Effects of spatial smoothing and physiological noise removal on brain activity with cigarette craving

Real-time fMRI (rtfMRI) neurofeedback (NF) facilitates volitional control over brain activity and the modulation of associated mental functions. The NF signals of traditional rtfMRI-NF studies predominantly reflect neuronal activity within ROIs. In this study, we describe a novel rtfMRI-NF approach that includes a functional connectivity (FC) component in the NF signal (FC-added rtfMRI-NF). We estimated the efficacy of the FC-added rtfMRI-NF method by applying it to nicotine-dependent heavy smokers in an effort to reduce cigarette craving. ACC and medial pFC as well as the posterior cingulate cortex and precuneus are associated with cigarette craving and were chosen as ROIs. Fourteen heavy smokers were randomly assigned to receive one of two types of NF: traditional activity-based rtfMRI-NF or FC-added rtfMRI-NF. Participants received rtfMRI-NF training during two separate visits after overnight smoking cessation, and cigarette craving score was assessed. The FC-added rtfMRI-NF resulted in greater neuronal activity and increased FC between the targeted ROIs than the traditional activity-based rtfMRI-NF and resulted in lower craving score. In the FC-added rtfMRI-NF condition, the average of neuronal activity and FC was tightly associated with craving score (Bonferroni-corrected p = .028). However, in the activity-based rtfMRI-NF condition, no association was detected (uncorrected p > .081). Non-rtfMRI data analysis also showed enhanced neuronal activity and FC with FC-added NF than with activity-based NF. These results demonstrate that FC-added rtfMRI-NF facilitates greater volitional control over brain activity and connectivity and greater modulation of mental function than activity-based rtfMRI-NF.

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Effect of spatial smoothing on physiological noise in high-resolution fMRI

NeuroImage ◽

10.1016/j.neuroimage.2006.04.182 ◽

2006 ◽

Vol 32 (2) ◽

pp. 551-557 ◽

Cited By ~ 99

Author(s):

Christina Triantafyllou ◽

Richard D. Hoge ◽

Lawrence L. Wald

Keyword(s):

High Resolution ◽

Spatial Smoothing ◽

Physiological Noise

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Correction for pulse height variability reduces physiological noise in functional MRI when studying spontaneous brain activity

Human Brain Mapping ◽

10.1002/hbm.20866 ◽

2009 ◽

pp. NA-NA ◽

Cited By ~ 3

Author(s):

Petra J. van Houdt ◽

Pauly P.W. Ossenblok ◽

Paul A.J.M. Boon ◽

Frans S.S. Leijten ◽

Demetrios N. Velis ◽

...

Keyword(s):

Functional Mri ◽

Brain Activity ◽

Pulse Height ◽

Physiological Noise ◽

Spontaneous Brain Activity

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Scale-resolved analysis of brain functional connectivity networks with spectral entropy

10.1101/813162 ◽

2019 ◽

Author(s):

Carlo Nicolini ◽

Giulia Forcellini ◽

Ludovico Minati ◽

Angelo Bifone

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Activity ◽

Ground Truth ◽

Structural Features ◽

Von Neumann Entropy ◽

Complete Graphs ◽

Theoretic Approach ◽

Physiological Noise ◽

Brain Functional Connectivity

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

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Holo-Hilbert Spectral-based Removal of Gamma-Band Noise from Simultaneous EEG-fMRI Recordings

10.1101/2021.04.28.440961 ◽

2021 ◽

Author(s):

Narges Moradi ◽

Pierre LeVan ◽

Burak Akin ◽

Bradley G. Goodyear ◽

Roberto C. Sotero

Keyword(s):

Brain Function ◽

Brain Activity ◽

Noise Removal ◽

Gamma Band ◽

Attention And Memory ◽

Mode Decomposition ◽

Health And Disease ◽

Eeg Recordings ◽

Frequency Modulations ◽

Band Signal

Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (30–100 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to the MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a method of MR-induced noise removal from the gamma band of EEG that is based on Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively). Instead of including all FM components, our method examines only gamma-band FM and AM components, removes components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method effectively reduces artifacts while preserving the original signal.

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

10.1101/180596 ◽

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.

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

10.1101/2020.06.01.128306 ◽

2020 ◽

Cited By ~ 1

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.

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

10.1101/2021.03.02.433573 ◽

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.

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