scholarly journals Reliability of graph analysis of resting state fMRI using test-retest dataset from the Human Connectome Project

NeuroImage ◽  
2016 ◽  
Vol 142 ◽  
pp. 172-187 ◽  
Author(s):  
M. Termenon ◽  
A. Jaillard ◽  
C. Delon-Martin ◽  
S. Achard
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Graph Analysis ◽  
Human Connectome Project

scholarly journals Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

2019 ◽  
Author(s):  
Jianfeng Zhang ◽  
Zirui Huang ◽  
Shankar Tumati ◽  
Georg Northoff
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Peak Period ◽  
Physiological Noise ◽  
Activation Patterns ◽  
Global Signal ◽  
Co Activation ◽  
Human Connectome Project ◽  
Partial Dissociation ◽  
Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

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 Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Lan Yang ◽  
Jing Wei ◽  
Ying Li ◽  
Bin Wang ◽  
Hao Guo ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Functional Magnetic Resonance ◽  
Intra Class Correlation ◽  
Retest Reliability ◽  
Brain Signals ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
The Brain

In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.

scholarly journals A distributed brain network predicts general intelligence from resting-state human neuroimaging data

2018 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Resting State ◽  
Brain Volume ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Reliable Estimate ◽  
General Intelligence ◽  
Total Brain Volume ◽  
General Ability ◽  
Human Connectome Project

AbstractIndividual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of longterm life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

scholarly journals A distributed brain network predicts general intelligence from resting-state human neuroimaging data

2018 ◽  
Vol 373 (1756) ◽  
pp. 20170284 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Brain Volume ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Reliable Estimate ◽  
General Intelligence ◽  
Total Brain Volume ◽  
Human Connectome Project

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N= 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.

scholarly journals Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns

2021 ◽  
Author(s):  
Taylor S Bolt ◽  
Jason Nomi ◽  
Danilo Bzdok ◽  
Catie Chang ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Spatiotemporal Patterns ◽  
Brain Organization ◽  
Functional Brain ◽  
Temporal Properties ◽  
Human Connectome Project ◽  
Functional Brain Organization

The characterization of intrinsic functional brain organization has been approached from a multitude of analytic techniques and methods. We are still at a loss of a unifying conceptual framework for capturing common insights across this patchwork of empirical findings. By analyzing resting-state fMRI data from the Human Connectome Project using a large number of popular analytic techniques, we find that all results can be seamlessly reconciled by three fundamental low-frequency spatiotemporal patterns that we have identified via a novel time-varying complex pattern analysis. Overall, these three spatiotemporal patterns account for a wide variety of previously observed phenomena in the resting-state fMRI literature including the task-positive/task-negative anticorrelation, the global signal, the primary functional connectivity gradient and the network community structure of the functional connectome. The shared spatial and temporal properties of these three canonical patterns suggest that they arise from a single hemodynamic mechanism.

scholarly journals Optimizing BOLD sensitivity in the 7T Human Connectome Project resting-state fMRI protocol using plug-and-play parallel transmission

NeuroImage ◽  
2019 ◽  
Vol 195 ◽  
pp. 1-10 ◽  
Author(s):  
Vincent Gras ◽  
Benedikt A. Poser ◽  
Xiaoping Wu ◽  
Raphaël Tomi-Tricot ◽  
Nicolas Boulant
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Plug And Play ◽  
Parallel Transmission ◽  
Human Connectome Project ◽  
Bold Sensitivity

scholarly journals The functional organisation of the hippocampus along its long axis is gradual and predicts recollection

2018 ◽  
Author(s):  
Izabela Przezdzik ◽  
Myrthe Faber ◽  
Guillén Fernández ◽  
Christian F. Beckmann ◽  
Koen V. Haak
Keyword(s):  
Resting State ◽  
Longitudinal Axis ◽  
Resting State Fmri ◽  
Alternative View ◽  
Mapping Data ◽  
Human Hippocampus ◽  
Human Connectome Project ◽  
Individual Variations ◽  
Functional Organisation ◽  
Better Than

AbstractUnderstanding the functional organisation of the hippocampus is crucial for understanding its role in cognition and disorders in which it is implicated. Different views have been proposed of how function is distributed along its long axis: one view suggests segregation, whereas the alternative view postulates a more gradual organisation. Here, we applied a novel ‘connectopic mapping’ data-analysis approach to the resting-state fMRI data of participants of the Human Connectome Project, and demonstrate that the functional organisation of the hippocampal longitudinal axis is gradual rather than segregated into parcels. In addition, we show that inter-individual variations in this gradual organisation predicts variations in recollection memory better than a characterisation based on parcellation. These results present an important step forward in understanding the functional organisation of the human hippocampus and have important implications for translating between rodent and human research.

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2019 ◽  
Author(s):  
John Fallon ◽  
Phil Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

AbstractIntrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural-connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural-connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale-related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6000 time-series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

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