Periodic changes in fMRI connectivity

By Daniel A. Handwerker, Vinai Roopchansingh, Javier Gonzalez-Castillo, Peter A. Bandettini. NeuroImage, 2012

387 citations recorded by the source corpus.

The first two decades of brain research using fMRI have been dominated by studies that measure signal changes in response to a presented task. A rapidly increasing number of studies are showing that consistent activation maps appear by assessment of signal correlations during time periods in which the subjects were not directed to perform any specific task (i.e. “resting state correlations”). Even though neural interactions can happen on much shorter time scales, most “resting state” studies assess these temporal correlations over a period of about 5 to 10 minutes. Here we investigate how these temporal correlations change on a shorter time scale. We examine changes in brain correlations to the posterior cingulate cortex (PCC) across a 10 minutes scan. We show: (1) fMRI correlations fluctuate over time, (2) these fluctuations can be periodic, and (3) correlations between the PCC and other brain regions fluctuate at distinct frequencies. While the precise frequencies of correlation fluctuations vary across subjects and runs, it is still possible to parse brain regions and combinations of brain regions based on fluctuation frequency differences. To evaluate the potential biological significance of these empirical observations, we then use synthetic time series data with identical amplitude spectra, but randomized phase to show that similar effects can still appear even if the timing relationships between voxels are randomized. This implies that observed correlation fluctuations could occur between regions with distinct amplitude spectra, whether or not there are dynamic changes in neural connectivity between such regions. As more studies of brain connectivity dynamics appear, particularly studies using correlation as a key metric, it is vital to better distinguish true neural connectivity dynamics from connectivity fluctuations that are inherently part of this method. Our results also highlight the rich information in the power spectra of fMRI data that can be used to parse brain regions.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fe11120572b76e63837d539967f3dc9f7b18a2ad

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In Cognitive Neuroscience

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Citations in the corpus

A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, neutral): “…metrics at grouplevel section for demonstration). fluctuations in patterns of functional connectivity (Allen et al., in press; Chang and Glover, 2010; Handwerker et al., 2012; Kang et al., 2011; Smith et al., 2012); most recently, Allen et al. (in press) demonstrated the presence of up to seven…”
Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (2014, background): “…variability (Fox andRaichle, 2007), reports have alsohighlighted the pitfall of over-interpreting fluctuations in FC as having a neural origin, when in fact these fluctuations can arise from basic BOLD signal properties (Handwerker et al., 2012; Hutchison et al., 2013; Keilholz et al., 2013).”
Network attributes for segregation and integration in the human brain. (2013, background): “More recently, empirical studies of resting-state fMRI have documented nonstationarities in functional couplings among remote brain regions [75], and these findings have been confirmed and extended across the human, macaque, and rat brain [76 ,77 ,78] (Figure 4).”
State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, positive): “In these regards, the analysis of functional characteristics inherent in rs-fMRI is playing a core role for brain disease diagnosis or prognosis (Handwerker et al., 2012; Li et al., 2012; Leonardi et al., 2013; Hjelm et al., 2014; Wee et al., 2014; Suk et al., 2015c).”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, methodology): “The study was approved by the local institutional ethics committee and all subjects provided informed consent.”
Dynamic functional connectivity of the default mode network tracks daydreaming (2014, background): “…demonstrated that FC fluctuates across shorter time-windows (e.g. 30–60 s) during rs-fMRI, with regions that are correlated with one another during some periods being uncorrelated and/or anticorrelated during other periods (Allen et al., 2014; Chang and Glover, 2010; Handwerker et al., 2012).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “In complementary work, Handwerker et al. (2012) tested the hypothesis that the frequency characteristics of sliding-window time series were dependent on the precise timing relationships between regional time courses by keeping the amplitude spectra of individual time series constant while…”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “All of these window lengths were within the sizes commonly reported in the literature that vary from 8 to 240 s (Chang and Glover, 2010; Kiviniemi et al., 2011; Handwerker et al., 2012; Chang et al., 2013; Keilholz et al., 2013; Thompson et al., 2013a, 2013b; Kucyi and Davis, 2014; Wilson et al., 20”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “…by MEG studies (de Pasquale et al., 2010, Brookes et al., 2011b, Hipp et al., 2012, Hipp and Siegel, 2015) and by investigations on the temporal dynamics of FC (Chang and Glover, 2010, Handwerker et al., 2012, Smith et al., 2012, Hutchison et al., 2013a, Allen et al., 2014, Zalesky et al., 2014).”
Spatio-Temporal Data Mining (2017, background): “For example, [Handwerker et al. 2012] demonstrated that the correlation between time series from the Posterior Cingulate region and other locations in the brain change with time.”