Dynamic Resting-State Connectivity Differences in Eyes Open Versus Eyes Closed Conditions
Abstract
Introduction: Previous studies have shown significant conditional differences between eyes open, fixated at an image (EO) and eyes closed (EC) in the acquired resting-state functional magnetic resonance imaging (rs-fMRI) data.
Aim: We recently showed significant functional network connectivity (FNC) differences between EO and EC across a variety of networks. In this study, we aim at further evaluating differences in dynamic FNC (dFNC) between EO and EC.
Materials and Methods: Rs-fMRI were collected from adolescents aged 9–15 years old during both EO and EC conditions, and dFNC was calculated by using the independent component analysis framework.
Results: We found that out of five states (clusters), state 1 was observed to be more dominant in the EO condition, whereas state 2 was observed to be more dominant in the EC condition. States 1 and 2 showed significant differences in the mean dwell time based on false discovery rate, and states 1, 2, 3, and 4 differed in the frequency of occurrences. These results are consistent with our previous study of static connectivity in suggesting that EO and EC differences not only are relatively strong but also importantly reveal that these differences vary over time, especially in one particularly transient connectivity pattern.
Conclusion: Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. Overall, our findings indicate that both static and dynamic rs-fMRI connectivity patterns are strongly impacted by basic conditional differences such as EO and EC.
Impact statement
Our findings not only suggest that eyes open, fixated at an image (EO) and eyes closed (EC) condition-related resting state functional magnetic resonance imaging differences are relatively strong, but they also reveal an important attribute of these conditions that these differences vary over time, especially in one particularly transient connectivity pattern. Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. We believe there is benefit in having the EO/EC as a contrast of interest in future studies, if time allows.
Introduction
Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly collected under one or more of three conditions, eyes closed (EC), eyes open without fixation, and eyes open, fixated on an image (EO). In the EC case, participants are asked to keep their EC and not to fall asleep during scanning. In the EO case, participants are asked to keep their eyes open during scanning. For the EO case, there is a target, usually a crosshair, and subjects are asked to keep their eyes fixated on that target. Several studies, including our own, showed that the conditions of the eyes can affect the resting-state connectivity patterns (Agcaoglu et al., 2019; Liu et al., 2013; Patriat et al., 2013; Van Dijk et al., 2010; Wei et al., 2018; Yan et al., 2009).
Liu et al. (2013) found reproducible patterns of EO and EC differences in three metrics, including amplitude of low-frequency fluctuations, regional homogeneity, and seed-based correlation by using a dataset containing 48 college students (aged 19–31 years, 24 females). Wei et al. (2018) found significantly greater brain activity in attentional system areas in the EO condition, but significantly lower brain activity in sensorimotor system areas compared with the EC condition by using the same dataset with Liu et al. (2013).
A few studies analyzed brain connectivity via group independent component analysis (gICA). Wu et al. (2010) found high functional network connectivity (FNC) and widespread alpha hemodynamic responses in EC condition, whereas for the EO condition they reported lowered hemodynamic responses and decreased FNC by using a dataset of 25 participants (age 29 ± 8 years, 17 males). The previous studies used relatively small datasets: Patriat et al. (2013) used a dataset consisting of 25 participants (age 35.5 ± 17.7 years, 10 females), Van Dijk et al. (2010) used a dataset of 30 participants for the EO–EC analysis (aging from 19 to 29 years), and Yan et al. (2009) used a dataset comprising 20 participants (10 females, age 18–24 years).
Recently, we investigated static FNC (sFNC) differences between EO and EC conditions by using a large fMRI dataset collected from adolescents aged 9–15 years old (i.e., the same data used in this work; Agcaoglu et al., 2019).
Dynamic FNC (dFNC) is an extension of FNC that utilizes a sliding window approach to estimate inter-network connectivity in each time window. This enables one to track connectivity changes during scanning, rather than obtaining an average estimate of the whole scan. Sakoglu et al. (2010) used a sliding window approach to show that multiple networks identified via whole-brain FNC dynamically change during scanning. Chang and Glover (2010) highlighted time-frequency dynamics within a set of regions of interest selected from several of the well-known resting networks and evaluated voxel-wise variability over time.
After that, several studies further examined these changes and showed that dFNC is a useful tool to investigate brain connectivity. For example, several studies (Damaraju et al., 2014; Espinoza et al., 2019; Rashid et al., 2014, 2018) found significant differences in dFNC between healthy controls and patients with schizophrenia, bipolar disorder, autism spectrum disorder, and Huntington's disease. A recent study from our group investigated the effect of EO versus EC using the dFNC framework (Allen et al., 2018) and the same dataset as Wu et al. (2010) and found an association between dynamic connectivity in concurrently collected electroencephalography (EEG) and fMRI data, as well as a large effect of vigilance on FNC.
In this study, we extend our previous sFNC work (Agcaoglu et al., 2019) by utilizing a dFNC framework to analyze connectivity differences over time between EO and EC conditions. Our study (173 subjects, age 11.95 ± 1.78 years) is different than the previous dFNC study comparing EC and EO (Allen et al., 2018; 20 subjects, age 29 ± 8 years) in both the targeted age range and richness of the dataset.
