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Neural computations of threat in the aftermath of combat trauma

Nature Neuroscience volume 22, pages 470–476 (2019)Cite this article

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An Author Correction to this article was published on 13 February 2019

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Abstract

By combining computational, morphological, and functional analyses, this study relates latent markers of associative threat learning to overt post-traumatic stress disorder (PTSD) symptoms in combat veterans. Using reversal learning, we found that symptomatic veterans showed greater physiological adjustment to cues that did not predict what they had expected, indicating greater sensitivity to prediction errors for negative outcomes. This exaggerated weighting of prediction errors shapes the dynamic learning rate (associability) and value of threat predictive cues. The degree to which the striatum tracked the associability partially mediated the positive correlation between prediction-error weights and PTSD symptoms, suggesting that both increased prediction-error weights and decreased striatal tracking of associability independently contribute to PTSD symptoms. Furthermore, decreased neural tracking of value in the amygdala, in addition to smaller amygdala volume, independently corresponded to higher PTSD symptom severity. These results provide evidence for distinct neurocomputational contributions to PTSD symptoms.

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Fig. 1: Experimental overview
Fig. 2: Computational model comparison and relationship to PTSD symptoms.
Fig. 3: Amygdala structure and value computation contribute to PTSD symptoms using a hybrid computational model of associability and value encoding.
Fig. 4: Neural computations of value, associability and prediction error and their relationship to CAPS symptoms for different ROIs.
Fig. 5: Associability-related neural activity in the right striatum partially mediates the relationship between prediction-error weights and CAPS.

Code availability

The code used for the analyses is available online at: http://osf.io/rxsw2/.

Data availability

Data used to support the conclusions of this study is available online at: http://osf.io/rxsw2/.

Change history

  • 13 February 2019

    The original and corrected figures are shown in the accompanying Author Correction.

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Acknowledgements

The main source of funding for this work was provided by NIMH 105535 R01 grant awarded to I. Harpaz-Rotem and D. Schiller (MPI) and funding provided by the Clinical Neurosciences Division of the National Center for PTSD. Additional support was provided by Klingenstein-Simons Fellowship Award in the Neurosciences to D. Schiller, The Brain and Behavior Research Foundation 23260 to I. Harpaz-Rotem, Chinese NSF grant 31421003 to J. Li, and the Swiss National Science Foundation grant SNF 161077 to P. Homan. The analytic work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai.

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Author notes

  1. These authors contributed equally: Ilan Harpaz-Rotem, Daniela Schiller.

Authors and Affiliations

  1. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Philipp Homan, Jingchu Hu & Daniela Schiller

  2. Departments of Comparative Medicine, Neuroscience and Psychology, Yale University, New Haven, CT, USA

    Ifat Levy

  3. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

    Eric Feltham, Charles Gordon, Robert H. Pietrzak, Steven Southwick, John H. Krystal & Ilan Harpaz-Rotem

  4. US Department of Veterans Affairs National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA

    Eric Feltham, Charles Gordon, Robert H. Pietrzak, Steven Southwick, John H. Krystal & Ilan Harpaz-Rotem

  5. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

    Jian Li

  6. Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Daniela Schiller

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Contributions

I.L., I.H.R., and D.S. designed the study. E.F., C.G., I.L., and I.H.R. collected the data. J.H. scored the data. P.H. analyzed the data. I.L., J.L, I.H.R., and D.S. contributed to data analysis. J.H.K., R.H.P., and S.S. contributed to the interpretation of the results. P.H., I.L., I.H.R., and D.S. wrote the first draft of the manuscript. All authors contributed to the final version of the manuscript.

Corresponding authors

Correspondence to Ilan Harpaz-Rotem or Daniela Schiller.

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Integrated supplementary information

Supplementary Figure 1 Distributions of psychopathology in the study sample show the wide range of symptoms in participants, from completely healthy to highly affected by PTSD.

ASI, Anxiety Sensitivity Index; BDI, Beck Depression Inventory; CAPS, Clinician-Administered PTSD Scale; CES, combat exposure score; STAIS, State Anxiety subscale of the Spielberger State-Trait Anxiety Inventory.

Supplementary Figure 2 Hierarchical Bayesian model recovers simulated learning parameters.

