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 Causally informed activity flow models provide mechanistic insight into the emergence of cognitive processes from brain network interactions

2021 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Takuya Ito ◽  
Ravi D. Mill ◽  
Stephen José Hanson ◽  
Michael W. Cole
Keyword(s):  
Functional Connectivity ◽  
Effective Connectivity ◽  
Pearson Correlation ◽  
Explanatory Power ◽  
Memory Task ◽  
Brain Regions ◽  
Brain Network ◽  
List Type ◽  
Flow Models ◽  
Insight Into

AbstractBrain activity flow models estimate the movement of task-evoked activity over brain connections to help explain the emergence of task-related functionality. Activity flow estimates have been shown to accurately predict task-evoked brain activations across a wide variety of brain regions and task conditions. However, these predictions have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. Starting from Pearson correlation (the current field standard), we progress from FC measures with poor to excellent causal grounding, demonstrating a continuum of causal validity using simulations and empirical fMRI data. Finally, we apply a causal FC method to a dorsolateral prefrontal cortex region, demonstrating causal network mechanisms contributing to its strong activation during a 2-back (relative to a 0-back) working memory task. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.Highlights-Activity flow models provide insight into how cognitive neural effects emerge from brain network interactions.-Functional connectivity methods grounded in causal principles facilitate mechanistic interpretations of task activity flow models.-Mechanistic activity flow models accurately predict task-evoked neural effects across a wide variety of brain regions and cognitive tasks.

scholarly journals Understanding psychophysiological interaction and its relations to beta series correlation

2018 ◽  
Author(s):  
Xin Di ◽  
Zhiguo Zhang ◽  
Bharat B Biswal
Keyword(s):  
Functional Connectivity ◽  
Effective Connectivity ◽  
General Linear Model ◽  
Fmri Data ◽  
Stop Signal Task ◽  
Physiological Variable ◽  
Context Sensitive ◽  
Psychophysiological Interaction ◽  
Direct Contrast ◽  
Multiple Conditions

AbstractPsychophysiological interaction (PPI) was proposed 20 years ago for study of task modulated connectivity on functional MRI (fMRI) data. A few modifications have since been made, but there remain misunderstandings on the method, as well as on its relations to a similar method named beta series correlation (BSC). Here, we explain what PPI measures and its relations to BSC. We first clarify that the interpretation of a regressor in a general linear model depends on not only itself but also on how other effects are modeled. In terms of PPI, it always reflects differences in connectivity between conditions, when the physiological variable is included as a covariate. Secondly, when there are multiple conditions, we explain how PPI models calculated from direct contrast between conditions could generate identical results as contrasting separate PPIs of each condition (a.k.a. “generalized” PPI). Thirdly, we explicit the deconvolution process that is used for PPI calculation, and how is it related to the trial-by-trial modeling for BSC, and illustrate the relations between PPI and those based upon BSC. In particular, when context sensitive changes in effective connectivity are present, they manifest as changes in correlations of observed trial-by-trial activations or functional connectivity. Therefore, BSC and PPI can detect similar connectivity differences. Lastly, we report empirical analyses using PPI and BSC on fMRI data of an event-related stop signal task to illustrate our points.

scholarly journals Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

2019 ◽  
Author(s):  
Caterina Gratton ◽  
Ally Dworetsky ◽  
Rebecca S. Coalson ◽  
Babatunde Adeyemo ◽  
Timothy O. Laumann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Large Data ◽  
Motion Artifacts ◽  
Single Band ◽  
Fmri Data ◽  
Head Motion ◽  
List Type ◽  
Motion Parameters ◽  
Filtering Approach

AbstractDenoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 – 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.Highlights-Single-band fMRI motion traces show factitious high-frequency content (HF-motion)-The magnitude of HF-motion relates to age and other demographic factors-HF-motion elevates framewise displacement (FD) and causes data loss-Substantial fMRI data can be recovered from censoring by filtering motion traces-Filtering motion traces reduces motion artifacts in 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 Assessing uncertainty in connective field estimations from resting state fMRI activity

2020 ◽  
Author(s):  
Azzurra Invernizzi ◽  
Nicolas Gravel ◽  
Koen V. Haak ◽  
Remco J. Renken ◽  
Frans W. Cornelissen
Keyword(s):  
Visual Field ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
List Type ◽  
Spatial Integration ◽  
Cortical Areas ◽  
Local Functional ◽  
Visual Cortical

AbstractConnective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modelling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data, although this is important since 3T scanners are much more abundant and more often used in clinical research than 7T ones. In this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans interleaved by the acquisition of visual stimulation in 12 healthy participants.Our results show that both approaches reveal good agreement between RS- and visual field (VF)-based maps. Moreover, the 3T observations were similar to those previously reported at 7T. In addition, to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.HighlightsLocal functional connectivity between visual cortical areas can be estimated from RS-fMRI data at 3T using both standard CF and Bayesian CF modelling.Bayesian CF modelling quantifies the model uncertainty associated with each CF parameter on RS and VF data, important in particular for future studies on clinical populations.3T observations were qualitatively similar to those previously reported at 7T.