In our study, we investigate the EO and EC differences in a cohort of 9- to 15-year-old adolescents, an age range that is generally considered to be a time of rapid physical and mental development. These data also use a more modern pulse sequence with a shorter repetition time (TR) (460 ms vs. 2 sec). Yang et al. (2007) found that a shorter TR revealed higher EO–EC differences in their study of amplitude of low-frequency fluctuations by using a dataset containing 15 participants (9 females, aged 22–27 years). We hypothesized that dFNC values will be strongly associated with age and gender, especially in cognitive control networks, with more association in the EO cases as reported in our sFNC study (Agcaoglu et al., 2019).
Materials and Methods
The study was approved and monitored by the Advarra Institutional Review Board for New Mexico and the Institutional Review Board of the University of Nebraska Medical Center for Nebraska. All participants provided signed informed consent before study procedures.
Participants
In this study, we utilized an existing dataset reported in a previous study (Agcaoglu et al., 2019). The rs-fMRI scans were collected from 182 adolescents at two different sites (Mind Research Network [MRN]/New Mexico and University of Nebraska Medical Center/Nebraska) under both EO and EC conditions as part of the National Science Foundation (NSF) supported DevCog project. Participants were instructed to close their eyes but remain awake during the EC condition and were asked to stare at a fixation cross during the EO scan. Initially, 358 different scans were used in the gICA state; for further analysis, we removed participants who did not have both EO and EC scans available.
Final analysis included 346 EO and EC scans from 173 participants (age range from 9.1 to 15.5 years, 11.95 ± 1.78). The retained participants had a maximum mean frame displacement (MFD) of 0.3 mm, and the mean of MFD was 0.074 mm with a standard deviation of 0.03 mm. Demographic information of the participants is presented in Table 1.
| No. of subjects | % | |||
|---|---|---|---|---|
| Gender | 173 | 100 | ||
| Male | 89 | 51.45 | ||
| Female | 84 | 48.55 | ||
| Handedness | ||||
| Left | 15 | %9 | ||
| Right | 151 | %91 | ||
| Age (years) | Mean | SD | Min | Max |
| Male | 12.03 | 1.88 | 9.1 | 15.5 |
| Female | 11.89 | 1.66 | 9.2 | 15.1 |
| Barratt simplified measure of social status | 43.70 | 12.80 | 11.50 | 66 |
Imaging parameters
Imaging data were collected at the MRN site on a 3T Siemens TIM Trio scanner and at the Nebraska site on a 3T Siemens Skyra scanner. A total of 650 volumes of multiband echo planar imaging blood oxygen level dependent (BOLD) data (a length of 4 min and 59 sec) were collected per condition and participant with TR of 0.46 sec, echo time = 29 ms, flip angle = 44°, and a slice thickness of 3 mm with no gap. Rs-fMRI scans were acquired by using a standard gradient-echo planar imaging paradigm; MRN site: field of view (FOV) of 246 × 246 mm (82 × 82 matrix), 56 sequential axial slices; Nebraska: FOV of 268 × 268 mm (82 × 82 matrix), and 48 sequential axial slices. The scanning order of the EO and EC sessions was counter-balanced across participants at each site. Eyes were monitored via an eye-tracker during the EO condition to ensure compliance with instructions.
Preprocessing
The data were preprocessed by using a combination of toolboxes. Images were distortion corrected by using FSL's applytopup; then, images were re-aligned to the single-band reference image (SBref) by using analysis of functional neuroimages (AFNI's) align_epi_anat.py (https://afni.nimh.nih.gov). Motion parameters were estimated relative to SBref. Next, data were registered to the MNI template by using AFNI's 3dNwarpApply, as estimated by AFNI's auto warp.py. The first 4 volumes of each session were discarded to account for the T1 equilibrium effect. Because participants consisted of children with an age range of 9.1–15.5 years, we re-warped the data to a study-specific template computed as the average of the first time point from each scan. Next, we smoothed the data to 6 mm full-width at half maximum.
Group independent component analysis
The preprocessed functional data were analyzed with gICA implemented in the Group ICA Of fMRI Toolbox (GIFT) software (Calhoun et al., 2001, 2002; Calhoun and Adali, 2012) and decomposed into 150 spatially independent components. Before gICA, a scan-specific principle component analysis (PCA) was utilized to reduce the dimensionality across the 646 time points to 200 maximally variable directions; then, a group PCA was applied to further reduce the dimensionality to 150 (Erhardt et al., 2011). One hundred fifty independent components were estimated by using the infomax algorithm (Bell and Sejnowski, 1995). The ICA algorithm 20 was run times in ICASSO (Himberg et al., 2004) to ensure stability of the estimation, and the most central run was selected from the resulting 20 runs (Ma et al., 2011).
After this group-level estimation, a spatially constrained ICA algorithm, called group information guided ICA as implemented in GIFT software (Du and Fan, 2013, Du et al., 2016; Salman et al., 2019), was used to estimate scan-specific spatial maps and time courses (TCs) from the group maps. Fifty-one components out of 150 were identified as the resting-state networks (RSNs), and these RSNs were grouped based on their anatomical and functional properties including 4 sub-cortical networks (SC), 3 auditory networks (Aud), 8 sensorimotor networks (SM), 18 visual networks (Vis), 4 default-mode networks (DMN), 12 cognitive control networks (CC), and 2 cerebellar networks (Cb). See Agcaoglu et al. (2019) for additional details of the gICA and RSN selection process.
