scholarly journals Structural templates for imaging EEG cortical sources in infants

2020 ◽  
Author(s):  
Christian O’Reilly ◽  
Eric Larson ◽  
John E. Richards ◽  
Mayada Elsabbagh
Keyword(s):  
Age Differences ◽  
Brain Activity ◽  
Multiple Time ◽  
List Type ◽  
Source Reconstruction ◽  
Pia Mater ◽  
Source Imaging ◽  
Cortical Sources ◽  
Topological Errors ◽  
Eeg Recordings

AbstractElectroencephalographic (EEG) source reconstruction is a powerful approach that helps to unmix scalp signals, mitigates volume conduction issues, and allows anatomical localization of brain activity. Algorithms used to estimate cortical sources require an anatomical model of the head and the brain, generally reconstructed using magnetic resonance imaging (MRI). When such scans are unavailable, a population average can be used for adults, but no average surface template is available for cortical source imaging in infants. To address this issue, this paper introduces a new series of 12 anatomical models for subjects between zero and 24 months of age. These templates are built from MRI averages and volumetric boundary element method segmentation of head tissues available as part of the Neurodevelopmental MRI Database. Surfaces separating the pia mater, the gray matter, and the white matter were estimated using the Infant FreeSurfer pipeline. The surface of the skin as well as the outer and inner skull surfaces were extracted using a cube marching algorithm followed by Laplacian smoothing and mesh decimation. We post-processed these meshes to correct topological errors and ensure watertight meshes. The use of these templates for source reconstruction is demonstrated and validated using 100 high-density EEG recordings in 7-month-old infants. Hopefully, these templates will support future studies based on EEG source reconstruction and functional connectivity in healthy infants as well as in clinical pediatric populations. Particularly, they should make EEG-based neuroimaging more feasible in longitudinal neurodevelopmental studies where it may not be possible to scan infants at multiple time points.Graphical abstractHighlightsTwelve surface templates for infants in the 0-2 years old range are proposedThese templates can be used for EEG source reconstruction using existing toolboxesA relatively modest impact of age differences was found in this age rangeCorrelation analysis confirms increasing source differences with age differencesSources reconstructed with infants versus adult templates significantly differ

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.

scholarly journals Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes

2020 ◽  
Author(s):  
Felix Bröhl ◽  
Christoph Kayser
Keyword(s):  
High Frequency ◽  
Brain Activity ◽  
Low Frequency ◽  
List Type ◽  
Theta Band ◽  
Acoustic Fluctuations ◽  
Eeg Recordings ◽  
Band Limited ◽  
Speech Envelope ◽  
Speech Tracking

AbstractThe representation of speech in the brain is often examined by measuring the alignment of rhythmic brain activity to the speech envelope. To conveniently quantify this alignment (termed ‘speech tracking’) many studies consider the overall speech envelope, which combines acoustic fluctuations across the spectral range. Using EEG recordings, we show that using this overall envelope can provide a distorted picture on speech encoding. We systematically investigated the encoding of spectrally-limited speech-derived envelopes presented by individual and multiple noise carriers in the human brain. Tracking in the 1 to 6 Hz EEG bands differentially reflected low (0.2 – 0.83 kHz) and high (2.66 – 8 kHz) frequency speech-derived envelopes. This was independent of the specific carrier frequency but sensitive to attentional manipulations, and reflects the context-dependent emphasis of information from distinct spectral ranges of the speech envelope in low frequency brain activity. As low and high frequency speech envelopes relate to distinct phonemic features, our results suggest that functionally distinct processes contribute to speech tracking in the same EEG bands, and are easily confounded when considering the overall speech envelope.HighlightsDelta/theta band EEG tracks band-limited speech-derived envelopes similar to real speechLow and high frequency speech-derived envelopes are represented differentiallyHigh-frequency derived envelopes are more susceptible to attentional and contextual manipulationsDelta band tracking shifts towards low frequency derived envelopes with more acoustic detail

scholarly journals SEREEGA: Simulating Event-Related EEG Activity

2018 ◽  
Author(s):  
Laurens R. Krol ◽  
Juliane Pawlitzki ◽  
Fabien Lotte ◽  
Klaus Gramann ◽  
Thorsten O. Zander
Keyword(s):  
Open Source ◽  
Brain Activity ◽  
Simulated Data ◽  
Ground Truth ◽  
List Type ◽  
Eeg Activity ◽  
Eeg Data ◽  
Different Types ◽  
Related Potentials ◽  
Eeg Recordings

AbstractElectroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which e ects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learn-ing algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of sim-ulated epochs of EEG data. It is modular and extensible, at initial release supporting ve different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general work ow of this toolbox, as well as a simulated data set demonstrating some of its functions.HighlightsSimulated EEG data has a known ground truth, which can be used to validate methods.We present a general-purpose open-source toolbox to simulate EEG data.It provides a single framework to simulate many different types of EEG recordings.It is modular, extensible, and already includes a number of head models and signals.It supports noise, oscillations, event-related potentials, connectivity, and more.

