Conceived and designed the experiments: OG CLG. Performed the experiments: OG. Analyzed the data: OG CLG. Wrote the paper: OG CLG.
The authors have declared that no competing interests exist.
Recent evidence points to two potentially fundamental aspects of the default network (DN), which have been relatively understudied. One is the temporal nature of the functional interactions among nodes of the network in the resting-state, usually assumed to be static. The second is possible influences of previous brain states on the spatial patterns (i.e., the brain regions involved) of functional connectivity (FC) in the DN at rest. The goal of the current study was to investigate modulations in both the spatial and temporal domains. We compared the resting-state FC of the DN in two runs that were separated by a 45 minute interval containing cognitive task execution. We used partial least squares (PLS), which allowed us to identify FC spatiotemporal patterns in the two runs and to determine differences between them. Our results revealed two primary modes of FC, assessed using a posterior cingulate seed – a robust correlation among DN regions that is stable both spatially and temporally, and a second pattern that is reduced in spatial extent and more variable temporally after cognitive tasks, showing switching between connectivity with certain DN regions and connectivity with other areas, including some task-related regions. Therefore, the DN seems to exhibit two
The default network (DN) is an ensemble of brain regions that shows deactivation during a wide range of externally-cued tasks compared to a task-free resting-state, and exhibits coherent low-frequency endogenous resting-state fluctuations
An equally important question, and one that has direct relevance for cognitive studies, is whether the DN alters its functional structure across time or in response to cognitive activity, i.e. whether two resting-state scans that are separated by time or task execution will show the same spatial composition. Despite studies showing that the DN's general architecture appears to be stable and consistent across individuals
The temporal nature of the DN also has received some recent attention. Barnes et al
The abovementioned preliminary evidence points to two potentially major DN characteristics that are in need of further investigation: the temporal dynamics of the network during rest, and possible task-related influences on the spatial FC patterns during the resting-state. However, no study to date has investigated modulations in both the spatial and temporal domains; this was the goal of the current study. We compared the resting-state FC of the DN in two runs that were separated by a 45 minute interval containing cognitive task execution. We used partial least squares (PLS) for this purpose, which allowed us to identify FC spatiotemporal patterns
Eighteen healthy right-handed young adults (age M = 24 years, SD = 3; 9 males) participated in this study after providing written informed consent. The Research Ethics Board of Baycrest Centre approved the study.
Each session included a high-resolution structural scan, followed by 10 functional runs, each lasting 5:40 minutes. The first and last runs were resting-state runs (Rest1 & Rest2), where subjects were instructed to lie still with their eyes closed, relax, and clear their minds, but to not actively suppress any thoughts that may spontaneously arise. Following scanning, subjects were asked if they fell asleep during the resting runs. Runs 2–9 were task runs, described below. Therefore, each session consisted of 2 resting-state runs, separated by 8 block-design runs of various tasks, a gap of about 45 minutes.
Each of the eight task-runs was composed of alternating 20 sec blocks of task and rest
Scanning was carried out with a Siemens Trio 3T scanner. Anatomical scans were acquired with a 3D MP-RAGE sequence (TR = 2 sec, TE = 2.63 msec, FOV = 25.6 cm2, 256×256 matrix, 160 slices of 1 mm thickness). Functional runs were acquired with an EPI sequence (170 volumes, TR = 2 sec, TE = 30 msec, flip angle = 70°, FOV = 20 cm2, 64×64 matrix, 30 slices of 5 mm thickness). Pulse and respiration were measured during scanning.
