Comparing test-retest reliability of dynamic functional connectivity methods

Choe, Ann S.; Nebel, Mary Beth; Barber, Anita D.; Cohen, Jessica R.; Xu, Yuting; Pekar, James J.; Caffo, Brian; Lindquist, Martin A.

doi:10.1016/j.neuroimage.2017.07.005

Cited by 179 publications

(188 citation statements)

References 79 publications

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“…The network metrics include clustering coefficient, characteristic path length, local efficiency, global efficiency, modularity, hierarchy, and degree, and they were calculated with the Matlab toolboxes Brain Connectivity Toolbox (Rubinov and Sporns, 2010 a ) and GRETNA (Wang et al, 2015). Mathematical definitions for each metric are presented in Appendix C. We note that the ICC is a commonly used measure designed to assess the similarity of network estimates across scanning sessions (Braun et al, 2012; Choe et al, 2017; Niu et al, 2013), and it is usually derived by calculating the proportion of the total variation attributed to variability across scanning sessions. Thus, small variation across sessions relative to variation between individuals produces high ICC values, indicating strong reproducibility.…”

Section: Resultsmentioning

confidence: 99%

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge

2018

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Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization by integrating information on both brain structure and function. In particular, incorporating anatomical knowledge leads to desirable outcomes such as increased accuracy in brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear advantages, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to inherent deficiencies in existing imaging technology. This calls for the development of advanced network modeling tools that appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach which models the brain functional networks via sparse precision matrices whose degree of edge specific shrinkage is a random variable that is modeled using both anatomical structure and an independent baseline component. The proposed approach adaptively shrinks functional connections and flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of misspecified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm which yields maximum a posteriori estimates. Extensive numerical studies involving multiple functional network structures reveal the clear advantages of the proposed approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is misspecified up to a certain degree. An application of the approach to data from the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.

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Section: Resultsmentioning

confidence: 99%

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge

2018

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“…For instance,Zhang et al, 2018 analyzed the reliability of both static FC and dynamic fluctuations (standard deviation, amplitude of low frequency fluctuations, and excursions) in a large population of adults (820 subjects from Human Connectome Project). They found the variations of connectivity dynamics were most robust when the sliding-window length was less than 40 s. However,Choe et al (2017) found, although the temporal features of connectivity dynamics were reliably reproducible, their dFC parameters of connectivity states (number of state transitions and dwell time) had poorer reliabilities across the sliding windows, taped sliding window, and dynamic conditional correlations approach. Thus, our results are informative to demonstrate the robustness of dFC properties in terms of their reproducibility during RW conditions across numbers of clusters or sliding-window lengths.…”

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confidence: 99%

“…The occurrence of these states predicted inter-subjects' behavioral lapsing and intra-subjects' response speeds (Patanaik et al, 2018;Yeo, Tandi, & Chee, 2015). However, several studies (Choe et al, 2017;Zhang, Baum, Adduru, Biswal, & Michael, 2018) debated the low replicabilities of both temporal brain fluctuations and their dFC parameters across sliding windows using some large samples of public datasets.…”

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“…Number of subjects = 13 and 14 in SD28 and SD52 study fraction of connectivity states potentially acting as robust statistical indicators with low intra-subject variabilities. Up to now, there havebeen several studies that attempted to examine the test-retest reliability of dynamic correlations and their connectivity states, as well as the summary measures of states(Abrol et al, 2017;Choe et al, 2017). For instance,Zhang et al, 2018 analyzed the reliability of both static FC and dynamic fluctuations (standard deviation, amplitude of low frequency fluctuations, and excursions) in a large population of adults (820 subjects from Human Connectome Project).…”

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Impact of acute sleep deprivation on dynamic functional connectivity states

