Comparative Analysis of Parallel Brain Activity Mapping Algorithms for High Resolution Brain Models

This paper proposes a comparative analysis between regular and parallel versions of FISTA and Tikhonov-like optimizations for solving the EEG brain mapping problem. Such comparison is performed in terms of computational time reduction and estimation error achieved by the parallelized methods. Two brain models (high- and low-resolution) are used to compare the algorithms. As a result, it can be seen that, if the number of parallel processes increases, computational time decreases significantly for all the head models used in this work, without compromising the reconstruction quality. In addition, it can be concluded that the use of a high-resolution head model produces an improvement in any source reconstruction method in terms of spatial resolution.

Download Full-text

High-Resolution Spatio-Temporal Functional Neuroimaging of Brain Activity

Critical Reviews in Biomedical Engineering ◽

10.1615/critrevbiomedeng.v30.i456.30 ◽

2002 ◽

Vol 30 (4-6) ◽

pp. 283-306 ◽

Cited By ~ 46

Author(s):

Bin He ◽

J. Lian

Keyword(s):

High Resolution ◽

Functional Neuroimaging ◽

Brain Activity ◽

Spatio Temporal

Download Full-text

HIGH-RESOLUTION SPECTRAL RECONSTRUCTION: METHOD AND APPLICATIONS FOR CONTAMINATION MEASUREMENT, DIGITAL SAMPLING, AND CONTINUITY ASSESSMENT

10.30632/spwla-5054 ◽

2020 ◽

Author(s):

Christopher Michael Jones ◽

◽

Yngve B Johansen ◽

Artur Kotwicki ◽

Cameron Rekully ◽

...

Keyword(s):

High Resolution ◽

Reconstruction Method ◽

Digital Sampling ◽

Spectral Reconstruction

Download Full-text

Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

Applied Sciences ◽

10.3390/app9224749 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4749

Author(s):

Lingyun Jiang ◽

Kai Qiao ◽

Linyuan Wang ◽

Chi Zhang ◽

Jian Chen ◽

...

Keyword(s):

Deep Learning ◽

Human Brain ◽

Brain Activity ◽

Feature Space ◽

Training Data ◽

Reconstruction Method ◽

Learning Method ◽

Training Samples ◽

Visual Reconstruction ◽

Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

Download Full-text

Levels of detail analysis of microwave scattering from human head models for brain stroke detection

PeerJ ◽

10.7717/peerj.4061 ◽

2017 ◽

Vol 5 ◽

pp. e4061 ◽

Cited By ~ 4

Author(s):

Awais Munawar Qureshi ◽

Zartasha Mustansar

Keyword(s):

Scattering Problem ◽

Human Head ◽

Computational Time ◽

Head Model ◽

Microwave Scattering ◽

Levels Of Detail ◽

Realistic Head Model ◽

Different Types ◽

Brain Stroke ◽

Brain Tissues

In this paper, we have presented a microwave scattering analysis from multiple human head models. This study incorporates different levels of detail in the human head models and its effect on microwave scattering phenomenon. Two levels of detail are taken into account; (i) Simplified ellipse shaped head model (ii) Anatomically realistic head model, implemented using 2-D geometry. In addition, heterogenic and frequency-dispersive behavior of the brain tissues has also been incorporated in our head models. It is identified during this study that the microwave scattering phenomenon changes significantly once the complexity of head model is increased by incorporating more details using magnetic resonance imaging database. It is also found out that the microwave scattering results match in both types of head model (i.e., geometrically simple and anatomically realistic), once the measurements are made in the structurally simplified regions. However, the results diverge considerably in the complex areas of brain due to the arbitrary shape interface of tissue layers in the anatomically realistic head model.After incorporating various levels of detail, the solution of subject microwave scattering problem and the measurement of transmitted and backscattered signals were obtained using finite element method. Mesh convergence analysis was also performed to achieve error free results with a minimum number of mesh elements and a lesser degree of freedom in the fast computational time. The results were promising and the E-Field values converged for both simple and complex geometrical models. However, the E-Field difference between both types of head model at the same reference point differentiated a lot in terms of magnitude. At complex location, a high difference value of 0.04236 V/m was measured compared to the simple location, where it turned out to be 0.00197 V/m. This study also contributes to provide a comparison analysis between the direct and iterative solvers so as to find out the solution of subject microwave scattering problem in a minimum computational time along with memory resources requirement.It is seen from this study that the microwave imaging may effectively be utilized for the detection, localization and differentiation of different types of brain stroke. The simulation results verified that the microwave imaging can be efficiently exploited to study the significant contrast between electric field values of the normal and abnormal brain tissues for the investigation of brain anomalies. In the end, a specific absorption rate analysis was carried out to compare the ionizing effects of microwave signals to different types of head model using a factor of safety for brain tissues. It is also suggested after careful study of various inversion methods in practice for microwave head imaging, that the contrast source inversion method may be more suitable and computationally efficient for such problems.