Post-gICA processing and FNC
After the gICA, the subject-specific TCs were post-processed. To do so, subject specific TCs were detrended, motion parameters were regressed (including their derivatives, their squares, and derivatives of their squares) and then despiked, which involved detecting spikes as determined by AFNI's 3dDespike algorithm and replacing spikes by values obtained from third-order spline fit to neighboring clean portions of the data. The correlation among brain networks is primarily driven by the low-frequency fluctuations in BOLD fMRI data (Cordes et al., 2001); therefore, we low-pass filtered TCs by using a fifth-order Butterworth low-pass filter with a cutoff of 0.15 Hz.
We calculated dFNC by using a sliding window approach with a third-order Gaussian window with 100 timepoints (46 sec). For each window, dFNC was calculated as the pairwise correlation between windowed TCs. We estimated the covariance from the regularized precision matrix or the inverse covariance matrix (Smith et al., 2011). Following the graphical LASSO method of Friedman et al. (2008), we placed a penalty on the L1 norm of the precision matrix to promote sparsity. The regularization parameter lambda (λ) was optimized separately for each subject by evaluating the log-likelihood of the unseen data in a cross-validation framework. Finally, dFNC values were Fisher-Z transformed and residualized with respect to age, gender, site, and motion (MFD).
Initially, the main modules of the dFNC matrix were organized similar to Allen et al. (2014) as subcortical, auditory, sensorimotor, visual, default mode, cognitive control, and cerebellar; then, they were re-organized after applying the Louvain algorithm from the brain connectivity toolbox to arrange the RSN ordering within these main modules.
Clustering
We applied k-means clustering by using the distance method of correlation. We used five cluster centroids to match previous studies investigating resting condition on resting-state dFNC (Allen et al., 2018). We used a k-means clustering approach to investigate FNC changes over time by clustering over time, and each centroid represents a connectivity state. We also compared EO and EC dFNC differences on domains such as the number of subjects entering each state and their percentage of occurrences, the number of subjects in each state over time, and the mean dwell time (MDT). The MDT is the average time a participant remains in each state before changing to another state.
We tested for mean dFNC differences between EO and EC for each state. To do so, first, for each subject and each state, the average of EO and EC dFNC windows belonging to that state was calculated. Because some subjects did not enter some states at all, the number of subject-mean dFNCs for EO and EC was not the same, and we used a two-sample t-test and corrected for multiple comparisons by using a false discovery rate (FDR).
In a similar manner, we also calculated the mean in each connectivity domain, compared mean domain dFNC differences between EO and EC for each state, and corrected for multiple comparisons with FDR. There were no significant differences in motion (t-test, p > 0.05) between the MFD computed from the EO and EC scans. We also checked the correlation between age and MFD for EO and EC cases separately. For EO, the association was not significant (r = −0.138, p = 0.069); whereas for the EC condition, there were some age-related associations (r = −0.165, p = 0.029). In addition, we repeated the k-means clustering with four and six cluster centroids; the results were generally similar (see Supplementary Data and Supplementary Figures S1–S8 for details).
Results
Resting-state networks
The gICA was applied to resting-state data from both conditions to derive intrinsic RSNs. The selected RSNs were then grouped based on their anatomical and functional properties, which revealed 4 SC, 3 Aud, 8 SM, 18 Vis, 4 DMN, 12 CC, and 2 Cb. These RSNs are displayed in Figure 1, and the corresponding anatomical regions and their peak locations are detailed in Table 2.
FIG. 1. Selected RSN, grouped according to their anatomical and functional location (Agcaoglu et al., 2019), 4 SC, 3 Aud, 8 SM, 18 Vis, 4 DMN, 12 CC, and 2 Cb. Aud, auditory networks; Cb, cerebellar networks; CC, cognitive control networks; DMN, default-mode networks; RSNs, resting-state networks; SC, sub-cortical networks; SM, sensorimotor networks; Vis, visual networks). Color images are available online.
