Material and physical model for evaluation of deep brain activity contribution to EEG recordings

2015 ◽  
Vol 08 (03) ◽  
pp. 1540003 ◽  
Author(s):  
Yan Ye ◽  
Xiaoping Li ◽  
Tiecheng Wu ◽  
Zhe Li ◽  
Wenwen Xie
Keyword(s):  
Neuronal Activity ◽  
Surface Potential ◽  
Brain Activity ◽  
Human Head ◽  
Brain Structures ◽  
Dipole Source ◽  
Head Model ◽  
Scalp Eeg ◽  
Eeg Recordings ◽  
Deep Brain

Deep brain activity is conventionally recorded with surgical implantation of electrodes. During the neurosurgery, brain tissue damage and the consequent side effects to patients are inevitably incurred. In order to eliminate undesired risks, we propose that deep brain activity should be measured using the noninvasive scalp electroencephalography (EEG) technique. However, the deeper the neuronal activity is located, the noisier the corresponding scalp EEG signals are. Thus, the present study aims to evaluate whether deep brain activity could be observed from EEG recordings. In the experiment, a three-layer cylindrical head model was constructed to mimic a human head. A single dipole source (sine wave, 10 Hz, altering amplitudes) was embedded inside the model to simulate neuronal activity. When the dipole source was activated, surface potential was measured via electrodes attached on the top surface of the model and raw data were recorded for signal analysis. Results show that the dipole source activity positioned at 66 mm depth in the model, equivalent to the depth of deep brain structures, is clearly observed from surface potential recordings. Therefore, it is highly possible that deep brain activity could be observed from EEG recordings and deep brain activity could be measured using the noninvasive scalp EEG technique.

scholarly journals Neural silences can be localized rapidly using noninvasive scalp EEG

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover
Keyword(s):  
Center Of Mass ◽  
Cost Effective ◽  
Human Head ◽  
Head Model ◽  
Cortical Function ◽  
Scalp Eeg ◽  
Localization Algorithms ◽  
Important Benefit ◽  
Eeg Recordings ◽  
Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

scholarly journals Neural silences can be localized rapidly using noninvasive scalp EEG

2020 ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover
Keyword(s):  
Center Of Mass ◽  
Cost Effective ◽  
Human Head ◽  
Head Model ◽  
Cortical Function ◽  
Scalp Eeg ◽  
Localization Algorithms ◽  
Physiological Properties ◽  
Eeg Recordings ◽  
Detection And Localization

A rapid and cost-effective noninvasive tool to detect and characterize suppressed neural activity can be of significant benefit for the diagnosis and treatment of many disorders. We propose a novel algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural “silences”, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a novel hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric patients with lobectomy, using less than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53mm), as well for 70 different simulated regions of silence based on a real human head model (11±0.5mm vs. 54±2.2mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

Tikhonov Regularization Methods for the Inverse Scalp Electroencephalography

2009 ◽  
Author(s):  
Menglu Wu ◽  
Xiaolin Chen
Keyword(s):  
Tikhonov Regularization ◽  
Brain Activity ◽  
Human Head ◽  
Diagnostic Value ◽  
Parameter Selection ◽  
Head Model ◽  
Regularization Methods ◽  
Scalp Eeg ◽  
Ill Posed ◽  
Curve Method

Electroencephalography (EEG) source localization of brain activity is of high diagnostic value. Noninvasive numerical procedures can be developed to help reconstruct the cortical brain activities from the low-spatial-resolution scalp EEG measurement. In this paper, Tikhonov regularization methods are employed to tackle the solution difficulty associated with the ill-posed reconstruction problem. Three different techniques, namely the L-curve method, the generalized cross validation (GCV) and the discrepancy principle (DP), are implemented to help identify an optimum parameter for the numerical regularization. The numerical procedures are verified by comparing reconstruction results with available theoretical potential solutions for a simplified concentric sphere head model. All three parameter selection methods achieve good results and the L-curve method produces the best regularization effect among the three when the noise level is high in the contaminated scalp data input. More studies are performed on a computational model of an anatomically realistic human head. Our results show that the combination of Tikhonov regularization with the L-curve parameter selection method can effectively regularize the ill-posed inverse EEG problem for brain potential reconstruction.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

scholarly journals MRI-Based Multiscale Model for Electromagnetic Analysis in the Human Head with Implanted DBS