scholarly journals Age differences in brain activity related to unsuccessful declarative memory retrieval

2015 ◽  
Vol 1612 ◽  
pp. 30-47 ◽  
Author(s):  
Cheryl L. Grady ◽  
Marie St-Laurent ◽  
Hana Burianová
Keyword(s):  
Age Differences ◽  
Memory Retrieval ◽  
Declarative Memory ◽  
Brain Activity

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

scholarly journals Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter

2020 ◽  
pp. 105830
Author(s):  
Laith Hamid ◽  
Nawar Habboush ◽  
Philipp Stern ◽  
Natia Japaridze ◽  
Ümit Aydin ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Brain Activity ◽  
Source Imaging ◽  
Deep Brain

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 Novel dry electrode EEG headbands for home use: Comparing performance and comfort

2020 ◽  
Vol 6 (3) ◽  
pp. 139-142
Author(s):  
Jens Haueisen ◽  
Patrique Fiedler ◽  
Anna Bernhardt ◽  
Ricardo Gonçalves ◽  
Carlos Fonseca
Keyword(s):  
Brain Activity ◽  
Skin Impedance ◽  
Signal Quality ◽  
The Novel ◽  
Application Time ◽  
Dry Electrodes ◽  
Power Spectral ◽  
Dry Electrode ◽  
Increasing Trend ◽  
Eeg Recordings

AbstractMonitoring brain activity at home using electroencephalography (EEG) is an increasing trend for both medical and non-medical applications. Gel-based electrodes are not suitable due to the gel application requiring extensive preparation and cleaning support for the patient or user. Dry electrodes can be applied without prior preparation by the patient or user. We investigate and compare two dry electrode headbands for EEG acquisition: a novel hybrid dual-textile headband comprising multipin and multiwave electrodes and a neoprene-based headband comprising hydrogel and spidershaped electrodes. We compare the headbands and electrodes in terms of electrode-skin impedance, comfort, electrode offset potential and EEG signal quality. We did not observe considerable differences in the power spectral density of EEG recordings. However, the hydrogel electrodes showed considerably increased impedances and offset potentials, limiting their compatibility with many EEG amplifiers. The hydrogel and spider-shaped electrodes required increased adduction, resulting in a lower wearing comfort throughout the application time compared to the novel headband comprising multipin and multiwave electrodes.

scholarly journals ConvDip: A convolutional neural network for better M/EEG Source Imaging

2020 ◽  
Author(s):  
Lukas Hecker ◽  
Rebekka Rupprecht ◽  
Ludger Tebartz van Elst ◽  
Juergen Kornmeier
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Convolutional Neural Network ◽  
Brain Activity ◽  
Clinical Diagnostics ◽  
Dipole Model ◽  
Source Imaging ◽  
Eeg Source Imaging ◽  
Eeg Data ◽  
Inverse Solutions

AbstractEEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the M/EEG one has to solve the inverse problem, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods (eLORETA and LCMV beamforming) on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. (4) It produces plausible inverse solutions for real-world EEG recordings and needs less than 40 ms for a single forward pass. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG and MEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

scholarly journals Electro-Metabolic Sensing Through Capillary ATP-Sensitive K+ Channels and Adenosine to Control Cerebral Blood Flow

2021 ◽  
Author(s):  
Maria Sancho ◽  
Nicholas R. Klug ◽  
Amreen Mughal ◽  
Thomas J. Heppner ◽  
David Hill-Eubanks ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Vascular Function ◽  
Brain Activity ◽  
List Type ◽  
Capillary Endothelial Cells ◽  
Metabolic Sensing ◽  
Cellular Components

SUMMARYThe dense network of capillaries composed of capillary endothelial cells (cECs) and pericytes lies in close proximity to all neurons, ideally positioning it to sense neuro/glial-derived compounds that regulate regional and global cerebral perfusion. The membrane potential (VM) of vascular cells serves as the essential output in this scenario, linking brain activity to vascular function. The ATP-sensitive K+ channel (KATP) is a key regulator of vascular VM in other beds, but whether brain capillaries possess functional KATP channels remains unknown. Here, we demonstrate that brain capillary ECs and pericytes express KATP channels that robustly control VM. We further show that the endogenous mediator adenosine acts through A2A receptors and the Gs/cAMP/PKA pathway to activate capillary KATP channels. Moreover, KATP channel stimulation in vivo causes vasodilation and increases cerebral blood flow (CBF). These findings establish the presence of KATP channels in cECs and pericytes and suggest their significant influence on CBF.HIGHLIGHTSCapillary network cellular components—endothelial cells and pericytes—possess functional KATP channels.Activation of KATP channels causes profound hyperpolarization of capillary cell membranes.Capillary KATP channels are activated by exogenous adenosine via A2A receptors and cAMP-dependent protein kinase.KATP channel activation by adenosine or synthetic openers increases cerebral blood flow.

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