scholarly journals Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

2020 ◽  
Author(s):  
Michalis Kassinopoulos ◽  
Georgios D. Mitsis
Keyword(s):  
Heart Rate ◽  
Brain Activity ◽  
Low Frequency ◽  
Blood Oxygenation ◽  
Breathing Patterns ◽  
Physiological Noise ◽  
Model Based ◽  
Human Connectome Project ◽  
Contrast Mechanism ◽  
Noise Correction

AbstractThe blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with RETROICOR, which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS – defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

scholarly journals Association Between Brain Activation and Functional Connectivity

2018 ◽  
Vol 29 (5) ◽  
pp. 1984-1996 ◽  
Author(s):  
Dardo Tomasi ◽  
Nora D Volkow
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Low Frequency ◽  
Brain Activation ◽  
Blood Oxygen Level Dependent ◽  
Common Source ◽  
Functional Connectivity Density ◽  
Density Mapping ◽  
Human Connectome Project ◽  
Bold Responses

Abstract The origin of the “resting-state” brain activity recorded with functional magnetic resonance imaging (fMRI) is still uncertain. Here we provide evidence for the neurovascular origins of the amplitude of the low-frequency fluctuations (ALFF) and the local functional connectivity density (lFCD) by comparing them with task-induced blood-oxygen level dependent (BOLD) responses, which are considered a proxy for neuronal activation. Using fMRI data for 2 different tasks (Relational and Social) collected by the Human Connectome Project in 426 healthy adults, we show that ALFF and lFCD have linear associations with the BOLD response. This association was significantly attenuated by a novel task signal regression (TSR) procedure, indicating that task performance enhances lFCD and ALFF in activated regions. We also show that lFCD predicts BOLD activation patterns, as was recently shown for other functional connectivity metrics, which corroborates that resting functional connectivity architecture impacts brain activation responses. Thus, our findings indicate a common source for BOLD responses, ALFF and lFCD, which is consistent with the neurovascular origin of local hemodynamic synchrony presumably reflecting coordinated fluctuations in neuronal activity. This study also supports the development of task-evoked functional connectivity density mapping.

scholarly journals Impact of physiological noise correction on detecting blood oxygenation level-dependent contrast in the breast

2016 ◽  
Vol 62 (1) ◽  
pp. 127-145
Author(s):  
Tess E Wallace ◽  
Roido Manavaki ◽  
Martin J Graves ◽  
Andrew J Patterson ◽  
Fiona J Gilbert
Keyword(s):  
Blood Oxygenation Level Dependent ◽  
Blood Oxygenation ◽  
Physiological Noise ◽  
Blood Oxygenation Level ◽  
Oxygenation Level ◽  
Noise Correction

scholarly journals Quasi-Periodic Patterns of Intrinsic Brain Activity in Individuals and their Relationship to Global Signal

2017 ◽  
Author(s):  
Behnaz Yousefi ◽  
Jaemin Shin ◽  
Eric H. Schumacher ◽  
Shella D. Keilholz
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Primary Source ◽  
Intrinsic Activity ◽  
Periodic Patterns ◽  
Physiological Noise ◽  
Global Signal ◽  
Human Connectome Project ◽  
Large Scale Networks ◽  
Induced Signal

AbstractQuasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain’s intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual’s global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion (or even high motion) and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

Periodicities in respiration and heart rate in newborns

1983 ◽  
Vol 61 (4) ◽  
pp. 329-335 ◽  
Author(s):  
John P. Finley ◽  
Sherwin T. Nugent
Keyword(s):  
Heart Rate ◽  
Newborn Infants ◽  
Low Frequency ◽  
Periodic Breathing ◽  
Respiratory Pattern ◽  
Breathing Patterns ◽  
Quiet Sleep ◽  
Amplitude Variation ◽  
Normal Breathing ◽  
Prolonged Apnea

Periodic breathing is common in normal infants, but may be associated with prolonged apnea leading to crib death. The mechanisms of periodic breathing and its relation to normal breathing patterns are unclear. We recorded respiratory and heart rate (HR) patterns of 11 healthy newborn infants during quiet sleep, in both normal and periodic breathing. Spectral analysis of the respiratory pattern revealed a low-frequency (LF) periodicity in normal breathing approximately equal to the frequency of periodic breathing when this occurs. Periodic breathing thus appears to be an exaggeration of an underlying slow amplitude variation which is present in regular breathing. LF periodicity also appeared in the HR pattern in both normal and periodic breathing, suggesting an LF modulation of cardiovascular control as well. The lack of a definite phase relation between HR and ventilation at LF may indicate dominant peripheral, rather than central, interactions between HR and respiration at these frequencies.

scholarly journals The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity

2021 ◽  
Author(s):  
Masaya Misaki ◽  
Jerzy Bodurka
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Noise Reduction ◽  
Real Time ◽  
Brain Activity ◽  
Time Estimation ◽  
Blood Oxygenation ◽  
Physiological Noise ◽  
Global Signal ◽  
The Impact

AbstractObjectiveComprehensive denoising is imperative in fMRI analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity.ApproachWe performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT).Main resultsAll the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETORICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time.SignificanceThe results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.