Preprocessing was performed with AFNI
Lastly, we temporally resampled all voxels' time series by dividing the time series into 30 “blocks” of 5 consecutive volumes each, normalizing each block to the first volume of that block, and then averaging all volumes of the block. This averaging produced an effective low-pass filter of 0.1 Hz and reduced temporal noise. Since respiratory and cardiac fluctuations were shown to bias time course correlations
We analyzed our data with partial least squares
PLS starts by creating a matrix of the correlations, across subjects, between seed activity and all other brain voxels for each “block”. This matrix is decomposed using singular value decomposition (SVD) to identify latent variables (LVs), which are orthogonal patterns of brain activity that characterize common or different patterns of group-level FC across “blocks”, thus assessing both spatial and temporal aspects of FC. For each LV, The SVD maximizes covariance and minimizes residuals between the seed activity and the spatiotemporal brain data. Each voxel has a weight, or salience, which is proportional to the covariance of its activity with the FC pattern on each LV. The significance for each LV as a whole is determined with a permutation test. The rows of the data matrix are re-ordered a number of times (i.e. the no. of permutations, in our case - 1000), and each time the SVD creates a new set of LVs, and the amount of covariance accounted for by these permuted LVs is compared to that of the original LV. The original value is assigned a probability based on the number of times the values from the permuted data exceed the original value. This is the permuted p value, representing the significance of the LV.
The reliability of each voxel's salience is determined with a bootstrap test. Subjects are randomly resampled with replacement a number of times (i.e. the no. of bootstraps, in our case - 1000), and their standard errors
To investigate the whole-brain FC of the DN, we performed several seed-PLS analyses using a PCC seed (−2, −50, 28, a coordinate identified in a previous study
The first analysis included Rest1 and Rest2, and was a data-driven examination of the spatial and temporal characteristics of DN functional connectivity in the pre-task and post-task resting-states. We report here the temporal and spatial patterns of the two primary LVs, accounting for 42% of the covariance in the data (each of the remaining LVs accounted for <5% of the covariance). The spatial patterns in these two LVs were further assessed by calculating a series of conjunction maps, aimed at identifying the spatial commonalities and differences between them. To make these conjunction maps, the BSR maps of LV1 and LV2 were multiplied to create a new BSR image. Any voxel/cluster that had high BSR values in both LVs received an even higher BSR in the new map, thus it contained “hotspots” common to the two LVs. We then applied two different masks to this image to identify voxels with common or different patterns of FC, including only those voxels that met the BSR thresholds for each LV considered separately.
We next carried out a contrast-driven analysis, using specific contrasts entered into the analysis rather than using a data-driven approach. The purpose of this analysis was to identify regions with overall differences in the strength of FC between rest runs. For this analysis, we directly contrasted Rest1 and Rest2, across all time points, by entering a series of −1's for the Rest1 blocks and 1's for the Rest2 blocks.
A second series of contrast-driven analyses was carried out to distinguish differences between rest runs that would be due to the influence of the intervening cognitive tasks from the effects of time in the scanner per se. These analyses included the first and last task runs, which we will refer to as Task1 and Task8, in addition to Rest1 and Rest2. These two task runs were separated by a temporal gap similar to the one separating the resting-state runs. The first contrast assessed a simple effect of time in the scanner (i.e. Rest1/Task1 vs. Task8/Rest2 = −1/−1/+1/+1) and the second assessed an interaction of time and type of run (Rest1/Rest2 = −1/+1 and Task1/Task8 = +1/−1, or together = −1 +1 −1 +1). That is, the second contrast was designed to identify those FC changes from Rest1 to Rest2 that differed from any change seen between the two task runs, and which could be attributed to some factor other than time in the scanner.
