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

2019 ◽  
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.

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

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.

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

2019 ◽  
Author(s):  
Jonathan D. Power ◽  
Benjamin M. Silver ◽  
Alex Martin ◽  
Rebecca M. Jones
Keyword(s):  
Young Adults ◽  
Resting State ◽  
Resting State Fmri ◽  
Mean Value ◽  
Abdominal Circumference ◽  
List Type ◽  
Resting State Functional Connectivity ◽  
Fmri Signal ◽  
Respiratory Events ◽  
Human Connectome Project

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

scholarly journals Computing personalized brain functional networks from fMRI using self-supervised deep learning

2021 ◽  
Author(s):  
Hongming Li ◽  
Srinivasan Dhivya ◽  
Zaixu Cui ◽  
Chuanjun Zhuo ◽  
Raquel E. Gur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Brain Function ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Functional Networks ◽  
Brain Functional Networks ◽  
Human Connectome Project ◽  
Functional Homogeneity ◽  
Decoder Architecture

ABSTRACTA novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

scholarly journals Human Connectome Project resting state fMRI data organized into 60 fine-scaled retinotopically organized regions in cortical areas V1, V2 and V3

2018 ◽  
Author(s):  
Debra Ann Dawson ◽  
Zixuan Yin ◽  
Jack Lam ◽  
Amir Shmuel
Keyword(s):  
Visual Cortex ◽  
Resting State ◽  
Resting State Fmri ◽  
Regions Of Interest ◽  
Fmri Data ◽  
Cortical Areas ◽  
Human Connectome Project ◽  
Public Data ◽  
Visual Areas ◽  
Data Release

AbstractThe data comprises 60 regions of interest (ROIs) from V1, V2, and V3 of the human visual cortex. Preprocessed data from the Human Connectome Project (HCP) 900 subjects public data release were utilized: 220 subjects were randomly selected, each with 4 scans of resting state fMRI data. Given that these subjects did not have retinotopy scans performed, the visual areas were defined using an anatomical template from Benson et al. (2014). Visual areas from each hemisphere were further divided along dorsal-ventral lines into quadrants, resulting in 4 quadrants per subject. Within each quadrant, fine scaled ROIs were defined by subdividing each visual area into 5 regions according to eccentricity. These data may be useful for studying retinotopically organized functional connectivity in the visual cortex using the HCP 3 Tesla dataset.

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 Representation Learning of Resting State fMRI with Variational Autoencoder

2020 ◽  
Author(s):  
Jung-Hoon Kim ◽  
Yizhen Zhang ◽  
Kuan Han ◽  
Minkyu Choi ◽  
Zhongming Liu
Keyword(s):  
Resting State ◽  
Latent Variables ◽  
Large Data ◽  
Representation Learning ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Human Connectome Project ◽  
Variational Autoencoder ◽  
Short Period ◽  
Task Positive Network

AbstractResting state functional magnetic resonance imaging (rs-fMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rs-fMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rs-fMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. Of the latent representation, its distribution reveals overlapping functional networks, and its geometry is unique to each individual. Our results support the functional opposition between the default mode network and the task-positive network, while such opposition is asymmetric and non-stationary. Correlations between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available per subject.

scholarly journals A Quantitative Data-Driven Analysis (QDA) Framework for Resting-state fMRI: a Study of the Impact of Adult Age

2021 ◽  
Author(s):  
Xia Li ◽  
Håkan Fischer ◽  
Amirhossein Manzouri ◽  
Kristoffer N.T. Månsson ◽  
Tie-Qiang Li
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Quantitative Data ◽  
Resting State Fmri ◽  
Data Driven ◽  
Fmri Data ◽  
Resting State Functional Connectivity ◽  
Sensorimotor Network ◽  
Adult Age ◽  
Connectivity Strength

AbstractPurposeThe objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC).MethodsWhole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N=227, aged 18-74 years old, male/female=99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index (CSI) and connectivity density index (CDI) utilizing the convolutions of the cross-correlation (CC) histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately.ResultsWith the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyrus (MFG), posterior cingulate cortex (PCC), right insula and inferior parietal lobule (IPL) of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: 1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects’ age; 2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; 3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age.ConclusionThe proposed QDA framework can produce threshold-free, voxel-wise analysis of R-fMRI data the RFC metrics. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.HighlightsA quantitative data-driven analysis (QDA) framework was proposed to analysis resting-state fMRI data.Threshold-free resting-state functional connectivity (RFC) metrics were derived to assess brain changes with adult age.Separate assessment of the positive and negative correlations improve sensitivity of the RFC metrics.The posterior cingulate and right insula cortices are anti-correlated and tend to manifest declines in both the negative and positive connectivity strength with adult age.Negative connectivity strength enhances with adult age in sensorimotor network.

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