ArticleNeuroImage. Clinical2015
Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings.
Article in NeuroImage. Clinical, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Putative Mechanisms of Electroconvulsive Therapy in Treatment-Resistant Schizophrenia Examined Using Magnetic Resonance Imaging.Biological psychiatry global open science · 2025Article
- Changes of soil carbon along precipitation gradients in three typical vegetation types in the Alxa desert region, China.Carbon balance and management · 2024Article
- Functional Connectivity Biomarkers in Schizophrenia.Advances in neurobiology · 2024Review
- A preliminary study to investigate resting state fMRI as a potential group differentiator for schizophrenia.Asian journal of psychiatry · 2020Article
- Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2020Article
- Treatment-Resistant Schizophrenia: Genetic and Neuroimaging Correlates.Frontiers in pharmacology · 2019Review
- Integrative computational evaluation of genetic markers for Alzheimer's disease.Saudi journal of biological sciences · 2018Article
- Attenuated resting-state functional connectivity in patients with childhood- and adult-onset schizophrenia.Schizophrenia research · 2018Article
- Integrative computational approach to evaluate risk genes for postmenopausal osteoporosis.IET systems biology · 2018Article
- Distinguishing Between Treatment-Resistant and Non-Treatment-Resistant Schizophrenia Using Regional Homogeneity.Frontiers in psychiatry · 2018Article
- [Degree centrality of the functional network in schizophrenia patients].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2017Article
- An Integrative Computational Approach to Evaluate Genetic Markers for Chronic Lymphocytic Leukemia.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder.Scientific reports · 2017Article
- Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data.NeuroImage · 2017Article
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Authors and funding
16 authors.
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Abstract
unlabelledAmong individuals diagnosed with schizophrenia, approximately 20%-33% are recognized as treatment-resistant schizophrenia (TRS) patients. These TRS patients suffer more severely from the disease but struggle to benefit from existing antipsychotic treatments. A few recent studies suggested that schizophrenia may be caused by impaired synaptic plasticity that manifests as functional dysconnectivity in the brain, however, few of those studies focused on the functional connectivity changes in the brains of TRS groups. In this study, we compared the whole brain connectivity variations in TRS patients, their unaffected siblings, and healthy controls. Connectivity network features between and within the 116 automated anatomical labeling (AAL) brain regions were calculated and compared using maps created with three contrasts: patient vs. control, patient vs. sibling, and sibling vs. CONTROL: To evaluate the predictive power of the selected features, we performed a multivariate classification approach. We also evaluated the influence of six important clinical measures (e.g. age, education level) on the connectivity features. This study identified abnormal significant connectivity changes of three patterns in TRS patients and their unaffected siblings: 1) 69 patient-specific connectivity (PCN); 2) 102 shared connectivity (SCN); and 3) 457 unshared connectivity (UCN). While the first two patterns were widely reported by previous non-TRS specific studies, we were among the first to report widespread significant connectivity differences between TRS patient groups and their healthy sibling groups. Observations of this study may provide new insights for the understanding of the neurophysiological mechanisms of TRS.
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