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Cited by 116Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

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The spatial structure of resting state connectivity stability on the scale of minutes

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Gonzalez-Castillo, Javier, Handwerker, Daniel A., Robinson, Meghan E., Hoy, Colin W., Buchanan, Laura C., Saad, Ziad S., Bandettini, Peter A.·2014·Frontiers in Neuroscience
DOI 10.3389/fnins.2014.00138PMC PMC4052097
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Abstract

Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5-10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32% of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.

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Crossref · 10.3389/fnins.2014.00138

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Figures

Figure 1 (A) Depiction of the 150-ROI Craddock Atlas on top of five sagittal slices in the MNI stereotaxic space. (B) Depiction of the remaining 130 ROIs from the atlas considered in this study. ROIs eliminated from the original atlas correspond mainly to the cerebellum and inferior temporal regions that were not part of the imaging FOV for all 12 participants. (C) Grouping of the remaining ROIs according to the Laird et al. (2011) functional network templates.
Figure 1 (A) Depiction of the 150-ROI Craddock Atlas on top of five sagittal slices in the MNI stereotaxic space. (B) Depiction of the remaining 130 ROIs from the atlas considered in this study. ROIs eliminated from the original atlas correspond mainly to the cerebellum and inferior temporal regions that were not part of the imaging FOV for all 12 participants. (C) Grouping of the remaining ROIs according to the Laird et al. (2011) functional network templates.CC BY · PubMed Central
Figure 2 Sliding-Window methods. (A) Example running window connectivity matrix for one representative subject on the left, and its associated vector of CVAR values on the right. The thresholds used to discard connections on the basis of excessive CVAR are depicted as red dashed lines. Eight connections that were discarded for this particular subject are marked as red dots. (B) Sliding window connectivity matrix and CVAR vector after removal of outlier connections. Now there are 5023 connections, instead of 5032, for this representative subject. (C) Sliding window connectivity matrix and CVAR vector after sorting connections according to their CVAR. Connections with negative CVAR are at the bottom of the graph, while connections with positive CVAR are on the top. The further a connection is from the horizontal axis where CVAR is the closest to zero (black dashed line), the more variable the strength of that connection across time. (D) Classification of connections in three possible groups for three other representative subjects, shown both as sorted sliding window connectivity matrices (left) and in a single matrix form (right) where the color of the cell for a connection denotes its group assignment according to our criteria. The three groups are: connections with negative CVAR (blue); lowest positive CVAR/most stable connections (green); largest positive CVAR/least stable connections (red). (E) Aggregated results across subjects. We do this by only selecting connections classified the same way across all 11 participants that were included in the sliding window analysis. Connections of the three types are shown both in matrix view (top) and in brain space (bottom).
Figure 2 Sliding-Window methods. (A) Example running window connectivity matrix for one representative subject on the left, and its associated vector of CVAR values on the right. The thresholds used to discard connections on the basis of excessive CVAR are depicted as red dashed lines. Eight connections that were discarded for this particular subject are marked as red dots. (B) Sliding window connectivity matrix and CVAR vector after removal of outlier connections. Now there are 5023 connections, instead of 5032, for this representative subject. (C) Sliding window connectivity matrix and CVAR vector after sorting connections according to their CVAR. Connections with negative CVAR are at the bottom of the graph, while connections with positive CVAR are on the top. The further a connection is from the horizontal axis where CVAR is the closest to zero (black dashed line), the more variable the strength of that connection across time. (D) Classification of connections in three possible groups for three other representative subjects, shown both as sorted sliding window connectivity matrices (left) and in a single matrix form (right) where the color of the cell for a connection denotes its group assignment according to our criteria. The three groups are: connections with negative CVAR (blue); lowest positive CVAR/most stable connections (green); largest positive CVAR/least stable connections (red). (E) Aggregated results across subjects. We do this by only selecting connections classified the same way across all 11 participants that were included in the sliding window analysis. Connections of the three types are shown both in matrix view (top) and in brain space (bottom).CC BY · PubMed Central