| RSN no. | Nv | Tmax | Coord. | BA |
|---|---|---|---|---|
| Sub-cortical networks | ||||
| 85 | ||||
| Left putamen | 1463 | 141.63 | −18 10 4 | |
| Right putamen | 735 | 82.27 | 20 12 0 | |
| 54 | ||||
| Right putamen | 1224 | 135.54 | 26 4 − 2 | |
| Left putamen | 1197 | 147.24 | −28 0 − 2 | |
| 58 | ||||
| Right thalamus | 1033 | 170.69 | 2 − 20 6 | |
| 68 | ||||
| Left thalamus | 1430 | 141.33 | −4 − 12 12 | |
| Auditory networks | ||||
| 62 | ||||
| Left superior temporal gyrus | 2267 | 84.25 | −52 − 18 6 | 22 |
| Right superior temporal gyrus | 2029 | 87.45 | 60 − 12 0 | 22 |
| 145 | ||||
| Right superior temporal gyrus | 3713 | 104.6 | 56 − 44 18 | 13 |
| Left middle temporal gyrus | 1378 | 51.41 | −58 − 54 12 | 22 |
| 125 | ||||
| Right insula lobe | 1908 | 119.75 | 42 − 18 12 | 13 |
| Left superior temporal gyrus | 1795 | 107.6 | −46 − 24 12 | 41 |
| Sensorimotor networks | ||||
| 9 | ||||
| Left paracentral lobule | 2599 | 94.33 | 0 − 24 72 | 6 |
| 8 | ||||
| Left postcentral gyrus | 1828 | 94.76 | −46 − 30 54 | 2 |
| Right postcentral gyrus | 383 | 37.47 | 54 − 20 48 | 1 |
| 98 | ||||
| Left inferior parietal lobule | 2438 | 96.03 | −54 − 30 46 | 2 |
| Right supramarginal gyrus | 1621 | 76.73 | 60 − 20 40 | 3 |
| 26 | ||||
| Right postcentral gyrus | 2515 | 96.98 | 44 − 30 58 | 2 |
| Left postcentral gyrus | 507 | 37.30 | −42 − 38 60 | 40 |
| 2 | ||||
| Left postcentral gyrus | 1080 | 91.31 | −54 − 8 34 | 6 |
| Right postcentral gyrus | 1014 | 90.70 | 60 − 6 30 | 6 |
| 73 | ||||
| Left paracentral lobule | 4219 | 125.8 | 0 − 24 54 | 6 |
| Left rolandic operculum | 157 | 40.96 | −40 − 26 18 | 13 |
| 124 | ||||
| Left inferior parietal lobule | 1073 | 76.71 | −58 − 42 42 | 40 |
| Right supramarginal gyrus | 873 | 73.92 | 60 − 38 40 | 40 |
| 77 | ||||
| Left SMA | 4587 | 101.23 | 0 6 52 | 6 |
| Right insula lobe | 516 | 53.27 | 48 10 − 2 | 22 |
| Visual networks | ||||
| 131 | ||||
| Left inferior temporal gyrus | 2183 | 74.55 | −52 − 50 − 12 | 37 |
| Right fusiform gyrus | 1668 | 60.82 | 44 − 30 − 18 | 20 |
| 76 | ||||
| Right calcarine gyrus | 2756 | 81.86 | 18 − 102 − 2 | 18 |
| 34 | ||||
| Left cuneus | 3412 | 82.78 | 2 − 80 24 | 18 |
| 42 | ||||
| Right fusiform gyrus | 1510 | 72.64 | 32 − 78 − 14 | 19 |
| Left cerebellum | 569 | 36.52 | −40 − 68-20 | 19 |
| 71 | ||||
| Left fusiform gyrus | 1920 | 77.93 | −30 − 56 − 14 | 19 |
| Right fusiform gyrus | 1422 | 69.95 | 30 − 48 − 18 | 37 |
| 91 | ||||
| Right lingual gyrus | 4008 | 91.16 | 24 − 72 − 12 | 19 |
| 111 | ||||
| Left lingual gyrus | 2891 | 109.15 | 0 − 78 4 | 18 |
| 69 | ||||
| Left cerebellum | 1662 | 143.19 | −6 − 50 − 2 | 30 |
| 82 | ||||
| Right cerebellum | 1674 | 142.59 | 8 − 50 − 2 | 30 |
| 70 | ||||
| Left lingual gyrus | 2152 | 76.07 | −18 − 86 − 18 | 18 |
| 33 | ||||
| Right calcarine gyrus | 3313 | 115.61 | 8 − 68 10 | 30 |
| 59 | ||||
| Right lingual gyrus | 2465 | 127.29 | 12 − 56 10 | 30 |
| Left middle occipital gyrus | 293 | 41.29 | −42 − 80 30 | 19 |
| 130 | ||||
| Right middle occipital gyrus | 3229 | 97.50 | 38 − 84 6 | 19 |
| Left middle occipital gyrus | 3092 | 82.44 | −36 − 86 6 | 19 |
| 100 | ||||
| Cerebellar vermis | 1270 | 192.57 | 2 − 42 4 | 29 |
| 129 | ||||
| Cerebellar vermis | 1448 | 140.21 | 6 − 56 0 | |
| 38 | ||||
| Left precuneus | 3174 | 79.17 | 0 − 66 58 | 7 |
| Right superior frontal gyrus | 241 | 32.07 | 30 4 60 | 6 |
| 37 | ||||
| Left posterior cingulate cortex | 2019 | 141.39 | 0 − 54 30 | 31 |
| Left angular gyrus | 509 | 45.23 | −52 − 68 28 | 39 |
| 27 | ||||
| Right middle cingulate cortex | 2693 | 88.57 | −4 − 24 28 | 23 |
| Left inferior parietal lobule | 207 | 35.50 | −36 − 62 48 | 7 |
| Default-mode networks | ||||
| 123 | ||||
| Right anterior cingulate cortex | 4398 | 118.94 | 2 42 10 | 32 |
| Right insula lobe | 538 | 59.55 | 36 18 − 12 | 47 |
| 49 | ||||
| Left mid-orbital gyrus | 2941 | 115.46 | 0 48 − 6 | 10 |
| Left middle temporal gyrus | 253 | 39.78 | −58 − 14 − 18 | 21 |
| 90 | ||||
| Left angular gyrus | 2579 | 91.98 | −52 − 62 30 | 39 |
| Left middle frontal gyrus | 2269 | 52.91 | −42 18 46 | 8 |
| 101 | ||||
| Right middle frontal gyrus | 2450 | 57.54 | 30 18 54 | 8 |
| Right inferior parietal lobule | 1892 | 99.84 | 54 − 56 40 | 40 |
| Cognitive control networks | ||||
| 83 | ||||
| Left middle temporal gyrus | 2105 | 93.39 | −46 6 − 30 | 21 |