The data-driven analysis of Rest1 vs. Rest2 revealed two prominent spatiotemporal FC patterns for the PCC seed. The primary LV (36% of the covariance, p = 0.001) showed mostly positive correlations between the PCC and the rest of the DN across the entire run, for both Rest1 and Rest2 (
LV1 - The primary resting-state spatiotemporal pattern of PCC correlations, showing positive FC across most of the ‘blocks’ in both resting runs. A) The spatial composition, capturing the DN. The red regions (with positive BSRs) indicate areas with positive correlation with the PCC seed (no negative BSRs met the threshold, value range displayed is consistent with
Region | Hem | X(mm) | Y(mm) | Z(mm) | BSR |
Angular gyrus | Left | −46 | −62 | 30 | 20.98 |
Calcarine gyrus | midline | 4 | −90 | 6 | 11.60 |
Cerebellum | Left | −22 | −78 | −28 | 10.05 |
Cerebellum | Right | 22 | −42 | −18 | 9.72 |
Cerebellum | Right | 6 | −50 | −40 | 11.79 |
Cerebellum | Right | 38 | −56 | −50 | 8.42 |
Fusiform gyrus – posterior region | Right | 24 | −82 | −24 | 11.96 |
Inferior frontal gyrus p. orbitalis | Left | −42 | 26 | −10 | 10.70 |
Inferior frontal gyrus p. triangularis | Left | −56 | 24 | 10 | 8.28 |
Inferior frontal gyrus p. triangularis | Right | 58 | 30 | 8 | 9.36 |
Medial frontal gyrus | Left | −6 | −28 | 66 | 8.43 |
Medial frontal gyrus | Left | −6 | 56 | 4 | 17.99 |
Medial frontal gyrus | midline | 2 | 48 | 26 | 17.76 |
Middle frontal gyrus | Right | 44 | 10 | 44 | 10.38 |
Middle temporal gyrus | Left | −66 | −34 | −2 | 13.72 |
Middle temporal gyrus | Left | −58 | −14 | −12 | 13.42 |
Middle temporal gyrus | Right | 56 | −28 | −10 | 11.12 |
Middle temporal gyrus | Right | 56 | 0 | −22 | 13.03 |
midline | −2 | −50 | 28 | 789.72 | |
Precentral gyrus | Left | −18 | −24 | 56 | 8.70 |
Precentral gyrus | Right | 28 | −26 | 54 | 7.69 |
Putamen/claustrum | Right | 34 | 0 | −12 | 10.10 |
SMA - BA6 | Right | 8 | −10 | 66 | 7.50 |
Superior frontal gyrus | Left | −16 | 40 | 38 | 17.20 |
Superior frontal gyrus | Right | 20 | 38 | 32 | 11.80 |
Superior temporal gyrus | Right | 48 | −52 | 20 | 19.05 |
Superior temporal gyrus | Right | 42 | 26 | −22 | 12.09 |
Thalamus | Left | −14 | −18 | −4 | 9.49 |
MNI coordinates. BSR>3.3 is equivalent to p<0.001. Hem = hemisphere; SMA = supplementary motor area; PCC = posterior cingulate cortex, the seed used in the FC analysis. See also
The secondary LV (6% of the covariance, p = 0.001) showed a pattern of mostly positive PCC connectivity during Rest1 with a subset of the regions seen in the first LV (red regions in
LV2 - The secondary resting-state spatiotemporal pattern of PCC correlations, showing a transition from relative stability of DN connectivity to switching between two different patterns of FC. A) The spatial pattern of FC seen in this LV. Activity in red regions (positive BSRs) is associated with increased activity in the PCC during those blocks with positive correlations between brain scores and PCC (seen in B), whereas increased activity in blue areas (negative BSRs) is correlated with increased activity in the PCC for blocks where the correlations are negative. B) Correlations across time. Rest1 shows relatively stable positive correlations between the PCC and other DN regions, while Rest2 shows switching between the two patterns of connectivity. Bars = 95% confidence intervals for the correlations.