Figure 3 (A) Weighted connectivity matrices in terms of Pearson's correlation (r) for four representative subjects when the complete time series (3950 data points) enter the analysis. (B) Binary connectivity matrices for the same four representative subjects after statistical thresholding. (C) Matrix showing the number of subjects for which a given connection was marked as statistically significant. (D) Matrix showing connections that were marked as statistically significant in at least seven subjects.
Figure 3 (A) Weighted connectivity matrices in terms of Pearson's correlation (r) for four representative subjects when the complete time series (3950 data points) enter the analysis. (B) Binary connectivity matrices for the same four representative subjects after statistical thresholding. (C) Matrix showing the number of subjects for which a given connection was marked as statistically significant. (D) Matrix showing connections that were marked as statistically significant in at least seven subjects.CC BY · PubMed Central
Figure 4 Similarity of whole-brain connectivity vs. window duration. Data for individual subjects are shown as red dashed lines. Average across all subjects is shown in black. The similarity of connectivity matrices clearly decreases as a function of window duration. The decreasing rate is particularly accentuated for durations below 10 min. As window duration decreases, a larger number of windows enter the analysis for a given duration. The number of windows contributing to each duration in each subject is provided at the bottom of the graph. All window durations within the same shaded region (white or gray) have the same number of windows contributing to the computation of similarity.
Figure 4 Similarity of whole-brain connectivity vs. window duration. Data for individual subjects are shown as red dashed lines. Average across all subjects is shown in black. The similarity of connectivity matrices clearly decreases as a function of window duration. The decreasing rate is particularly accentuated for durations below 10 min. As window duration decreases, a larger number of windows enter the analysis for a given duration. The number of windows contributing to each duration in each subject is provided at the bottom of the graph. All window durations within the same shaded region (white or gray) have the same number of windows contributing to the computation of similarity.CC BY · PubMed Central
Figure 5 (A) Distributions of correlation values across time for all the connections in a representative subject are depicted in black. To highlight the mostly uni-modal shape of individual histograms, 11 randomly selected histograms are highlighted using dashed colored lines. (B) Same histograms as in (A), but this time each histogram is colored according to the membership of each connection to one of three groups: blue = negative CVAR connection; red = most variable positive CVAR connection; green = most stable positive CVAR connection. Grouping of connections show a compact profile with all connections from the same group clustering together. (C) Same as (B) for a second representative subject.
Figure 5 (A) Distributions of correlation values across time for all the connections in a representative subject are depicted in black. To highlight the mostly uni-modal shape of individual histograms, 11 randomly selected histograms are highlighted using dashed colored lines. (B) Same histograms as in (A), but this time each histogram is colored according to the membership of each connection to one of three groups: blue = negative CVAR connection; red = most variable positive CVAR connection; green = most stable positive CVAR connection. Grouping of connections show a compact profile with all connections from the same group clustering together. (C) Same as (B) for a second representative subject.CC BY · PubMed Central
Figure 6 (A) Most variable positive connections for window length = 60 s. Connections classified as most variable in all 11 participants are shown over 3D renderings of a brain surface. (B) The same information shown as a 2D matrix. Colors corresponding to networks on the axes of the matrix are used to color nodes of that network in brain space. (C) Most variable connections for window length = 120 s. (D) Most variable connections with window length = 180 s.
Figure 6 (A) Most variable positive connections for window length = 60 s. Connections classified as most variable in all 11 participants are shown over 3D renderings of a brain surface. (B) The same information shown as a 2D matrix. Colors corresponding to networks on the axes of the matrix are used to color nodes of that network in brain space. (C) Most variable connections for window length = 120 s. (D) Most variable connections with window length = 180 s.CC BY · PubMed Central
Figure 7 Number of subjects for which a given connection was classified as most variable (A), most stable (B), and with negative CVAR (C). Connections that were consistently classified in the same group for all 11 subjects are marked with a black outline. These are the same connections shown in Figure 6 (most variable), Figure 8 (most stable), and Figure 11 (negative CVAR). Connections that were classified in the same group for seven or more subjects appear in different shades of red (most variable), green (most stable), or blue (negative CVAR) in the corresponding panel.
