ArticleComputational and mathematical methods in medicine2017
Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning.Brain and behavior · 2025Article
- Network Analysis of Depression Using Magnetoencephalogram Based on Polynomial Kernel Granger Causality.Entropy (Basel, Switzerland) · 2023Article
- Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders includingNeurobiology of stress · 2023Article
- Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.Molecular psychiatry · 2023Article
- Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.Medical image analysis · 2023Article
- Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression.Brain imaging and behavior · 2022Article
- Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review).Sovremennye tekhnologii v meditsine · 2022Review
- Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome.Diagnostics (Basel, Switzerland) · 2021Review
- Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies.Frontiers of medicine · 2021Review
- Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.Human brain mapping · 2020Article
- Distinct topological properties of cue-evoked attention processing network in persisters and remitters of childhood ADHD.Cortex; a journal devoted to the study of the nervous system and behavior · 2018Article
- Machine learning in major depression: From classification to treatment outcome prediction.CNS neuroscience & therapeutics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.