Participants
Data for the current analyses were collected as part of the multisite emergency department (ED) AURORA study. The AURORA study represents a significant research effort aimed at enhancing our understanding, prevention, and recovery strategies for individuals who have undergone a traumatic event. In AURORA study, trauma-exposed civilians brought to one of 29 participating EDs across the United States were recruited for this large, longitudinal study (details in38). This study involved more than 4000 participants from the AURORA project, who provided clinical data at various intervals: 2 weeks (WK2), 4 weeks (WK4), 3 months (M3), 6 months (M6), and 12 months (M12) as illustrated in Fig. 1A. Additionally, neuroimaging data from ~ 400 participants were collected at WK2 from five different scanning locations, which include Atlanta (Georgia), Belmont (Massachusetts), Philadelphia (Pennsylvania), St. Louis (Missouri), and Detroit (Michigan). The recruitment for this study took place between September 2017 and December 2020 (Final freeze 4 Psychometric release). We excluded those with low-quality resting-state fMRI and missing clinical information at the imaging acquisition date. This process resulted in 275 participants (181 females) being included in this analysis. Table 1 summarizes the demographic characteristics of the participants included in this study.
Table 1
Participant demographics and clinical information
Characteristics | Mean (SD) or N (%) |
Demographic characteristics |
Age | 34.55(12.78) |
Sex assigned at birth, male/female | 94 (34.18%)/181(65.82%) |
*Race/ethnicity | |
Hispanic | 42 (15.27%) |
White | 85 (30.91%) |
Black | 131 (47.64%) |
Others | 15 (5.45%) |
Missing | 2 (0.73%) |
Years of education | 15.16(2.31) |
Income level | |
<$19,000 | 74 (25.96%) |
$19,001-$35,000 | 85 (30.91%) |
$35,001-$50,000 | 40 (14.55%) |
$50,001-$75,000 | 30 (10.91%) |
$75,001-$100,000 | 17 (6.18%) |
>-$100,000 | 20 (7.27%) |
Missing | 9 (3.27) |
Clinical characteristics |
PCL-5 score | |
WK2 (N = 275) | 30.12 (17.58) |
WK8 (N = 243) | 26.60 (17.30) |
M3 (N = 226) | 23.53 (17.40) |
M6 (N = 208) | 21.00 (17.33) |
M12 (N = 176) | 20.33 (17.93) |
*Self-reported . SD: standard deviation, BMI: body mass index, WK2: 2 weeks after trauma, WK8: 8 weeks after trauma, M3: 3 months after trauma, M6: 6 months after trauma, M12: 12 months after trauma.
Three distinct dFNC states were identified
After calculating the dFNC of each participant, we grouped their dFNC into three different dynamic network connectivity states (Fig. 1B). Figure 2 presents an overview of the identified states. Each state represents 1378 connectivity measures among seven networks across the entire brain. These networks included subcortical network (SCN), auditory network (ADN), sensorimotor network (SMN), visual network (VSN), cognitive control network (CCN), default-mode network (DMN), and cerebellar network (CBN). The top panel highlights three distinct dFNC states, while the bottom panel shows the data with connectivities between − 0.3 and 0.3 removed for clarity. State 2 and state 3 exhibit a stronger positive connectivity among sensory networks, including visual, auditory, and sensorimotor networks. Conversely, in state 1, we observed more disconnections among these networks. We observed an increase in within-CCN connectivity and enhanced connectivity between the DMN and sensory networks in state 3. Additionally, we noted a greater connectivity between the CBN and SCN in state 3 compared to the other two states. Overall, our analysis suggests that state 2 and state 3 exhibit characteristics of inter-network states, evidenced by the increased connectivity across the seven networks. In contrast, state 1 is indicative of an intra-network state, as it demonstrates predominantly within-network connectivity patterns.
Dynamic FNC occupancy rates link with PCL-5 scores.
By utilizing the three identified brain states for the entire group and the state vector, estimated for each individual, which represents the state of the brain network at any given time point, we calculated three occupancy rates (OCRs) for each participant. The OCR of each state represents the proportion of time each participant spends in that state (see Method Section and Supplementary Fig. 1). Figure 3A shows the correlation between OCRs and PCL-5 scores at various time points. The associations were computed using General Linear Model (GLM) accounting for age, sex, years of education, scanning site, income, marital status, employment status, and percentile ADI, and the resulting t-statistics were transformed to correlation (r). A positive significant association was found between the OCR of state 1 and the PCL-5 scores at M3 (r = 0.192, β = 0.0039, SE = 0.0012, 95% CI: 0.0015 ~ 0.0062, pcorrected = 0.021, N = 226 after excluding sample with missing scores, see Table 1). These results indicate that the participants with higher PTSD symptom severity spend more time in state 1, which is indicative of an intera-network brain state.
