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. 2025 Feb;3(2):185-198.
doi: 10.1038/s44220-024-00377-0. Epub 2025 Jan 20.

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma

Affiliations

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma

Mohammad Se Sendi et al. Nat Ment Health. 2025 Feb.

Abstract

Post-traumatic stress (PTS) encompasses a range of psychological responses following trauma, which may lead to more severe outcomes such as post-traumatic stress disorder (PTSD). Identifying early neuroimaging biomarkers that link brain function to PTS outcomes is critical for understanding PTSD risk. This longitudinal study examines the association between brain dynamic functional network connectivity (dFNC) and current/future PTS symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.197, p corrected = 0.0079) and future (r=-0.176, p corrected = 0.0176) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.205, p corrected = 0.0079). We additionally observed that the association between the network dynamics of the inter-network and intra-network brain state with symptom severity is more pronounced in female group. Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger effect in female group, underscoring the importance of sex differences.

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Conflict of interest statement

Competing Interests Statement Dr. Sendi has served as a consulatant for Niji Corp for unrelated work. Dr. Daskalakis is on the scientific advisory board for Sentio Solutions, Inc. and Circular Genomics, Inc. Over the past 3 years, Dr. Pizzagalli has received consulting fees from Boehringer Ingelheim, Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (former BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka Pharmaceuticals, Sage Therapeutics, and Takeda Pharmaceuticals; honoraria from the Psychonomic Society and the American Psychological Association (for editorial work) and Alkermes, and research funding from the Bird Foundation, Brain and Behavior Research Foundation, DARPA, Millennium Pharmaceuticals, and the National Institute of Mental Health. In addition, he has received stock options from Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (former BlackThorn Therapeutics), and Neuroscience Software. Dr. Neylan has received research support from NIH, VA, and Rainwater Charitable Foundation, and consulting income from Jazz Pharmaceuticals. In the last three years Dr Clifford has received research funding from the NSF, NIH and LifeBell AI, and unrestricted donations from AliveCor Inc, Amazon Research, the Center for Discovery, the Gates Foundation, Google, the Gordon and Betty Moore Foundation, MathWorks, Microsoft Research, Nextsense Inc, One Mind Foundation, and the Rett Research Foundation. Dr Clifford has financial interest in AliveCor Inc and Nextsense Inc. He also is the CTO of MindChild Medical with significant stock. These relationships are unconnected to the current work. Dr. Germine is on the board of the Many Brains Project. Her family also has equity in Intelerad Medical Systems, Inc. Dr. Rauch reported serving as secretary of the Society of Biological Psychiatry; serving as a board member of Community Psychiatry and Mindpath Health; serving as a board member of National Association of Behavioral Healthcare; serving as secretary and a board member for the Anxiety and Depression Association of America; serving as a board member of the National Network of Depression Centers; receiving royalties from Oxford University Press, American Psychiatric Publishing Inc, and Springer Publishing; and receiving personal fees from the Society of Biological Psychiatry, Community Psychiatry and Mindpath Health, and National Association of Behavioral Healthcare outside the submitted work. Dr. Jones has no competing interests related to this work, though he has been an investigator on studies funded by AstraZeneca, Vapotherm, Abbott, and Ophirex. Dr. Harte has no competing interest related to this work, though in the last three years he has received research funding from Aptinyx and Arbor Medical Innovations, and consulting payments from Aptinyx. In the past 3 years, Dr. Kessler was a consultant for Cambridge Health Alliance, Canandaigua VA Medical Center, Holmusk, Partners Healthcare, Inc., RallyPoint Networks, Inc., and Sage Therapeutics. He has stock options in Cerebral Inc., Mirah, PYM, and Roga Sciences. Dr. Koenen’s research has been supported by the Robert Wood Johnson Foundation, the Kaiser Family Foundation, the Harvard Center on the Developing Child, Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, the National Institutes of Health, One Mind, the Anonymous Foundation, and Cohen Veterans Bioscience. She has been a paid consultant for Baker Hostetler, Discovery Vitality, and the Department of Justice. She has been a paid external reviewer for the Chan Zuckerberg Foundation, the University of Cape Town, and Capita Ireland. She has had paid speaking engagements in the last three years with the American Psychological Association, European Central Bank. Sigmund Freud University – Milan, Cambridge Health Alliance, and Coverys. She receives royalties from Guilford Press and Oxford University Press. Dr. McLean has served as a consultant for Walter Reed Army Institute for Research, Arbor Medical Innovations, and BioXcel Therapeutics, Inc. Dr. Ressler has performed scientific consultation for Bioxcel, Bionomics, Acer, and Jazz Pharma; serves on Scientific Advisory Boards for Sage, Boehringer Ingelheim, Senseye, and the Brain Research Foundation, and he has received sponsored research support from Alto Neuroscience. The remaining authors declare no competing interests.