Fronczek‐Poncelet

Lange

et al. 2019

Human Brain Mapping

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Sleep deprivation (SD) could amplify the temporal fluctuation of spontaneous brain activities that reflect different arousal levels using a dynamic functional connectivity (dFC) approach. Therefore, we intended to evaluate the test-retest reliability of dFC characteristics during rested wakefulness (RW), and to explore how the properties of these dynamic connectivity states were affected by extended durations of acute sleep loss (28/52 hr). We acquired resting-state fMRI and neuropsychological datasets in two independent studies: (a) twice during RW and once after 28 hr of SD (n = 15) and (b) after 52 hr of SD and after 14 hr of recovery sleep (RS; n = 14).Sliding-window correlations approach was applied to estimate their covariance matrices and corresponding three connectivity states were generated. The test-retest reliability of dFC properties demonstrated mean dwell time and fraction of connectivity states were reliable. After SD, the mean dwell time of a specific state, featured by strong subcortical-cortical anticorrelations, was significantly increased. Conversely, another globally hypoconnected state was significantly decreased. Subjective sleepiness and objective performances were separately positive and negative correlated with the increased and decreased state. Two brain connectivity states and their alterations might be sufficiently sensitive to reflect changes in the dynamics of brain mental activities after sleep loss. K E Y W O R D Sacute sleep deprivation, dynamic connectivity states, light sleep/drowsiness, resting-state fMRI, test-retest reliability

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“…constructed biologically-plausible models that are not constrained by individual-level 23 human brain activity or used data-driven statistical characterizations of individuals that are 24 not mechanistic. We aim to bridge this gap through the development of a new modeling 25 approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein 26 we fit nonlinear dynamical systems models directly to human brain imaging data. The 27 MINDy framework is able to produce these data-driven network models for hundreds to 28 thousands of interacting brain regions in just 1-3 minutes per subject.…”

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Estimation and Validation of Individualized Dynamic Brain Models with Resting State fMRI

Singh

Braver

Cole

et al. 2019

Preprint

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20A key challenge for neuroscience is to develop generative, causal models of the human 21 nervous system in an individualized, data-driven manner. Previous initiatives have either 22 33 34 MAIN TEXT 35 36 45 Generative models are then formed by integrating these cellular-level observations with known 46 neuronal biophysics at the spatial scale of individual neurons or small populations (e.g. [1]). 47 48 In contrast, another set of large initiatives has instead focused on modeling individual 49 human brain function using an approach often referred to as "connectomics" (e.g., Human 50 Page 2 of 38 Connectome Project, [2]). This approach relies on descriptive statistics, typically correlation 51 between fluctuating activity signals in brain regions assessed during the resting state (``resting 52 state functional connectivity" or rsFC; [3]). As a result, it is sometimes difficult to make 53 mechanistic inferences based upon functional connectivity correlations ( [4]). Moreover, neural 54 processes are notoriously nonlinear and inherently dynamic, meaning that stationary descriptions, 55 such as correlation/functional connectivity, may be unable to fully capture brain mechanisms. 56 Nevertheless, rsFC remains the dominant framework for describing connectivity patterns in 57 individual human brains. 59Despite the promise of human connectomics, there have been only a few attempts to equip 60 human fMRI studies with the sorts of generative neural population models that have powered 61 insights into non-human nervous systems. Notable advances have occurred in data-driven 62 approaches, with methods being developed to identify directed, causal influences between brain 63 regions (e.g. [5]). Conversely, neural mass modeling approaches have also been extended to study 64 human brain activity in a generative fashion, and these have provided new insights into the 65 computational mechanisms underlying fMRI and MEG/EEG activity dynamics ( [6] [7] [8]). 66 Neural mass modeling approaches have also been applied to individuals by incorporating single 67 subject diffusion data and anatomy ( [6]), but, unlike (linear) data-driven approaches (e.g. 68 Dynamic Causal Modeling; [5]), these models have not been directly fit to the individual brain 69 activity dynamics they seek to capture. However, both of these approaches have important 70 limitations. In particular, the data-driven approaches are subject to potential misinferences due to 71 assumptions of linearity ( [9]) or limitation to a relatively small number of brain regions (e.g. [5]). 72 Likewise, with neural mass modeling approaches, their ability to quantitatively recreate key 73 features of individual-level functional connectivity has also been limited ( [10] [7] [8]). This may 74 be because the most common approach is to parameterize connectivity from estimates of white 75 matter integrity from diffusion imaging, which also can lead to potential misinference, since these 76 connectivity estimates are constrained to be symmetric and positive ( [11]). ...

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Comparing test-retest reliability of dynamic functional connectivity methods

Cited by 179 publications

References 79 publications

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge

Impact of acute sleep deprivation on dynamic functional connectivity states

Estimation and Validation of Individualized Dynamic Brain Models with Resting State fMRI

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