We observed significant negative association between the OCR of state 3 and the PCL-5 scores at WK2 (r=-0.179, β= -0.0029, SE = 0.0009, 95% CI: -0.0048~-0.0010, pcorrected= 0.021, N = 275). We also found a negative correlation between state 3 OCR and PCL-5 of M3 (r=-0.166, β=-0.0030, SE = 0.0011,95% CI: -0.0052~-0.0008, pcorrected = 0.029, N = 226). This indicates that individuals with higher PCL-5 scores spent less time in state 3, which is indicative of an inter-network brain state. Overall, our findings highlight the relationships between the OCR and PCL-5 scores, suggesting potential connections between dynamic functional network connectivity and symptoms of PTSD at different time points.
Sex modulates the relationship between OCRs and PCL-5 scores.
To examine the influence of sex on the relationship between OCRs and PCL-5 scores, we conducted GLM analyses for males (N = 94) and females (N = 181), separately. In these analyses, we included age, years of education, scanning site, income, marital status, employment status, and ADI as covariates. The correlation results between OCRs and PCL-5 scores for females and males are presented in Fig. 3B and Fig. 3C, respectively. While no significant association was found between OCRs and PCL-5 scores in the male group, we did observe significant associations between the OCR of state 1 and state 3 with PCL-5 scores at WK2 and M3. Notably, only the association between OCR of state 3 and PCL-5 at WK2 remained significant after applying FDR correction (r=-0.244, β= -0.0030, SE = 0.0011, 95% CI: -0.0048~-0.0010, pcorrected = 0.014, N = 181). We also observed a positive link between state 1 OCR and WK2 PCL-5 (r = 0.153, β = 0.0027, SE = 0.0013, 95% CI: -0.0003 ~ 0.0040, puncorrected = 0.0402, N = 181). Additionally, OCR of state 1 showed a positive link with M3 PCL-5 (r = 0.178, β = 0.0034, SE = 0.0014, 95% CI: 0.0015 ~ 0.0062, puncorrected = 0.0167, N = 154) and OCR of state 3 showed a negative link with M3 PCL-5 (r=-0.164, β=-0.0028, SE = 0.0012, 95% CI: -0.0052~-0.0008, puncorrected = 0.0273, N = 154). However, none of them were significant after FDR correction.
To verify that the strong correlation in females is not due to their larger sample size compared to males, we tested the correlations between state 3 OCR and WK2 PCL-5 scores in both groups. Using Fisher's z-transformation and calculating standard errors, we found a significant difference in the correlations between females and males (|Z-test statistic| = 1.734, p = 0.041), suggesting that the relationship between OCRs and PCL-5 scores at WK2 differs significantly between sexes.
Both posttraumatic stress (PTS) and non-PTS group generate similar dFNC states
We categorized participants into posttraumatic stress or PTS (N = 124) and non-PTS (N = 151) groups based on their WK 2 PCL-5 scores, with a cutoff point of 31. Those scoring above 31 were classified as PTS, while those below were considered non-PTS 39. We used the term PTS instead of PTSD because the classification was based on PCL-5 scores at the time of imaging (i.e., WK2), before an official PTSD diagnosis till WK8. We then examined state pattern differences between the two groups by performing separate k-means clustering analyses on their dFNC data.
Figure 4 demonstrates a notable similarity in brain states between the PTS and non-PTS groups, as anticipated. We quantified the similarity by calculating the Pearson correlation coefficient between corresponding states' FNC. The correlations between state 1 of the non-PTS group and state 1 of the PTS group, state 2 of the non-PTS group and state 2 of the PTS group, and state 3 of the non-PTS group and state 3 of the PTS group were 0.9632 (N = 1378, where N is number of connections, p ~ 0), 0.9880 (N = 1378, p ~ 0), and 0.8938 (N = 1378, p ~ 0), respectively (see Fig. 4A and 4B). The p-value, displayed as zero in MATLAB, indicates a very small value, suggesting strong statistical significance and reinforcing the robustness of our findings. Comparing the OCR of states in the non-PTS and PTS groups, we found a consistent pattern: state 1 consistently showed the highest OCR, while state 2 exhibited the lowest OCR in both groups (Fig. 4C and 4D). These results suggest a consistent OCR pattern across states in both groups, indicating a high degree of similarity in identified brain states between the non-PTS and PTS groups. Additionally, our findings that individuals with PTS tend to spend more time in state 1 compared to those without PTS corroborate our main finding that have established a connection between the heightened OCR of this state and PCL-5, hinting at the potential clinical relevance of this brain state in PTS.