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Maria Ida Iacono ◽  
Nikos Makris ◽  
Luca Mainardi ◽  
Leonardo M. Angelone ◽  
Giorgio Bonmassar
Keyword(s):  
Electric Field ◽  
Ex Vivo ◽  
Human Head ◽  
Multiscale Model ◽  
Head Model ◽  
Tissue Heating ◽  
Electromagnetic Simulations ◽  
Anatomical Detail ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

Deep brain stimulation (DBS) is an established procedure for the treatment of movement and affective disorders. Patients with DBS may benefit from magnetic resonance imaging (MRI) to evaluate injuries or comorbidities. However, the MRI radio-frequency (RF) energy may cause excessive tissue heating particularly near the electrode. This paper studies how the accuracy of numerical modeling of the RF field inside a DBS patient varies with spatial resolution and corresponding anatomical detail of the volume surrounding the electrodes. A multiscale model (MS) was created by an atlas-based segmentation using a 1 mm3head model (mRes) refined in the basal ganglia by a 200 μm2ex-vivo dataset. Four DBS electrodes targeting the left globus pallidus internus were modeled. Electromagnetic simulations at 128 MHz showed that the peak of the electric field of the MS doubled (18.7 kV/m versus 9.33 kV/m) and shifted 6.4 mm compared to the mRes model. Additionally, the MS had a sixfold increase over the mRes model in peak-specific absorption rate (SAR of 43.9 kW/kg versus 7 kW/kg). The results suggest that submillimetric resolution and improved anatomical detail in the model may increase the accuracy of computed electric field and local SAR around the tip of the implant.

scholarly journals Four-sphere head model for EEG signals revisited

2017 ◽  
Author(s):  
Solveig Næss ◽  
Chaitanya Chintaluri ◽  
Torbjørn V. Ness ◽  
Anders M. Dale ◽  
Gaute T. Einevoll ◽  
...  
Keyword(s):  
Analytical Formula ◽  
Mathematical Expression ◽  
Head Model ◽  
The Finite Element Method ◽  
Sphere Model ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Spherical Head ◽  
Analytical Formulas ◽  
Eeg Recordings

AbstractElectric potential recorded at the scalp (EEG) is dominated by contributions from current dipoles set by active neurons in the cortex. Estimation of these currents, called ’inverse modeling’, requires a ’forward’ model, which gives the potential when the positions, sizes, and directions of the current dipoles are known. Different models of varying complexity and realism are used in the field. An important analytical example is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. This model has been used extensively in the analysis of EEG recordings. Since it is analytical, it can also serve as a benchmark against which numerical schemes, such as the Finite Element Method (FEM), can be tested. While conceptually clear, the mathematical expression for the scalp potentials in the four-sphere model is quite cumbersome, and we observed the formulas presented in the literature to contain errors. We here derive and present the correct analytical formulas for future reference. They are compared with the results of FEM simulations of four-sphere model. We also provide scripts for computing EEG potentials in this model with the correct analytical formula and using FEM.

scholarly journals Miniscope GRIN Lens System for Calcium Imaging of Neuronal Activity from Deep Brain Structures in Behaving Animals

2018 ◽  
Vol 86 (1) ◽  
pp. e56 ◽  
Author(s):  
Lifeng Zhang ◽  
Bo Liang ◽  
Giovanni Barbera ◽  
Sarah Hawes ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Brain Structures ◽  
Lens System ◽  
Grin Lens ◽  
Deep Brain

scholarly journals Effects of Head Models and Dipole Source Parameters on EEG Fields

2015 ◽  
Vol 9 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Li Peng ◽  
Mingming Peng ◽  
Anhuai Xu
Keyword(s):  
Source Parameters ◽  
Human Head ◽  
Basis Functions ◽  
Dipole Source ◽  
Head Model ◽  
Peak Potential ◽  
Head Shape ◽  
The Face ◽  
Pls Method ◽  
Peak Value