Region | Hem | X(mm) | Y(mm) | Z(mm) | BSR |
Amygdala | Left | −18 | 2 | −20 | −5.69 |
Cerebellum | Left | −8 | −72 | −36 | −5.57 |
Cerebellum | Right | 32 | −72 | −50 | −5.10 |
Cerebellum | Right | 28 | −46 | −36 | −3.84 |
Cuneus | midline | −4 | −68 | 2 | −7.19 |
Fusiform gyrus | Left | −30 | −50 | −18 | −6.62 |
Inferior frontal gyrus p. triangularis | Left | −40 | 34 | 16 | −6.66 |
Inferior frontal gyrus p. triangularis | Right | 46 | 22 | 6 | −6.09 |
Inferior occipital gyrus | Left | −32 | −78 | −8 | −4.99 |
Inferior parietal lobule | Left | −62 | −34 | 30 | −5.23 |
Inferior parietal lobule | Right | 24 | −54 | 38 | −4.03 |
Inferior parietal lobule | Right | 62 | −22 | 38 | −5.30 |
Insula | Left | −30 | 18 | 6 | −7.56 |
Insula | Left | −36 | −4 | 10 | −6.19 |
Insula | Right | 38 | −12 | −2 | −6.13 |
Middle cingulate cortex | Left | −6 | 6 | 40 | −6.17 |
Middle cingulate cortex | Left | −8 | −26 | 44 | −5.22 |
Middle frontal gyrus | Right | 32 | 0 | 38 | −5.54 |
Middle frontal gyrus | Right | 52 | 46 | 8 | −5.10 |
Middle frontal gyrus | Right | 42 | 46 | 20 | −4.40 |
Middle frontal gyrus | Right | 28 | 44 | 20 | −5.19 |
Middle temporal gyrus | Right | 56 | −52 | −6 | −3.76 |
Postcentral gyrus | Left | −66 | −18 | 30 | −5.61 |
Postcentral gyrus | Right | 60 | −12 | 16 | −7.00 |
Precentral gyrus | Left | −48 | −2 | 46 | −6.99 |
Precentral gyrus | Left | −56 | 6 | 4 | −7.49 |
Precentral gyrus | Left | −20 | −20 | 54 | −6.61 |
Precentral gyrus | Right | 58 | 2 | 28 | −5.65 |
Precentral gyrus | Right | 20 | −26 | 58 | −5.58 |
Precentral gyrus | Right | 48 | −4 | 50 | −4.37 |
Precuneus | Left | −16 | −58 | 48 | −6.30 |
Precuneus | Right | 14 | −46 | 58 | −5.77 |
Superior frontal gyrus | Left | −22 | 2 | 48 | −6.99 |
Superior temporal gyrus | Left | −46 | −18 | 6 | −5.62 |
Left | −42 | −64 | 28 | 12.07 | |
Right | 44 | −48 | 26 | 9.32 | |
midline | 2 | 54 | −6 | 7.13 | |
midline | 2 | 56 | 12 | 6.47 | |
midline | −2 | −50 | 28 | 398.19 | |
Left | −12 | 50 | 36 | 7.56 |
MNI coordinates. BSR>3.3 is equivalent to p<0.001. Hem = hemisphere; PCC = posterior cingulate cortex, the seed used in the FC analysis. Labels in
To assess whether the distribution of correlations differed between Rest1 and Rest2, we used a non-parametric test to compare the correlations seen in
Correlation values for all 10-sec blocks, sorted and plotted from lowest (most negative) to highest (most positive), to show the distributions in LV1 and LV2. A) LV1 – Rest1 correlations (squares) are not significantly different from Rest2 correlations (triangles). B) LV2 – Rest1 correlations (squares) are more positive than Rest2 correlations (triangles).
The next step was to elaborate and differentiate between stable and variable DN regions; we use “stable” to refer to areas that showed positive network connectivity with other DN regions on both LV1 and LV2, and “variable” to describe those regions with different connectivity on LV1 and LV2. To do this, we created two conjunction maps.
