Predicting response to the antidepressant bupropion using pretreatment fMRI

Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher, Cooper Mellema, Madhukar H. Trivedi, Albert Montillo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Major depressive disorder is a primary cause of disability in adults with a lifetime prevalence of 6–21% worldwide. While medical treatment may provide symptomatic relief, response to any given antidepressant is unpredictable and patient-specific. The standard of care requires a patient to sequentially test different antidepressants for 3 months each until an optimal treatment has been identified. For 30–40% of patients, no effective treatment is found after more than one year of this trial-and-error process, during which a patient may suffer loss of employment or marriage, undertreated symptoms, and suicidal ideation. This work develops a predictive model that may be used to expedite the treatment selection process by identifying for individual patients whether the patient will respond favorably to bupropion, a widely prescribed antidepressant, using only pretreatment imaging data. This is the first model to do so for individuals for bupropion. Specifically, a deep learning predictor is trained to estimate the 8-week change in Hamilton Rating Scale for Depression (HAMD) score from pretreatment task-based functional magnetic resonance imaging (fMRI) obtained in a randomized controlled antidepressant trial. An unbiased neural architecture search is conducted over 800 distinct model architecture and brain parcellation combinations, and patterns of model hyperparameters yielding the highest prediction accuracy are revealed. The winning model identifies bupropion-treated subjects who will experience remission with the number of subjects needed-to-treat (NNT) to lower morbidity of only 3.2 subjects. It attains a substantially high neuroimaging study effect size explaining 26% of the variance ($$R^2 = 0.26$$ ) and the model predicts post-treatment change in the 52-point HAMD score with an RMSE of 4.71. These results support the continued development of fMRI and deep learning-based predictors of response for additional depression treatments.

Original languageEnglish (US)
Title of host publicationPredictive Intelligence in Medicine - 2nd International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsIslem Rekik, Ehsan Adeli, Sang Hyun Park
PublisherSpringer
Pages53-62
Number of pages10
ISBN (Print)9783030322809
DOIs
StatePublished - Jan 1 2019
Event2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11843 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/13/19

Keywords

  • Deep learning
  • Depression
  • fMRI
  • Neural architecture search
  • Neuroimaging
  • Treatment response

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Predicting response to the antidepressant bupropion using pretreatment fMRI'. Together they form a unique fingerprint.

  • Cite this

    Nguyen, K. P., Fatt, C. C., Treacher, A., Mellema, C., Trivedi, M. H., & Montillo, A. (2019). Predicting response to the antidepressant bupropion using pretreatment fMRI. In I. Rekik, E. Adeli, & S. H. Park (Eds.), Predictive Intelligence in Medicine - 2nd International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 53-62). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11843 LNCS). Springer. https://doi.org/10.1007/978-3-030-32281-6_6