Abstract
Background
Posttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no current
accurate prediction of who develops it after trauma. Neurobiologically, individuals
with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between
the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior
cingulate), and these aberrations correlate with severity of illness. Previous small-scale
research (n < 25) has also shown that hippocampal rsFC measured acutely after trauma is predictive
of future severity using a region-of-interest–based approach. While this is a promising
biomarker, to date, no study has used a data-driven approach to test whole-brain hippocampal
FC patterns in forecasting the development of PTSD symptoms.
Methods
A total of 98 adults at risk of PTSD were recruited from the emergency department
after traumatic injury and completed resting-state functional magnetic resonance imaging
(8 min) within 1 month; 6 months later, they completed the Clinician-Administered
PTSD Scale for DSM-5 for assessment of PTSD symptom severity. Whole-brain rsFC values
with bilateral hippocampi were extracted (using CONN) and used in a machine learning
kernel ridge regression analysis (PRoNTo); a k-folds (k = 10) and 70/30 testing versus training split approach were used for cross-validation
(1000 iterations to bootstrap confidence intervals for significance values).
Results
Acute hippocampal rsFC significantly predicted Clinician-Administered PTSD Scale for
DSM-5 scores at 6 months (r = 0.30, p = .006; mean squared error = 120.58, p = .006; R2 = 0.09, p = .025). In post hoc analyses, hippocampal rsFC remained significant after controlling
for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity
at 6 months (B = 0.59, SE = 0.20, p = .003).
Conclusions
Findings suggest that functional connectivity of the hippocampus across the brain
acutely after traumatic injury is associated with prospective PTSD symptom severity.
Keywords
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Article Info
Publication History
Published online: August 31, 2021
Accepted:
August 22,
2021
Received in revised form:
July 18,
2021
Received:
March 14,
2021
Identification
Copyright
© 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
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- Identifying Neurobiological Markers of Posttraumatic Stress Disorder Using Resting-State Functional Magnetic Resonance Imaging Data: The Promise of Data-Driven Computational ApproachesBiological Psychiatry: Cognitive Neuroscience and NeuroimagingVol. 7Issue 2
- PreviewCurrent psychiatric research, evaluation, and treatment rely on a diagnostic system based almost exclusively on subjective data, such as self-reported experiences and questionnaires, rather than on more objective markers. Recent advances in computational power have enabled the increasing use of data-driven computational approaches aiming to identify objective neural markers for diagnosing psychopathology (i.e., classifying subjects as either patients or healthy control subjects), predicting illness onset and development, and predicting treatment response.
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