Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study

Schmaal, Lianne; Marquand, André F.; Rhebergen, Didi; Tol, Marie-José van; Ruhé, Henricus G.; Wee, Nic J.A. van der; Veltman, Dick J.; Penninx, Brenda W. J. H.

doi:10.1016/j.biopsych.2014.11.018

Cited by 93 publications

(72 citation statements)

References 41 publications

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“…This has shown some promising recent results, indicating that it may become possible to predict individual trajectories of patients with schizophrenia (Anticevic et al, 2015) or mood disorders (Lythe et al, 2015;Schmaal et al, 2015) from neuroimaging data, or forecast individual treatment responses to psychotherapy (Mansson et al, 2015), antidepressants (DeBattista et al, 2011;McGrath et al, 2013;Miller et al, 2013) and antipsychotics (Hadley et al, 2014;Nejad et al, 2013).…”

Section: Accepted Manuscriptmentioning

confidence: 97%

Computational neuroimaging strategies for single patient predictions

et al. 2017

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Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

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Section: Accepted Manuscriptmentioning

confidence: 97%

Computational neuroimaging strategies for single patient predictions

et al. 2017

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“…Using an ensemble feature selection strategy and an advanced support vector machine approach, Sui et al(98) combined resting-state fMRI, EEG and sMRI data to classify schizophrenia from healthy controls and achieved the best performance with 91% accuracy compared to using a single modality. By adopting Gaussian process classifiers to evaluate the prognostic value of neuroimaging data and clinical characteristics, Schmaal et al(99) discovered that prediction of the naturalistic course of depression over 2 years is improved by considering different task contrasts or data sources, especially those derived from neural responses to emotional facial expressions. Finally, Pettersson-Yeo et al(100) used a multimodal SVM approach to examine the ability of sMRI, fMRI, dMRI and cognitive data to differentiate between ultra-high-risk (UHR) and first-episode (FEP) psychosis at the single-subject level, supporting clinical development of SVM to help inform identification of FEP and UHR.…”

Section: Emerging Approachesmentioning

confidence: 99%

Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness

Calhoun

Sui

2016

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

307

283

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It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.

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“…For example, in order to identify three course trajectories of MDD (including chronic, gradual improving, and fast remission patients), Schmaal et al [33] investigated a machine learning approach that employs the Gaussian process algorithm as a classifier to integrate functional and structural magnetic resonance imaging (MRI) data. Gaussian process classifiers, which are similar to SVMs, are a form of multivariate pattern recognition methods providing the support of predictive probabilities for class membership [33]. Their study used Gaussian process classifiers to assess three course trajectories of MDD by utilizing prognostic values of clinical datasets (including duration, comorbidity, and baseline severity) as well as MRI datasets [33].…”

Section: Applications In Prognosis Predictionmentioning

confidence: 99%

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Lin

Lane

2020

IJMS

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A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

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Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study

Cited by 93 publications

References 41 publications

Computational neuroimaging strategies for single patient predictions

Computational neuroimaging strategies for single patient predictions

Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

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