| Right medial temporal pole | 1588 | 96.98 | 48 10 − 26 | 21 |
| 114 | ||||
| Left superior medial gyrus | 3955 | 103.29 | 0 60 22 | 10 |
| Left temporal pole | 733 | 40.71 | −36 22 − 20 | 47 |
| 63 | ||||
| Right middle frontal gyrus | 6987 | 115.49 | 32 58 4 | 10 |
| Right inferior parietal lobule | 72 | 26.70 | 50 − 50 48 | 40 |
| 48 | ||||
| Left superior medial gyrus | 2744 | 86.68 | 0 66 18 | 10 |
| Right cerebellum | 77 | 31.19 | 48 − 72 − 38 | |
| 120 | ||||
| Left inferior frontal gyrus (p. Triangularis) | 4236 | 91.65 | −48 30 18 | 46 |
| Right inferior frontal gyrus (p. Triangularis) | 855 | 52.20 | 50 22 28 | 46 |
| 146 | ||||
| Right inferior frontal gyrus (p. Opercularis) | 7279 | 114.14 | 50 18 6 | 45 |
| Left insula lobe | 590 | 46.17 | −34 24 − 2 | 13 |
| 119 | ||||
| Left insula lobe | 2186 | 116.66 | −40 18 − 6 | 47 |
| Right insula lobe | 1381 | 88.71 | 44 16 − 2 | 47 |
| 96 | ||||
| Left inferior parietal lobule | 3989 | 78.25 | −24 − 72 46 | 7 |
| Left precentral gyrus | 711 | 47.22 | −52 10 34 | 9 |
| 102 | ||||
| Right inferior parietal lobule | 3397 | 82.40 | 44 − 42 48 | 40 |
| Right inferior frontal gyrus (p. Opercularis) | 1660 | 51.33 | 54 12 30 | 9 |
| 133 | ||||
| Right rolandic operculum | 2745 | 102.29 | 54 4 4 | 22 |
| Left rolandic operculum | 766 | 51.31 | −54 0 4 | 22 |
| 55 | ||||
| Right superior parietal lobule | 3916 | 71.19 | 18 − 54 66 | 7 |
| Right cerebellum | 106 | 33.04 | 26 − 44 − 48 | |
| 136 | ||||
| Left angular gyrus | 6603 | 74.10 | −52 − 78 28 | 39 |
| Right middle occipital gyrus | 807 | 58.53 | 44 − 78 34 | 19 |
| Cerebellar networks | ||||
| 84 | ||||
| Right cerebellum | 4173 | 150.37 | 30 − 68 − 38 | |
| 110 | ||||
| Left cerebellum | 3906 | 126.64 | −30 − 66 − 38 | |
Clustering results
The five dynamic state centers are presented in Figure 2. When we compared these state centers, state 1 showed the highest Vis–Vis connectivity compared with the other states, and it also has a negative correlation with Vis–SM, Vis–Aud, and Vis–SC. State 2 has the highest connectivity among SM–SM and SM–Aud compared with the other states. State 3 differs from the other states in that it has all negative correlations between Vis and all CC networks, whereas the other states show more positively correlated networks overall.
FIG. 2. Center of the five clusters, states collapsed across both conditions. State 1 has the highest Vis–Vis connectivity compared with the other states and has a negative correlation with Vis–SM, Vis–Aud, and Vis–SC. State 2 has the highest connectivity among SM–SM and SM–Aud compared with the other states. State 3 has a negative correlation between Vis and all CC networks, whereas the other states show a more positive correlation between Vis and CC networks. Color images are available online.
We also compared the total number of subjects entering each state at least one time for each task, and the results are presented in Table 3. We found that 154 out of 173 subjects enter state 1 at least one time for EO scans, whereas 85 out of 173 subjects enter state 1 for EC scans. Forty-two subjects enter state 2 for EO, whereas 97 subjects enter this state for the EC condition. State 3 (152 and 150 for EO and EC, respectively), state 4 (129 and 128 for EO and EC, respectively), and state 5 (158 and 143 for EO and EC, respectively) have almost equal number of subjects entering the state across the two conditions. Moreover, we compared the total number of occurrences of each state for each condition. Results are displayed in both Table 2 and as a graph in Figure 3 (top left). State 1 has 33% of occurrences in the EO case, whereas it has only 8% of occurrences in the EC case. The condition is reversed in state 2: EO has only 5% of occurrences in state 2, whereas EC has 22% of occurrences. All states but state 5 showed 0.05 FDR significant differences between EO and EC on occurrences. The MDT for each state and each condition is displayed in Figure 3 (bottom left), and states 1 and 2 differed (FDR <0.05) between EO and EC conditions. Figure 3 (right column) shows the state transition matrix for EO and EC cases, and only one index, translation probability from states 3 to 1, showed significant (FDR <0.05) differences between EO and EC cases. Figure 4 shows the paired t-test results between EO and EC state transition matrices.
