scholarly journals Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing

NeuroImage ◽  
2018 ◽  
Vol 173 ◽  
pp. 361-369 ◽  
Author(s):  
Kaisu Lankinen ◽  
Jukka Saari ◽  
Yevhen Hlushchuk ◽  
Pia Tikka ◽  
Lauri Parkkonen ◽  
...  
Keyword(s):  
Fmri Signal ◽  
Time Courses ◽  
Signal Time

scholarly journals Motion correction of free-breathing magnetic resonance renography using model-driven registration

2021 ◽  
Author(s):  
Dimitra Flouri ◽  
Daniel Lesnic ◽  
Constantina Chrysochou ◽  
Jehill Parikh ◽  
Peter Thelwall ◽  
...  
Keyword(s):  
Motion Correction ◽  
Ground Truth ◽  
Free Breathing ◽  
Patient Data ◽  
Reference Object ◽  
Free Form ◽  
Model Driven ◽  
Time Courses ◽  
Signal Time ◽  
Parametric Maps

Abstract Introduction Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR). Materials and methods MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data). Results DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses. Discussion MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.

Blood Oxygenation Level Dependent Signal Time Courses During Prolonged Visual Stimulation

1998 ◽  
Vol 16 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Alistair M. Howseman ◽  
David A. Porter ◽  
Chloe Hutton ◽  
Oliver Josephs ◽  
Robert Turner
Keyword(s):  
Visual Stimulation ◽  
Blood Oxygenation Level Dependent ◽  
Blood Oxygenation ◽  
Blood Oxygenation Level ◽  
Oxygenation Level ◽  
Dependent Signal ◽  
Time Courses ◽  
Signal Time

scholarly journals Skilled lipreaders read and listen to lips

2017 ◽  
Author(s):  
S. Saalasti ◽  
J. Alho ◽  
J.M. Lahnakoski ◽  
M. Bacha-Trams ◽  
E. Glerean ◽  
...  
Keyword(s):  
Brain Activity ◽  
Temporal Cortex ◽  
Neural Mechanisms ◽  
Middle Temporal ◽  
Auditory Speech ◽  
The Neural Network ◽  
Neural Substrate ◽  
Time Courses ◽  
Speech Segments ◽  
Signal Time

ABSTRACTOnly a few of us are skilled lipreaders while most struggle at the task. To illuminate the poorly understood neural substrate of this variability, we estimated the similarity of brain activity during lipreading, listening, and reading of the same 8-min narrative with subjects whose lipreading skill varied extensively. The similarity of brain activity was estimated by voxel-wise comparison of the BOLD signal time courses. Inter-subject correlation of the time courses revealed that lipreading and listening are supported by the same brain areas in temporal, parietal and frontal cortices, precuneus and cerebellum. However, lipreading activated only a small part of the neural network that is active during listening/reading the narrative, demonstrating that neural processing during lipreading vs. listening/reading differs substantially. Importantly, skilled lipreading was specifically associated with bilateral activity in the superior and middle temporal cortex, which also encode auditory speech. Our novel results both confirm previous results from few previous studies using isolated speech segments as stimuli but also extend in an important way understanding of neural mechanisms of lipreading.

Temporal and Intensity Coding of Pain in Human Cortex

1998 ◽  
Vol 80 (6) ◽  
pp. 3312-3320 ◽  
Author(s):  
Carlo A. Porro ◽  
Valentina Cettolo ◽  
Maria Pia Francescato ◽  
Patrizia Baraldi
Keyword(s):  
Ascorbic Acid ◽  
Pain Intensity ◽  
Premotor Cortex ◽  
Acid Group ◽  
Spatial Extent ◽  
Intensity Curve ◽  
Human Cortex ◽  
Intensity Coding ◽  
Time Courses ◽  
Signal Time

