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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.01246

Whole-brain exploratory analysis of functional task response following erythropoietin treatment in mood disorders: a supervised machine learning approach

  • 1Copenhagen Affective Disorder Research Center, Region Hovedstad Psychiatry, Denmark
  • 2Department of Psychology, Faculty of Social Sciences, University of Copenhagen, Denmark
  • 3Danish Research Centre for Magnetic Resonance (DRCMR), Denmark
  • 4Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
  • 5Department of Neurology, Bispebjerg Hospital, Denmark

A core symptom of mood disorders is cognitive impairment in attention, memory and executive functions. Erythropoietin (EPO) is a candidate treatment for cognitive impairment in unipolar and bipolar disorders (UD and BD) and modulates cognition-related neural activity across a fronto-temporo-parietal network. This report investigates predicting the pharmacological treatment from functional magnetic resonance imaging (fMRI) data using a supervised machine learning approach. A total of 84 patients with UD or BD were included in a randomized double-blind parallel-group study in which they received eight weekly infusions of either EPO (40 000 IU) or saline. Task fMRI data were collected before EPO/saline infusions started (baseline) and six weeks after last infusion (follow-up). During the scanning sessions, participants were given an n-back working memory and a picture encoding task. Linear classification models with different regularization techniques were used to predict treatment status from both cross-sectional data (at follow-up) and longitudinal data (difference between baseline and follow-up). For the n-back and picture encoding tasks, data were available and analysed for 52 (EPO; n = 28, Saline; n = 24) and 59 patients (EPO; n = 31, Saline; n = 28), respectively. We found limited evidence that the classifiers used could predict treatment status at a reliable level of performance (≤ 60 % accuracy) when tested using repeated cross-validation. There was no difference in using cross-sectional versus longitudinal data. Whole-brain multivariate decoding applied to pharmaco-fMRI in small to moderate samples seems to be suboptimal for exploring data driven neuronal treatment mechanisms.

Keywords: Erythropoeitin, Functional Magnet Resonance Imaging, machine learning, Mood disorder, cognitive dysfunction

Received: 20 Mar 2019; Accepted: 05 Nov 2019.

Copyright: © 2019 Nielsen, Madsen, Vinberg, Kessing, Siebner and Miskowiak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: PhD. Søren F. Nielsen, Copenhagen Affective Disorder Research Center, Region Hovedstad Psychiatry, Copenhagen, Denmark,