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Related Experiment Video

Updated: Feb 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting treatment response to antidepressant medication using early changes in emotional processing.

Michael Browning1, Jonathan Kingslake2, Colin T Dourish2

  • 1Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Trust, Warneford Hospital, Oxford, United Kingdom; P1vital Ltd, Manor House, Howbery Park, Wallingford, Oxfordshire, United Kingdom.

European Neuropsychopharmacology : the Journal of the European College of Neuropsychopharmacology
|November 27, 2018
PubMed
Summary

Predicting antidepressant response early is crucial. Changes in emotional processing within one week can accurately forecast treatment effectiveness, shortening delays in finding the right medication for depression.

Keywords:
AntidepressantDepressionEmotional biasMachine learningPredictionTreatment

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Clinical Psychology

Background:

  • Antidepressant treatment response is typically delayed, often requiring weeks to assess efficacy.
  • Many patients do not respond to the initial antidepressant medication prescribed, leading to prolonged treatment delays.
  • Antidepressants initiate changes in emotional stimulus processing early in treatment.

Purpose of the Study:

  • To determine if early changes in emotional processing and subjective symptoms predict antidepressant treatment response.
  • To develop a predictive test to shorten the time to effective antidepressant therapy for depressed patients.

Main Methods:

  • Seventy-four primary care patients with depression completed emotional bias and symptom measures before and one week after starting antidepressants.
  • Treatment response was assessed at 4-6 weeks.
  • Classifiers were validated using a leave-one-out procedure and tested on an independent sample of 239 patients.

Main Results:

  • A combination of facial emotion recognition and subjective symptoms predicted response with 77% accuracy in the training sample.
  • The predictive model achieved 60% accuracy in an independent study sample, outperforming baseline response rates.
  • The facial emotion recognition task demonstrated high acceptability and data quality.

Conclusions:

  • Changes in emotional processing within the first week of treatment serve as a sensitive early indicator of antidepressant efficacy.
  • Early treatment-induced alterations in emotional processing can guide antidepressant therapy selection.
  • This approach has the potential to reduce the time depressed patients need to achieve mental health recovery.