scholarly journals A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

2017 ◽  
Author(s):  
Jeremy R. Manning ◽  
Xia Zhu ◽  
Theodore L. Willke ◽  
Rajesh Ranganath ◽  
Kimberly Stachenfeld ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Probabilistic Approach ◽  
Covariance Structure ◽  
Fmri Data ◽  
Cognitive State ◽  
Bayesian Hierarchical ◽  
Connectivity Patterns ◽  
Hierarchical Matrix

AbstractRecent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participant’s cognitive state (for review see [35]). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subject’s network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes. The number of nodes, along with their locations, sizes, and activities (over time) are learned from the data. Because the number of nodes is typically substantially smaller than the number of fMRI voxels, HTFA can be orders of magnitude more efficient than traditional voxel-based functional connectivity approaches. In one case study, we show that HTFA recovers the known connectivity patterns underlying a collection of synthetic datasets. In a second case study, we illustrate how HTFA may be used to discover dynamic full-brain activity and connectivity patterns in real fMRI data, collected as participants listened to a story. In a third case study, we carried out a similar series of analyses on fMRI data collected as participants viewed an episode of a television show. In these latter case studies, we found that the HTFA-derived activity and connectivity patterns can be used to reliably decode which moments in the story or show the participants were experiencing. Further, we found that these two classes of patterns contained partially non-overlapping information, such that decoders trained on combinations of activity-based and dynamic connectivity-based features performed better than decoders trained on activity or connectivity patterns alone. We replicated this latter result with two additional (previously developed) methods for efficiently characterizing full-brain activity and connectivity patterns.

scholarly journals Methodology for tDCS integration with fMRI

2019 ◽  
Author(s):  
Zeinab Esmaeilpour ◽  
A. Duke Shereen ◽  
Peyman Ghobadi-Azbari ◽  
Abhishek Datta ◽  
Adam J. Woods ◽  
...  
Keyword(s):  
Behavioral Interventions ◽  
Computational Models ◽  
Brain Activity ◽  
State Level ◽  
Resting State Fmri ◽  
Underlying Mechanism ◽  
Cognitive State ◽  
Neural Activation ◽  
Imaging Data

AbstractIntegration of tDCS with fMRI holds promise for investigation the underlying mechanism of stimulation effect. There are 118 published tDCS studies (up to 1st Oct 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state-level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review we explore methodological parameter space of tDCS integration with fMRI. Existing tDCS-fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a case study with both task and resting state fMRI before and after tDCS in a cross-over design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability in responsiveness. Through the case study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS-fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS-fMRI can address important questions on the functional mechanisms of tDCS action (e.g. target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

scholarly journals A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ali Yener Mutlu ◽  
Edward Bernat ◽  
Selin Aviyente
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Dynamic Network ◽  
Principal Component ◽  
Brain Network ◽  
Event Related Potential ◽  
Time Varying ◽  
Error Related Negativity ◽  
Connectivity Patterns

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

scholarly journals Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Andreas A. Ioannides ◽  
Stavros I. Dimitriadis ◽  
George A. Saridis ◽  
Marotesa Voultsidou ◽  
Vahe Poghosyan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Clinical Applications ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Brain ◽  
Typical Subject ◽  
Different Parts ◽  
Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

scholarly journals Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability

2016 ◽  
Vol 37 (2) ◽  
pp. 471-484 ◽  
Author(s):  
Jonathan R Bumstead ◽  
Adam Q Bauer ◽  
Patrick W Wright ◽  
Joseph P Culver
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Functional Connectivity ◽  
Large Variability ◽  
Mayer Waves ◽  
Intrinsic Signal ◽  
Optical Intrinsic Signal ◽  
Connectivity Patterns

Resting-state functional connectivity is a growing neuroimaging approach that analyses the spatiotemporal structure of spontaneous brain activity, often using low-frequency (<0.08 Hz) hemodynamics. In addition to these fluctuations, there are two other low-frequency hemodynamic oscillations in a nearby spectral region (0.1–0.4 Hz) that have been reported in the brain: vasomotion and Mayer waves. Despite how close in frequency these phenomena exist, there is little research on how vasomotion and Mayer waves are related to or affect resting-state functional connectivity. In this study, we analyze spontaneous hemodynamic fluctuations over the mouse cortex using optical intrinsic signal imaging. We found spontaneous occurrence of oscillatory hemodynamics ∼0.2 Hz consistent with the properties of Mayer waves reported in the literature. Across a group of mice (n = 19), there was a large variability in the magnitude of Mayer waves. However, regardless of the magnitude of Mayer waves, functional connectivity patterns could be recovered from hemodynamic signals when filtered to the lower frequency band, 0.01–0.08 Hz. Our results demonstrate that both Mayer waves and resting-state functional connectivity patterns can co-exist simultaneously, and that they can be separated by applying bandpass filters.

Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns from fMRI Data

2020 ◽  
pp. 1-1
Author(s):  
Li Xiao ◽  
Aiying Zhang ◽  
Biao Cai ◽  
Julia M. Stephen ◽  
Tony W. Wilson ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Fmri Data ◽  
Graph Learning ◽  
Connectivity Patterns

scholarly journals A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

NeuroImage ◽  
2018 ◽  
Vol 180 ◽  
pp. 243-252 ◽  
Author(s):  
Jeremy R. Manning ◽  
Xia Zhu ◽  
Theodore L. Willke ◽  
Rajesh Ranganath ◽  
Kimberly Stachenfeld ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Probabilistic Approach ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity

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 Classification of emotions based on functional connectivity patterns of the human brain

2020 ◽  
Author(s):  
Heini Saarimäki ◽  
Enrico Glerean ◽  
Dmitry Smirnov ◽  
Henri Mynttinen ◽  
Iiro P. Jääskeläinen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Pattern Recognition ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Connectivity Patterns

AbstractNeurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity signatures differ across emotions. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks, and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We conclude that functional connectivity patterns consistently differ across different emotions particularly within the default mode system.

scholarly journals Task activations produce spurious but systematic inflation of task functional connectivity estimates

2018 ◽  
Author(s):  
Michael W. Cole ◽  
Takuya Ito ◽  
Douglas Schultz ◽  
Ravi Mill ◽  
Richard Chen ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Computational Model ◽  
Brain Activity ◽  
Pearson Correlation ◽  
Order Effect ◽  
Evoked Response ◽  
Fmri Data ◽  
List Type ◽  
Functional Relationships ◽  
Psychophysiological Interaction

AbstractMost neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of taskevoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference “likely active or interacting during the task”, rather than the intended inference “likely interacting during the task”. Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model’s predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.HighlightsComputational model shows task inflation of functional connectivity estimatesHemodynamic responses cause task activations to further inflate estimatesStandard approaches to remove task activations leave many false positivesMethods that flexibly fit hemodynamic response shape effectively correct inflationCorrection of functional connectivity inflation verified with empirical fMRI data

scholarly journals Spatiotemporal Trajectories in Resting-state FMRI Revealed by Convolutional Variational Autoencoder

2021 ◽  
Author(s):  
Xiaodi Zhang ◽  
Eric Maltbie ◽  
Shella Keilholz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Spatiotemporal Patterns ◽  
Network Activity ◽  
Fmri Data ◽  
Variational Autoencoder ◽  
Spatiotemporal Trajectories ◽  
The Brain

AbstractRecent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.

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