scholarly journals Motion and physiological noise effects on amygdala real-time fMRI neurofeedback learning

2018 ◽  
Author(s):  
Lydia Hellrung ◽  
Viola Borchardt ◽  
Florian N. Götting ◽  
Jörg Stadler ◽  
Claus Tempelmann ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Self Regulation ◽  
Bold Signal ◽  
Physiological Factors ◽  
Physiological Noise ◽  
Human Connectome Project ◽  
Study Results ◽  
Signal Changes ◽  
Motion Parameters

AbstractReal-time fMRI neurofeedback allows to learn control over activity in a localized brain region. However, with fMRI, physiological factors such as the cardiac cycle and respiration interfere with the measurement of brain activation. In conventional fMRI studies this is usually mitigated by inclusion of motion parameters and/or physiological parameters as nuisance regressors at the analysis stage, allowing to correct for and filter out such confounders. In real-time fMRI, however, such an approach is not routinely feasible due to the necessity to process all signals during the runtime of an experiment. The absence of on-line correction can therefore compromise real-time fMRI study outcomes reporting volitional self-regulation capability as BOLD signal changes. This is especially true for BOLD signal changes in subcortical regions situated close to blood vessels or air cavities, such as the amygdala. We therefore aimed to establish the effects of motion, heart rate, heart rate variability, and respiratory volume on learning effects, which means here an increase in BOLD signal change over NF training, in an amygdala neurofeedback experiment. Specifically, we investigate motion parameters from two emotion regulation studies - performed at 3T and 7T scanners - and additionally acquired physiological variance for the latter one. Our results revealed differences in these parameters between groups and especially between regulation and resting periods within each participant. However, strictly considering these parameters as nuisance regressors in data analysis revealed that the learning of volitional self-regulation of the amygdala is not driven by motion and physiological changes. As validation of our real-time findings, we compare them to the gold standard of assessment of motion and physiology from the Human Connectome Project. Based on this, we recommend to carefully report neurofeedback study results including physiological nuisance regression. To our knowledge, this is the first study investigating the effects of motion and physiological noise correction on neurofeedback BOLD effects.

scholarly journals Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

2017 ◽  
Author(s):  
Matthew F. Glasser ◽  
Timothy S. Coalson ◽  
Janine D. Bijsterbosch ◽  
Samuel J. Harrison ◽  
Michael P. Harms ◽  
...  
Keyword(s):  
Brain Activity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Physiological Noise ◽  
Temporal Noise ◽  
Global Signal ◽  
Human Connectome Project ◽  
Before And After ◽  
Components Analysis ◽  
Subject Motion

AbstractTemporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal “noise” from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread “global” structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary—to do or not to do global signal regression—given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a “best of both worlds” solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

The Attenuation of Very Low Frequency Brain Oscillations in Transitions from a Rest State to Active Attention

2009 ◽  
Vol 23 (4) ◽  
pp. 191-198 ◽  
Author(s):  
Suzannah K. Helps ◽  
Samantha J. Broyd ◽  
Christopher J. James ◽  
Anke Karl ◽  
Edmund J. S. Sonuga-Barke
Keyword(s):  
Brain Activity ◽  
Sampling Rate ◽  
Low Frequency ◽  
Future Research ◽  
Adhd Symptoms ◽  
Default Mode ◽  
Very Low Frequency ◽  
Wide Range ◽  
Interference Hypothesis ◽  
Mode Interference

Background: The default mode interference hypothesis ( Sonuga-Barke & Castellanos, 2007 ) predicts (1) the attenuation of very low frequency oscillations (VLFO; e.g., .05 Hz) in brain activity within the default mode network during the transition from rest to task, and (2) that failures to attenuate in this way will lead to an increased likelihood of periodic attention lapses that are synchronized to the VLFO pattern. Here, we tested these predictions using DC-EEG recordings within and outside of a previously identified network of electrode locations hypothesized to reflect DMN activity (i.e., S3 network; Helps et al., 2008 ). Method: 24 young adults (mean age 22.3 years; 8 male), sampled to include a wide range of ADHD symptoms, took part in a study of rest to task transitions. Two conditions were compared: 5 min of rest (eyes open) and a 10-min simple 2-choice RT task with a relatively high sampling rate (ISI 1 s). DC-EEG was recorded during both conditions, and the low-frequency spectrum was decomposed and measures of the power within specific bands extracted. Results: Shift from rest to task led to an attenuation of VLFO activity within the S3 network which was inversely associated with ADHD symptoms. RT during task also showed a VLFO signature. During task there was a small but significant degree of synchronization between EEG and RT in the VLFO band. Attenuators showed a lower degree of synchrony than nonattenuators. Discussion: The results provide some initial EEG-based support for the default mode interference hypothesis and suggest that failure to attenuate VLFO in the S3 network is associated with higher synchrony between low-frequency brain activity and RT fluctuations during a simple RT task. Although significant, the effects were small and future research should employ tasks with a higher sampling rate to increase the possibility of extracting robust and stable signals.

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