Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability

Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

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Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

10.1101/2020.02.04.934554 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alba Xifra-Porxas ◽

Michalis Kassinopoulos ◽

Georgios D. Mitsis

Keyword(s):

Functional Connectivity ◽

Brain Connectivity ◽

Brain Activity ◽

Large Data ◽

Head Motion ◽

Time Varying ◽

Resting State Networks ◽

Human Connectome Project ◽

Noninvasive Biomarker ◽

Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

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Brain connectivity at rest predicts individual differences in normative activity during movie watching

10.1101/2021.01.06.425410 ◽

2021 ◽

Author(s):

David C Gruskin ◽

Gaurav H Patel

Keyword(s):

Spatial Distribution ◽

Individual Differences ◽

Resting State ◽

Functional Organization ◽

Brain Connectivity ◽

Brain Activity ◽

Sensory Information ◽

Resting State Functional Connectivity ◽

Similar Region ◽

Human Connectome Project

When multiple individuals are exposed to the same sensory event, some are bound to have less typical experiences than others. These atypical experiences are underpinned by atypical stimulus-evoked brain activity, the extent of which is often indexed by intersubject correlation (ISC). Previous research has attributed individual differences in ISC to variation in trait-like behavioral phenotypes. Here, we extend this line of work by showing that an individual's degree and spatial distribution of ISC are closely related to their brain's intrinsic functional architecture. Using resting state and movie watching fMRI data from 176 Human Connectome Project participants, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC with considerable accuracy. Similar region-level analyses demonstrate that the amount of ISC a brain region exhibits during movie watching is associated with its connectivity to others at rest, and that the nature of these connectivity-activity relationships varies as a function of the region's role in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings suggest that the brain's ability to process complex sensory information is tightly linked to its baseline functional organization and motivate a more comprehensive understanding of individual responses to naturalistic stimuli.

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Modulation of the spontaneous brain activity and functional connectivity in the triple resting-state networks following the visual oddball paradigm

10.1101/2021.01.26.428223 ◽

2021 ◽

Author(s):

Hasan Sbaihat ◽

Ravichandran Rajkumar ◽

Shukti Ramkiran ◽

Abed Al-Nasser Assi ◽

N. Jon Shah ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Cognitive Activity ◽

Large Body ◽

Oddball Paradigm ◽

Resting State Networks ◽

Wide Range ◽

Visual Oddball Paradigm ◽

Visual Oddball

AbstractThe default mode network (DMN), the salience network (SN), and the central executive network (CEN) could be considered as the core resting-state brain networks (RSN) due to their involvement in a wide range of cognitive tasks. Despite the large body of knowledge relating to their regional spontaneous activity (RSA) and functional connectivity (FC) of these networks, less is known about the influence of task-associated activity on these parameters and on the interaction between these three networks. We have investigated the effects of the visual-oddball paradigm on three fMRI measures (amplitude of low-frequency fluctuations for RSA, regional homogeneity for local FC, and degree centrality for global FC) in these three core RSN networks. A rest-task-rest paradigm was used and the RSNs were identified using independent component analysis (ICA) on the resting-state data. We found that the task-related brain activity induced different patterns of significant changes within the three RS networks. Most changes were strongly associated with the task performance. Furthermore, the task-activity significantly increased the inter-network correlations between the SN and CEN as well as between the DMN and CEN, but not between the DMN and SN. A significant dynamical change in RSA, alongside local and global FC within the three core resting-state networks following a simple cognitive activity may be an expression of the distinct involvement of these networks in the performance of the task and their various outcomes.

