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 Disambiguating the role of blood flow and global signal with Partial Information Decomposition

2019 ◽  
Author(s):  
Nigel Colenbier ◽  
Frederik Van de Steen ◽  
Lucina Q. Uddin ◽  
Russell A. Poldrack ◽  
Vince D. Calhoun ◽  
...  
Keyword(s):  
Blood Flow ◽  
Functional Connectivity ◽  
Arrival Time ◽  
Partial Information ◽  
Physiological Noise ◽  
Global Signal ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Impact

AbstractIn resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.

scholarly journals Functional connectivity networks with and without global signal correction

2013 ◽  
Author(s):  
Satoru Hayasaka
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Correlation Coefficients ◽  
Blood Oxygenation ◽  
Fmri Data ◽  
Series Data ◽  
Network Analyses ◽  
Global Signal ◽  
Signal Correction ◽  
The Impact

In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.

The Impact of Video Speed on the Estimation of Time Duration in Sport

2021 ◽  
pp. 1-11
Author(s):  
Lisa-Marie Schütz ◽  
Geoffrey Schweizer ◽  
Henning Plessner
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Slow Motion ◽  
Sport Performance ◽  
Time Duration ◽  
Video Presentation ◽  
Sporting Activities ◽  
The Impact ◽  
Estimation Of Time

The authors investigated the impact of video speed on judging the duration of sport performance. In three experiments, they investigated whether the speed of video presentation (slow motion vs. real time) has an influence on the accuracy of time estimation of sporting activities (n1 = 103; n2 = 100; n3 = 106). In all three studies, the time estimation was more accurate in real time than in slow motion, in which time was overestimated. In two studies, the authors initially investigated whether actions in slow motion are perceived to last longer because the distance they cycled or ran is perceived to be longer (n4 = 92; n5 = 106). The results support the hypothesis that the duration of sporting activities is estimated more accurately when they are presented in real time than in slow motion. Sporting officials’ judgments that require accurate time estimation may thus be biased when based on slow-motion displays.

scholarly journals Declined functional connectivity of white matter during rest and working memory tasks associates with cognitive impairments in schizophrenia

2020 ◽  
Author(s):  
Yurui Gao ◽  
Muwei Li ◽  
Anna S Huang ◽  
Adam W Anderson ◽  
Zhaohua Ding ◽  
...  
Keyword(s):  
Working Memory ◽  
White Matter ◽  
Functional Connectivity ◽  
Resting State ◽  
Spatial Working Memory ◽  
Cognitive Impairments ◽  
Memory Task ◽  
Brain Network ◽  
Blood Oxygenation ◽  
Cerebral Peduncle

BACKGROUND: Schizophrenia, characterized by cognitive impairments, arises from a disturbance of brain network. Pathological changes in white matter (WM) have been indicated as playing a role in disturbing neural connectivity in schizophrenia. However, deficits of functional connectivity (FC) in individual WM bundles in schizophrenia have never been explored; neither have cognitive correlates with those deficits. METHODS: Resting-state and spatial working memory task fMRI images were acquired on 67 healthy subjects and 84 patients with schizophrenia. The correlations in blood-oxygenation-level-dependent (BOLD) signals between 46 WM and 82 gray matter regions were quantified, analyzed and compared between groups under three scenarios (i.e., resting state, retention period and entire time of a spatial working memory task). Associations of FC in WM with cognitive assessment scores were evaluated for three scenarios. RESULTS: FC deficits were significant (p<.05) in external capsule, cingulum, uncinate fasciculus, genu and body of corpus callosum under all three scenarios. Deficits were also present in the anterior limb of the internal capsule and cerebral peduncle in task scenario. Decreased FCs in specific WM bundles associated significantly (p<.05) with cognitive impairments in working memory, processing speed and/or cognitive control. CONCLUSIONS: Decreases in FC are evident in several WM bundles in patients with schizophrenia and are significantly associated with cognitive impairments during both rest and working memory tasks. Furthermore, working memory tasks expose FC deficits in more WM bundles and more cognitive associates in schizophrenia than resting state does.

scholarly journals The impact of physiological noise on hemodynamic-derived estimates of directed functional connectivity

2019 ◽  
Vol 224 (9) ◽  
pp. 3145-3157 ◽  
Author(s):  
F. Konrad Schumacher ◽  
Carmen Steinborn ◽  
Cornelius Weiller ◽  
Björn O. Schelter ◽  
Matthias Reinhard ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Physiological Noise ◽  
The Impact

scholarly journals Real-time estimation of the influenza-associated excess mortality in Hong Kong