Figures

Fig. 1:
Fig. 1:. Data collection procedure and analytic pipeline:
A) The PTSD Checklist for DSM-5 (PCL-5) was utilized to evaluate PTSD symptoms at various time points, encompassing pre-trauma (PRE), week 2 (WK2), week 8 (WK8), month 3 (M3), month 6 (M6), and month 12 (M12). During the study visit at WK2 a subgroup of participants underwent MRI scans, either in the morning or the afternoon. B) We utilized the NeuroMark pipeline to extract robust intrinsic connectivity networks (ICNs), totaling 53 components, which demonstrate consistent replication across independent datasets. These 53 distinct components were initially identified through group-ICA analysis using the NeuroMark template. These components were subsequently categorized into seven distinct networks, which include the subcortical network (SCN), auditory network (ADN), visual sensory network (VSN), sensorimotor network (SMN), cognitive control network (CCN), default mode network (DMN), and cerebellar network (CBN). C) Dynamic functional network connectivity (dFNC) analytic pipeline: Step 1: Initially, the time-course signal of 53 intrinsic connectivity networks (ICNs) was identified through group-ICA in the Neuromak template. Subsequently, the identified 53 ICNs were subjected to a taper sliding window segmentation to calculate FNC. Each subject yielded 210 FNCs, each with a size of 53 × 53. Step 2: To cluster the FNCs into three distinct groups, the FNC matrices were vectorized and concatenated, followed by the utilization of k-means clustering with correlation as the distance metric. Step 3: From the state vector, occupancy rate (OCR) was computed for each subject, resulting in a total of three OCR variables for each subject. In order to investigate the relationship between OCRs with the PTSD clinical measure (i.e, PCL-5), we used GLM to compute the associations, taking into account factors such as age, sex, years of education, scanning site, income, marital status, employment status, type of truama, and percentile ADI. The resulting t-statistics from this analysis were then converted to correlation (r) values.
Fig. 1:
Fig. 1:. Data collection procedure and analytic pipeline:
A) The PTSD Checklist for DSM-5 (PCL-5) was utilized to evaluate PTSD symptoms at various time points, encompassing pre-trauma (PRE), week 2 (WK2), week 8 (WK8), month 3 (M3), month 6 (M6), and month 12 (M12). During the study visit at WK2 a subgroup of participants underwent MRI scans, either in the morning or the afternoon. B) We utilized the NeuroMark pipeline to extract robust intrinsic connectivity networks (ICNs), totaling 53 components, which demonstrate consistent replication across independent datasets. These 53 distinct components were initially identified through group-ICA analysis using the NeuroMark template. These components were subsequently categorized into seven distinct networks, which include the subcortical network (SCN), auditory network (ADN), visual sensory network (VSN), sensorimotor network (SMN), cognitive control network (CCN), default mode network (DMN), and cerebellar network (CBN). C) Dynamic functional network connectivity (dFNC) analytic pipeline: Step 1: Initially, the time-course signal of 53 intrinsic connectivity networks (ICNs) was identified through group-ICA in the Neuromak template. Subsequently, the identified 53 ICNs were subjected to a taper sliding window segmentation to calculate FNC. Each subject yielded 210 FNCs, each with a size of 53 × 53. Step 2: To cluster the FNCs into three distinct groups, the FNC matrices were vectorized and concatenated, followed by the utilization of k-means clustering with correlation as the distance metric. Step 3: From the state vector, occupancy rate (OCR) was computed for each subject, resulting in a total of three OCR variables for each subject. In order to investigate the relationship between OCRs with the PTSD clinical measure (i.e, PCL-5), we used GLM to compute the associations, taking into account factors such as age, sex, years of education, scanning site, income, marital status, employment status, type of truama, and percentile ADI. The resulting t-statistics from this analysis were then converted to correlation (r) values.