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Latent functional connectivity underlying multiple brain states

10.1101/2021.04.05.438534 ◽

2021 ◽

Author(s):

Ethan M McCormick ◽

Katelyn L Arnemann ◽

Takuya Ito ◽

Stephen Jose Hanson ◽

Michael W Cole

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Network Architecture ◽

Brain Activity ◽

Brain Network ◽

Mathematical Framework ◽

Human Connectome Project ◽

Brain States ◽

Task Conditions ◽

Psychometric G

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to primarily reflect the brain's intrinsic network architecture, which is thought to be broadly relevant to brain function because it persists across brain states. However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting patterns of connectivity shared across many brain states, may better capture intrinsic FC relative to measures derived from resting state alone. We estimated latent FC in relation to 7 highly distinct task states (24 task conditions) and resting state using fMRI data from 352 participants from the Human Connectome Project. Latent FC was estimated independently for each connection by applying leave-one-task-out factor analysis on the state FC estimates. Compared to resting-state connectivity, we found that latent connectivity improves generalization to held-out brain states, better explaining patterns of both connectivity and task-evoked brain activity. We also found that latent connectivity improved prediction of behavior, measured by the general intelligence factor psychometric g. Our results suggest that patterns of FC shared across many brain states, rather than just resting state, better reflects general, state-independent connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

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Auditory Stimulation Modulates Resting-State Functional Connectivity in Unresponsive Wakefulness Syndrome Patients

Frontiers in Neuroscience ◽

10.3389/fnins.2021.554194 ◽

2021 ◽

Vol 15 ◽

Author(s):

Melanie Boltzmann ◽

Simone B. Schmidt ◽

Christoph Gutenbrunner ◽

Joachim K. Krauss ◽

Martin Stangel ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Auditory Stimulation ◽

Unresponsive Wakefulness Syndrome ◽

Resting State Networks ◽

Resting State Functional Connectivity ◽

Fmri Study ◽

Scanner Noise ◽

High Level

Passive listening to music is associated with several psychological and physical benefits in both, healthy and diseased populations. In this fMRI study, we examined whether preferred music has effects on the functional connectivity within resting-state networks related to consciousness. Thirteen patients in unresponsive wakefulness syndrome (UWS) and 18 healthy controls (HC) were enrolled. Both groups were exposed to different auditory stimulation (scanner noise, preferred music, and aversive auditory stimulation). Functional connectivity was analyzed using a seed-based approach. In HC, no differences were found between the three conditions, indicating that their networks are already working at high level. UWS patients showed impaired functional connectivity within all resting-state networks. In addition, functional connectivity of the auditory network was modulated by preferred music and aversive auditory stimulation. Hence, both conditions have the potential to modulate brain activity of UWS patients.

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Learning differentially reorganizes brain activity and connectivity

10.1101/2020.02.23.961623 ◽

2020 ◽

Author(s):

Maxwell A. Bertolero ◽

Azeez Adebimpe ◽

Ankit N. Khambhati ◽

Marcelo G. Mattar ◽

Daniel Romer ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Learning Performance ◽

Functional Architecture ◽

Time Space ◽

Novel Objects ◽

Value Learning ◽

The Brain

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity—as a function of time, space, and performance—that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.

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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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Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

10.1101/2021.06.23.449584 ◽

2021 ◽

Author(s):

Usama Pervaiz ◽

Diego Vidaurre ◽

Chetan Gohil ◽

Stephen M. Smith ◽

Mark W Woolrich

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Dynamic Modelling ◽

Fmri Data ◽

Generative Adversarial Networks ◽

Activity Levels ◽

Time Varying ◽

Human Connectome Project ◽

The Mean ◽

Over Time

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time-varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability

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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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Discordant attributes of structural and functional connectivity in a two-layer multiplex network

10.1101/273136 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sol Lim ◽

Filippo Radicchi ◽

Martijn P van den Heuvel ◽

Olaf Sporns

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Structural Connectivity ◽

Brain Regions ◽

Resting State Networks ◽

Structural Failure ◽

Resonance Imaging ◽

Multiplex Network ◽

Human Connectome Project ◽

Magnetic Resonance Imaging Mri

AbstractSeveral studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine whether node strength assortativity within and between the SC and FC layer may confer increased robustness against structural failure. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may explain laterality of some neurological dysfunctions and recovery.

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