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.

Keywords: dynamic functional network connectivity; eyes closed; eyes open; independent component analysis; resting state.

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.

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.

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.

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.

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.

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.