The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants

AbstractThe developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20 to 45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.HighlightsAn automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI dataIncludes integrated dynamic distortion and slice-to-volume motion correctionA robust multimodal registration approach which includes custom neonatal templatesIncorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspectionData analysis of 538 infants imaged at 26-45 weeks post-menstrual age

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

10.1101/2020.05.04.072868 ◽

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.

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Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex

10.1101/651117 ◽

2019 ◽

Author(s):

Claudio Toro-Serey ◽

Sean M. Tobyne ◽

Joseph T. McGuire

Keyword(s):

Prefrontal Cortex ◽

Medial Prefrontal Cortex ◽

Cognitive Functions ◽

Fmri Data ◽

Default Network ◽

List Type ◽

Individual Level ◽

Human Connectome Project ◽

Spectral Partitioning ◽

The Individual

AbstractRegions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into “DN” and “non-DN” subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.HighlightsThe topography of Default Network cortical regions varies across individuals.A community detection algorithm, spectral partitioning, was applied to rs-fMRI data.The algorithm identified individualized Default Network regions in mPFC and PCC.Default Network topography varied across individuals in mPFC, moreso than in PCC.Overlap of task effects with DN regions should be assessed at the individual level.

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Resting-State Functional Brain Connectivity Best Predicts the Personality Dimension of Openness to Experience

Personality Neuroscience ◽

10.1017/pen.2018.8 ◽

2018 ◽

Vol 1 ◽

Cited By ~ 49

Author(s):

Julien Dubois ◽

Paola Galdi ◽

Yanting Han ◽

Lynn K. Paul ◽

Ralph Adolphs

Keyword(s):

Personality Traits ◽

Resting State ◽

Small Sample ◽

Openness To Experience ◽

Fmri Data ◽

Personality Factors ◽

Personality Factor ◽

Personality Dimension ◽

Human Connectome Project ◽

Neuroimaging Data

AbstractPersonality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 884 young healthy adults in the Human Connectome Project database. We attempted to predict personality traits from the “Big Five,” as assessed with the Neuroticism/Extraversion/Openness Five-Factor Inventory test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness, and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two intersubject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 hr of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; three denoising strategies; two alignment schemes; three models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O:r=.24,R2=.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR:r=.26,R2=.044). Other factors (Extraversion, Neuroticism, Agreeableness, and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the Neuroticism/Extraversion/Openness Five-Factor Inventory factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=.27,R2=.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

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Dynamic mode decomposition of resting-state and task fMRI

10.1101/431718 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jeremy Casorso ◽

Xiaolu Kong ◽

Wang Chi ◽

Dimitri Van De Ville ◽

B.T. Thomas Yeo ◽

...

Keyword(s):

Time Series ◽

Resting State ◽

Motor Task ◽

Component Analysis ◽

Temporal Organization ◽

Fmri Data ◽

Dynamic Component ◽

Human Connectome Project ◽

Fmri Time Series

AbstractComponent analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain networks shaping functional connectivity (FC). Importantly, these approaches are static in the sense that they ignore the temporal information contained in fMRI time series. Therefore, the corresponding components provide a static characterization of FC. Building upon recent findings suggesting that FC dynamics encode richer information about brain functional organization, we use a dynamic extension of component analysis to identify dynamic modes (DMs) of fMRI time series. We demonstrate the feasibility and relevance of this approach using resting-state and motor-task fMRI data of 730 healthy subjects of the Human Connectome Project (HCP). In resting-state, dominant DMs have strong resemblance with classical resting-state networks, with an additional temporal characterization of the networks in terms of oscillatory periods and damping times. In motor-task conditions, dominant DMs reveal interactions between several brain areas, including but not limited to the posterior parietal cortex and primary motor areas, that are not found with classical activation maps. Finally, we identify two canonical components linking the temporal properties of the resting-state DMs with 158 behavioral and demographic HCP measures. Altogether, these findings illustrate the benefits of the proposed dynamic component analysis framework, making it a promising tool to characterize the spatio-temporal organization of brain activity.

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Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques

Frontiers in Neuroinformatics ◽

10.3389/fninf.2021.740143 ◽

2021 ◽

Vol 15 ◽

Author(s):

Ramon Casanova ◽

Robert G. Lyday ◽

Mohsen Bahrami ◽

Jonathan H. Burdette ◽

Sean L. Simpson ◽

...

