Structural Brain Network Disturbances in the Psychosis Spectrum

Dellen, Edwin van; Bohlken, Marc M.; Draaisma, Laurijn R.; Tewarie, Prejaas; Lutterveld, Remko van; Mandl, René C.W.; Stam, Cornelis J.; Sommer, Iris E.

doi:10.1093/schbul/sbv178

Cited by 34 publications

(33 citation statements)

References 45 publications

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“…Otte et al (2015) showed that MST characteristics are sensitive to aging effects, which was replicated in the present work. A recent study in patients with psychotic symptoms showed that MST analysis is a viable method to directly compare structural brain networks of different populations, and to capture psychosis‐related alterations in brain network topology (van Dellen et al, 2015). MST analysis has also proven useful in several empirical studies on functional brain networks, showing a less integrated network in patients with neurological and psychiatric disorders including schizophrenia, epilepsy, Parkinson's disease, and multiple sclerosis (Stam, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, the MST is a unique subset of connections provided that all connection weights are unique, which makes it possible to create an empirical reference network of the connections in a healthy human brain. Recent studies have shown that MST analysis can be used to capture network alterations due to aging and disease in both functional and structural imaging data (Boersma et al, 2010;Otte et al, 2015;van Dellen et al, 2014van Dellen et al, , 2015.…”

Section: ; Vanmentioning

confidence: 99%

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Minimum spanning tree analysis of the human connectome

Dellen

Sommer

Bohlken

et al. 2018

Human Brain Mapping

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One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion‐weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null‐model. The MST of individual subjects matched this reference MST for a mean 58%–88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so‐called rich club nodes (a subset of high‐degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical–subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models.

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Section: Discussionmentioning

confidence: 99%

Section: ; Vanmentioning

confidence: 99%

Minimum spanning tree analysis of the human connectome

Dellen

Sommer

Bohlken

et al. 2018

Human Brain Mapping

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“…Importantly, the IFN approach avoids another pitfall found in regression-based methods: reduced comparability between samples. Because the number of edges is kept constant and does not vary on the basis of sample size, network measures are less biased when attempting to compare between crosssectional or clinical and non-clinical samples (Tewarie et al, 2015;van Dellen et al, 2015). In the TMFG method, for example, the WSS-SF network, with 60 variables, will always have 174 edges (3n -6 edges).…”

Section: Information Filtering Networkmentioning

confidence: 99%

Network structure of the Wisconsin Schizotypy Scales–Short Forms: Examining psychometric network filtering approaches

et al. 2018

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Schizotypy is a multidimensional construct that provides a useful framework for understanding the etiology, development, and risk for schizophrenia-spectrum disorders. Past research has applied traditional methods, such as factor analysis, to uncovering common dimensions of schizotypy. In the present study, we aimed to advance the construct of schizotypy, measured by the Wisconsin Schizotypy Scales-Short Forms (WSS-SF), beyond this general scope by applying two different psychometric network filtering approaches-the state-of-the-art approach (lasso), which has been employed in previous studies, and an alternative approach (information-filtering networks; IFNs). First, we applied both filtering approaches to two large, independent samples of WSS-SF data (ns = 5,831 and 2,171) and assessed each approach's representation of the WSS-SF's schizotypy construct. Both filtering approaches produced results similar to those from traditional methods, with the IFN approach producing results more consistent with previous theoretical interpretations of schizotypy. Then we evaluated how well both filtering approaches reproduced the global and local network characteristics of the two samples. We found that the IFN approach produced more consistent results for both global and local network characteristics. Finally, we sought to evaluate the predictability of the network centrality measures for each filtering approach, by determining the core, intermediate, and peripheral items on the WSS-SF and using them to predict interview reports of schizophrenia-spectrum symptoms. We found some similarities and differences in their effectiveness, with the IFN approach's network structure providing better overall predictive distinctions. We discuss the implications of our findings for schizotypy and for psychometric network analysis more generally.

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“…Psychotic symptoms are within the general population, 92,95,101,108,181,204,205 on a continuum, 204,205 and risk factors for developing psychotic disorders. 95,101 Psychotic symptoms are also symptoms of psychopathological states such as psychosis and psychotic disorders, 98,105,[111][112][113]115,127,138,145,146,149,154,156,162,164,167,169,174,177,179,184,188,189,[191][192][193][196][197][198] dementia, 103,107,116,117,125,135,137,139,140,…”

Section: Common Terminologymentioning

confidence: 99%

Terminology and assessment tools of psychosis: A systematic narrative review

Seiler

Nguyen

Yung

et al. 2020

Psychiatry Clin Neurosci

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Aim Phenomena within the psychosis continuum that varies in frequency/duration/intensity have been increasingly identified. Different terms describe these phenomena, however there is no standardization within the terminology. This review evaluated the definitions and assessment tools of seven terms – (i) ‘psychotic experiences’; (ii) ‘psychotic‐like experiences’; (iii) ‘psychotic‐like symptoms’; (iv) ‘attenuated psychotic symptoms’; (v) ‘prodromal psychotic symptoms’; (vi) ‘psychotic symptomatology’; and (vii) ‘psychotic symptoms’. Methods EMBASE, MEDLINE, and CINAHL were searched during February–March 2019. Inclusion criteria included 1989–2019, full text, human, and English. Papers with no explicit definition or assessment tool, duplicates, conference abstracts, systematic reviews, meta‐analyses, or no access were excluded. Results A total of 2238 papers were identified and of these, 627 were included. Definitions and assessment tools varied, but some trends were found. Psychotic experiences and psychotic‐like experiences were transient and mild, found in the general population and those at‐risk. Psychotic‐like symptoms were subthreshold and among at‐risk populations and non‐psychotic mental disorders. Attenuated psychotic symptoms were subthreshold but associated with distress, risk, and help‐seeking. Prodromal psychotic symptoms referred to the prodrome of psychotic disorders. Psychotic symptomatology included delusions and hallucinations within psychotic disorders. Psychotic symptoms was the broadest term, encompassing a range of populations but most commonly involving hallucinations, delusions, thought disorder, and disorganization. Discussion A model for conceptualizing the required terms is proposed and future directions needed to advance this field of research are discussed.

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Structural Brain Network Disturbances in the Psychosis Spectrum

Abstract: Weaker connections and decreased centrality of parietal hubs characterize the structural brain network in subjects with psychotic symptoms. These differences are more notable in clinical than in subclinical subjects with psychotic experiences.

Cited by 34 publications

References 45 publications

Minimum spanning tree analysis of the human connectome

Minimum spanning tree analysis of the human connectome

Network structure of the Wisconsin Schizotypy Scales–Short Forms: Examining psychometric network filtering approaches

Terminology and assessment tools of psychosis: A systematic narrative review

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