Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and “missed” respiratory events

AbstractBreathing rate and depth influence the concentration of carbon dioxide in the blood, altering cerebral blood flow and thus functional magnetic resonance imaging (fMRI) signals. Such respiratory fluctuations can have substantial influence in studies of fMRI signal covariance in subjects at rest, the so-called “resting state functional connectivity” technique. If respiration is monitored during fMRI scanning, it is typically done using a belt about the subject’s abdomen to record abdominal circumference. Several measures have been derived from these belt records, including the windowed envelope of the waveform (ENV), the windowed variance in the waveform (respiration variation, RV), and a measure of the amplitude of each breath divided by the cycle time of the breath (respiration volume per time, RVT). Any attempt to gauge respiratory contributions to fMRI signals requires a respiratory measure, but little is known about how these measures compare to each other, or how they perform beyond the small studies in which they were initially proposed. In this paper, we examine the properties of these measures in hundreds of healthy young adults scanned for an hour each at rest, a subset of the Human Connectome Project chosen for having high-quality physiological records. We find: 1) ENV, RV, and RVT are all similar, though ENV and RV are more similar to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic fMRI signal changes, head motions, and image quality abnormalities time-locked to deep breaths evident in the belt traces; 3) all measures can “miss” respiratory events evident in the belt traces; 4) RVT “misses” deep breaths (i.e., yawns and sighs) more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-minute scan, decreasing in mean value. We discuss the implication of these findings for the literature, and ways to move forward in modeling respiratory influences on fMRI scans.Highlights- Examines 3 respiratory measures in resting state fMRI scans of healthy young adults- All respiratory measures “miss” respiratory events, some more than others- Respiration volume per time (RVT) frequently “misses” deep breaths- All respiratory measures decrease systematically over 14.4 minute scans- Systematic decreases are due to decreased breathing depth and rate

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Prevalent and sex-biased breathing patterns modify functional connectivity MRI in young adults

Nature Communications ◽

10.1038/s41467-020-18974-9 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Charles J. Lynch ◽

Benjamin M. Silver ◽

Marc J. Dubin ◽

Alex Martin ◽

Henning U. Voss ◽

...

Keyword(s):

Young Adults ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Resonance Imaging ◽

Breathing Patterns ◽

Resting State Functional Connectivity ◽

Brain Organization ◽

Normal Breathing ◽

Functional Connectivity Mri

Abstract Resting state functional connectivity magnetic resonance imaging (fMRI) is a tool for investigating human brain organization. Here we identify, visually and algorithmically, two prevalent influences on fMRI signals during 440 h of resting state scans in 440 healthy young adults, both caused by deviations from normal breathing which we term deep breaths and bursts. The two respiratory patterns have distinct influences on fMRI signals and signal covariance, distinct timescales, distinct cardiovascular correlates, and distinct tendencies to manifest by sex. Deep breaths are not sex-biased. Bursts, which are serial taperings of respiratory depth typically spanning minutes at a time, are more common in males. Bursts share features of chemoreflex-driven clinical breathing patterns that also occur primarily in males, with notable neurological, psychiatric, medical, and lifespan associations. These results identify common breathing patterns in healthy young adults with distinct influences on functional connectivity and an ability to differentially influence resting state fMRI studies.

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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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Predicting Individual Task Contrasts From Resting-state Functional Connectivity using a Surface-based Convolutional Network

10.1101/2021.04.19.440523 ◽

2021 ◽

Author(s):

Gia Ngo ◽

Meenakshi Khosla ◽

Keith Jamison ◽

Amy Kuceyeski ◽

Mert R Sabuncu

Keyword(s):

Resting State ◽

Predictive Accuracy ◽

Functional Neuroimaging ◽

Resting State Fmri ◽

Training Data ◽

Resting State Functional Connectivity ◽

Convolutional Network ◽

Individual Level ◽

Human Connectome Project ◽

Individual Task

Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. Our model achieves state of the art predictive accuracy on independent test data from the Human Connectome Project and yields individual-level predicted maps that are on par with the target-repeat reliability of the measured contrast maps. We also demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.

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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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A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity

10.1101/837609 ◽

2019 ◽

Cited By ~ 2

Author(s):

Michalis Kassinopoulos ◽

Georgios D. Mitsis

Keyword(s):

Resting State ◽

Signal To Noise Ratio ◽

Resting State Fmri ◽

Motion Artifacts ◽

Fmri Data ◽

Quality Of Data ◽

Resting State Functional Connectivity ◽

Physiological Processes ◽

Human Connectome Project ◽

Low Pass

AbstractIt is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the best data quality, is achieved when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz, but not with scrubbing.

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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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Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

10.1101/2020.02.17.953547 ◽

2020 ◽

Cited By ~ 1

Author(s):

Norio Takata ◽

Nobuhiko Sato ◽

Yuji Komaki ◽

Hideyuki Okano ◽

Kenji F. Tanaka

Keyword(s):

Functional Connectivity ◽

Mouse Brain ◽

Resting State ◽

Brain Structures ◽

Resting State Fmri ◽

Brain Atlas ◽

List Type ◽

Resting State Functional Connectivity ◽

Whole Brain ◽

Link Type

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1,381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.Highlights–A flexible annotation atlas (FAA) for the mouse brain is proposed.–FAA is expected to improve whole brain ROI-definition consistency among laboratories.–The ROI can be combined or divided objectively while maintaining anatomical hierarchy.–FAA realizes functional connectivity analysis across the anatomical hierarchy.–Codes for FAA reconstruction is available at https://github.com/ntakata/flexible-annotation-atlas–Datasets for resting-state fMRI in awake mice are available at https://openneuro.org/datasets/ds002551

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