FIG. 3. Top left; occurrence frequency of each state for the EO and EC cases shows FDR significant differences for states 1, 2, 3, and 4 between EO and EC cases. Bottom left, mean dwell time of each state for EO and EC cases; state 1 and 2 show significant differences between EO and EC conditions. Right column shows the state transition matrix in −log10 format; transition probability from states 3 to 1 shows FDR significant difference between EO and EC cases (marked with ‘o’). The asterisk (*) shows those have FDR significant difference. EC, eyes closed; EO, eyes open; FDR, false discovery rate. Color images are available online.
FIG. 4. Paired t-test differences for the state transition matrix, EO and EC differences (EO–EC), are displayed as −sign(t-stats) × log10(p). Transition from states 3 to 1 shows FDR significant differences with resting condition (marked with ‘o’). Color images are available online.
| No. of subjects entered state | State 1 | State 2 | State 3 | State 4 | State 5 |
|---|---|---|---|---|---|
| EO | 154 | 42 | 152 | 129 | 158 |
| EC | 85 | 97 | 150 | 128 | 143 |
| Total % of occurrences | State 1 | State 2 | State 3 | State 4 | State 5 |
| EO | %33 | %5 | %21 | %18 | %23 |
| EC | %8 | %22 | %28 | %23 | %19 |
Figure 5 shows the number of subjects in each state over time. The most remarkable change is in state 2, which shows an increase in occupancy as time proceeds. This is more significant for the EC condition, but it is also observable in the EO condition. We also observed a decrease in the number of subjects in state 3 over time for the EC condition. State 3 also had a decrease in the number of subjects over time for the EC condition. States 1, 4, and 5 did not show a linear trend (increase or decrease) as time proceeded, although they did show changes over time.
FIG. 5. Number of subjects in each state over time. We observe an increase in the number of subjects in state 2 as time increases. This is not only more significant for the EC condition but also observable in the EO condition. State 3 also shows a decrease in the number of subjects over time for the EC condition. Color images are available online.
Figure 6 shows the results from our previously published sFNC analysis (Agcaoglu et al., 2019) for comparison with Figure 2, as well as the dynamic state center differences between states 1 and 2. The differences between states 1 and 2 look similar to the sFNC EO versus EC comparison. Overall, we can conclude that state 1 is observed to be dominant in the EO dFNC and we label this an “EO State,” whereas state 2 is observed to be dominant in the EC dFNC and we label this an “EC State.”
FIG. 6. Top row, sFNC results for EO and EC cases (Agcaoglu et al., 2019). States 1 and 2 resemble these results with small differences. Middle row shows the paired t-test results between EO and EC. Bottom row shows the differences between state center 1 and state center 2. The state center differences also resemble the paired t-test results between EO and EC sFNCs. sFNC, static functional network connectivity. Color images are available online.
Figure 7 shows the results comparing the mean dFNC differences per subject between EO and EC cases for each state. The upper triangular matrix presents the unthresholded results, and the lower triangular matrix presents the results corrected at an FDR of 0.01. All states show a significantly higher correlation between Vis–SM and Vis–Aud, and in most states for Vis–DMN for the EC case. We also generally see higher Vis–Vis correlations. Figure 8 shows the domain average results comparing the mean dFNC window differences per subject between EO and EC cases, the upper triangular matrix presents the unthresholded results, and the lower triangular matrix presents the results corrected with 0.01 FDR.
FIG. 7. Two-sample t-test difference between EO and EC samples (EO–EC) in each state; upper triangular matrix shows the unthresholded results, and lower triangular matrix displays 0.01 FDR-corrected results as −sign(t-statistics) × log10(p). All states show FDR significant differences between EO and EC conditions; the differences in states 1 and 2 are very modular and affect a wide range of areas. States 2 and 4 show fewer differences compared with other states. Color images are available online.
FIG. 8. Two-sample t-test difference between EO and EC domain averages (EO–EC) in each state; upper triangular matrix shows the un-thresholded results, and lower triangular matrix displays 0.01 FDR-corrected results as −sign(t-statistics) × log10(p). Color images are available online.
Finally, we also examined the effect of age, gender, and socioeconomic status scores on MDT, frequency of occurrence, and transition matrix by using a regression model separately for EO and EC conditions and corrected for multiple comparison (p < 0.05), but no FDR significant results were found.
Discussion
In this study, we analyzed dFNC differences between EO and EC conditions in a relatively large dataset collected from 173 adolescents aged 9.1–15.5 years old. Results supported widespread resting functional connectivity differences between eyes open/EC, consistent with our previous study of static connectivity (Agcaoglu et al., 2019); importantly, here we additionally show that these differences vary over time with the largest changes localized to one particular transient connectivity pattern. We found that among five dynamic states, state 1 was observed to be dominant during the EO condition. This state showed the highest Vis–Vis connectivity and the lowest Vis–SM and Vis–Aud connectivity. State 2 was observed to be dominant during the EC condition and showed the highest SM–SM, Aud–Aud, SM–Aud, Vis–SM, and Vis–Aud connectivity. Also, state 2 showed a high anticorrelation between the SC–Aud and SC–SM networks.