Download Full-text

Comparative analysis of native and permethylated human milk oligosaccharides by liquid chromatography coupled to high resolution mass spectrometry

Journal of Chromatography B ◽

10.1016/j.jchromb.2017.03.028 ◽

2017 ◽

Vol 1071 ◽

pp. 49-57 ◽

Cited By ~ 18

Author(s):

Stéphanie Oursel ◽

Sophie Cholet ◽

Christophe Junot ◽

François Fenaille

Keyword(s):

Mass Spectrometry ◽

Liquid Chromatography ◽

Comparative Analysis ◽

High Resolution ◽

Human Milk ◽

High Resolution Mass Spectrometry ◽

Human Milk Oligosaccharides ◽

Milk Oligosaccharides ◽

High Resolution Mass ◽

Resolution Mass

Download Full-text

An objective and efficient method for estimating probabilistic coastal inundation hazards

Natural Hazards ◽

10.1007/s11069-019-03807-w ◽

2019 ◽

Vol 99 (2) ◽

pp. 1105-1130 ◽

Cited By ~ 3

Author(s):

Kun Yang ◽

Vladimir Paramygin ◽

Y. Peter Sheng

Keyword(s):

High Resolution ◽

Storm Surge ◽

Computational Cost ◽

Joint Probability ◽

Water Levels ◽

Computational Time ◽

Probability Method ◽

Highly Efficient ◽

Inundation Maps ◽

Flood Elevation

Abstract The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

Download Full-text

Comparative Analysis of Image Fusion Methods According to Spectral Responses of High-Resolution Optical Sensors

Korean Journal of Remote Sensing ◽

10.7780/kjrs.2014.30.2.6 ◽

2014 ◽

Vol 30 (2) ◽

pp. 227-239 ◽

Cited By ~ 2

Author(s):

Ha-Seong Lee ◽

Kwan-Young Oh ◽

Hyung-Sup Jung

Keyword(s):

Comparative Analysis ◽

High Resolution ◽

Image Fusion ◽

Optical Sensors ◽

Fusion Methods ◽

Spectral Responses

Download Full-text

A Software Toolkit for TMS Electric-Field Modeling with Boundary Element Fast Multipole Method: An Efficient MATLAB Implementation

10.1101/2020.02.09.941021 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sergey N. Makarov ◽

William A. Wartman ◽

Mohammad Daneshzand ◽

Kyoko Fujimoto ◽

Tommi Raij ◽

...

Keyword(s):

High Resolution ◽

Boundary Element ◽

Electric Fields ◽

Fast Multipole Method ◽

Human Head ◽

Head Model ◽

Fast Multipole ◽

Software Toolkit ◽

Model Repository ◽

Multipole Method

AbstractBackgroundTranscranial magnetic stimulation (TMS) is currently the only non-invasive neurostimulation modality that enables painless and safe supra-threshold stimulation by employing electromagnetic induction to efficiently penetrate the skull. Accurate, fast, and high resolution modeling of the electric fields (E-fields) may significantly improve individualized targeting and dosing of TMS and therefore enhance the efficiency of existing clinical protocols as well as help establish new application domains.ObjectiveTo present and disseminate our TMS modeling software toolkit, including several new algorithmic developments, and to apply this software to realistic TMS modeling scenarios given a high-resolution model of the human head including cortical geometry and an accurate coil model.MethodThe recently developed charge-based boundary element fast multipole method (BEM-FMM) is employed as an alternative to the 1st order finite element method (FEM) most commonly used today. The BEM-FMM approach provides high accuracy and unconstrained field resolution close to and across cortical interfaces. Here, the previously proposed BEM-FMM algorithm has been improved in several novel ways.Results and ConclusionsThe improvements resulted in a threefold increase in computational speed while maintaining the same solution accuracy. The computational code based on the MATLAB® platform is made available to all interested researchers, along with a coil model repository and examples to create custom coils, head model repository, and supporting documentation. The presented software toolkit may be useful for post-hoc analyses of navigated TMS data using high-resolution subject-specific head models as well as accurate and fast modeling for the purposes of TMS coil/hardware development.

Download Full-text

Multiscale Modeling of EEG/MEG Response of a Compact Cluster of Tightly Spaced Pyramidal Neocortical Neurons

Brain and Human Body Modeling 2020 ◽

10.1007/978-3-030-45623-8_11 ◽

2020 ◽

pp. 195-211

Author(s):

Sergey N. Makarov ◽

Jyrki Ahveninen ◽

Matti Hämäläinen ◽

Yoshio Okada ◽

Gregory M. Noetscher ◽

...

Keyword(s):

High Resolution ◽

Boundary Element ◽

Multiscale Modeling ◽

Cluster Size ◽

Fast Multipole Method ◽

Head Model ◽

Current Dipole ◽

Neocortical Neurons ◽

Dipole Sources ◽

Compact Cluster

AbstractIn this study, the boundary element fast multipole method or BEM-FMM is applied to model compact clusters of tightly spaced pyramidal neocortical neurons firing simultaneously and coupled with a high-resolution macroscopic head model. The algorithm is capable of processing a very large number of surface-based unknowns along with a virtually unlimited number of elementary microscopic current dipole sources distributed within the neuronal arbor.The realistic cluster size may be as large as 10,000 individual neurons, while the overall computation times do not exceed several minutes on a standard server. Using this approach, we attempt to establish how well the conventional lumped-dipole model used in electroencephalography/magnetoencephalography (EEG/MEG) analysis approximates a compact cluster of realistic neurons situated either in a gyrus (EEG response dominance) or in a sulcus (MEG response dominance).

Download Full-text