelines For Modelingsupporting

confidence: 68%

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

et al. 2017

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Transcranial electric stimulation aims to stimulate the brain by applying weak electrical currents at the scalp. However, the magnitude and spatial distribution of electric fields in the human brain are unknown. We measured electric potentials intracranially in ten epilepsy patients and estimated electric fields across the entire brain by leveraging calibrated current-flow models. When stimulating at 2 mA, cortical electric fields reach 0.8 V/m, the lower limit of effectiveness in animal studies. When individual whole-head anatomy is considered, the predicted electric field magnitudes correlate with the recorded values in cortical (r = 0.86) and depth (r = 0.88) electrodes. Accurate models require adjustment of tissue conductivity values reported in the literature, but accuracy is not improved when incorporating white matter anisotropy or different skull compartments. This is the first study to validate and calibrate current-flow models with in vivo intracranial recordings in humans, providing a solid foundation to target stimulation and interpret clinical trials.

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Section: Guidelines For Modelingsupporting

confidence: 68%

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

et al. 2017

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“…Three-layer finite element method (FEM) head meshes using tetrahedral elements (Acar and Makeig, 2010) were used to generate 3-year-old and 4-yearold template head models. A 64-channel Biosemi EEG electrode location template with six EXG ocular reference channels was used to co-register electrode locations to the scalp mesh of the head models.…”

Section: Equivalent Dipole Model Fitting Of Ic Scalp Mapsmentioning

confidence: 99%

“…Sperli et al, 2006;Bathelt et al, 2013). Current optimal methods include boundary-element and finiteelement models, constrained by conductivity estimates of interposed tissues (Acar and Makeig, 2010). Using an age-appropriate anatomical model, it should be possible to estimate the source locations of coherent EEG activity in toddlers.…”

Section: Introductionmentioning

confidence: 99%

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

Acar

Makeig

et al. 2015

NeuroImage

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Contemporary active-EEG and EEG-imaging methods show particular promise for studying the development of action planning and social-action representation in infancy and early childhood. Action-related mu suppression was measured in eleven 3-year-old children and their mothers during a 'live,' largely unscripted social interaction. High-density EEG was recorded from children and synchronized with motion-captured records of children's and mothers' hand actions, and with video recordings. Independent Component Analysis (ICA) was used to separate brain and non-brain source signals in toddlers' EEG records. EEG source dynamics were compared across three kinds of epochs: toddlers' own actions (execution), mothers' actions (observation), and between-turn intervals (no action). Mu (6-9 Hz) power was suppressed in left and right somatomotor cortex during both action execution and observation, as reflected by independent components of individual children's EEG data. These mu rhythm components were accompanied by beta-harmonic (~16 Hz) suppression, similar to findings from adults. The toddlers' power spectrum and scalp density projections provide converging evidence of adult-like musuppression features. Mu-suppression components' source locations were modeled using an age-specific 4-layer forward head model. Putative sources clustered around somatosensory cortex, near the hand/arm region. The results demonstrate that action-locked, event-related EEG dynamics can be measured, and sourceresolved, from toddlers during social interactions with relatively unrestricted social behaviors.

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“…A TBI-tailored version of the METUFEM software package [16,17] is used to compute the forward matrix A of dimensions m × n, where m and n are the number of sensors and sources, respectively. In each volume element within the head, the electric potential Φ is computed using linear interpolation functions [16].…”

Section: Eeg Forward Modelingmentioning

confidence: 99%

Integration of Multimodal Neuroimaging and Electroencephalography for the Study of Acute Epileptiform Activity After Traumatic Brain Injury

Irimia

Goh

Vespa

et al. 2015

Lecture Notes in Computer Science

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Abstract. The integration of multidimensional, longitudinal data acquired using the combined use of structural neuroimaging [e.g. magnetic resonance imaging (MRI), computed tomography (CT)] and neurophysiological recordings [e.g. electroencephalography (EEG)] poses substantial challenges to neuroinformaticians and to biomedical scientists who interact frequently with such data. In traumatic brain injury (TBI) studies, this challenge is even more severe due to the substantial heterogeneity of TBIs across patients and to the variety of neurophysiological responses to injury. Additionally, the study of acute epileptiform activity prompted by TBI poses logistic, analytic and data integration difficulties. Here we describe our proposed solutions to the integration of structural neuroimaging with neurophysiological recordings to study epileptiform activity after TBI. Based on techniques for TBI-robust segmentation and electrical activity localization, we have developed an approach to the joint analysis of MRI/CT/EEG data to identify the foci of seizure-related activity and to facilitate the study of TBI-related neuropathophysiology.

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Neuroelectromagnetic Forward Head Modeling Toolbox

Cited by 114 publications

References 40 publications

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

Integration of Multimodal Neuroimaging and Electroencephalography for the Study of Acute Epileptiform Activity After Traumatic Brain Injury

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