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The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression

Chapter
First Online:
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1305)

Summary

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.

Keywords

Machine learning Neuroimaging Depression MRI 
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© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Department of PsychiatryGachon University College of Medicine, Gil Medical CenterIncheonRepublic of Korea
  2. 2.Department of PsychiatryKorea University Ansan Hospital, College of MedicineAnsanRepublic of Korea

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