scholarly journals Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Anna Pidnebesna ◽  
David Tomeček ◽  
Jaroslav Tintěra ◽  
Jaroslav Hlinka
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Connectivity Pattern ◽  
Head Motion ◽  
Connectivity Matrix ◽  
Normal Brain ◽  
Functional Connections ◽  
Human Connectome Project

AbstractFunctional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brain’s connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

scholarly journals Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

2021 ◽  
Author(s):  
Shachar Gal ◽  
Yael Coldham ◽  
Michal Bernstein-Eliav ◽  
Ido Tavor
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Predictive Modelling ◽  
Resting State Fmri ◽  
Resting State Functional Connectivity ◽  
Promising Alternative ◽  
Functional Connections ◽  
Human Connectome Project ◽  
Intelligence Scores

The search for an 'ideal' approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

scholarly journals Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hang Joon Jo ◽  
Stephen J. Gotts ◽  
Richard C. Reynolds ◽  
Peter A. Bandettini ◽  
Alex Martin ◽  
...  
Keyword(s):  
Resting State ◽  
Correlation Coefficients ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Head Motion ◽  
Time Points ◽  
Global Signal ◽  
Motion Time ◽  
Correlation Estimate ◽  
The Brain

Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We replicate the previously reported head-motion bias on correlation coefficients and then show that while motion can be the source of artifact in correlations, the distance-dependent bias is exacerbated by GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that the local white matter regressor (WMeLOCAL), a subset of ANATICOR, results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

scholarly journals Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

2020 ◽  
Author(s):  
Arun S. Mahadevan ◽  
Ursula A. Tooley ◽  
Maxwell A. Bertolero ◽  
Allyson P. Mackey ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Partial Correlation ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Alternative Methods ◽  
Fmri Data ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

scholarly journals Customized head molds reduce motion during resting state fMRI scans

2018 ◽  
Author(s):  
Jonathan D Power ◽  
Benjamin Silver ◽  
Melanie R. Silverman ◽  
Eliana L. Ajodan ◽  
Dienke J. Bos ◽  
...  
Keyword(s):  
Resting State ◽  
Standard Procedure ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Quality Indices ◽  
Age Range ◽  
Head Coil

Head motion causes artifacts in functional magnetic resonance imaging (fMRI) scans, a problem especially relevant for task-free resting state paradigms and for developmental, aging, and clinical populations. In a cohort spanning 7-28 years old (mean age 15) we produced customized head-anatomy-specific Styrofoam molds for each subject that inserted into an MRI head coil. We scanned these subjects under two conditions: using our standard procedure of packing the head coil with foam padding about the head to reduce head motion, and using the customized molds to reduce head motion. Here we report the effects found in our first 13 subjects. In 12 of 13 subjects, the molds reduced head motion throughout the scan, and reduced the fraction of a scan with substantial motion (i.e., volumes with motion notably above baseline levels of motion). Motion was reduced in all 6 head position estimates, especially in rotational, left-right, and superior-inferior directions. Motion was reduced throughout the full age range studied, including children, adolescents, and young adults. In terms of the fMRI data itself, quality indices improved with the head mold on, scrubbing analyses detected less distance-dependent artifact in scans with the head mold on, and distant-dependent artifact was less evident in the scans with the molds on, both for the entire scan and also during only low-motion volumes. Subjects found the molds comfortable. Head molds are thus effective tools for reducing head motion, and motion artifacts, during fMRI scans.

Functional Connectivity Pattern of the Internal Hippocampal Network in Awake Pigeons: A Resting-State fMRI Study

2017 ◽  
Vol 90 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Mehdi Behroozi ◽  
Felix Ströckens ◽  
Martin Stacho ◽  
Onur Güntürkün
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Hippocampal Formation ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
Lateral Septum ◽  
Connectivity Pattern ◽  
Oxygenation Level ◽  
Hippocampal Network

In the last two decades, the avian hippocampus has been repeatedly studied with respect to its architecture, neurochemistry, and connectivity pattern. We review these insights and conclude that we unfortunately still lack proper knowledge on the interaction between the different hippocampal subregions. To fill this gap, we need information on the functional connectivity pattern of the hippocampal network. These data could complement our structural connectivity knowledge. To this end, we conducted a resting-state fMRI experiment in awake pigeons in a 7-T MR scanner. A voxel-wise regression analysis of blood oxygenation level-dependent (BOLD) fluctuations was performed in 6 distinct areas, dorsomedial (DM), dorsolateral (DL), triangular shaped (Tr), dorsolateral corticoid (CDL), temporo-parieto-occipital (TPO), and lateral septum regions (SL), to establish a functional connectivity map of the avian hippocampal network. Our study reveals that the system of connectivities between CDL, DL, DM, and Tr is the functional backbone of the pigeon hippocampal system. Within this network, DM is the central hub and is strongly associated with DL and CDL BOLD signal fluctuations. DM is also the only hippocampal region to which large Tr areas are functionally connected. In contrast to published tracing data, TPO and SL are only weakly integrated in this network. In summary, our findings uncovered a structurally otherwise invisible architecture of the avian hippocampal formation by revealing the dynamic blueprints of this network.

scholarly journals Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

2020 ◽  
Author(s):  
Behnaz Yousefi ◽  
Shella Keilholz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Whole Brain ◽  
Functional Connections ◽  
Driving Mechanisms

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

scholarly journals A Whole-Brain Regression Method to Identify Individual and Group Variations in Functional Connectivity

2020 ◽  
Author(s):  
Yi Zhao ◽  
Brian S. Caffo ◽  
Bingkai Wang ◽  
Chiang-shan R. Li ◽  
Xi Luo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Statistical Power ◽  
Resting State Fmri ◽  
Regression Method ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Group Ica ◽  
Brain Functional Connectivity

AbstractResting-state functional connectivity is an important and widely used measure of individual and group differences. These differences are typically attributed to various demographic and/or clinical factors. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a generalized linear model method that regresses whole-brain functional connectivity on covariates. Our approach builds on two methodological components. We first employ whole-brain group ICA to reduce the dimensionality of functional connectivity matrices, and then search for matrix variations associated with covariates using covariate assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results show that the approach enjoys improved statistical power in detecting interaction effects of sex and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.

scholarly journals The Ubiquitous Vigilance Signal in fMRI Time Series Data

2021 ◽  
Author(s):  
Javier Gonzalez-Castillo ◽  
Isabel Fernandez ◽  
Daniel A Handwerker ◽  
Peter A Bandettini
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Time Series Data ◽  
Resting State Fmri ◽  
Series Data ◽  
Similar Frequency ◽  
Global Signal ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
Eye Closure

Vigilance and wakefulness modulate estimates of functional connectivity, and, if unaccounted for, they can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project. Our results confirm the presence of those fluctuations in the HCP sample despite this data having relatively small inflow weighting. Moreover, we show that fluctuations of a similar frequency appear in large portions of grey matter with different temporal delays, and that they can substantially influence estimates of functional connectivity. Finally, we demonstrate that the temporal evolution of this signal cannot only help us reproduce previously reported overall sleep patterns in resting-state data, but also predict individual periods of eye closure with 70% accuracy, matching predictions attainable using the amplitude of the global signal (a common fMRI marker of arousal). In summary, our results demonstrate the ubiquitous presence of this signal in a large, publicly available, fMRI sample, its value as a marker of arousal in absence of a better metric, its relationship to the global signal, and its potential nuisance effects on functional connectivity estimates when ignored.

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