A Sliding Time-Window ICA Reveals Spatial Variability of the Default Mode Network in Time

Kiviniemi, Vesa; Vire, Tapani; Remes, Jukka; Elseoud, Ahmed Abou; Starck, Tuomo; Tervonen, Osmo; Nikkinen, Juha

doi:10.1089/brain.2011.0036

Cited by 243 publications

(224 citation statements)

References 45 publications

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“…Standard stationary FC analyses assume temporal stationarity and are blind to the temporal evolution of FC. Several recent studies have since confirmed the non-stationarity of FC (Handwerker et al, 2012;Hutchison et al, in press;Kang et al, 2011;Kiviniemi et al, 2011;Li et al, in press;Majeed et al, 2011). We will use the terms dynamic FC to specifically refer to fluctuating connectivity during rest, and stationary FC to refer to connectivity estimated under the assumption of temporal stationarity.…”

Section: Introductionmentioning

confidence: 99%

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

et al. 2013

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Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.© 2013 Elsevier Inc. All rights reserved. IntroductionSpontaneous fluctuations of the functional MRI (fMRI) bloodoxygen-level-dependent (BOLD) signal are not random but temporally coherent between distinct brain regions. While these fluctuations were long considered as "noise", Biswal et al. (1995) showed that fluctuations of motor areas were correlated even in the absence of a motor task. Several other networks of coherent BOLD activity between remote brain regions have since been identified, including visual, auditory, language and attention networks, and a network called the "default mode network" (DMN) which reduces its activity during attentiondemanding tasks. These networks of regions with coherent activity during rest are consistent across subjects and closely resemble the brain's functional organization of evoked responses (Damoiseaux et al., 2006;Fox and Raichle, 2007;Laird et al., 2011;Smith et al., 2009). Coherent BOLD activity persists during sleep and in anesthetized monkeys, suggesting that it reflects a fundamental property of the brain's functional organization (Larson-Prior et al., 2009;Vincent et al., 2007).Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al., 2011), cognitive and affective states (Cribben et al., 2012;Ekman et al., 2012;Eryilmaz et al., 2011;Richiardi et al., 2011;Shirer et al., 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al., 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dy...

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Section: Introductionmentioning

confidence: 99%

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

et al. 2013

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“…This evidence led to the introduction of a new concept in evaluating brain regions connectivity named dynamic FC which has encouraged the introduction of a large number of methods to detect it. Sliding window, for instance, has been a widely used method to assess dynamic FC [12][13][14][15][16]. Dynamic connectivity regression (DCR) is another method recommended by [4,5] to discover FC change points between brain areas.…”

Section: Introductionmentioning

confidence: 99%

Model-based estimation of dynamic functional connectivity in resting-state functional magnetic resonance imaging

2017

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Recently, we have witnessed an increase in scientific interest in understanding the dynamic nature of brain networks by evaluating dynamic functional connectivity (FC) using functional magnetic resonance imaging (fMRI). In this work, we introduce two multivariate volatility models, standardized dynamic conditional correlation, and standardized exponentially weighted moving average, both of which are built upon the framework of dynamic conditional correlation and exponentially weighted moving average models, respectively. In these two models, we use standardized residuals with the goal of determining whether the use of standardized residuals reduces the mean square rate error. Moreover, in traditional simulation studies, time series were considered with zero conditional expectation and static conditional variance which do not capture the nature of the real data. This is because of hemodynamic response function in the brain and dynamic functional connectivity of each brain region with itself during the experiment time, respectively. That is why, next, some new simulation studies are introduced which are more similar to blood-oxygen-level-dependent time series of brain regions. Then, methods' proficiency is analyzed using previous and new simulation studies. Results show that, in both former and latter simulations, the new methods work better. Finally, the best model is utilized to model FC in an Iranian resting-state fMRI data.

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“…In contrast, many recent studies have emphasized the importance of treating FC as a dynamical quantity, that is, evolving in time (Hutchison et al 2013;Park and Friston 2013). Different tools have been proposed to introduce temporal variations into the analyses of FC, such as sliding windows (Sakoglu et al 2010;Bassett et al 2011;Jones et al 2012;Shirer et al 2012;Allen et al 2012;Handwerker et al 2012), dynamic conditional correlation (Lindquist et al 2014), single-volume co-activation patterns (CAPs) (Tagliazucchi et al 2012;Liu and Duyn 2013;Amico et al 2014), as well as a combination of sliding windows and other methods, such as Independent Component Analysis (Kiviniemi et al 2011) or Principal Component Analysis (Leonardi et al 2013). For a review of these methods, see (Hutchison et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

et al. 2015

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This paper studies the link between restingstate functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected restingstate FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks Steven Laureys and Rodolphe Sepulchre contributed equally. Electronic supplementary material 123Brain Struct Funct DOI 10.1007/s00429-015-1083-y suggesting that they could be capturing consciousness-related processes such as mind wandering.

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A Sliding Time-Window ICA Reveals Spatial Variability of the Default Mode Network in Time

Cited by 243 publications

References 45 publications

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

Model-based estimation of dynamic functional connectivity in resting-state functional magnetic resonance imaging

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

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