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.

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 Aberrant Intraregional Brain Activity and Functional Connectivity in Patients With Diarrhea-Predominant Irritable Bowel Syndrome

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiao-Fei Chen ◽  
Yun Guo ◽  
Xing-Qi Lu ◽  
Le Qi ◽  
Kuang-Hui Xu ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Functional Connectivity ◽  
Brain Activity ◽  
Gastrointestinal Symptoms ◽  
Mediating Effect ◽  
Fmri Data ◽  
Lingual Gyrus ◽  
Postcentral Gyrus ◽  
Irritable Bowel ◽  
The Right

Background and PurposeThe appearance and aggravation of diarrhea-predominant irritable bowel syndrome (IBS-D) have proven to be closely related to psychosocial factors. We aimed to measure altered spontaneous brain activity and functional connectivity (FC) in patients with IBS-D using resting-state functional magnetic resonance imaging (RS-fMRI) and to analyze the relationship between these parameters and emotional symptoms.MethodsThirty-six adult IBS-D patients and thirty-six demographic-matched healthy controls (HCs) underwent RS-fMRI scans. After processing RS-fMRI data, the values of the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) of the two groups were compared. The abnormal regions were selected as the regions of interest to compare whole-brain seed-based FC between the groups. The relationships between RS-fMRI data and mood and gastrointestinal symptoms were analyzed using correlation and mediation analyses.ResultsCompared with HCs, IBS-D patients showed increased ALFF in the right cerebellum posterior lobe, the right lingual gyrus/calcarine, the right postcentral gyrus, the right superior frontal gyrus (SFG), and middle frontal gyrus (MFG), with decreased ALFF in the right inferior parietal lobule, the right striatum, the right anterior cingulated cortex, the right insula, the right hippocampus, the right thalamus, the right midbrain, and the left precuneus. IBS-D patients showed increased ReHo in the bilateral lingual gyrus/calcarine, the bilateral SFG, the right MFG, and the right postcentral gyrus, with decreased ReHo in the orbital part of the left inferior frontal gyrus and the right supplementary motor area. Patients showed enhanced FC between the left precuneus and the bilateral orbitofrontal cortex (OFC). There was a positive correlation between increased ALFF values in the right midbrain and anxiety-depression symptoms in IBS-D patients, and the mediating effect of gastrointestinal symptoms indirectly caused this correlation.ConclusionIBS-D patients had dysregulated spontaneous activity and FC in regions related to pain regulation and emotional arousal involved in prefrontal–limbic–midbrain circuit and somatosensory processing. The development of mood disorders in IBS-D patients may be partly related to the dysfunction of components in the dopamine pathway (especially the midbrain, OFC) due to visceral pain.

An Adaptive and Learning System for Feedback-Controlled Evoked Response Studies

1981 ◽  
Vol 20 (03) ◽  
pp. 169-173
Author(s):  
J. Wagner ◽  
G. Pfurtscheixer
Keyword(s):  
Brain Activity ◽  
Evoked Response ◽  
Learning System ◽  
Time Interval ◽  
Electrical Brain Activity ◽  
Long Time ◽  
The Subject ◽  
Eeg Power ◽  
Stimulation System ◽  
Background Eeg

The shape, latency and amplitude of changes in electrical brain activity related to a stimulus (Evoked Potential) depend both on the stimulus parameters and on the background EEG at the time of stimulation. An adaptive, learnable stimulation system is introduced, whereby the subject is stimulated (e.g. with light), whenever the EEG power is subthreshold and minimal. Additionally, the system is conceived in such a way that a certain number of stimuli could be given within a particular time interval. Related to this time criterion, the threshold specific for each subject is calculated at the beginning of the experiment (preprocessing) and adapted to the EEG power during the processing mode because of long-time fluctuations and trends in the EEG. The process of adaptation is directed by a table which contains the necessary correction numbers for the threshold. Experiences of the stimulation system are reflected in an automatic correction of this table. Because the corrected and improved table is stored after each experiment and is used as the starting table for the next experiment, the system >learns<. The system introduced here can be used both for evoked response studies and for alpha-feedback experiments.

Sign in / Sign up

Export Citation Format

Share Document