scholarly journals Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models

2019 ◽  
Author(s):  
Heather Shappell ◽  
Brian S. Caffo ◽  
James J. Pekar ◽  
Martin A. Lindquist
Keyword(s):  
Functional Connectivity ◽  
Markov Models ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Data ◽  
Time Varying ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Network States

AbstractThe study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject’s most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.

scholarly journals Behavioural relevance of spontaneous, transient brain network interactions in fMRI

2019 ◽  
Author(s):  
D. Vidaurre ◽  
A. Llera ◽  
S.M. Smith ◽  
M.W. Woolrich
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Networks ◽  
Structural Data ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Human Connectome Project ◽  
Brain Data ◽  
Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

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 State-Unspecific Modes of Whole-Brain Functional Connectivity Predict Intelligence and Life Outcomes

2018 ◽  
Author(s):  
Yu Takagi ◽  
Jun-ichiro Hirayama ◽  
Saori C Tanaka
Keyword(s):  
Functional Connectivity ◽  
Individual Variation ◽  
Brain Networks ◽  
Life Outcomes ◽  
Behavioral Measures ◽  
Experimental Conditions ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Brain Functional Connectivity

AbstractRecent functional magnetic resonance imaging (fMRI) studies have increasingly revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have been inherently limited to state-specific characterizations of related brain networks and their functions, several recent studies have examined the potential state-unspecific nature of functional brain networks, such as their global similarities across different experimental conditions (i.e., states) including both task and rest. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variation in whole-brain functional connectivity patterns, called “Common Neural Modes (CNMs)”, from a large fMRI dataset including eight task/rest states, obtained from the Human Connectome Project. Furthermore, we tested how CNMs account for variability in individual behavioral measures. The results revealed that three CNMs were robustly extracted under various different preprocessing conditions. Each of these CNMs was significantly correlated with different aspects of behavioral measures of both fluid and crystalized intelligence. The three CNMs were also able to predict several life outcomes, such as income and life satisfaction, achieving the highest performance when combined with behavioral intelligence measures as inputs. Our findings highlight the importance of state-unspecific brain networks to characterize fundamental individual variation.

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

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.

PHASE SYNCHRONIZATION IN BRAIN NETWORKS DERIVED FROM CORRELATION BETWEEN PROBABILITIES OF RECURRENCES IN FUNCTIONAL MRI DATA

2013 ◽  
Vol 23 (02) ◽  
pp. 1350003 ◽  
Author(s):  
D. RANGAPRAKASH ◽  
XIAOPING HU ◽  
GOPIKRISHNA DESHPANDE
Keyword(s):  
Linear Correlation ◽  
Resting State ◽  
Brain Function ◽  
Phase Synchronization ◽  
Human Subjects ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

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

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

scholarly journals Decoding task-specific cognitive states with slow, directed functional networks in the human brain

2019 ◽  
Author(s):  
Devarajan Sridharan ◽  
Shagun Ajmera ◽  
Hritik Jain ◽  
Mali Sundaresan
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Human Brain ◽  
Brain Networks ◽  
Fmri Data ◽  
Invasive Technique ◽  
Functional Interactions ◽  
Cognitive States ◽  
Human Connectome Project ◽  
The Brain

AbstractFlexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity; only the latter approach permits inferring directed functional interactions. Yet, the fMRI hemodynamic response is slow, and sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milliseconds). It is, therefore, widely held that lag-based fMRI functional connectivity, measured with approaches like as Granger-Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim has proven challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. We address this challenge by combining machine learning with GC functional connectivity estimation. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants, drawn from the Human Connectome Project database. A linear classifier, trained on either instantaneous or lag-based GC, reliably discriminated among seven different task and resting brain states, with over 80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, provide robust markers of behaviorally relevant cognitive states.Author SummaryFunctional MRI (fMRI) is a leading, non-invasive technique for mapping networks in the human brain. Yet, fMRI signals are noisy and sluggish, and fMRI scans are acquired at a timescale of seconds, considerably slower than the timescale of neural spiking (milliseconds). Can fMRI, then, be used to infer dynamic processes in the brain such as the direction of information flow among brain networks? We sought to answer this question by applying machine learning to fMRI scans acquired from 1000 participants in the Human Connectome Project (HCP) database. We show that directed brain networks, estimated with a technique known as Granger-Geweke Causality (GC), accurately predicts individual subjects’ task-specific cognitive states inside the scanner, and also explains variations in a variety of behavioral scores across individuals. We propose that directed functional connectivity, as estimated with fMRI-GC, is relevant for understanding human cognitive function.

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