Altered White Matter Connectivity Within and Between Networks in Antipsychotic-Naive First-Episode Schizophrenia

Li, Fēi; Lui, Su; Yao, Li; Ji, Gong‐Jun; Liao, Wei; Sweeney, John A.; Gong, Qiyong

doi:10.1093/schbul/sbx048

Cited by 36 publications

(25 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…White-matter alterations that occur in the early stages of psychosis may parallel the course of the illness and differentiate the clinical subtypes 9,10,63 . In this cross-sectional work, we investigated the white-matter connectivity of early-psychosis patients (EPPs) in different clinical stages after the first psychotic episode and in comparison with healthy controls (HCs).…”

Section: Discussionmentioning

confidence: 99%

Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging

et al. 2019

View full text Add to dashboard Cite

Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters (fractional anisotropy, apparent diffusion coefficient) were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode (stages IIIa, IIIb, IIIc) than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting (stages IIIb, IIIc) and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model.

show abstract

Section: Discussionmentioning

confidence: 99%

Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging

et al. 2019

View full text Add to dashboard Cite

show abstract

“…g) Participation coefficient:: One might also wish to measure how the connections emanating from a node are spread among nodes in the different communities, a notion that is formalized in the participation coefficient [103]: Pi=1−∑k=1Kfalse(SiCkSifalse)2,where S iC k is the strength of edges of node i to nodes in community C k . This statistic has been used to better understand how learning is impacted by patterns of intermodular connectivity [193], how brain function is altered in antipsychotic-naive first-episode schizophrenia patients [131], and how structural modular segregation mediates the relationship between age and executive function in youth [24].…”

Section: Summarizing Community Structure In Brain Graphsmentioning

confidence: 99%

Applications of Community Detection Techniques to Brain Graphs: Algorithmic Considerations and Implications for Neural Function

et al. 2018

View full text Add to dashboard Cite

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

show abstract

“…Research on subclinical negative symptoms is limited, let alone in association with DWI network alterations. Two studies in individuals at high-risk for psychosis reported a negative association between subclinical negative symptoms and rich-club organization (Schmidt et al 2017;Li et al 2018), while another study reported no associations between network connectivity and subclinical negative symptoms (Zhao et al 2017). In psychotic disorder, absence of an association between negative symptoms and network-based measures (Levitt et al 2017), as well as a negative association between network connectivity and negative symptoms has been described (Qifeng Wang et al 2012a).…”

Section: Associations Between Symptoms and Network Measures In Indivimentioning

confidence: 99%

“…Additionally, decreased local efficiency (defined as how well information circulates over the network (Latora and Marchiori 2003)) has been described in frontal, temporal, (para)-limbic and putamen regions (Q. F. Wang et al 2012b;Yan et al 2015). Findings on the clustering coefficient (CC; measure of local cohesiveness/interconnectedness) are inconsistent, showing increased (Zhang et al 2012), decreased (Li et al 2018) and unchanged CC (Yeo et al 2016). Furthermore, studies have reported negative associations between structural network efficiency on the one hand, and negative and positive symptoms in patients with psychotic disorder on the other (Skudlarski et al 2010;Qifeng Wang et al 2012a).…”

Section: Introductionmentioning

confidence: 99%

White matter microstructure and network-connectivity in emerging adults with subclinical psychotic experiences

Michielse

Lange

Bakker

et al. 2019

Brain Imaging and Behavior

View full text Add to dashboard Cite

Group comparisons of individuals with psychotic disorder and controls have shown alterations in white matter microstructure. Whether white matter microstructure and network connectivity is altered in adolescents with subclinical psychotic experiences (PE) at the lowest end of the psychosis severity spectrum is less clear. DWI scan were acquired in 48 individuals with PE and 43 healthy controls (HC). Traditional tensor-derived indices: Fractional Anisotropy, Axial Diffusivity, Mean Diffusivity and Radial Diffusivity, as well as network connectivity measures (global/local efficiency and clustering coefficient) were compared between the groups. Subclinical psychopathology was assessed with the Community Assessment of Psychic Experiences (CAPE) and Montgomery-Åsberg Depression Rating Scale (MADRS) questionnaires and, in order to capture momentary subclinical expression of psychosis, the Experience Sampling Method (ESM) questionnaires. Within the PE-group, interactions between subclinical (momentary) symptoms and brain regions in the model of tensor-derived indices and network connectivity measures were investigated in a hypothesis-generating fashion. Whole brain analyses showed no group differences in tensor-derived indices and network connectivity measures. In the PE-group, a higher positive symptom distress score was associated with both higher local efficiency and clustering coefficient in the right middle temporal pole. The findings indicate absence of microstructural white matter differences between emerging adults with subclinical PE and controls. In the PE-group, attenuated symptoms were positively associated with network efficiency/cohesion, which requires replication and may indicate network alterations in emerging mild psychopathology.

show abstract

Altered White Matter Connectivity Within and Between Networks in Antipsychotic-Naive First-Episode Schizophrenia

Cited by 36 publications

References 51 publications

Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging

Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging

Applications of Community Detection Techniques to Brain Graphs: Algorithmic Considerations and Implications for Neural Function

White matter microstructure and network-connectivity in emerging adults with subclinical psychotic experiences

Contact Info

Product

Resources

About