OPEN Foundation

J. Stroud

Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression

Abstract

BACKGROUND:
Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not.
METHODS:
A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response.
RESULTS:
Speech analytics and machine learning successfully differentiated depressed patients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision).
CONCLUSIONS:
Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity.
LIMITATIONS:
The sample size was small and replication is required to strengthen inferences on these results.
Carrillo, F., Sigman, M., Slezak, D. F., Ashton, P., Fitzgerald, L., Stroud, J., … & Carhart-Harris, R. L. (2018). Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. Journal of affective disorders230, 84-86. 10.1016/j.jad.2018.01.006
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Psilocybin with psychological support improves emotional face recognition in treatment-resistant depression

Abstract

Rationale

Depressed patients robustly exhibit affective biases in emotional processing which are altered by SSRIs and predict clinical outcome.

Objectives

The objective of this study is to investigate whether psilocybin, recently shown to rapidly improve mood in treatment-resistant depression (TRD), alters patients’ emotional processing biases.

Methods

Seventeen patients with treatment-resistant depression completed a dynamic emotional face recognition task at baseline and 1 month later after two doses of psilocybin with psychological support. Sixteen controls completed the emotional recognition task over the same time frame but did not receive psilocybin.

Results

We found evidence for a group × time interaction on speed of emotion recognition (p = .035). At baseline, patients were slower at recognising facial emotions compared with controls (p < .001). After psilocybin, this difference was remediated (p = .208). Emotion recognition was faster at follow-up compared with baseline in patients (p = .004, d = .876) but not controls (p = .263, d = .302). In patients, this change was significantly correlated with a reduction in anhedonia over the same time period (r = .640, p = .010).

Conclusions

Psilocybin with psychological support appears to improve processing of emotional faces in treatment-resistant depression, and this correlates with reduced anhedonia. Placebo-controlled studies are warranted to follow up these preliminary findings.

Stroud, J. B., Freeman, T. P., Leech, R., Hindocha, C., Lawn, W., Nutt, D. J., … & Carhart-Harris, R. L. (2017). Psilocybin with psychological support improves emotional face recognition in treatment-resistant depression. Psychopharmacology, 1-8. 10.1007/s00213-017-4754-y
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