January 25, 2021

Background

When a loved one dies…

  • For most, grief gradually resolves over time1
  • But some (~7-10%) continue to struggle2

How does grief get “complicated?”

Major task of bereavement adaptation is to integrate loss:

  • Flexibly switch between focusing on loss- and restoration-related stressors; appraisals.3–6,8
  • Identity and self-narrative can accomodate the loss.9–14
  • Find new ways to get attachment needs met.15–18

How does grief get “complicated?”

Prolonged salience + perseverative thought get in the way of adaptation:

  • Unhelpful avoidance reinforces the salience of deceased-related cues, possibly d/t continued monitoring & more frequent intrusions19–21
  • Mental simulation of anticipated reward (yearning) highlights discrepancy in current vs. desired state22,23
  • Death of primary attachment figure –> dysregulated oxytocin signaling24,25

 

Christoff, K., Irving, Z., Fox, K. et al. Nat Rev Neurosci. 17, 718–731 (2016).
https://doi.org/10.1038/nrn.2016.113


  • Network connectivity altered in disorders w/negative affect & perseverative thought26–28

 

Christoff, K., Irving, Z., Fox, K. et al. Nat Rev Neurosci. 17, 718–731 (2016).
https://doi.org/10.1038/nrn.2016.113
  • Oxytocin modulates brain networks to facilitate social/emotional stimulus processing; regulate internal vs. external attention29–31
  • Affects self-oriented processing, encoding, retrieval, interoception24,32,33

Summary

  • Theories of adaptation in bereavement highlight a need for flexible shifting between mental states.
  • Prolonged motivational salience of the deceased partner may be a complicating factor, particularly when coupled with perseverative thinking.
  • Investigate how large-scale brain network activity and oxytocin neuropeptide are involved in complicated grief symptom severity.

Study and Aims

In the present study, older adults with and without complicated grief participated in two resting state fMRI sessions, as part of a larger within-subjects crossover study using intranasal oxytocin as an experimental probe.

Aim 1 involves only data from the placebo scan, whereas Aim 2 involves data from both oxytocin and placebo scan.

Aim 1

To identify whether static and/or dynamic resting state functional connectivity (FNC/dFNC) is associated with complicated grief symptoms.

Aim 2

To investigate if/how intranasal oxytocin alters FNC/dFNC in older adults, & if oxytocin effects are moderated by complicated grief symptoms.

Participants

N = 38 (71% female)

  • Time since death M = 15.4 months (SD = 8.2, range: 6 - 36)

  • Relationship length M = 38.5 years (SD = 12.4, range: 17 - 59)

  • Age M = 69.2 years (SD = 6.5, range: 57 - 79)

  • 94% non-Hispanic White

  • 80% retired

  • 63% four-year college degree or higher

  • CG (n = 15) vs. non-CG (n = 23):

    • Higher depression symptoms in CG group (p < .001)

    • Men overrepresented in CG group (p = .073)

Design

 

 

fMRI Data Processing

  • Nipype-based, open-source pipelines for quality control (MRIQC)34 and preprocessing (fMRIPrep).35

  • Analysis:

    • Group ICA with back-reconstruction to identify brain networks (GIFT36)

    • Look at network correlations averaged across entire scan (static FNC)

    • Sliding window time-varying connectivity analysis (dynamic FNC)


Results

 

Selected components

Spatial maps of independent components thresholded at Z = 2 and displayed on a standard T1 template image

Spatial maps of independent components thresholded at Z = 2 and displayed on a standard T1 template image

Static FNC

dFNC state 1

  • Overall stability, with CoN-dACC & R FPN more variable (p <.05)

dFNC state 2

  • Large positive fluctuations between most component pairs (p <.05)

dFNC state 3

  • Negative fluctuations in R FPN - DNCore (p <.05)
  • Positive fluctuations in R FPN - CoN-dACC (p <.05)

dFNC state 4

  • CoNdACC greater positive variability with DNCore and R FPN (p <.05)

Aim 1

To identify whether static and/or dynamic resting state functional connectivity (FNC/dFNC) is associated with complicated grief symptoms.

