On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

Thompson, William Hedley; Fransson, Peter

doi:10.1089/brain.2016.0454

Cited by 38 publications

(38 citation statements)

References 44 publications

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“…Previous studies have shown that the dFCvar and sFC strength are significantly anticorrelated [Thompson and Fransson, ], demonstrating that the altered sFC strength in the ASD group may drive atypical dFCvar in this study. To determine the relationship between dFC hypervariance and sFC under‐connectivity in ASD, a partial correlation analysis was done to explore the difference of dFCvar between HC and ASD while controlling the sFC strength.…”

Section: Methodssupporting

confidence: 55%

Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism

Chen

Nomi

Uddin

et al. 2017

Human Brain Mapping

112

111

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Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with altered brain connectivity. Previous neuroimaging research demonstrates inconsistent results, particularly in studies of functional connectivity in ASD. Typically, these inconsistent findings are results of studies using static measures of resting-state functional connectivity. Recent work has demonstrated that functional brain connections are dynamic, suggesting that static connectivity metrics fail to capture nuanced time-varying properties of functional connections in the brain. Here we used a dynamic functional connectivity approach to examine the differences in the strength and variance of dynamic functional connections between individuals with ASD and healthy controls (HCs). The variance of dynamic functional connections was defined as the respective standard deviations of the dynamic functional connectivity strength across time. We utilized a large multi-center dataset of 507 male subjects (209 with ASD and 298 HC, from 6 to 36 years old) from the Autism Brain Imaging Data Exchange (ABIDE) to identify six distinct whole-brain dynamic functional connectivity states. Analyses demonstrated greater variance of widespread long-range dynamic functional connections in ASD (p < 0.05, NBS method) and weaker dynamic functional connections in ASD (p < 0.05, NBS method) within specific whole-brain connectivity states. Hyper-variant dynamic connections were also characterized by weaker connectivity strength in ASD compared with HC. Increased variance of dynamic functional connections was also related to ASD symptom severity (ADOS total score) (p < 0.05), and was most prominent in connections related to the medial superior frontal gyrus and temporal pole. These results demonstrate that greater intra-individual dynamic variance is a potential biomarker of ASD.

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Section: Methodssupporting

confidence: 55%

Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism

Chen

Nomi

Uddin

et al. 2017

Human Brain Mapping

112

111

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“…The consequence of this is that direct comparisons of the DFC variance between cohorts/conditions becomes hard to interpret as dynamic fluctuations, especially when the length of the data varies. However, this is the case for most methods and it should be remembered that the variance is proportional to the static functional connectivity (7,9,10). Simply put, the JC method (like all other methods) should not be used for a direct contrast of the variance of DFC time series.…”

Section: Discussionmentioning

confidence: 99%

“…Dynamic functional connectivity (DFC) is being applied to an increasing number of topics studying the brain's networks. Topics that have been explored with DFC include development (1), various pathologies (2,3), affect (4), attention (5), levels of consciousness (6), and temporal properties of the brain's networks (7)(8)(9). There are many concerns raised regarding methodological issues.…”

Section: Introductionmentioning

confidence: 99%

“…Methods used to derive DFC estimates are as diverse as its range of applications. Examples of different methods include: the sliding window method, sometimes tapered (15), temporal derivatives (16), methods using Euclidean distance between spatial configurations (8), k-means clustering methods (7,17), eigenconnectivities (18), point process methods (19,20), Kalman filters (21,22). flexible least squares (23), temporal ICA (24), sliding window ICA (25), dynamic conditional correlation (26), phase differences (27) wavelet coherence (4), hidden Markov models (28), and variational Bayes hidden Markov models (29).…”

Section: Introductionmentioning

confidence: 99%

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A simulation and comparison of dynamic functional connectivity methods

Thompson¹,

Richter

Plavén-Sigray³

et al. 2017

Preprint

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WHT and PF designed the simulation analysis. WHT and CGR designed and tested the jackknife correlation method. WHT and PPS designed the statistical models. WHT performed the analysis. WHT and PF wrote the paper. CGR and PPS critically revised the paper. AbstractThere is a current interest in quantifying brain dynamic functional connectivity (DFC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brain's dynamics. However, given that the ground truth for DFC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exists many DFC methods it is difficult to assess differences in dynamic brain connectivity between studies. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating DFC (sliding window, tapered sliding window, temporal derivative, spatial distance and jackknife correlation). In particular, we were interested in each methods' ability to track changes in covariance over time, which is a key property in DFC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future DFC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. In this paper, we present dfcbenchmarker, which is a Python package where researchers can easily submit and compare their own DFC methods to evaluate its performance.

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“…For the Box Cox transform the λ parameter was fit by taking maximum likelihood after a grid-search procedure through -5 and 5 in 0.1 increments for each edge. Prior to the Box Cox transformation, the smallest value was scaled to 1 to make sure the Box Cox transform performed similarly throughout the time series (78). Each connectivity time series was then standardized by subtracting the mean and dividing by the standard deviation.…”

Section: Creating Time-graphlets (T-graphlets)mentioning

confidence: 99%

From static to temporal network theory – applications to functional brain connectivity

Thompson

Brantefors

Fransson

2016

Preprint

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Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, there has been a growing interest to examine the temporal dynamics of the brain's network activity. While different approaches to capture fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. Temporal network theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences and engineering. The objective of this paper is twofold: (i) to present a detailed description of the central tenets and outline measures from temporal network theory; (ii) apply these measures to a resting-state fMRI dataset to illustrate their utility. Further, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this paper are freely available as a python package Teneto. (7,8,9), yielding knowledge about functional network properties (10,11,12,13) which has been applied to clinical populations (14,15).In parallel to research on the brain's connectome, there has been a focus on studying the dynamics of brain activity. When the brain is modeled as a dynamic system, a diverse range of properties can be explored, prominent examples of this are metastability (16,17,18,19,20) and oscillations (21,22,23). Brain oscillations, inherently dynamic, have become a vital ingredient in proposed mechanisms ranging from psychological processes such as memory (24, 25, 26), attention (27, 28), to basic neural communication of a top-down and bottom-up type of information transfer (29,30,31,32,33,34,35,36).Recently, approaches to study brain connectomics and the dynamics of neuronal communication have started to merge. A significant amount of work has recently been carried out that aims to quantify dynamic fluctuations of network activity in the brain using fMRI (37,38,39,40,41,42) as well as MEG (7,43,9,44,35). This research area to unify brain connectommics with the dynamic properties of neuronal communication has been called the "dynome" (45) and the "chronnectome" (46). As the brain can quickly fluctuate between different tasks, the overarching aim of this area of research is to understand the dynamic interplay of the brain's networks. The intent of this research is that it will yield insight about the complex and dynamic cognitive human abilities.

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On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

Cited by 38 publications

References 44 publications

Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism

Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism

A simulation and comparison of dynamic functional connectivity methods

From static to temporal network theory – applications to functional brain connectivity

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