scholarly journals Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

2020 ◽  
Author(s):  
Luke Tait ◽  
Ayşegül Özkan ◽  
Maciej J Szul ◽  
Jiaxiang Zhang
Keyword(s):  
Spatial Resolution ◽  
Resting State ◽  
Functional Neuroimaging ◽  
Superior Performance ◽  
High Temporal Resolution ◽  
Source Reconstruction ◽  
Reconstruction Algorithms ◽  
Source Imaging ◽  
Level Data ◽  
Data Driven Approach

AbstractNon-invasive functional neuroimaging of the human brain at rest can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with very high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms for task-based MEG data are available. However, no consensus yet exists on the optimum algorithm for resting-state data.Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. In the context of human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project’s multimodal parcellation of the human cortex. This procedure produced a reduced cortical atlas with 250 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners.For both voxel-wise and parcellated source reconstructions, we showed that the eLORETA inverse algorithm had zero localization error, high spatial resolution, and superior performance in predicting sensor-level activity. Our comprehensive comparisons and recommandations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.

scholarly journals Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics

2018 ◽  
Vol 20 (1) ◽  
pp. 171-196 ◽  
Author(s):  
Bin He ◽  
Abbas Sohrabpour ◽  
Emery Brown ◽  
Zhongming Liu
Keyword(s):  
Temporal Resolution ◽  
Functional Neuroimaging ◽  
Dimensional Space ◽  
Brain Activity ◽  
Brain Dynamics ◽  
High Temporal Resolution ◽  
Spatiotemporal Resolution ◽  
Source Imaging ◽  
Brain Functions ◽  
Wide Range

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

scholarly journals Multimodal Study of Resting-State Functional Connectivity Networks using EEG electrodes position as seed

2017 ◽  
Author(s):  
Gonzalo M. Rojas ◽  
Carolina Alvarez ◽  
Carlos Montoya ◽  
María de la Iglesia-Vayá ◽  
Jaime Cisternas ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Neural Activity ◽  
Resting State ◽  
Blood Oxygenation ◽  
High Temporal Resolution ◽  
Resting State Functional Connectivity ◽  
Diagnosis Method ◽  
Eeg Connectivity

AbstractElectroencephalography (EEG) is the standard diagnosis method for a wide variety of diseases such as epilepsy, sleep disorders, encephalopathies, and coma, among others. Resting-state functional magnetic resonance (rs-fMRI) is currently a technique used in research in both healthy individuals as well as patients. EEG and fMRI are procedures used to obtain direct and indirect measurements of brain neural activity: EEG measures the electrical activity of the brain using electrodes placed on the scalp, and fMRI detects the changes in blood oxygenation that occur in response to neural activity. EEG has a high temporal resolution and low spatial resolution, while fMRI has high spatial resolution and low temporal resolution. Thus, the combination of EEG with rs-fMRI using different methods could be very useful for research and clinical applications. In this article, we describe and show the results of a new methodology for processing rs-fMRI using seeds positioned according to the 10-10 EEG standard. We analyze the functional connectivity and adjacency matrices obtained using 65 seeds based on 10-10 EEG scheme and 21 seeds based on 10-20 EEG. Connectivity networks are created using each 10-20 EEG seeds and are analyzed by comparisons to the seven networks that have been found in recent studies. The proposed method captures high correlation between contralateral seeds, ipsilateral and contralateral occipital seeds, and some in the frontal lobe.

scholarly journals High-numerical-aperture macroscope optics for time-resolved experiments

2019 ◽  
Vol 26 (4) ◽  
pp. 1161-1172 ◽  
Author(s):  
Minna Bührer ◽  
Marco Stampanoni ◽  
Xavier Rochet ◽  
Felix Büchi ◽  
Jens Eller ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Numerical Aperture ◽  
Superior Performance ◽  
High Temporal Resolution ◽  
The Novel ◽  
Time Resolved ◽  
High Numerical Aperture ◽  
Custom Made ◽  
Very High

A novel high-quality custom-made macroscope optics, dedicated to high-resolution time-resolved X-ray tomographic microscopy at the TOMCAT beamline at the Swiss Light Source (Paul Scherrer Institut, Switzerland), is introduced. The macroscope offers 4× magnification, has a very high numerical aperture of 0.35 and it is modular and highly flexible. It can be mounted both in a horizontal and vertical configuration, enabling imaging of tall samples close to the scintillator, to avoid edge-enhancement artefacts. The macroscope performance was characterized and compared with two existing in-house imaging setups, one dedicated to high spatial and one to high temporal resolution. The novel macroscope shows superior performance for both imaging settings compared with the previous systems. For the time-resolved setup, the macroscope is 4 times more efficient than the previous system and, at the same time, the spatial resolution is also increased by a factor of 6. For the high-spatial-resolution setup, the macroscope is up to 8.5 times more efficient with a moderate spatial resolution improvement (factor of 1.5). This high efficiency, increased spatial resolution and very high image quality offered by the novel macroscope optics will make 10–20 Hz high-resolution tomographic studies routinely possible, unlocking unprecedented possibilities for the tomographic investigations of dynamic processes and radiation-sensitive samples.