Head model and an efficient method for computing the forward EEG (electroencephalography)problem are essential to dipole source localization(DSL). In this paper, we use less expensive ovoid geometry to approximate human head, aiming at investigating the effects of head shape and dipole source parameters on EEG fields. The application of point least squares (PLS) based on meshless method was introduced for solving EEG forward problem and numerical simulation is implemented in three kinds of ovoid head models. We present the performances of the surface potential in the face of varying dipole source parameters in detail. The results show that the potential patterns are similar for different dipole position in different head shapes, but the peak value of potential is significantly influenced by the head shape. Dipole position induces a great effect on the peak value of potential and shift of peak potential. The degree of variation between sphere head model and non-sphere head models is seen at the same time. We also show that PLS method with the trigonometric basis is superior to the constant basis, linear basis, and quadratic basis functions in accuracy and efficiency.

scholarly journals Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons

2018 ◽  
Author(s):  
Aman Aberra ◽  
Boshuo Wang ◽  
Warren M Grill ◽  
Angel V Peterchev
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Fields ◽  
Cortical Neurons ◽  
Magnetic Stimulation ◽  
Brain Activity ◽  
Pyramidal Cells ◽  
Human Head ◽  
Mechanistic Explanation ◽  
Head Model ◽  
Neural Activation

Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements it activates and how stimulation parameters affect the neural response. We integrated detailed neuronal models with TMS-induced electric fields in the human head to quantify the effects of TMS on cortical neurons. TMS activated with lowest intensity layer 5 pyramidal cells at their intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 2/3 pyramidal cells and inhibitory basket cells may be activated too, whereas direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, contrary to theories implicating the field component normal to the cortical surface. Varying the induced current's direction caused a waveform-dependent shift in the activation site and provided a mechanistic explanation for experimentally observed differences in thresholds and latencies of muscle responses. This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other forms of cortical stimulation.

scholarly journals Theta bursts precede, and spindles follow, cortical and thalamic downstates in human NREM sleep

2018 ◽  
Author(s):  
Chris Gonzalez ◽  
Rachel Mak-McCully ◽  
Burke Rosen ◽  
Sydney S. Cash ◽  
Patrick Chauvel ◽  
...  
Keyword(s):  
Brain Activity ◽  
Intractable Epilepsy ◽  
Nrem Sleep ◽  
Human Cortex ◽  
Scalp Eeg ◽  
Temporal Coupling ◽  
Healthy Cohort ◽  
Eeg Recordings ◽  
Generative Mechanisms

Abstract:Since their discovery, slow oscillations have been observed to group spindles during non-REM sleep. Previous studies assert that the slow oscillation downstate (DS) is preceded by slow spindles (10-12Hz), and followed by fast spindles (12-16Hz). Here, using both direct transcortical recordings in patients with intractable epilepsy (n=10, 8 female), as well as scalp EEG recordings from a healthy cohort (n=3, 1 female), we find in multiple cortical areas that both slow and fast spindles follow the DS. Although discrete oscillations do precede DSs, they are theta bursts (TB) centered at 5-8Hz. TBs were more pronounced for DSs in NREM stage N2 compared with N3. TB with similar properties occur in the thalamus, but unlike spindles they have no clear temporal relationship with cortical TB. These differences in corticothalamic dynamics, as well as differences between spindles and theta in coupling high frequency content, are consistent with NREM theta having separate generative mechanisms from spindles. The final inhibitory cycle of the TB coincides with the DS peak, suggesting that in N2, TB may help trigger the DS. Since the transition to N1 is marked by the appearance of theta, and the transition to N2 by the appearance of DS and thus spindles, a role of TB in triggering DS could help explain the sequence of electrophysiological events characterizing sleep. Finally, the coordinated appearance of spindles and DSs are implicated in memory consolidation processes, and the current findings redefine their temporal coupling with theta during NREM sleep.Significance StatementSleep is characterized by large slow waves which modulate brain activity. Prominent among these are ‘downstates,’ periods of a few tenths of a second when most cells stop firing, and ‘spindles,’ oscillations at about twelve times a second lasting for about a second. In this study, we provide the first detailed description of another kind of sleep wave: ‘theta bursts,’ a brief oscillation at about six cycles per second. We show, recording during natural sleep directly from the human cortex and thalamus, as well as on the human scalp, that theta bursts precede, and spindles follow downstates. Theta bursts may help trigger downstates in some circumstances, and organize cortical and thalamic activity so that memories can be consolidated during sleep.

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