Evidence map›Paper›PMID 31491813›Full record

ArticleNeuroImage. Clinical2019

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT).

Olga Tymofiyeva, Justin P Yuan, Chiung-Yu Huang, Colm G Connolly, Eva Henje Blom, Duan Xu, Tony T Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  15. A lateralized model of the pain-depression dyad.Neuroscience and biobehavioral reviews · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Olga TymofiyevaDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA. Electronic address: Olga.Tymofiyeva@ucsf.edu.
Justin P YuanDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA.
Chiung-Yu HuangDepartment of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA 94143, USA.
Colm G ConnollyDepartment of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Biomedical Sciences, Florida State University College of Medicine, 1115 West Call Street, Tallahassee, FL 32306, USA.
Eva Henje BlomDepartment of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Clinical Science/Child- and Adolescent Psychiatry, Umeå University, SE-901 87 Umeå, Sweden.
Duan XuDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA.
Tony T YangDepartment of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA.

Funding

Repair in High Risk Newborns with Congential Heart DiseaseP01NS082330 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI XU, DUAN · 2014 to 2018
$6.0M
Towards Baby Brain Connectome: a Study of Newborn Brain NetworksR01HD072074 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HESS, CHRISTOPHER PAUL, XU, DUAN · 2013 to 2017
$3.0M
Neural mechanisms of meditation training in healthy and depressed adolescents: An MRI connectome studyR61AT009864 · NCCIH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TYMOFIYEVA, OLGA, YANG, TONY TUNG-I · 2019 to 2020
$822k
A Network Approach to Study Brain Plasticity in Children with Cognitive TrainingR21AT009173 · NCCIH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TYMOFIYEVA, OLGA · 2016 to 2017
$436k
NCCIH NIH HHS R21 AT009173NCCIH NIH HHS R61 AT009864NICHD NIH HHS R01 HD072074NINDS NIH HHS P01 NS082330
6 · The paper itself

Abstract

purposeAdolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. "Who will improve with CBT?" is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT.

methodsWe applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation.

resultsThe classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus.

conclusionsOur results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression.

Indexed as

Cognitive Behavioral TherapyConnectomeDiffusion Tensor ImagingMachine LearningAdolescentBrainFemaleHumansMajor Depressive DisorderMaleOutcome Assessment, Health CareAdolescent depressionBrain networkCBTConnectomicsDiffusion MRIMachine learning

Identifiers

PMID31491813
PMCPMC6627980

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.