Fig. 2:
Fig. 2:. Three dynamic functional connectivity states identified in AURORA dataset.
A) Three dynamic functional connectivity (dFNC) state identified using k-means clustering method for N=275 incldung both PTS and non-PTS individual. B) To enhance clarity, the dFNC state displayed by removing connectivities with values between −0.3 and 0.3. States 2 and 3 exhibit stronger positive connectivity among sensory networks (visual, auditory, and sensorimotor). State 1, on the other hand, shows more disconnections within these networks. State 3 demonstrates increased within-CCN connectivity and enhanced connectivity between the DMN and sensory networks compared to state 1 and satte 2. State 3 also exhibits greater connectivity between the CBN and SCN compared to the other two states. Overall, our analysis identifies states 2 and 3 as inter-network brain states while state 1 appears to be an intra-network brain state based on connectivity patterns. The color bar indicates the strength of the connectivity. SCN: Subcortical network; ADN: auditory network; SMN: sensorimotor network; VSN: visual network; CCN: cognitive control network; DMN: default-mode network; and CBN: cerebellar network.
Fig. 3:
Fig. 3:. Dynamic functional network connectivity occupancy rates (OCRs) link with PCL-5 .
We employed a General Linear Model to explore the association between OCRs and PCL-5 scores using data from all participants at WK2 (N=275), WK8 (N=243), M3 (N=226), M6 (N=208), and M12 (N=176). We included age, sex at birth, years of education, income, employment status, marital status, scanning site, type of trauma, and percentile ADI as covariates. In the sex-stratified analysis, sex was excluded as a covariate. With three predictors and five time points, we have 15 tests. These partial correlations were analyzed using a two-sided test to assess the significance of associations in both directions. In each panel, all 15 p-values have been adjusted for multiple comparisons using FDR correction. A) We found a positive association between the OCR of state 1 and PCL-5 scores at M3 (r =0.205, β = 0.0042, SE=0.0012, 95% CI: 0.0018–0.0066, pcorrected=0.0079, N=226). We also found a significant negative association between the OCR of state 3 and PCL-5 scores of WK2 (r=−0.197, β=−0.0033, SE=0.0009, 95% CI: −0.0052~−0.0013, pcorrected =0.0079, N = 275), and between state 3 OCR and PCL-5 scores at M3 (r=−0.176, β=−0.0032, SE= 0.0011, 95% CI: −0.0054~ −0.0011, pcorrected=0.0176, N=226). B) A positive association is observed between the OCR of state 1 and PCL-5 scores both WK2 (r=0.187, β= 0.0034, SE=0.0014, 95% CI: 0.0001~0.0045, pcorrected=0.044, N=181) and M3 (r=0.224, β=0.0044, SE=0.0014, 95% CI: 0.00180–0.0066, pcorrected=0.019, N=154). Conversely, a negative correlation is seen between the OCR of state 3 and PCL-5 scores at both WK2 (r=−0.269, β=−0.0043, SE=0.0011, 95% CI: −0.0052~−0.0013, pcorrected = 0.004, N=181) and M3 (r=−0.208, β= −0.0036, SE=0.0013, 95% CI: −0.0055~−0.0011, pcorrected = 0.014, N=154). C) We did not find any significant result for the male group. Time points: WK2 (week 2 after trauma), WK8 (week 8 after trauma), M3 (month 3 after trauma), M6 (month 6 after trauma), and M12 (month 12 after trauma). The color bar represents correlation strength, with solid boxes indicating significant results after FDR correction and dashed boxes marking significant results that did not survive correction.
Fig. 4:
Fig. 4:. Both non-PTS and PTS group generate similar dynamic functional connectivity (dFNC) state.
A) the dFNC states identified only in non-PTS group (N=151). B) the dFNC states identified only in PTS group (N=124). C) The OCR in different state of non-PTS group ( N=151). D) The OCR in different state of PTS group ( N=124). The bar plot show the mean of OCR and the error bar shows the ±SD (standard deviation) from the mean. The color bar indicates the strength of the connectivity. SCN: Subcortical network; ADN: auditory network; SMN: sensorimotor network; VSN: visual network; CCN: cognitive control network; DMN: default-mode network; and CBN: cerebellar network.

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