The first conjunction map identified DN regions common to both LVs, i.e. - voxels that had positive BSRs (red regions seen in
Conjunction analyses of LV1 and LV2, highlighting stable and variable DN regions. A) Stable DN regions – voxels showing positive brain scores, and positive FC, on both LVs (red regions in both
Region | Hem | X(mm) | Y(mm) | Z(mm) |
Angular gyrus | left | −48 | −62 | 35 |
Inferior parietal lobule | left | −50 | −57 | 46 |
Medial frontal gyrus | midline | 1 | 45 | −12 |
Medial frontal gyrus | right | 8 | 56 | 22 |
Middle cingulate cortex | midline | 4 | −26 | 36 |
Middle frontal gyrus | left | −37 | 30 | 43 |
Middle temporal gyrus | left | −65 | −3 | −23 |
Posterior cingulate cortex | midline | 1 | −46 | 18 |
Posterior cingulate cortex | right | 14 | −47 | 29 |
Precuneus | midline | −1 | −49 | 31 |
Precuneus | right | 6 | −63 | 44 |
Superior frontal gyrus | left | −16 | 49 | 34 |
Superior frontal gyrus | right | 23 | 49 | 38 |
Superior temporal gyrus | right | 55 | −58 | 29 |
Thalamus | midline | 1 | −16 | 17 |
Anterior cingulate cortex | right | 8 | 14 | 24 |
Cuneus | left | −7 | −62 | 3 |
Cuneus | right | 6 | −92 | 8 |
Cuneus | right | 16 | −74 | 15 |
Fusiform gyrus | left | −40 | −47 | −24 |
Insula | left | −31 | −24 | 9 |
Lingual gyrus | left | −12 | −49 | −13 |
Lingual gyrus | midline | 1 | −86 | 1 |
Lingual gyrus | right | 12 | −56 | 0 |
Middle cingulate cortex | right | 10 | 21 | 36 |
Precentral gyrus | right | 23 | −21 | 62 |
Precentral gyrus | right | 67 | 0 | 6 |
SMA - BA6 | midline | 4 | −5 | 54 |
Superior frontal gyrus | right | 8 | 14 | 57 |
Superior frontal gyrus | right | 25 | −15 | 66 |
Superior temporal gyrus | left | −56 | −24 | 5 |
Superior temporal gyrus | right | 57 | −21 | 2 |
MNI coordinates. BSR>3.3 is equivalent to p<0.001. Hem = hemisphere; SMA = supplementary motor area; PCC = posterior cingulate cortex, the seed used in the FC analysis. See also
The second map (
The direct comparison of Rest1 and Rest2, across all time points, is shown in
Areas showing greater FC with PCC in Rest2 than Rest1, as found in the contrast analysis, and shown in red (BSRs>3.3). No negative BSRs met the threshold.
Region | Hem | X(mm) | Y(mm) | Z(mm) | BSR |
Amygdala | left | −22 | −4 | −26 | −4.75 |
Lingual gyrus | left | −8 | −72 | −8 | −5.88 |
Paracentral lobule | right | 10 | −20 | 68 | −5.90 |
Parahippocampal gyrus | left | −26 | −24 | −24 | −4.82 |
Postcentral fyrus | right | 52 | −10 | 16 | −5.23 |
Precentral gyrus | right | 28 | −18 | 46 | −5.26 |
SMA - BA6 | midline | −4 | 12 | 50 | −4.99 |
Superior temporal gyrus | left | −34 | 2 | −20 | −4.76 |
Fusiform gyrus | left | −32 | −42 | −12 | −5.96 |
Lingual gyrus | left | −4 | −66 | 4 | −4.80 |
Precuneus | right | 14 | −58 | 46 | −5.04 |
Precuneus | left | −10 | −54 | 44 | −4.45 |
SMA - BA6 | left | −4 | 12 | 50 | −5.47 |
Medial frontal gyrus | Right | 8 | −24 | 66 | −5.16 |
Paracentral lobule | Left | −18 | −20 | 56 | −6.15 |
Precentral gyrus | Right | 60 | 8 | 10 | −4.86 |
Supramarginal gyrus | Right | 52 | −50 | 36 | −5.44 |
MNI coordinates. BSR>3.3 is equivalent to p<0.001. Hem = hemisphere; SMA = supplementary motor area; PCC = posterior cingulate cortex, the seed used in the FC analysis. See also
To address this issue, we tested for common differences between FC in the two rest conditions and the first and last task runs, as well as differences unique to the two rests. The first contrast identified those areas with similar changes between the first and second rest runs and the first and last task runs, i.e., those changes likely due to time in the scanner. These areas are shown in
Areas showing greater FC with PCC in Rest2 and Task8, compared to Rest1 and Task1, and shown in red (BSRs>3.3). No negative BSRs met the threshold.
