ArticleHuman brain mapping2016
Individualized covariance profile of cortical morphology for auditory hallucinations in first-episode psychosis.
Article in Human brain mapping, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Structural covariance networks in schizophrenia: A systematic review Part II.Schizophrenia research · 2022Pooled it
- Individualized cortical gradient and network topology reveal symptom-linked disruptions and neurobiological subtypes in schizophrenia.medRxiv : the preprint server for health sciences · 2026Article
- Reduced Whole-Brain Network Segregation and Dorsal Anterior-Cingulate Cortex Neurochemical Alterations in Chronic Smokers.Brain and behavior · 2026Article
- Reassessing Asymmetry Reduction in Psychosis: Cingulate Folding and Gyrification Covariance in Patients with Auditory Hallucinations.Schizophrenia bulletin · 2026Article
- Abnormalities in cerebellar subregions' volume and cerebellocerebral structural covariance in autism spectrum disorder.Autism research : official journal of the International Society for Autism Research · 2025Article
- Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence.Brain imaging and behavior · 2024Article
- Atypical characteristic changes of surface morphology and structural covariance network in developmental dyslexia.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Exploring Alzheimer's disease: a comprehensive brain connectome-based survey.Psychoradiology · 2024Review
- Altered structural brain networks in linguistic variants of frontotemporal dementia.Brain imaging and behavior · 2022Article
- Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium.Molecular psychiatry · 2022Article
- Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia.Brain sciences · 2021Article
- Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder.Journal of clinical medicine · 2020Article
- Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.Brain : a journal of neurology · 2020Article
- Meta-analytic Evidence for the Plurality of Mechanisms in Transdiagnostic Structural MRI Studies of Hallucination Status.EClinicalMedicine · 2019Article
- Hubness of strategic planning and sociality influences depressive mood and anxiety in College Population.Scientific reports · 2017Article
- Autonomic Regulation and Auditory Hallucinations in Individuals With Schizophrenia: An Experience Sampling Study.Schizophrenia bulletin · 2017Article
- Intra-individual structural covariance network in patients with chronic neck and shoulder pain: a longitudinal brain structure analysis.Frontiers in neurologyArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neocortical phenotype of cortical surface area (CSA) and thickness (CT) are influenced by distinctive genetic factors and undergo differential developmental trajectories, which could be captured using the individualized cortical structural covariance (ISC). Disturbed patterns of neocortical development and maturation underlie the perceptual disturbance of psychosis including auditory hallucination (AH). To demonstrate the utility of selected ISC features as primal biomarker of AH in first-episode psychosis (FEP) subjects experiencing AH (FEP-AH), we employed herein a support vector machine (SVM). A total of 147 subjects (FEP-AH, n = 27; FEP-NAH, n = 24; HC, n = 96) underwent T1 -weighted magnetic resonance imaging at 3T. The FreeSurfer software suite was used for cortical parcellation, with the CSA-ISC and CT-ISC then calculated. The most informative ISCs showing statistical significance (P < 0.001) across every run of leave-one-out group-comparison were aligned according to the absolute value of averaged t-statistics and were packaged into candidate feature sets for classification analysis using the SVM. An optimal feature set comprising three CSA-ISCs, including the intraparietal sulcus, Broca's complex, and the anterior insula, distinguished FEP-AH from FEP-NAH subjects with 83.6% accuracy (sensitivity = 82.8%; specificity = 85.7%). Furthermore, six CT-ISCs encompassing the executive control network and Wernicke's module classified FEP-AH from FEP-NAH subjects with 82.3% accuracy (sensitivity = 79.5%; specificity = 88.6%). Finally, extended sets of ISCs related to the default-mode network distinguished FEP-AH or FEP-NAH from HC subjects with 89.0-93.0% accuracy (sensitivity = 88.4-93.4%; specificity = 89.0-94.1%). This study established a distinctive intermediate phenotype of biological proneness for AH in FEP using CSA-ISCs as well as a state marker of disease progression using CT-ISCs.
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