Poster No:
431
Submission Type:
Abstract Submission
Authors:
Julian Gaviria Lopez1, Brenda Penninx2
Institutions:
1Amsterdam UMC, Geneva, Geneva, 2Amsterdam UMC, Amsterdam, Amsterdam
First Author:
Co-Author:
Introduction:
Recent fMRI studies have shown modest differences in stationary functional connectivity between individuals with anxiety and depression and healthy individuals. These findings do not provide consistent insights into the underlying mechanisms or risk factors for these disorders. Additionally, there are few longitudinal neuroimaging studies, limiting our understanding of mood-state transitions. In this study, we analyzed the dynamic functional connectivity of the brain (dFC) in patients with anxiety and depression over a two-year period.
Aims
(I) To describe brain functional systems associated with the natural course of anxiety and depression.
(II) To investigate the relationship between symptom severity and dFC.
(III) To quantify the variability of dFC brain networks over time.
Methods:
Participants. Dutch speakers aged between 18 and 57 years from the Netherlands Study of Depression and Anxiety [NESDA (Penninx et al., 2008)], a large-scale observational cohort study aimed at evaluating the long-term trajectories of depressive and anxiety disorders. We utilized data collected at baseline and two years later across three medical centers in the Netherlands. Two groups were recruited. A control group comprising healthy individuals (N=54, 31 females), and a second group of participants who met the DSM-IV criteria for a major depressive disorder (MDD) and/or one or more anxiety disorders [panic disorder, social anxiety disorder, and generalized anxiety disorder (N=87. 45 females)]. Exclusion criteria: presence or history of major internal or neurological disorders, drug or alcohol abuse, hypertension, primary diagnosis of psychotic disorder, obsessive-compulsive disorder, bipolar disorder, or severe addiction disorder, and general MRI contraindications. Participants receiving treatment with serotonin reuptake inhibitors (SSRIs) or serotonin-norepinephrine reuptake inhibitors (SNRIs) on stable use (i.e., daily use for more than 50% of days in the past month) were included. Pharmacological intake was controlled in the statistical analyses. Demographic and clinical variables: Age, sex, years of education, depressive symptom severity (Inventory of Depressive Symptoms (John Rush et al., 1986)], the NEO Five-Factor Personality Dimensions Inventory (Costa Jr. & McCrae, 1995), the Beck Anxiety Inventory (Beck et al., 1988). Brain marker: We analyzed seed-free functional co-activation patterns [CAPs (Bolton et al., 2020)] to characterize dFC within whole-brain networks during resting state fMRI recordings. To evaluate changes in brain CAPs over two years, we measured three metrics: Occurrences: Total number of frames assigned to each CAP during fMRI sessions. Entries: Number of transitions into a specific co-activation state. Duration: Time (in seconds) a participant remains in a specific brain state (i.e., a CAP).
Results:
(I) Patients reported higher rates of occurrences and entries in two different CAPs compared to controls. One CAP included regions associated with the salient and somatomotor network (CAPSN-SMN), while the second CAP overlapped with areas related to the default mode network (CAPDN).
(II) The occurrence rates of CAPDN were associated with depression scores.
(III) In patients, the duration rates of CAPSN-SMN and CAPDN recorded during the 2-year follow-up were longer than those at baseline.
Conclusions:
(I) Our research shows transient and long-term changes in the connectivity of two brain systems, CAPSN-SMN and CAPDN, which reflect the neural mechanisms linked to depression and anxiety.
(II) The connection between CAPDN and depression severity suggests further research into the predictive capacity of brain dFC markers related to the onset and remission of depressive symptoms over time.
(III) Our results provide insight into the long-term dynamic nature of brain networks and their interactions in the resting state, surpassing traditional static functional connectivity in pathological conditions such as anxiety and depression.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Psychiatric Disorders
Other - Depression; Anxiety; dynamic functional connectivity
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
No
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
No
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
fMRIprep
Provide references using APA citation style.
Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56(6), 893–897. https://doi.org/10.1037/0022-006X.56.6.893
Bolton, T. A., Tuleasca, C., Wotruba, D., Rey, G., Dhanis, H., Gauthier, B., Delavari, F., Morgenroth, E., Gaviria, J., & Blondiaux, E. (2020). TbCAPs: A toolbox for co-activation pattern analysis. NeuroImage, 211, 116621.
Costa Jr., P. T., & McCrae, R. R. (1995). Domains and Facets: Hierarchical Personality Assessment Using the Revised NEO Personality Inventory. Journal of Personality Assessment, 64(1), 21–50. https://doi.org/10.1207/s15327752jpa6401_2
John Rush, A., Giles, D. E., Schlesser, M. A., Fulton, C. L., Weissenburger, J., & Burns, C. (1986). The inventory for depressive symptomatology (IDS): Preliminary findings. Psychiatry Research, 18(1), 65–87. https://doi.org/10.1016/0165-1781(86)90060-0
Penninx, B. W. J. H., Beekman, A. T. F., Smit, J. H., Zitman, F. G., Nolen, W. A., Spinhoven, P., Cuijpers, P., De Jong, P. J., Van Marwijk, H. W. J., Assendelft, W. J. J., Van Der Meer, K., Verhaak, P., Wensing, M., De Graaf, R., Hoogendijk, W. J., Ormel, J., & Van Dyck, R. (2008). The Netherlands Study of Depression and Anxiety (NESDA): Rationale, objectives and methods. International Journal of Methods in Psychiatric Research, 17(3), 121–140. https://doi.org/10.1002/mpr.256.
No