States 1 and 2 showed FDR significant differences in the dwell time and states 1, 2, 3, and 4 showed FDR significant differences in the frequency of occurrence between EO and EC conditions.
The centroid of state 1 (observed to be dominant during the EO condition) resembled the average sFNC results for the EO condition, and the centroid of state 2 (observed to be dominant during the EC condition) resembled the average sFNC results for the EC condition. This tells us that the previous static results, though accurate, are not providing a complete picture. The fact that different states capture changes across time tells us that these differences are manifested as time-varying changes in connectivity (i.e., they are large changes that are visible in the static results, but not occurring with the same temporal signature). These results, thus, provide evidence that having eyes open versus EC during resting-state scanning has a significant impact on the connectivity dynamics.
An important finding of this study is that the number of subjects in each state shows a linear increase or decrease for the EC condition in states 2 and 3. These distinctive linear trends do not occur for any states in the EO condition, although they did fluctuate over time. State 2 was unique in that it showed high anticorrelation between the SC–Aud and SC–SM networks, especially RSN 58 and 68 (right and left thalamus) showed anti-correlation to Aud and SM.
Changing from positive correlation to anti-correlation in thalamus to cortical connectivity has been identified as a feature of the transition from vigilance to sleep (Allen et al., 2018). State 2 in our study resembles state 4 in Allen et al. (2018), which was associated with a decrease in vigilance, an increase in delta and theta power, and a decrease in alpha power within the simultaneously collected EEG. However, in our study, state 2 showed a high increase in occurrences over time in the EC case and a slight increase in occurrences in the EO case, whereas state 4 in Allen et al. (2018) showed a high increase in the EO case and a slight increase in the EC case. Allen et al. (2018) used a dataset from an adult cohort (age 29 ± 8.8 years), whereas our dataset consists of adolescents, which may explain the difference, but further studies are needed.
This is an example of how the current dFNC analysis provides additional information beyond an sFNC analysis; a dFNC analysis enables us to track the changes in brain connectivity during scanning. Also, it provides the opportunity to examine patterns of inter-state changes (i.e., determine the state that follows or precedes another state; the state transition matrix), which appears to be sensitive to conditional differences such as those studied here, as we observed significant differences between EO and EC in the transition from states 3 to 1.
Our results further suggest that brain connectivity for the EC condition changes during scanning, which may be due to changes in the default or idling brain condition, and our results show interesting differences between Vis–Vis, Vis–SM, and Vis–Aud, and between EC and EO.
Finally, in this study, although the main focus was to compare EO/EC differences in dFNC, we were also expecting some development (age) effect on dFNC. However, we did not find any significant association between dFNC and age in our cohort of 9- to 15-year-old adolescents. This age range is generally considered to be a time of rapid physical and mental development, as well as the onset of puberty. Such rapid development and individual differences in the onset of puberty could be a source of high between-subject variability, and this could underlie our finding of no age-related FNC differences between EO and EC. Nevertheless, further studies are needed to address this, and a future study of longitudinal changes would especially help inform the field regarding the relationship between developmental changes and dynamic brain connectivity.
Limitations
Before concluding, it is important to acknowledge some limitations of this study. First, although the sliding window technique is widely used and accepted in the field, there are some controversies regarding the method, such as the choice of the window size. Our method used a relatively larger window size, which may result in a loss of information if the change in FNC is fast (Faghiri et al., 2020). Another controversial point is about the design of appropriate null models. Miller et al. (2018) showed that windowed FNC can be challenging to simulate via null models. Another debated point is the degree to which fluctuations between windows are driven by factors such as task, arousal, or spontaneous thought. Much of these are reviewed in a recent collaborative paper (Lurie et al., 2020). There are also some newer approaches that can potentially be used to avoid selecting a specific window (Faghiri et al., 2020; Yaesoubi et al., 2018).
Second, we used a group ICA model order of 150, but examining the dFNC with lower and higher gICA model orders would also be interesting. Based on our (and others) extensive previous work on ICA analysis showing the replicability of brain networks at different model orders, we know that the primary impact of adding more components or removing some from the model can mainly provide an opportunity to study brain connectivity at different spatial scales. Although a multi-scale approach would be interesting, given the large number of results, we believed that a follow-up study focusing specifically on this question would be most appropriate.
Third, we did not have a measure of pubertal status on our participants. Future developmental studies in this area should implement a pubertal scale or measure hormones such as testosterone to estimate pubertal stage across their sample. Also, there was an association between age and MFD for the EC case.
Finally, though the ICA approach has, by design, identified maximally spatially independent networks, spatial similarities or independence of the selected RSNs with previous studies were not precisely checked. A detailed spatial analysis of each of the networks would be very interesting and future studies should directly investigate this. We are currently also working on auto-labeling tools that may help automate the selection process in the future.