Porro, Carlo A., Valentina Cettolo, Maria Pia Francescato, and Patrizia Baraldi. Temporal and intensity coding of pain in human cortex. J. Neurophysiol. 80:3312–3320, 1998. We used a high-resolution functional magnetic resonance imaging (fMRI) technique in healthy right-handed volunteers to demonstrate cortical areas displaying changes of activity significantly related to the time profile of the perceived intensity of experimental somatic pain over the course of several minutes. Twenty-four subjects (ascorbic acid group) received a subcutaneous injection of a dilute ascorbic acid solution into the dorsum of one foot, inducing prolonged burning pain (peak pain intensity on a 0–100 scale: 48 ± 3, mean ± SE; duration: 11.9 ± 0.8 min). fMRI data sets were continuously acquired for ∼20 min, beginning 5 min before and lasting 15 min after the onset of stimulation, from two sagittal planes on the medial hemispheric wall contralateral to the stimulated site, including the cingulate cortex and the putative foot representation area of the primary somatosensory cortex (SI). Neural clusters whose fMRI signal time courses were positively or negatively correlated ( P < 0.0005) with the individual pain intensity curve were identified by cross-correlation statistics in all 24 volunteers. The spatial extent of the identified clusters was linearly related ( P < 0.0001) to peak pain intensity. Regional analyses showed that positively correlated clusters were present in the majority of subjects in SI, cingulate, motor, and premotor cortex. Negative correlations were found predominantly in medial parietal, perigenual cingulate, and medial prefrontal regions. To test whether these neural changes were due to aspecific arousal or emotional reactions, related either to anticipation or presence of pain, fMRI experiments were performed with the same protocol in two additional groups of volunteers, subjected either to subcutaneous saline injection (saline: n = 16), inducing mild short-lasting pain (peak pain intensity 23 ± 4; duration 2.8 ± 0.6 min) or to nonnoxious mechanical stimulation of the skin (controls: n = 16) at the same body site. Subjects did not know in advance which stimulus would occur. The spatial extent of neural clusters whose signal time courses were positively or negatively correlated with the mean pain intensity curve of subjects injected with ascorbic acid was significantly larger ( P < 0.001) in the ascorbic acid group than both saline and controls, suggesting that the observed responses were specifically related to pain intensity and duration. These findings reveal distributed cortical systems, including parietal areas as well as cingulate and frontal regions, involved in dynamic encoding of pain intensity over time, a process of great biological and clinical relevance.

Transient and sustained BOLD signal time courses affect the detection of emotion-related brain activation in fMRI

NeuroImage ◽  
2014 ◽  
Vol 103 ◽  
pp. 522-532 ◽  
Author(s):  
Christian Paret ◽  
Rosemarie Kluetsch ◽  
Matthias Ruf ◽  
Traute Demirakca ◽  
Raffael Kalisch ◽  
...  
Keyword(s):  
Brain Activation ◽  
Bold Signal ◽  
Time Courses ◽  
Signal Time

scholarly journals Canonical EEG Microstate Dynamic Properties and Their Associations with fMRI Signals at Resting Brain

2020 ◽  
Author(s):  
Obada Al Zoubi ◽  
Masaya Misaki ◽  
Aki Tsuchiyagaito ◽  
Ahmad Mayeli ◽  
Vadim Zotev ◽  
...  
Keyword(s):  
Functional State ◽  
Emotional Processing ◽  
Time Course ◽  
Temporal Dynamics ◽  
Dynamic Properties ◽  
Mental Illnesses ◽  
Hemodynamic Response Function ◽  
Functional Interpretation ◽  
Fmri Signal ◽  
Time Courses

AbstractElectroencephalography microstates (EEG-ms) capture and reflect the spatio-temporal neural dynamics of the brain. A growing literature is employing EEG-ms-based analyses to study various mental illnesses and to evaluate brain mechanisms implicated in cognitive and emotional processing. The spatial and functional interpretation of the EEG-ms is still being investigated. Previous works studied the association of EEG-ms time courses with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal and suggested an association between EEG-ms and resting-state networks (RSNs). However, the distinctive association between EEG-ms temporal dynamics and brain neuronal activities is still not clear, despite the assumption that EEG-ms are an electrophysiological representation of RSNs activity. Recent works suggest a role for brain spontaneous EEG rhythms in contributing to and modulating canonical EEG-ms topographies and determining their classes (coined A through D) and metrics. This work simultaneously utilized EEG and fMRI to understand the EEG-ms and their properties further. We adopted the canonical EEG-ms analysis to extract three types of regressors for EEG-informed fMRI analyses: EEG-ms direct time courses, temporal activity per microstate, and pairwise temporal transitions among microstates (the latter two coined activity regressors). After convolving EEG-ms regressors with a hemodynamic response function, a generalized linear model whole-brain voxel-wise analysis was conducted to associate EEG-ms regressors with fMRI signals. The direct time course regressors replicated prior findings of the association between the fMRI signal and EEG-ms time courses but to a smaller extent. Notably, EEG-ms activity regressors were mostly anticorrelated with fMRI, including brain regions in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with no significant overlap for default mode, limbic or frontoparietal networks. A similar pattern emerged in using the transition regressors among microstates but not in self-transitions. The relatively short duration of each EEG-ms and the significant association of EEG-ms activity regressors with fMRI signals suggest that EEG-ms manifests successive transition from one brain functional state to another rather than being associated with specific brain functional state or RSN networks.