Finally, a contrast to identify areas with a time×run type interaction was carried out. This is a stringent test of those effects limited to the difference between Rest1 and Rest2 because it requires the areas that increase in strength between the resting runs to weaken between the two task runs. This contrast (p = 0.004) showed a set of regions with stronger PCC connectivity in Rest2, relative to Rest1, but weaker connectivity in Task8 relative to Task1 (
Areas showing stronger FC with PCC in Rest2 relative to Rest1, and weaker FC in Task8 relative to Task1, as found in the interaction contrast analysis, and shown in red (BSRs>3.3). No negative BSRs met the threshold.
Most of the regions identified by these direct contrasts were also identified in the second LV of the data-driven analysis. This suggests that the observed overall FC change in these regions, with the exclusion of regions identified as showing stronger FC in both rest and task runs (
The study of the DN, and the brain states that it supports, is expanding in recent years. However, two possibly fundamental aspects of the behavior of this network have been relatively understudied. One is the temporal nature of the functional interactions among nodes of the network, and the second is whether DN functional connectivity can be influenced by a preceding brain state. In our study, we aimed to address the possibility of variability in both spatial and temporal domains. We compared the resting-state FC of the DN using two runs that were separated by a 45 minute interval containing task execution. We found that the DN is not temporally static, but can vary dynamically over time. The results revealed two primary modes of FC as assessed using the PCC as a seed – a robust correlation among DN regions, and a switching between connectivity among certain DN regions and connectivity among other areas, including some task-positive regions. The first FC pattern represents a stable feature of the DN, suggesting that the DN indeed maintains some temporally stable functional connections. However, the second FC pattern may represent a dynamic behavior of certain DN regions that occurs during rest periods that follow tasks, suggesting an interaction between task-positive regions and DN regions that carries over into resting-state periods.
Therefore, the DN seems to exhibit two
A few recent studies suggest that the DN is spatially stable at rest, across time spans of minutes, hours, or even months
There was consistency between the FC patterns identified in LV2 of the data-driven analysis and the subsequent contrast analyses in terms of the regions showing different FC between Rest1 and Rest2. For example, both LV2 and the direct contrast of Rest1 and Rest2 showed more FC for the left medial temporal region, medial frontal/SMA, and lingual gyrus during the post-task resting run. The contrasts comparing FC during rest and task runs suggested that strengthened FC in the lingual gyrus and medial frontal areas might be a consequence of time spent in the scanner, whereas stronger FC in SMA, anterior temporal regions and right supramarginal gyrus is more likely to be an influence of intervening task on resting FC. Some of these regions that exhibited greater FC with the PCC in Rest2, or more variable FC in Rest2, also exhibited increased activity (compared to baseline) during internally-oriented tasks (SMA, inferior frontal gyri) or during externally-oriented tasks (right supramarginal gyrus) in our previous study
It is unlikely that the carry-over we observed is a product of the sluggish temporal nature of the BOLD signal, especially since we observed it as a secondary dynamic to the more robust, stable pattern of FC. In addition, this carry-over is unlikely to constitute simple residual processing from the previous task-induced brain state, since it was present off and on during the entire second resting scan. If it had been residual task-related processing, it would have probably exhibited a “recovery” behavior, similar to that shown in Barnes et al's study
Finally, our study highlights some practical issues which should be taken into consideration in future work involving resting-state FC. First, our observation of different DN FC patterns between pre- and post-task resting-states shows that not all “rests” are the same. From a practical standpoint, researchers should take note that FC calculated from a resting run that follows some cognitive activity may be influenced by this previous brain state. A particularly striking example is our observation that the PCC is more strongly functionally correlated with some TPN regions after a series of tasks has been performed. Moreover, this influence might not necessarily just be carried over to a post-task resting-state, but also to any post-task state, be it rest or a new task. Therefore, a previous task might impact brain activity during the performance of a current task, something which is rarely if ever assessed.
Second, the fact that we observed two simultaneous resting-state dynamics highlights the possibility that brain networks/areas may be involved in multiple processing modes at any given point in time. This notion is consistent with the idea of neural context
The authors would like to thank Annette Weeks-Holder and staff of the Baycrest fMRI centre for technical assistance and Dr. Stephen Strother for helpful discussion of this work.