Figure 7 Number of subjects for which a given connection was classified as most variable (A), most stable (B), and with negative CVAR (C). Connections that were consistently classified in the same group for all 11 subjects are marked with a black outline. These are the same connections shown in Figure 6 (most variable), Figure 8 (most stable), and Figure 11 (negative CVAR). Connections that were classified in the same group for seven or more subjects appear in different shades of red (most variable), green (most stable), or blue (negative CVAR) in the corresponding panel.CC BY · PubMed Central
Figure 8 (A) Most stable positive connections for window length = 60 s. Connections classified as most stable in all 11 participants are shown over 3D renderings of a brain surface. (B) The same information shown as a 2D matrix. Colors corresponding to networks on the axes of the matrix are used to color nodes of that network in brain space. (C) Most stable connections for window length = 120 s. (D) Most stable connections with window length = 180 s.
Figure 8 (A) Most stable positive connections for window length = 60 s. Connections classified as most stable in all 11 participants are shown over 3D renderings of a brain surface. (B) The same information shown as a 2D matrix. Colors corresponding to networks on the axes of the matrix are used to color nodes of that network in brain space. (C) Most stable connections for window length = 120 s. (D) Most stable connections with window length = 180 s.CC BY · PubMed Central
Figure 9 (A) Most stable positive connections when only connections within the lowest 25% of CVAR values are selected in each subject. (B) Most stable positive connections when only connections within the lowest 12.5% of CVAR values are selected in each subject. As the selection criterion becomes more stringent, a smaller number of connections make it to the group level maps presented here. When fewer connections are present, the symmetric inter-hemispheric pattern becomes clearer.
Figure 9 (A) Most stable positive connections when only connections within the lowest 25% of CVAR values are selected in each subject. (B) Most stable positive connections when only connections within the lowest 12.5% of CVAR values are selected in each subject. As the selection criterion becomes more stringent, a smaller number of connections make it to the group level maps presented here. When fewer connections are present, the symmetric inter-hemispheric pattern becomes clearer.CC BY · PubMed Central
Figure 10 Percentage of within-network (diagonal) and across-network (non-diagonal) most stable positive connections. Two groups of networks with high percentages of across-network connections being temporally stable are highlighted with red (MV1, MV2, and MV3) and green (AUD, SPP) dashed lines.
Figure 10 Percentage of within-network (diagonal) and across-network (non-diagonal) most stable positive connections. Two groups of networks with high percentages of across-network connections being temporally stable are highlighted with red (MV1, MV2, and MV3) and green (AUD, SPP) dashed lines.CC BY · PubMed Central
Figure 11 (A) Negative CVAR connections for window length = 60 s. Connections with negative CVAR in all 11 participants are shown over 3D renderings of a brain surface. (B) The same information shown as a 2D matrix. Colors corresponding to networks on the axes of the matrix are used to color nodes of that network in brain space. (C) Negative CVAR connections for window length = 120 s. (D) Negative CVAR connections with window length = 180 s.
Figure 11 (A) Negative CVAR connections for window length = 60 s. Connections with negative CVAR in all 11 participants are shown over 3D renderings of a brain surface. (B) The same information shown as a 2D matrix. Colors corresponding to networks on the axes of the matrix are used to color nodes of that network in brain space. (C) Negative CVAR connections for window length = 120 s. (D) Negative CVAR connections with window length = 180 s.CC BY · PubMed Central
Figure 12 Intra-ROI correlation maps to the ROI representative time-series for 2 ROIs of interest. Voxels with the highest correlation to the representative time series (red color) are those in and around the third ventricle.
Figure 12 Intra-ROI correlation maps to the ROI representative time-series for 2 ROIs of interest. Voxels with the highest correlation to the representative time series (red color) are those in and around the third ventricle.CC BY · PubMed Central

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Metadata

Authors
Gonzalez-Castillo, J., Handwerker, D. A., Robinson, M. E., Hoy, C. W., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A.
Affiliations
National Institutes of Health (US)
ORCID
Venue
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Published
2014-06-11
Volume
8
Pages
138-138
ISSN
1662-453X (ISSN-L)
DOI
10.3389/fnins.2014.00138
PMC
PMC4052097
OpenAlex
W1986813656
Cited by
116
References
58
Citations by year
2026: 4 · 2025: 5 · 2024: 4 · 2023: 7 · 2022: 7 · 2021: 8 · 2020: 8 · 2019: 9 · 2018: 14 · 2017: 17 · 2016: 18 · 2015: 15
FWCI
9.29
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health
  • National Institute of Mental Health
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
resting-state functional connectivity, negative correlation, scan time, whole-brain connectivity

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