2019 ◽  
Vol 147 ◽  
Author(s):  
Jessica Y. Wong ◽  
Edward Goldstein ◽  
Vicky J. Fang ◽  
Benjamin J. Cowling ◽  
Peng Wu
Keyword(s):  
Hong Kong ◽  
Real Time ◽  
Excess Mortality ◽  
Time Estimation ◽  
Surveillance Data ◽  
Control Measures ◽  
Influenza Surveillance ◽  
Mortality Data ◽  
Real Time Estimation ◽  
The Impact

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

scholarly journals Benchmarking common preprocessing strategies in early childhood functional connectivity MRI

2020 ◽  
Author(s):  
Kirk Graff ◽  
Ryann Tansey ◽  
Amanda Ip ◽  
Christiane Rohr ◽  
Dennis Dimond ◽  
...  
Keyword(s):  
Early Childhood ◽  
Functional Connectivity ◽  
Young Children ◽  
Noise Removal ◽  
Head Motion ◽  
List Type ◽  
Unique Challenge ◽  
Global Signal ◽  
Passive Viewing ◽  
The Impact

AbstractFunctional connectivity magnetic resonance imaging (FC-MRI) has been widely used to investigate neurodevelopment. However, FC-MRI is vulnerable to head motion, which is associated with age and distorts FC estimates. Numerous preprocessing strategies have been developed to mitigate confounds, each with advantages and drawbacks. Preprocessing strategies for FC-MRI have typically been validated and compared using resting state data from adults. However, FC-MRI in young children presents a unique challenge due to relatively high head motion and a growing use of passive viewing paradigms to mitigate motion. This highlights a need to compare processing choices in pediatric samples. To this end, we leveraged longitudinal, passive viewing fMRI data collected from 4 to 8-year-old children. We systematically investigated combinations of widely used and debated preprocessing strategies, namely global signal regression, volume censoring, ICA-AROMA, and bandpass filtering. We implemented commonly used metrics of noise removal (i.e. quality control-functional connectivity), metrics sensitive to individual differences (i.e. connectome fingerprinting), and, because data was collected during a passive viewing task, we also assessed the impact on stimulus-evoked responses (i.e. intersubject correlations; ISC). We found that the most efficacious pipeline included censoring, global signal regression, bandpass filtering, and head motion parameter regression. Despite the drawbacks of noise-mitigation steps, our findings show benefits for both noise removal and information retention in a high-motion early childhood sample.Highlights- We evaluated 27 preprocessing pipelines in passive viewing data from young children- Pipelines were evaluated on noise-removed and information retained- Pipelines that included censoring and GSR outperformed alternatives across benchmarks- For high-motion scans, preprocessing choices substantially alter connectomes

scholarly journals White matter tract-oriented deformation is dependent on real-time axonal fiber orientation

2020 ◽  
Author(s):  
Zhou Zhou ◽  
August G. Domel ◽  
Xiaogai Li ◽  
Gerald Grant ◽  
Svein Kleiven ◽  
...  
Keyword(s):  
White Matter ◽  
Real Time ◽  
Fiber Orientation ◽  
Volume Ratio ◽  
Public Health Issue ◽  
Fiber Direction ◽  
Brain Response ◽  
High Deformation ◽  
Fiber Tracts ◽  
The Impact

AbstractTraumatic axonal injury (TAI) is a critical public health issue with its pathogenesis remaining largely elusive. Finite element (FE) head models are promising tools to bridge the gap between mechanical insult, localized brain response, and resultant injury. In particular, the FE-derived deformation along the direction of white matter (WM) tracts (i.e., tract-oriented strain) has been shown to be an appropriate predictor for TAI. However, the evolution of fiber orientation in time during the impact and its potential influence on the tract-oriented strain remains unknown. To address this question, the present study leveraged an embedded element approach to track real-time fiber orientation during impacts. A new scheme to calculate the tract-oriented strain was proposed by projecting the strain tensors from pre-computed simulations along the temporal fiber direction instead of its static counterpart directly obtained from diffuse tensor imaging. The results revealed that incorporating the real-time fiber orientation not only altered the direction but also amplified the magnitude of the tract-oriented strain, resulting in a generally more extended distribution and a larger volume ratio of WM exposed to high deformation along fiber tracts. These effects were exacerbated with the impact severities characterized by the acceleration magnitudes. Results of this study provide insights into how best to incorporate fiber orientation in head injury models and derive the WM tract-oriented deformation from computational simulations, which is important for furthering our understanding of the underlying mechanisms of TAI.

Sign in / Sign up

Export Citation Format

Share Document