ArticlePloS one2012
Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 4 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 4 syntheses or guidelines pooled it, 98 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Insula activity in resting-state differentiates bipolar from unipolar depression: a systematic review and meta-analysis.Scientific reports · 2021Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies.Biological psychiatry · 2017Pooled it
- Group differences in MEG-ICA derived resting state networks: Application to major depressive disorder.NeuroImage · 2015Trial
- A pilot study of inter-regional phase amplitude coupling as comorbid depression biomarker in temporal lobe epilepsy.Epilepsy & behavior : E&B · 2025Article
- Brain multi-contrast, multi-atlas segmentation of diffusion tensor imaging and ensemble learning automatically diagnose late-life depression.Scientific reports · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Vortioxetine Modulates the Regional Signal in First-Episode Drug-Free Major Depressive Disorder at Rest.Frontiers in psychiatry · 2022Article
- The implication of a diversity of non-neuronal cells in disorders affecting brain networks.Frontiers in cellular neuroscience · 2022Review
- Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome.Diagnostics (Basel, Switzerland) · 2021Review
- Age-Related Hearing Loss and the Development of Cognitive Impairment and Late-Life Depression: A Scoping Overview.Seminars in hearing · 2021Review
- Estimating Brain Connectivity With Diffusion-Weighted Magnetic Resonance Imaging: Promise and Peril.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Review
- Treatment-naïve first episode depression classification based on high-order brain functional network.Journal of affective disorders · 2019Article
- Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2019Article
- Machine learning in major depression: From classification to treatment outcome prediction.CNS neuroscience & therapeutics · 2018Review
- Current Clinical Applications of Diffusion-Tensor Imaging in Neurological Disorders.Journal of clinical neurology (Seoul, Korea) · 2018Review
- Neuronal connectivity in major depressive disorder: a systematic review.Neuropsychiatric disease and treatment · 2018Review
- Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women.Psychosomatic medicine · 2017Article
- Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.Psychiatry research. Neuroimaging · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients=86.4%, controls=96.2%; permutation test, p<0.0001) of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.
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