scholarly journals Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

2017 ◽  
Author(s):  
DW Carmichael ◽  
S Vulliemoz ◽  
T Murta ◽  
U. Chaudhary ◽  
S Perani ◽  
...  
Keyword(s):  
Human Brain ◽  
Sensorimotor Cortex ◽  
Brain Activity ◽  
Spatial Scales ◽  
Electrophysiological Response ◽  
Intracranial Eeg ◽  
Eeg Activity ◽  
High Gamma ◽  
Gamma Power ◽  
Patients With Epilepsy

AbstractThere are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales by intracranial electroencephalography and fMRI. By comparing individual features and summary descriptions of intracranial EEG activity we determined which best predict fMRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. We also then examine the specificity of this relationship to spatial colocalisation of the two signals.We acquired electrocorticography and fMRI simultaneously (ECoG-fMRI) in the sensorimotor cortex of 3 patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was colocalised with fMRI measures across all subjects. The best model of fMRI changes was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than a model based on the classical frequency bands both during task and rest periods or models derived on a summary of cross spectral changes (e.g. ‘root mean squared EEG frequency’). This suggests that the region specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

scholarly journals The Dynamics of Error Processing in the Human Brain as Reflected by High-Gamma Activity in Noninvasive and Intracranial EEG

2017 ◽  
Author(s):  
Martin Völker ◽  
Lukas D. J. Fiederer ◽  
Sofie Berberich ◽  
Jiří Hammer ◽  
Joos Behncke ◽  
...  
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Brain Activity ◽  
Local Level ◽  
Error Processing ◽  
Intracranial Eeg ◽  
High Gamma ◽  
Related Response ◽  
Spatio Temporal ◽  
Oscillatory Brain Activity

AbstractError detection in motor behavior is a fundamental cognitive function heavily relying on cortical information processing. Neural activity in the high-gamma frequency band (HGB) closely reflects such local cortical processing, but little is known about its role in error processing, particularly in the healthy human brain. Here we characterize the error-related response of the human brain based on data obtained with noninvasive EEG optimized for HGB mapping in 31 healthy subjects (15 females, 16 males), and additional intracranial EEG data from 9 epilepsy patients (4 females, 5 males). Our findings reveal a comprehensive picture of the global and local dynamics of error-related HGB activity in the human brain. On the global level as reflected in the noninvasive EEG, the error-related response started with an early component dominated by anterior brain regions, followed by a shift to parietal regions, and a subsequent phase characterized by sustained parietal HGB activity. This phase lasted for more than 1 s after the error onset. On the local level reflected in the intracranial EEG, a cascade of both transient and sustained error-related responses involved an even more extended network, spanning beyond frontal and parietal regions to the insula and the hippocampus. HGB mapping appeared especially well suited to investigate late, sustained components of the error response, possibly linked to downstream functional stages such as error-related learning and behavioral adaptation. Our findings establish the basic spatio-temporal properties of HGB activity as a neural correlate of error processing, complementing traditional error-related potential studies.Significance StatementThere is great interest to understand how the human brain reacts to errors in goal-directed behavior. An important index of cortical and subcortical information processing is fast oscillatory brain activity, particularly in the high-gamma band (above 50 Hz). Here we show that it is possible to detect signatures of errors in event-related high-gamma responses with noninvasive techniques, characterize these responses comprehensively, and validate the EEG procedure for the detection of such signals. In addition, we demonstrate the added value of intracranial recordings pinpointing the fine-grained spatio-temporal patterns in error-related brain networks. We anticipate that the optimized noninvasive EEG techniques as described here will be helpful in many areas of cognitive neuroscience where fast oscillatory brain activity is of interest.

scholarly journals EEG activity during balance platform test in humans

2019 ◽  
pp. 132-136 ◽  
Author(s):  
Vladimir Khorev ◽  
Artem Badarin ◽  
Vladimir Antipov ◽  
Vladimir Maksimenko ◽  
Semen Kurkin
Keyword(s):  
Cerebral Cortex ◽  
Human Brain ◽  
Brain Activity ◽  
Body Posture ◽  
Future Research ◽  
Eeg Activity ◽  
Common Features ◽  
Computer Interfaces ◽  
New Directions ◽  
Brain States

