Advancing motion denoising of multiband resting state functional connectivity fMRI data

AbstractMultiband-accelerated fMRI provides dramatically improved temporal and spatial resolution of resting state functional connectivity (RSFC) studies of the human brain, but poses unique challenges for denoising of subject motion induced data artifacts, a major confound in RSFC research. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, making these criteria inappropriate for quantifying pipeline performance. We further develop two new quantitative metrics that are free from these issues and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop rigorous, quantitative methods for determining optimal censoring thresholds and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.

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Task-Related Effects on the Temporal and Spatial Dynamics of Resting-State Functional Connectivity in the Default Network

PLoS ONE ◽

10.1371/journal.pone.0013311 ◽

2010 ◽

Vol 5 (10) ◽

pp. e13311 ◽

Cited By ~ 70

Author(s):

Omer Grigg ◽

Cheryl L. Grady

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Spatial Dynamics ◽

Default Network ◽

Resting State Functional Connectivity ◽

Temporal And Spatial

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Resting-State Functional Connectivity in the Human Connectome Project

Harvard Review of Psychiatry ◽

10.1097/hrp.0000000000000166 ◽

2017 ◽

Vol 25 (5) ◽

pp. 209-217 ◽

Cited By ~ 12

Author(s):

Deanna M. Barch

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Functional Connectivity ◽

Human Connectome Project

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General Functional Connectivity: shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

10.1101/330530 ◽

2018 ◽

Cited By ~ 2

Author(s):

Maxwell L. Elliott ◽

Annchen R. Knodt ◽

Megan Cooke ◽

M. Justin Kim ◽

Tracy R. Melzer ◽

...

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Resting State ◽

Biomarker Discovery ◽

Resting State Functional Connectivity ◽

Heritable Variation ◽

State Data ◽

Human Connectome Project ◽

Intrinsic Connectivity ◽

And Task

AbstractIntrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

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Individual-Specific fMRI-Subspaces Improve Functional Connectivity Prediction of Behavior

10.1101/515742 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rajan Kashyap ◽

Ru Kong ◽

Sagarika Bhattacharjee ◽

Jingwei Li ◽

Juan Zhou ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Posterior Cingulate Cortex ◽

List Type ◽

Resting State Functional Connectivity ◽

Behavioral Measures ◽

Specific Effects ◽

Human Connectome Project ◽

Common Group ◽

Behavioral Prediction

AbstractThere is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.HighlightsWe decomposed rs-fMRI signals into common subspace & individual-specific subspaceCommon subspace is shared across all Human Connectome Project (HCP) participantsCommon subspaces are different across runs, suggesting state-specific effectsIndividual-specific subspaces are unique to individualsRemoval of common subspace signals improve behavioral prediction by 11.7%

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Decreased brain connectivity in smoking contrasts with increased connectivity in drinking

eLife ◽

10.7554/elife.40765 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 7

Author(s):

Wei Cheng ◽

Edmund T Rolls ◽

Trevor W Robbins ◽

Weikang Gong ◽

Zhaowen Liu ◽

...

Keyword(s):

Functional Connectivity ◽

General Population ◽

Orbitofrontal Cortex ◽

Resting State ◽

Brain Connectivity ◽

Inferior Frontal Gyrus ◽

Cingulate Cortex ◽

Resting State Functional Connectivity ◽

Human Connectome Project ◽

Longitudinal Dataset

In a group of 831 participants from the general population in the Human Connectome Project, smokers exhibited low overall functional connectivity, and more specifically of the lateral orbitofrontal cortex which is associated with non-reward mechanisms, the adjacent inferior frontal gyrus, and the precuneus. Participants who drank a high amount had overall increases in resting state functional connectivity, and specific increases in reward-related systems including the medial orbitofrontal cortex and the cingulate cortex. Increased impulsivity was found in smokers, associated with decreased functional connectivity of the non-reward-related lateral orbitofrontal cortex; and increased impulsivity was found in high amount drinkers, associated with increased functional connectivity of the reward-related medial orbitofrontal cortex. The main findings were cross-validated in an independent longitudinal dataset with 1176 participants, IMAGEN. Further, the functional connectivities in 14-year-old non-smokers (and also in female low-drinkers) were related to who would smoke or drink at age 19. An implication is that these differences in brain functional connectivities play a role in smoking and drinking, together with other factors.

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

10.1101/2020.01.16.909580 ◽

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.

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

10.1101/2021.11.01.466749 ◽

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.

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Functional Connectivity of the Human Paraventricular Thalamic Nucleus: Insights From High Field Functional MRI

Frontiers in Integrative Neuroscience ◽

10.3389/fnint.2021.662293 ◽

2021 ◽

Vol 15 ◽

Author(s):

Sarah M. Kark ◽

Matthew T. Birnie ◽

Tallie Z. Baram ◽

Michael A. Yassa

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Thalamic Nucleus ◽

Middle Temporal Gyrus ◽

Resting State Functional Connectivity ◽

Functional Roles ◽

Context Sensitive ◽

State Dependent ◽

Human Connectome Project ◽

Future Work

The paraventricular thalamic nucleus (PVT) is a small but highly connected nucleus of the dorsal midline thalamus. The PVT has garnered recent attention as a context-sensitive node within the thalamocortical arousal system that modulates state-dependent motivated behaviors. Once considered related to generalized arousal responses with non-specific impacts on behavior, accumulating evidence bolsters the contemporary view that discrete midline thalamic subnuclei belong to specialized corticolimbic and corticostriatal circuits related to attention, emotions, and cognition. However, the functional connectivity patterns of the human PVT have yet to be mapped. Here, we combined high-quality, high-resolution 7T and 3T resting state MRI data from 121 young adult participants from the Human Connectome Project (HCP) and thalamic subnuclei atlas masks to investigate resting state functional connectivity of the human PVT. The 7T results demonstrated extensive positive functional connectivity with the brainstem, midbrain, ventral and dorsal medial prefrontal cortex (mPFC), anterior and posterior cingulate, ventral striatum, hippocampus, and amygdala. These connections persist upon controlling for functional connectivity of the rest of the thalamus. Whole-brain contrasts provided further evidence that, compared to three nearby midline thalamic subnuclei, functional connectivity of the PVT is strong with the hippocampus, amygdala, ventral and dorsal mPFC, and middle temporal gyrus. These findings suggest that, even during rest, the human PVT is functionally coupled with many regions known to be structurally connected to rodent and non-human primate PVT. Further, cosine similarity analysis results suggested the PVT is integrated into the default mode network (DMN), an intrinsic connectivity network associated with episodic memory and self-referential thought. The current work provides a much-needed foundation for ongoing and future work examining the functional roles of the PVT in humans.

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