Default-cingulo-opercular FNC predicts grief severity

 

Functional connectivity between the DNCore and CoNdACC components remained a significant predictor of grief severity when age, sex, and depressive symptoms score were included as covariates in the model (b = 8.48, 95%CI = [0.74, 16.21], SE = 3.80, t = 2.23, p = 0.03). The overall model explained 63% of the variance in grief severity (F(4,33) = 16.76, adjusted R2 = 0.63, p <.001.

Higher grief predicts longer dwell time in positively interconnected state

 

Participants with higher ICG scores spent more time in the highly interconnected state (dFNC state 2) than participants with lower ICG scores, b = 8.36, SE = 3.89, t(35) = 2.15, p = .039.

Aim 2

To investigate if/how intranasal oxytocin alters FNC/dFNC in older adults, & if oxytocin effects are moderated by complicated grief symptoms.

Oxytocin increased default - cingulo-opercular connectivity

Figure 9.

Figure 9.

 

Intranasal oxytocin had a significant positive effect on DNretrosplenial - CoNdACC FNC, b = 0.07, SE = 0.03, 95% CI [0.02, 0.12], p = .008.

…but no oxytocin x complicated grief interaction.

Grief severity had no significant effect on functional connectivity, alone or in interaction with session.

No effect of oxytocin on dFNC

  • No main or interaction effect of oxytocin on dwell time in any state.

  • No effects of complicated grief severity or session on n transitions between states.

Conclusions

Summary

Aim 1

  • Resting state static and dynamic FNC measures were associated with complicated grief symptom severity in widowed older adults:
    • Less functional segregation between DNCore and CoNdACC in higher-grief participants.

    • Higher-grief participants spent longer periods in state of interconnection between 3 major networks.

  • Higher-grief participants did not transition between dFNC states more frequently.

  • Less anti-correlation between DN and SN/CoN may support idea that prolonged attachment figure salience and internal thought are mutually reinforcing in CG16

Summary

Aim 2

  • Intranasal oxytocin increased static FNC between DNretrosplenial and CoNdACC (but small effect).
    • Intranasal oxytocin reconfigures large-scale brain networks to facilitate socio-emotional information processing even in the absence of immediate social stimuli.31,37
    • OXTR mRNA highly expressed in ACC and RSC;38 regions important for social cognition, learning, behavior.39
    • Grief severity did not moderate intranasal oxytocin effect on FNC/dFNC.

Limitations

  • Cross-sectional; did not assess thought content
  • Network identification (“salience” vs. “cingulo-opercular”)40
  • Motion artifact influence on FNC;41–43 test-retest reliability of dynamic FNC.44,45
  • Sample demographics & generalizability

Future directions

  • Replicate placebo-session results in larger sample (Rotterdam Scan Study; n = >350 bereaved older adults).
  • Establish whether observed functional connectivity differences are present pre-morbidly, relate to age-related cognitive changes, and if they reflect specific aspects of internal thought in older adults with CG vs. non-CG grief.

This study illustrates differences in static and dynamic resting state functional connectivity measures in bereaved older adults with higher vs. lower complicated grief symptom severity.

Taken together, results suggest that complicated grief symptoms are associated with reduced inter-network modularity, particularly among retrosplenial default and cingulo-opercular network regions whose anticorrelation was significantly decreased after intranasal oxytocin administration.

Findings suggest that interactions between large-scale brain networks are altered in complicated grief, and that the neuropeptide oxytocin might be involved.

 

Acknowledgements

Mary-Frances O’Connor
John JB Allen
Jessica Andrews-Hanna
Elena Plante
Brian Arizmendi

Scott Squire
Dianne Patterson & UA Brain Mapping Workgroup

The participants in this project, for their stories, insights, and willingness to contribute to our research.



This research was funded by:

National Institute on Aging - NIH (1F31AG062067, PI: Seeley)
DANA Foundation (Neuroscience Research Grant, PI: O’Connor)

Additional training funded by:
University of Washington eScience Institute
Academy of Psychological Clinical Science
Stanford Center for Reproducible Neuroscience
John and Laura Arnold Foundation
New York Academy of Sciences


Contact:
sarenseeley@email.arizona.edu

Website:
https://sarenseeley.github.io

Supplemental slides

Models

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