Keyword(s):

Manifold Learning ◽

Resting State ◽

Brain Networks ◽

Memory Task ◽

Fmri Data ◽

High Dimensional ◽

Functional Brain ◽

Learning Techniques ◽

Human Connectome Project ◽

Functional Brain Networks

Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways to analyze and interpret the data. Here, we investigate the potential of two powerful non-linear manifold learning techniques for functional brain networks representation: (1) T-stochastic neighbor embedding (t-SNE) and (2) Uniform Manifold Approximation Projection (UMAP) a recent breakthrough in manifold learning.Methods: fMRI data from the Human Connectome Project (HCP) and an independent study of aging were used to generate functional brain networks. We used fMRI data collected during resting state data and during a working memory task. The relative performance of t-SNE and UMAP were investigated by projecting the networks from each study onto 2D manifolds. The levels of discrimination between different tasks and the preservation of the topology were evaluated using different metrics.Results: Both methods effectively discriminated the resting state from the memory task in the embedding space. UMAP discriminated with a higher classification accuracy. However, t-SNE appeared to better preserve the topology of the high-dimensional space. When networks from the HCP and aging studies were combined, the resting state and memory networks in general aligned correctly.Discussion: Our results suggest that UMAP, a more recent development in manifold learning, is an excellent tool to visualize functional brain networks. Despite dramatic differences in data collection and protocols, networks from different studies aligned correctly in the embedding space.

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Using carpet plots to analyze transit times of low frequency oscillations in resting state fMRI

Scientific Reports ◽

10.1038/s41598-021-86402-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bradley Fitzgerald ◽

Jinxia Fiona Yao ◽

Thomas M. Talavage ◽

Lia M. Hocke ◽

Blaise deB Frederick ◽

...

Keyword(s):

Resting State ◽

Physiological Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Detection Algorithm ◽

Fmri Data ◽

Intensity Change ◽

Dynamic Susceptibility ◽

Low Frequency Oscillations ◽

Transit Times

AbstractA “carpet plot” is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Here, we evaluate the use of carpet plots paired with a developed slope-detection algorithm as a means to study LFOs in resting state fMRI (rs-fMRI) data with the help of dynamic susceptibility contrast (DSC) MRI data. Carpet plots were constructed by ordering voxels according to signal delay time for each voxel. The slope-detection algorithm was used to identify and calculate propagation times, or “transit times”, of tilted vertical edges across which a sudden signal change was observed. We aim to show that this metric has applications in understanding LFOs in fMRI data, possibly reflecting changes in blood flow speed during the scan, and for evaluating alternative blood-tracking contrast agents such as inhaled CO2. We demonstrate that the propagations of LFOs can be visualized and automatically identified in a carpet plot as tilted lines of sudden intensity change. Resting state carpet plots produce edges with transit times similar to those of DSC carpet plots. Additionally, resting state carpet plots indicate that edge transit times vary at different time points during the scan.

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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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The Noise-Resilient Brain: Resting-State Oscillatory Activity Predicts Words-In-Noise Recognition

10.1101/705053 ◽

2019 ◽

Author(s):

Thomas Houweling ◽

Robert Becker ◽

Alexis Hervais-Adelman

Keyword(s):

Individual Differences ◽

Phonological Processing ◽

Resting State ◽

Recognition Performance ◽

Brain Activity ◽

Superior Temporal Gyrus ◽

Oscillatory Activity ◽

List Type ◽

Gamma Frequency ◽

Human Connectome Project

AbstractThe role of neuronal oscillations in the processing of speech has recently come to prominence. Since resting-state (RS) brain activity has been shown to predict both task-related brain activation and behavioural performance, we set out to establish whether inter-individual differences in spectrally-resolved RS-MEG power are associated with variations in words-in-noise recognition in a sample of 88 participants made available by the Human Connectome Project. Positive associations with resilience to noise were observed with power in the range 21 and 29Hz in a number of areas along the left temporal gyrus and temporo-parietal association areas peaking in left posterior superior temporal gyrus (pSTG). Significant associations were also found in the right posterior superior temporal gyrus in the frequency range 30 to 40Hz. We propose that individual differences in words-in-noise performance are related to baseline excitability levels of the neural substrates of phonological processing.HighlightsPower of resting MEG activity predicts Words-In-Noise recognition performanceSignificant associations in higher beta and lower gamma frequency bandStrongest in left-lateralised perisylvian cluster peaking in posterior STGEffects are spectrally and spatially consistent with phoneme-level processing

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

10.1101/613851 ◽

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

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

10.1101/2021.09.25.461829 ◽

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.

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