To validate our Hierarchical Bayesian hybrid model before testing it in the clinical dataset, we simulated skin conductance data in 20 participants. We estimated the initial associability α0, κ, the normalization factor, and η, the prediction-error weight. a. Simulated data in 20 subjects. b. Correlations between estimated and simulated values. The Bayesian model successfully recovered the simulated values. Pearson correlation coefficients with two-tailed significance tests are shown. Error shadings correspond to standard errors. c. Estimated parameters with 90% credible intervals and simulated parameters. All simulated parameters were within the 90% credible intervals of the estimated parameters, indicating successful parameter recovery. ***, P < 0.001.

Supplementary Figure 3 Average skin conductance response across subjects and the best-fit associability trace for all trials without a shock.

As there were two experimental orders, time courses are displayed for each order separately (N = 54 participants). All traces were z-transformed and mean-centered for displaying purpose.

Supplementary Figure 4 Model comparison using maximum likelihood estimation (MLE) and the relationship of model fits to PTSD symptoms.

a. MLE analysis is consistent with the Bayesian analysis and a previous study. In line with a previous study,8 the hybrid model with the lowest Bayesian Information Criterion (BIC) was the hybrid (α + V) model, outperforming the other hybrid models as well as the Rescorla–Wagner (RW) model. The asterisk indicates the winning model. b. No evidence that model fits interacted with the symptom levels of PTSD. The correlation between model fits and symptoms was essentially flat for each of the 4 models. Error shadings correspond to standard errors. PTSD, posttraumatic stress disorder. CAPS, Clinician-Administered PTSD Scale; RW, Rescorla–Wagner model.

Supplementary Figure 5 Hierarchical Bayesian model recovers simulated learning parameters of a Rescorla–Wagner model.

To validate our Hierarchical Bayesian model before testing it in the clinical dataset, we simulated skin conductance data in 30 participants. We estimated the learning rate α (the free parameter in the Rescorla–Wagner model) using our Hierarchical Bayesian model coded in Stan and also compared it to a Maximum Likelihood Estimation (MLE) using the non-linear optimizer fmincon in MATLAB. The Bayesian model successfully recovered the simulated values.

Supplementary Figure 6 Average skin conductance response across subjects and the best-fit expected value trace for all trials without a shock using a Rescorla-Wagner model.

As there were two experimental orders, time courses are displayed for each order separately (N = 54 participants). All traces were z-transformed and mean-centered for displaying purpose.

Supplementary Figure 7 Amygdala structure and value computation contribute to PTSD symptoms using a Rescorla–Wagner model.

a. Regions of interest used in the computational imaging analysis. The amygdala (red) was defined functionally, using the contrast of conditioned stimulus vs. baseline. b-d. Amygdala volume and value-dependent neural activity independently contribute to PTSD symptoms. Partial correlations are shown (N = 54 participants) with Pearson correlation coefficients and two-tailed significance tests. Right amygdala volume and activity as well as left amygdala activity correlated negatively with the PTSD symptoms as measured with the CAPS. Regressions were adjusted for learning rate, age, gender, head movement, and total intracranial volume. Error shadings correspond to standard errors. CAPS, Clinician-Administered PTSD Scale; adj., adjusted; **, P < 0.01; *, P < 0.05.

Supplementary Figure 8 Individual amygdala regions of interest for two illustrative participants.

To ensure accurate appearance in the published version, please use the Symbol font for all symbols and Greek letters.Brain slices are shown at x = 24, y = −4, z = −16 in the Montreal Neurological Institute (MNI) coordinate system. using the neurological convention (that is, left is left).

Supplementary Figure 9 Regression of prediction error (δ) and associability (α) shows that the two learning components were not significantly correlated.

Data for both experimental orders (A and B) is shown (N = 69 trials) together with Pearson correlation coefficients and two-tailed significance tests. Error shadings correspond to standard errors.

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Supplementary Figures 1–9 and Supplementary Table 1

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Homan, P., Levy, I., Feltham, E. et al. Neural computations of threat in the aftermath of combat trauma. Nat Neurosci 22, 470–476 (2019). https://doi.org/10.1038/s41593-018-0315-x

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