In order to analyze different human brain states related to perception and maintaining of body posture, we implemented an experiment with a balance platform. It is known the cerebral cortex regulates subcortical postural centers to maintain upright balance and posture and balance demands. However, the cortical mechanisms that support standing balance remain elusive. In this work, we present an EEG-based analysis during execution of balance responses with distinct postural demands. The results suggest the existence of common features in the EEG structure associated with distinct activity during balance maintaining. This may give new directions for future research in the field of brain activity, and for the development of brain-computer interfaces.

scholarly journals The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial EEG

NeuroImage ◽  
2018 ◽  
Vol 173 ◽  
pp. 564-579 ◽  
Author(s):  
Martin Völker ◽  
Lukas D.J. Fiederer ◽  
Sofie Berberich ◽  
Jiří Hammer ◽  
Joos Behncke ◽  
...  
Keyword(s):  
Human Brain ◽  
Gamma Activity ◽  
Error Processing ◽  
Intracranial Eeg ◽  
High Gamma

scholarly journals Electrophysiological investigation of the contribution of attention to altered pain inhibition processes in patients with irritable bowel syndrome

2020 ◽  
Vol 70 (1) ◽  
Author(s):  
Nabi Rustamov ◽  
Alice Wagenaar-Tison ◽  
Elysa Doyer ◽  
Mathieu Piché
Keyword(s):  
Irritable Bowel Syndrome ◽  
Selective Attention ◽  
Brain Activity ◽  
Gastrointestinal Disorder ◽  
Anterior Cingulate ◽  
Control Group ◽  
High Gamma ◽  
Gamma Power ◽  
Irritable Bowel ◽  
Pain Inhibition

Abstract Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder associated with chronic abdominal pain and altered pain processing. The aim of this study was to examine whether attentional processes contribute to altered pain inhibition processes in patients with IBS. Nine female patients with IBS and nine age-/sex-matched controls were included in a pain inhibition paradigm using counter-stimulation and distraction with electroencephalography. Patients with IBS showed no inhibition of pain-related brain activity by heterotopic noxious counter-stimulation (HNCS) or selective attention. In the control group, HNCS and selective attention decreased the N100, P260 and high-gamma oscillation power. In addition, pain-related high-gamma power in sensorimotor, anterior cingulate and left dorsolateral prefrontal cortex was decreased by HNCS and selective attention in the control group, but not in patients with IBS. These results indicate that the central pain inhibition deficit in IBS reflects interactions between several brain processes related to pain and attention.

scholarly journals Interactions between motor thalamic field potentials and single unit spiking predict behavior in rats

2019 ◽  
Author(s):  
Matt Gaidica ◽  
Amy Hurst ◽  
Christopher Cyr ◽  
Daniel K. Leventhal
Keyword(s):  
Task Performance ◽  
Brain Activity ◽  
Field Potential ◽  
Spike Timing ◽  
Delta Phase ◽  
Beta Power ◽  
High Gamma ◽  
Gamma Power ◽  
Complex Relationships ◽  
And Behavior

AbstractThe thalamus plays a central role in generating circuit-level neural oscillations believed to coordinate brain activity over large spatiotemporal scales. Such thalamic influences are well-documented for sleep rhythms and in sensory systems, but the relationship between thalamic activity, motor circuit local field potential (LFP) oscillations, and behavior is unknown. We recorded wideband motor thalamic (Mthal) electrophysiology as healthy rats performed a two-alternative forced choice task. The power of delta (1−4 Hz), beta (13−30 Hz), low gamma (30−70 Hz), and high gamma (70−200 Hz) oscillations were strongly modulated by task performance. As in cortex, delta phase predicted beta/low gamma power and reaction time. Furthermore, delta phase differentially predicted spike timing in functionally distinct populations of Mthal neurons, which also predicted task performance and beta power. These complex relationships suggest mechanisms for commonly observed LFP-LFP and spike-LFP interactions, as well as subcortical influences on motor output.

