Machine learning for psychiatry: getting doctors at the black box?

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Abstract

Recent developments in the field of machine learning have spurred high hopes for diagnostic support for psychiatric patients based on brain MRI. But while technical advances are undoubtedly remarkable, the current trajectory of mostly proof-of-concept studies performed on retrospective, often repository-derived data, may not be well suited to yield a substantial impact in clinical practice. Here we review these developments and challenges, arguing for the need of stronger involvement of and input from medical doctors in order to pave the way for machine learning in clinical psychiatry.

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Eric Topol (2019)

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

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How the timing and quality of early experiences influence the development of brain architecture.

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Early life events can exert a powerful influence on both the pattern of brain architecture and behavioral development. In this study a conceptual framework is provided for considering how the structure of early experience gets "under the skin." The study begins with a description of the genetic framework that lays the foundation for brain development, and then proceeds to the ways experience interacts with and modifies the structures and functions of the developing brain. Much of the attention is focused on early experience and sensitive periods, although it is made clear that later experience also plays an important role in maintaining and elaborating this early wiring diagram, which is critical to establishing a solid footing for development beyond the early years.

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Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Mohammad Arbabshirani, Sergey M. Plis, Jing Sui … (2017)

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

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Author and article information

Contributors

Simon B. Eickhoff: s.eickhoff@fz-juelich.de

Journal

Journal ID (nlm-ta): Mol Psychiatry

Journal ID (iso-abbrev): Mol Psychiatry

Title: Molecular Psychiatry

Publisher: Nature Publishing Group UK (London )

ISSN (Print): 1359-4184

ISSN (Electronic): 1476-5578

Publication date (Electronic): 10 November 2020

Publication date PMC-release: 10 November 2020

Publication date (Print): 2021

Volume: 26

Issue: 1

Pages: 23-25

Affiliations

[1 ]GRID grid.6936.a, ISNI 0000000123222966, Department of Neuroradiology, Klinikum rechts der Isar, , Technical University of Munich, School of Medicine, ; Munich, Germany

[2 ]GRID grid.411327.2, ISNI 0000 0001 2176 9917, Institute of Systems Neuroscience, Medical Faculty, , Heinrich Heine University Düsseldorf, ; Düsseldorf, Germany

[3 ]GRID grid.8385.6, ISNI 0000 0001 2297 375X, Institute of Neuroscience and Medicine, , Brain & Behaviour (INM-7), Research Centre Jülich, ; Jülich, Germany

Author information

Dennis M. Hedderich http://orcid.org/0000-0001-8994-5593

Article

Publisher ID: 931

DOI: 10.1038/s41380-020-00931-z

PMC ID: 7815505

PubMed ID: 33173196

SO-VID: 4ea1a653-2854-4b60-b10d-9f14f00f1ad0

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 7 May 2020

Date revision received : 14 September 2020

Date accepted : 22 October 2020

Funding

Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);

Award ID: EI 816/21-1

Award Recipient : Simon B. Eickhoff

Funded by: FundRef https://doi.org/10.13039/501100000780, European Commission (EC);

Award ID: 945539

Award Recipient : Simon B. Eickhoff

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ScienceOpen disciplines: Molecular medicine

Keywords: diagnostic markers,neuroscience

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ScienceOpen disciplines: Molecular medicine

Keywords: diagnostic markers, neuroscience

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Machine learning for psychiatry: getting doctors at the black box?

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Most cited references 5

High-performance medicine: the convergence of human and artificial intelligence

How the timing and quality of early experiences influence the development of brain architecture.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

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Cited by 9

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