Conclusion
In conclusion, our study indicated that there are significant differences in dFNC between EO and EC rs-fMRI, consistent with our previous study of static connectivity (Agcaoglu et al., 2019). Also, our analysis revealed that these differences vary over time, especially in one particularly transient connectivity pattern. Overall, our findings demonstrate that both static and dynamic rs-fMRI connectivity patterns are strongly impacted by basic condition differences such as EO and EC. One key advantage of EO is that subjects are less likely to fall asleep; however, given the substantial differences in this study, which are unlikely to be due to sleep based on post-scan information, we believe there is benefit in having the EO/EC as a contrast of interest in future studies, if time allows.
Controlling basic condition may also reduce the variability reported across resting-state studies and, in turn, may reduce the overall time required to obtain stable state metrics, which is especially important for studies in children. We also did not find any significant effect of age, suggesting that in these rapidly developing ages of adolescence a high between-subject variability occurs.
Authors' Contributions
V.D.C., J.M.S., T.W.W., and Y.P.W. designed the study, wrote the protocol, and collected the rs-fMRI data; O.A. and V.D.C. processed the data and designed the experiment; and O.A. ran the analysis and wrote the first draft of the article. All authors contributed to and approved the final version of the article.
Author Disclosure Statement
No competing financial interests exist.
Funding Information
This work was supported in part by the National Institutes of Health (Grant Nos. P20GM103472, R01MH121101, P20GM130447, and R01EB020407), and NSF (Grant No. 1539067).
Supplementary Material
References
- . 2019. Resting state connectivity differences in eyes open versus eyes closed conditions. Hum Brain Mapp 40:2488–2498. Crossref, Medline, Google Scholar
- . 2018. EEG signatures of dynamic functional network connectivity states. Brain Topogr 31:101–116. Crossref, Medline, Google Scholar
- . 2014. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676. Crossref, Medline, Google Scholar
- . 1995. An information maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159. Crossref, Medline, Google Scholar
- . 2012. Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng 5:60–73. Crossref, Medline, Google Scholar
- . 2002. Group ICA of functional MRI data: separability, stationarity, and inference. In Proceedings of the International Conference on ICA and BSS, San Diego, CA, USA, pp. 155–160. Google Scholar
- . 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151. Crossref, Medline, Google Scholar
- . 2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50:81–98. Crossref, Medline, Google Scholar
- 2001. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol 22:1326–1333. Medline, Google Scholar
- 2014. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin 5:298–308. Crossref, Medline, Google Scholar
- . 2016. Artifact removal in the context of group ICA: a comparison of single-subject and group approaches. Hum Brain Mapp 37:1005–1025. Crossref, Medline, Google Scholar
- . 2013. Group information guided ICA for fMRI data analysis. Neuroimage 69:157–197. Crossref, Medline, Google Scholar
- . 2011. Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 32:2075–2095. Crossref, Medline, Google Scholar
- 2019. Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures. Hum Brain Mapp 40:1955–1968. Crossref, Medline, Google Scholar
- 2020. Weighted average of shared trajectory: a new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time. J Neurosci Methods 334:108600. Crossref, Medline, Google Scholar
- . 2008. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9:432–441. Crossref, Medline, Google Scholar
- . 2004. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 22:1214–1222. Crossref, Medline, Google Scholar
- . 2013. Eyes-open/eyes-closed dataset sharing for reproducibility evaluation of resting state fMRI data analysis methods. Neuroinformatics 11:469–476. Crossref, Medline, Google Scholar
- 2020. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 4:30–69. Crossref, Medline, Google Scholar
- . 2011. Automatic identification of functional clusters in FMRI data using spatial dependence. IEEE Trans Biomed Eng 58:3406–3417. Crossref, Medline, Google Scholar
- . 2018. Resting-state fMRI dynamics and null models: perspectives, sampling variability, and simulations. Front Neurosci 12:551. Crossref, Medline, Google Scholar
- 2013. The effect of resting condition on resting-state fMRI reliability and consistency: a comparison between resting with eyes open, closed, and fixated. Neuroimage 78:463–473. Crossref, Medline, Google Scholar
- 2018. Connectivity dynamics in typical development and its relationship to autistic traits and autism spectrum disorder. Hum Brain Mapp 39:3127–3142. Crossref, Medline, Google Scholar
- . 2014. Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects. Front Hum Neurosci 8:897. Crossref, Medline, Google Scholar
- . 2010. A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. MAGMA 23:351–366. Crossref, Medline, Google Scholar
- 2019. Group ICA for identifying biomarkers in schizophrenia: ‘adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression. Neuroimage Clin 22:101747. Crossref, Medline, Google Scholar
- 2011. Network modelling methods for FMRI. Neuroimage 54:875–891. Crossref, Medline, Google Scholar
- . 2010. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 103:297–321. Crossref, Medline, Google Scholar
- . 2018. Eyes-open and eyes-closed resting states with opposite brain activity in sensorimotor and occipital regions: multidimensional evidences from machine learning perspective. Front Hum Neurosci 12:422. Crossref, Medline, Google Scholar
- . 2010. Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study. Neuroimage 52:1252–1260. Crossref, Medline, Google Scholar
- . 2018. A window-less approach for capturing time-varying connectivity in fMRI data reveals the presence of states with variable rates of change. Hum Brain Mapp 39:1626–1636. Crossref, Medline, Google Scholar
- 2009. Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS One 4:e5743. Crossref, Medline, Google Scholar
- 2007. Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage 36:144–152. Crossref, Medline, Google Scholar
