Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications

By Sandra Vieira, Walter Hugo Lopez Pinaya, Andrea Mechelli. 2017

574 citations recorded by the source corpus.

HighlightsOverview of deep learning basic concepts: architecture, learning and testing.Literature review of deep learning in neuroimaging studies of brain‐based disorders.Discussion about future research and challenges of deep learning in neuroimaging. ABSTRACT Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. Given its ability to detect abstract and complex patterns, DL has been applied in neuroimaging studies of psychiatric and neurological disorders, which are characterised by subtle and diffuse alterations. Here we introduce the underlying concepts of DL and review studies that have used this approach to classify brain‐based disorders. The results of these studies indicate that DL could be a powerful tool in the current search for biomarkers of psychiatric and neurologic disease. We conclude our review by discussing the main promises and challenges of using DL to elucidate brain‐based disorders, as well as possible directions for future research.

Subfields: Cognitive Neuroscience

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review (2017, result): “…we aimed at encouraging further studies, especially for comparing and integrating this algorithm with other machine learning approaches, such as, the deep learning, which recently showed its potentiality in the investigation of neuroimaging correlates (Shen et al., 2017; Vieira et al., 2017).”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, methodology): “The International Society for Bipolar Disorders Big Data Task Force (17) AI is well-suited to handle “big data,” granular, or digitized data (28). a The potential of a subset of ML, deep learning (DL), in psychiatry is just beginning to be realized [see (29)]. r DL has been used to support diagnosti”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “Several critical issues challenge the using of deep learning in classification (Schmidhuber, 2015; Vieira et al., 2017).”
Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, background): “Furthermore, deep learning is capable of learningsubtle hiddenpatterns from high dimensionalneuroimaging data, perhaps providing cues for understanding the neural basis of neuropsychiatric disorders (Arbabshirani et al., 2017; Guo et al., 2017; Vieira et al., 2017).”
MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. (2020, background): “The emergence of deep learning as a powerful machine learning method offers great promise for transcending these limitations (Vieira et al., 2017).”