scholarly journals Subthalamic nucleus and sensorimotor cortex activity during speech production

2018 ◽  
Author(s):  
A Chrabaszcz ◽  
WJ Neumann ◽  
O Stretcu ◽  
WJ Lipski ◽  
A Bush ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Speech Production ◽  
Subthalamic Nucleus ◽  
Sensorimotor Cortex ◽  
Vocal Tract ◽  
Field Potential ◽  
Gamma Activity ◽  
High Gamma ◽  
Gamma Power ◽  
Deep Brain

ABSTRACTThe sensorimotor cortex is somatotopically organized to represent the vocal tract articulators, such as lips, tongue, larynx, and jaw. How speech and articulatory features are encoded at the subcortical level, however, remains largely unknown. We analyzed local field potential (LFP) recordings from the subthalamic nucleus (STN) and simultaneous electrocorticography recordings from the sensorimotor cortex of 11 patients (1 female) with Parkinson’s disease during implantation of deep brain stimulation (DBS) electrodes, while patients read aloud three-phoneme words. The initial phonemes involved either articulation primarily with the tongue (coronal consonants) or the lips (labial consonants). We observed significant increases in high gamma (60–150 Hz) power in both the STN and the sensorimotor cortex that began before speech onset and persisted for the duration of speech articulation. As expected from previous reports, in the sensorimotor cortex, the primary articulator involved in the production of the initial consonant was topographically represented by high gamma activity. We found that STN high gamma activity also demonstrated specificity for the primary articulator, although no clear topography was observed. In general, subthalamic high gamma activity varied along the ventral-dorsal trajectory of the electrodes, with greater high gamma power recorded in the more dorsal locations of the STN. These results demonstrate that articulator-specific speech information is contained within high gamma activity of the STN, with similar temporal but less specific topographical organization, compared to similar information encoded in the sensorimotor cortex.SIGNIFICANCE STATEMENTClinical and electrophysiological evidence suggests that the subthalamic nucleus is involved in speech, however, this important basal ganglia node is ignored in current models of speech production. We previously showed that subthalamic nucleus neurons differentially encode early and late aspects of speech production, but no previous studies have examined subthalamic functional organization for speech articulators. Using simultaneous local field potential recordings from the sensorimotor cortex and the subthalamic nucleus in patients with Parkinson’s disease undergoing deep brain stimulation surgery, we discovered that subthalamic nucleus high gamma activity tracks speech production at the level of vocal tract articulators, with high gamma power beginning to increase prior to the onset of vocalization, similar to cortical articulatory encoding.

scholarly journals The Dynamics of Language Network Interactions in Lexical Selection: An Intracranial EEG Study

2020 ◽  
Author(s):  
Yujing Wang ◽  
Anna Korzeniewska ◽  
Kiyohide Usami ◽  
Alyssandra Valenzuela ◽  
Nathan E Crone
Keyword(s):  
Temporal Cortex ◽  
Sentence Completion ◽  
Local Network ◽  
Lexical Selection ◽  
Population Activity ◽  
Intracranial Eeg ◽  
Lexical Representations ◽  
High Gamma ◽  
Gamma Power ◽  
Intracranial Recordings

Abstract Speaking in sentences requires selection from contextually determined lexical representations. Although posterior temporal cortex (PTC) and Broca’s areas play important roles in storage and selection, respectively, of lexical representations, there has been no direct evidence for physiological interactions between these areas on time scales typical of lexical selection. Using intracranial recordings of cortical population activity indexed by high-gamma power (70–150 Hz) modulations, we studied the causal dynamics of cortical language networks while epilepsy surgery patients performed a sentence completion task in which the number of potential lexical responses was systematically varied. Prior to completion of sentences with more response possibilities, Broca’s area was not only more active, but also exhibited more local network interactions with and greater top-down influences on PTC, consistent with activation of, and competition between, more lexical representations. These findings provide the most direct experimental support yet for network dynamics playing a role in lexical selection among competing alternatives during speech production.

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

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.

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