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

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

eLife ◽

10.7554/elife.62324 ◽

2021 ◽

Vol 10 ◽

Author(s):

Alba Xifra-Porxas ◽

Michalis Kassinopoulos ◽

Georgios D Mitsis

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Large Data ◽

Time Varying ◽

Resting State Networks ◽

Human Connectome Project ◽

Noninvasive Biomarker ◽

Recurrent Patterns

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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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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Multivariate Heteroscedasticity Models for Functional Brain Connectivity

10.1101/154468 ◽

2017 ◽

Author(s):

Christof Seiler ◽

Susan Holmes

Keyword(s):

Functional Connectivity ◽

Power Analysis ◽

Brain Connectivity ◽

Brain Activity ◽

Resting State Fmri ◽

Functional Brain ◽

False Discovery ◽

Human Connectome Project ◽

Falling Asleep ◽

Low Dimensional

ABSTRACTFunctional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI). We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP) comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.

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Association Between Brain Activation and Functional Connectivity

Cerebral Cortex ◽

10.1093/cercor/bhy077 ◽

2018 ◽

Vol 29 (5) ◽

pp. 1984-1996 ◽

Cited By ~ 13

Author(s):

Dardo Tomasi ◽

Nora D Volkow

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Low Frequency ◽

Brain Activation ◽

Blood Oxygen Level Dependent ◽

Common Source ◽

Functional Connectivity Density ◽

Density Mapping ◽

Human Connectome Project ◽

Bold Responses

Abstract The origin of the “resting-state” brain activity recorded with functional magnetic resonance imaging (fMRI) is still uncertain. Here we provide evidence for the neurovascular origins of the amplitude of the low-frequency fluctuations (ALFF) and the local functional connectivity density (lFCD) by comparing them with task-induced blood-oxygen level dependent (BOLD) responses, which are considered a proxy for neuronal activation. Using fMRI data for 2 different tasks (Relational and Social) collected by the Human Connectome Project in 426 healthy adults, we show that ALFF and lFCD have linear associations with the BOLD response. This association was significantly attenuated by a novel task signal regression (TSR) procedure, indicating that task performance enhances lFCD and ALFF in activated regions. We also show that lFCD predicts BOLD activation patterns, as was recently shown for other functional connectivity metrics, which corroborates that resting functional connectivity architecture impacts brain activation responses. Thus, our findings indicate a common source for BOLD responses, ALFF and lFCD, which is consistent with the neurovascular origin of local hemodynamic synchrony presumably reflecting coordinated fluctuations in neuronal activity. This study also supports the development of task-evoked functional connectivity density mapping.

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Functional Connectome of the Five-Factor Model of Personality

Personality Neuroscience ◽

10.1017/pen.2017.2 ◽

2018 ◽

Vol 1 ◽

Cited By ~ 16

Author(s):

Nicola Toschi ◽

Roberta Riccelli ◽

Iole Indovina ◽

Antonio Terracciano ◽

Luca Passamonti

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Factor Model ◽

Brain Connectivity ◽

Five Factor Model ◽

Mechanistic Explanation ◽

Data Sets ◽

Personality Differences ◽

Human Connectome Project ◽

Maximal Information Coefficient

Abstract A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analysing big data-sets with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and nonlinear statistical dependencies across the graph “nodes”, which were defined as distinct large-scale brain circuits identified via independent component analysis. Multivariate regression models and “train/test” approaches were used to examine the associations between FFM traits and connectomic indices as well as to assess the generalizability of the main findings, while accounting for age and sex variability. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This offers a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.

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Unique mapping of structural and functional connectivity on cognition

10.1101/296913 ◽

2018 ◽

Author(s):

J. Zimmermann ◽

J.G. Griffiths ◽

A.R. McIntosh

Keyword(s):

Functional Connectivity ◽

Brain Connectivity ◽

Structural Connectivity ◽

Brain Regions ◽

Unique Mapping ◽

Human Connectome Project ◽

Behavioural Patterns ◽

Wide Range ◽

Psychological Tasks ◽

New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

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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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The relationship between spatial configuration and functional connectivity of brain regions

10.1101/210195 ◽

2017 ◽

Author(s):

Janine D. Bijsterbosch ◽

Mark W. Woolrich ◽

Matthew F. Glasser ◽

Emma C. Robinson ◽

Christian F. Beckmann ◽

...

Keyword(s):

Functional Connectivity ◽

Spatial Configuration ◽

Brain Connectivity ◽

Brain Activity ◽

Spatial Arrangement ◽

Brain Regions ◽

Imaging Data ◽

Functional Regions ◽

Cortical Regions ◽

The Relationship

AbstractBrain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used "functional connectivity fingerprints" to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

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A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping

10.1101/2021.08.12.456134 ◽

2021 ◽

Author(s):

SUBBA REDDY OOTA ◽

Archi Yadav ◽

Arpita Dash ◽

Surampudi Bapi Raju ◽

Avinash Sharma

Keyword(s):

Deep Learning ◽

Functional Connectivity ◽

Complex Dynamics ◽

Brain Activity ◽

Structural Connectivity ◽

Reaction Diffusion ◽

Complex Nature ◽

Relational Structures ◽

Human Connectome Project ◽

The Brain

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks along with graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 100 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of human brain function.

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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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