Studying depression using imaging and machine learning methods

Patel, Meenal J.; Khalaf, Alexander; Aizenstein, Howard J.

doi:10.1016/j.nicl.2015.11.003

Cited by 155 publications

(75 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter two techniques can be considered as variants of k‐fold CV. Holdout approach is performed on data with large sample size ( k = 1), while leave‐one‐out is used on data with small sample size ( k = sample size) …”

Section: Research Overviewmentioning

confidence: 99%

Machine learning in major depression: From classification to treatment outcome prediction

2018

View full text Add to dashboard Cite

show abstract

Section: Research Overviewmentioning

confidence: 99%

Machine learning in major depression: From classification to treatment outcome prediction

2018

View full text Add to dashboard Cite

show abstract

“…Features extracted from handwriting have shown to indicate anxiety and stress . Neuroimaging data have also been provided many promising results, but this article focuses on non‐neuroimaging sensors such as voice. Finally, text obtained either from transcribed audio recordings, blogs, or social media has been used to detect many psychiatric disorders including psychotic, depressive, and anxiety disorders from morphological, syntactic, semantic, and discursive features (for reviews, see References ).…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

316

268

View full text Add to dashboard Cite

Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

show abstract

“…These approaches have utilized functional brain imaging (Craddock et al, 2009; Zeng et al, 2012) and structural brain images (Ardekani et al, 2011). This approach is starting to be applied to MDD (for review see (Patel et al, 2016)).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Schnyer

Clasen

Gonzalez

et al. 2017

Psychiatry Research: Neuroimaging

View full text Add to dashboard Cite

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n = 25) and healthy controls (n = 25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

show abstract

Studying depression using imaging and machine learning methods

Cited by 155 publications

References 49 publications

Machine learning in major depression: From classification to treatment outcome prediction

Machine learning in major depression: From classification to treatment outcome prediction

Automated assessment of psychiatric disorders using speech: A systematic review

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Contact Info

Product

Resources

About