scholarly journals High spatiotemporal vessel-specific hemodynamic mapping with multi-echo single-vessel fMRI

2019 ◽  
Vol 40 (10) ◽  
pp. 2098-2114 ◽  
Author(s):  
Yi He ◽  
Maosen Wang ◽  
Xin Yu
Keyword(s):  
Volume Fraction ◽  
Quantitative Estimation ◽  
T2 Mapping ◽  
Sampling Rate ◽  
Spatiotemporal Resolution ◽  
Single Vessel ◽  
Fmri Signal ◽  
Noninvasive Mapping ◽  
Blood Inflow ◽  
Time Courses

High-resolution fMRI enables noninvasive mapping of the hemodynamic responses from individual penetrating vessels in animal brains. Here, a 2D multi-echo single-vessel fMRI (MESV-fMRI) method has been developed to map the fMRI signal from arterioles and venules with a 100 ms sampling rate at multiple echo times (TE, 3–30 ms) and short acquisition windows (<1 ms). The T2*-weighted signal shows the increased extravascular effect on venule voxels as a function of TE. In contrast, the arteriole voxels show an increased fMRI signal with earlier onset than venules voxels at the short TE (3 ms) with increased blood inflow and volume effects. MESV-fMRI enables vessel-specific T2* mapping and presents T2*-based fMRI time courses with higher contrast-to-noise ratios (CNRs) than the T2*-weighted fMRI signal at a given TE. The vessel-specific T2* mapping also allows semi-quantitative estimation of the oxygen saturation levels (Y) and their changes (ΔY) at a given blood volume fraction upon neuronal activation. The MESV-fMRI method enables vessel-specific T2* measurements with high spatiotemporal resolution for better modeling of the fMRI signal based on the hemodynamic parameters.

scholarly journals Simulating brain signals with predefined mutual correlations

2021 ◽  
Author(s):  
Alexander Moiseev
Keyword(s):  
Correlation Matrix ◽  
Arbitrary Number ◽  
Seed Set ◽  
Technical Note ◽  
Linear Algorithm ◽  
Brain Signals ◽  
Time Courses ◽  
Signal Time

This technical note describes a simple linear algorithm which allows generating arbitrary number of signal time courses whose mutual correlations are defined by a given mathematically consistent correlation matrix. This is achieved by mixing some seed set of waveforms typically reflecting desired specific features of the target brain signals. If needed the resulting signals can be properly tapered to be neurophysiologically plausible. For multi-epoch designs the generated waveforms may also include a "phase-locked" component which does not vary between the epochs.

scholarly journals Effects of fluorescent glutamate indicators on neurotransmitter diffusion and uptake

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Moritz Armbruster ◽  
Chris G Dulla ◽  
Jeffrey S Diamond
Keyword(s):  
Neurotransmitter Release ◽  
Glutamate Uptake ◽  
Receptor Activation ◽  
Experimental Conditions ◽  
Mouse Cortex ◽  
Quantal Analysis ◽  
Time Courses ◽  
Kinetics Of ◽  
And Diffusion ◽  
Signal Time

Genetically encoded fluorescent glutamate indicators (iGluSnFRs) enable neurotransmitter release and diffusion to be visualized in intact tissue. Synaptic iGluSnFR signal time courses vary widely depending on experimental conditions, often lasting 10–100 times longer than the extracellular lifetime of synaptically released glutamate estimated with uptake measurements. iGluSnFR signals typically also decay much more slowly than the unbinding kinetics of the indicator. To resolve these discrepancies, here we have modeled synaptic glutamate diffusion, uptake and iGluSnFR activation to identify factors influencing iGluSnFR signal waveforms. Simulations suggested that iGluSnFR competes with transporters to bind synaptically released glutamate, delaying glutamate uptake. Accordingly, synaptic transporter currents recorded from iGluSnFR-expressing astrocytes in mouse cortex were slower than those in control astrocytes. Simulations also suggested that iGluSnFR reduces free glutamate levels in extrasynaptic spaces, likely limiting extrasynaptic receptor activation. iGluSnFR and lower affinity variants, nonetheless, provide linear indications of vesicle release, underscoring their value for optical quantal analysis.

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