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Neuroimaging Biomarker of Major Depressive Disorder

Published online by Cambridge University Press:  23 March 2020

N. Ichikawa ,
Y. Okamoto ,
G. Okada ,
G. Lisi ,
N. Yahata ,
J. Morimoto ,
M. Kawato ,
K. Matsuo ,
H. Yamagata  and
Y. Watanabe
...Show all authors
N. Ichikawa*
Affiliation:
Hiroshima University Graduate School of Biomedical & Health Sciences, Psychiatry and Neurosciences, Hiroshima, Japan
Y. Okamoto
Affiliation:
Hiroshima University Graduate School of Biomedical & Health Sciences, Psychiatry and Neurosciences, Hiroshima, Japan
G. Okada
Affiliation:
Hiroshima University Graduate School of Biomedical & Health Sciences, Psychiatry and Neurosciences, Hiroshima, Japan
G. Lisi
Affiliation:
ATR Computational Neuroscience Labs, Department of Brain Robot Interface, Kyoto, Japan
N. Yahata
Affiliation:
National Institute of Radiological Sciences, Molecular Imaging Center, Chiba, Japan
J. Morimoto
Affiliation:
ATR Computational Neuroscience Labs, Department of Brain Robot Interface, Kyoto, Japan
M. Kawato
Affiliation:
ATR, Brain Information Communication research Laboratory Group, Kyoto, Japan
K. Matsuo
Affiliation:
Yamaguchi University Graduate School of Medicine, Division of Neuropsychiatry, Department of Neuroscience, Ube, Japan
H. Yamagata
Affiliation:
Yamaguchi University Graduate School of Medicine, Division of Neuropsychiatry, Department of Neuroscience, Ube, Japan
Y. Watanabe
Affiliation:
Yamaguchi University Graduate School of Medicine, Division of Neuropsychiatry, Department of Neuroscience, Ube, Japan
S. Yamawaki
Affiliation:
Hiroshima University Graduate School of Biomedical & Health Sciences, Psychiatry and Neurosciences, Hiroshima, Japan
*
* Corresponding author.

Abstract

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Introduction

Recent studies have shown that it is important to understand the brain mechanism specifically by focusing on the common and unique functional connectivity in each disorder including depression.

Objectives

To specify the biomarker of major depressive disorder (MDD), we applied the sparse machine learning algorithm to classify several types of affective disorders using the resting state fMRI data collected in multiple sites, and this study shows the results of depression as a part of those results.

Aims

The aim of this study is to understand some specific pattern of functional connectivity in MDD, which would support diagnosis of depression and development of focused and personalized treatments in the future.

Methods

The neuroimaging data from patients with major depressive disorder (MDD, n = 100) and healthy control adults (HC: n = 100) from multiple sites were used for the training dataset. A completely separate dataset (n = 16) was kept aside for testing. After all preprocessing of fMRI data, based on one hundred and forty anatomical region of interests (ROIs), 9730 functional connectivities during resting states were prepared as the input of the sparse machine-learning algorithm.

Results

As results, 20 functional connectivities were selected with the classification performance of Accuracy: 83.0% (Sensitivity: 81.0%, Specificity: 85.0%). The test data, which was completely separate from the training data, showed the performance accuracy of 83.3%.

Conclusions

The selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Type
EV826
Information
European Psychiatry , Volume 33 , Issue S1: Abstracts of the 24th European Congress of Psychiatry , March 2016 , pp. S492 - S493
Copyright
Copyright © European Psychiatric Association 2016
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