Human versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design

2021 ◽  
pp. 1-38
Author(s):  
Joshua Gyory ◽  
Nicolas F Soria Zurita ◽  
Jay Martin ◽  
Corey Balon ◽  
Christopher McComb ◽  
...  
Keyword(s):  
Real Time ◽  
Design Process ◽  
Process Management ◽  
Superior Performance ◽  
Team Members ◽  
Engineering Teams ◽  
Complex Design ◽  
Data Driven Approach ◽  
Complex Engineering ◽  
Process Managers

Abstract Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an Artificial Intelligent (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams' actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers' intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from the both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.

scholarly journals Salience Network Disruption in US Army Soldiers with PTSD

2018 ◽  
Author(s):  
Chadi Abdallah ◽  
Christopher Averill ◽  
Amy Ramage ◽  
Lynnette Averill ◽  
Selin Goktas ◽  
...  
Keyword(s):  
Resting State ◽  
Right Hemisphere ◽  
Brain Connectivity ◽  
Brain Network ◽  
Data Driven ◽  
Salience Network ◽  
Symptom Provocation ◽  
Us Army ◽  
Data Driven Approach ◽  
Army Soldiers

BACKGROUND: Better understanding of the neurobiology of posttraumatic stress disorder (PTSD) may be critical to developing novel, effective therapeutics. Here, we conducted a data-driven investigation using a well-established, graph- based topological measure of nodal strength to determine the extent of functional dysconnectivity in a cohort of active duty US Army soldiers with PTSD compared to controls. METHODS: 102 participants with (n=50) or without PTSD (n=52) completed functional magnetic resonance imaging (fMRI) at rest and during symptom provocation using subject-specific script imagery. Vertex/voxel global brain connectivity with global signal regression (GBCr), a measure of nodal strength, was calculated as the average of its functional connectivity with all other vertices/voxels in the brain gray matter. RESULTS: In contrast to during resting-state, where there were no group differences, we found a significantly higher GBCr, in PTSD participants compared to controls, in areas within the right hemisphere, including anterior insula, caudal- ventrolateral prefrontal, and rostral-ventrolateral parietal cortices. Overall, these clusters overlapped with the ventral and dorsal salience networks. Post hoc analysis showed increased GBCr in these salience clusters during symptom provocation compared to resting-state. In addition, resting-state GBCr in the salience clusters predicted GBCr during symptom provocation in PTSD participants but not in controls. CONCLUSION: In PTSD, increased connectivity within the salience network has been previously hypothesized, based primarily on seed-based connectivity findings. The current results strongly support this hypothesis using whole-brain network measure in a fully data-driven approach. It remains to be seen in future studies whether these identified salience disturbances would normalize following treatment.

scholarly journals Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

2020 ◽  
Author(s):  
Paolo Colosio ◽  
Marco Tedesco ◽  
Xavier Fettweis ◽  
Roberto Ranzi
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Spatial Resolution ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Melting ◽  
Passive Microwave ◽  
High Temporal Resolution ◽  
Ice Dynamics ◽  
Enhanced Resolution

Abstract. Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

scholarly journals Simultaneous EEG and fMRI: towards the characterization of structure and dynamics of brain networks

2013 ◽  
Vol 15 (3) ◽  
pp. 381-386 ◽  
Keyword(s):  
Spatial Resolution ◽  
Brain Function ◽  
Methodological Approach ◽  
Brain Networks ◽  
Indirect Measurement ◽  
Synaptic Activity ◽  
High Temporal Resolution ◽  
Neural Mass ◽  
The Brain

Progress in the understanding of normal and disturbed brain function is critically dependent on the methodological approach that is applied. Both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are extremely efficient methods for the assessment of human brain function. The specific appeal of the combination is related to the fact that both methods are complementary in terms of basic aspects: EEG is a direct measurement of neural mass activity and provides high temporal resolution. FMRI is an indirect measurement of neural activity and based on hemodynamic changes, and offers high spatial resolution. Both methods are very sensitive to changes of synaptic activity, suggesting that with simultaneous EEG and fMRI the same neural events can be characterized with both high temporal and spatial resolution. Since neural oscillations that can be assessed with EEG are a key mechanism for multi-site communication in the brain, EEG-fMRI can offer new insights into the connectivity mechanisms of brain networks.

scholarly journals Imaging of neural oscillations with embedded inferential and group prevalence statistics

2017 ◽  
Author(s):  
Peter W. Donhauser ◽  
Esther Florin ◽  
Sylvain Baillet
Keyword(s):  
Brain Activity ◽  
Signal Strength ◽  
Brain Regions ◽  
Oscillatory Activity ◽  
Neural Oscillations ◽  
Source Reconstruction ◽  
Signal To Noise ◽  
Source Imaging ◽  
Methodological Challenges ◽  
Oscillatory Power

AbstractMagnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. Imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges1. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.Author summaryThe oscillatory activity of the brain produces a repertoire of signal dynamics that is rich and complex. Noninvasive recording techniques such as scalp magnetoencephalography and electroencephalography (MEG, EEG) are key methods to advance our comprehension of the role played by neural oscillations in brain functions and dysfunctions. Yet, there are methodological challenges in mapping these elusive components of brain activity that have remained unresolved. We introduce a new mapping technique, called imaging with embedded statistics (iES), which alleviates these difficulties. With iES, signal detection is constrained explicitly to the operational hypotheses of the study design. We show, in a variety of experimental contexts, how iES emphasizes the oscillatory components of brain activity, if any, that match the experimental hypotheses, even in deeper brain regions where signal strength is expected to be weak in MEG. Overall, the proposed method is a new imaging tool to respond to a wide range of neuroscience questions concerning the scaffolding of brain dynamics via anatomically-distributed neural oscillations.

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