scholarly journals Network Analysis Reveals the Latent Structure of Negative Symptoms in Schizophrenia

2018 ◽  
Vol 45 (5) ◽  
pp. 1033-1041 ◽  
Author(s):  
Gregory P Strauss ◽  
Farnaz Zamani Esfahlani ◽  
Silvana Galderisi ◽  
Armida Mucci ◽  
Alessandro Rossi ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Network Analysis ◽  
Negative Symptom ◽  
Negative Symptoms ◽  
Null Model ◽  
Detection Algorithm ◽  
Latent Structure ◽  
Symptom Scale ◽  
Mathematical Techniques ◽  
Community Detection Algorithm

Abstract Prior studies using exploratory factor analysis provide evidence that negative symptoms are best conceptualized as 2 dimensions reflecting diminished motivation and expression. However, the 2-dimensional model has yet to be evaluated using more complex mathematical techniques capable of testing structure. In the current study, network analysis was applied to evaluate the latent structure of negative symptoms using a community-detection algorithm. Two studies were conducted that included outpatients with schizophrenia (SZ; Study 1: n = 201; Study 2: n = 912) who were rated on the Brief Negative Symptom Scale (BNSS). In both studies, network analysis indicated that the 13 BNSS items divided into 6 negative symptom domains consisting of anhedonia, avolition, asociality, blunted affect, alogia, and lack of normal distress. Separation of these domains was statistically significant with reference to a null model of randomized networks. There has been a recent trend toward conceptualizing the latent structure of negative symptoms in relation to 2 distinct dimensions reflecting diminished expression and motivation. However, the current results obtained using network analysis suggest that the 2-dimensional conceptualization is not complex enough to capture the nature of the negative symptom construct. Similar to recent confirmatory factor analysis studies, network analysis revealed that the latent structure of negative symptom is best conceptualized in relation to the 5 domains identified in the 2005 National Institute of Mental Health consensus development conference (anhedonia, avolition, asociality, blunted affect, and alogia) and potentially a sixth domain consisting of lack of normal distress. Findings have implications for identifying pathophysiological mechanisms and targeted treatments.

scholarly journals Network Analysis Reveals Which Negative Symptom Domains Are Most Central in Schizophrenia vs Bipolar Disorder

2019 ◽  
Vol 45 (6) ◽  
pp. 1319-1330 ◽  
Author(s):  
Gregory Paul Strauss ◽  
Farnaz Zamani Esfahlani ◽  
Brian Kirkpatrick ◽  
Daniel N Allen ◽  
James M Gold ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Network Analysis ◽  
Negative Symptom ◽  
Negative Symptoms ◽  
Symptom Scale ◽  
Treatment Development ◽  
Symptom Domains ◽  
Network Connectedness ◽  
Network Properties ◽  
Males And Females

Abstract Network analysis was used to examine how densely interconnected individual negative symptom domains are, whether some domains are more central than others, and whether sex influenced network structure. Participants included outpatients with schizophrenia (SZ; n = 201), a bipolar disorder (BD; n = 46) clinical comparison group, and healthy controls (CN; n = 27) who were rated on the Brief Negative Symptom Scale. The mutual information measure was used to construct negative symptom networks. Groups were compared on macroscopic network properties to evaluate overall network connectedness, and microscopic properties to determine which domains were most central. Macroscopic analyses indicated that patients with SZ had a less densely connected negative symptom network than BD or CN groups, and that males with SZ had less densely connected networks than females. Microscopic analyses indicated that alogia and avolition were most central in the SZ group, whereas anhedonia was most central in BD and CN groups. In addition, blunted affect, alogia, and asociality were most central in females with SZ, and alogia and avolition were most central in males with SZ. These findings suggest that negative symptoms may be highly treatment resistant in SZ because they are not very densely connected. Less densely connected networks may make treatments less likely to achieve global reductions in negative symptoms because individual domains function in isolation with little interaction. Sex differences in centralities suggest that the search for pathophysiological mechanisms and targeted treatment development should be focused on different sets of symptoms in males and females.

scholarly journals Factor Analysis of Negative Symptom Items in the Structured Interview for Prodromal Syndromes

2018 ◽  
Vol 45 (5) ◽  
pp. 1042-1050 ◽  
Author(s):  
Matilda Azis ◽  
Gregory P Strauss ◽  
Elaine Walker ◽  
William Revelle ◽  
Richard Zinbarg ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Functional Outcome ◽  
Negative Symptom ◽  
Negative Symptoms ◽  
Psychotic Disorders ◽  
The United States ◽  
Structured Interview ◽  
Symptom Dimensions ◽  
Increased Risk ◽  
Active Research

Abstract Background Negative symptoms occur early in the clinical high risk (CHR) state and indicate increased risk of conversion to psychotic disorder and poor functional outcome. However, while the negative symptom domain has shown to be parsimoniously explained by a 2-factor construct in schizophrenia, there has yet to be an established factor structure of negative symptoms in CHR. Methods 214 individuals meeting the Structured Interview for Psychosis-Risk Syndromes (SIPS) criteria for CHR were recruited through 3 active research programs in the United States. Exploratory Factor Analysis was conducted on the 6 negative symptom items of the SIPS, and factors were evaluated with respect to functional outcome and depression. Results Factor analysis indicated a 2-factor hierarchical model with 2 negative symptom dimensions reflecting volition (Occupational Functioning and Avolition) and emotion (Expression of Emotion, Experience of Emotion and Social Anhedonia). Linear Regression showed that the emotion factor was associated with poor social function, and the volition factor was associated with poor role function and depression. Conclusions Similar to factor solutions identified in adults diagnosed with psychotic disorders, results indicated that the SIPS negative symptom subscale is not a unidimensional construct. Rather, the SIPS negative subscale has 2 distinct factors that have different associations with clinical outcome and should be interpreted independently. Results have significant relevance for informing the valid assessment and conceptual interpretation of early clinical phenomenology in the psychosis prodrome.

scholarly journals Aberrant Frontostriatal Connectivity in Negative Symptoms of Schizophrenia

2018 ◽  
Vol 45 (5) ◽  
pp. 1051-1059 ◽  
Author(s):  
Dinesh K Shukla ◽  
Joshua John Chiappelli ◽  
Hemalatha Sampath ◽  
Peter Kochunov ◽  
Stephanie M Hare ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Negative Symptom ◽  
Negative Symptoms ◽  
Imaging Study ◽  
Anterior Cingulate ◽  
Functional Magnetic Resonance ◽  
Resting State Functional Connectivity ◽  
Symptom Scale ◽  
Distinct Component ◽  
The Right

AbstractNegative symptoms represent a distinct component of psychopathology in schizophrenia (SCZ) and are a stable construct over time. Although impaired frontostriatal connectivity has been frequently described in SCZ, its link with negative symptoms has not been carefully studied. We tested the hypothesis that frontostriatal connectivity at rest may be associated with the severity of negative symptoms in SCZ. Resting state functional connectivity (rsFC) data from 95 mostly medicated patients with SCZ and 139 healthy controls (HCs) were acquired. Negative symptoms were assessed using the Brief Negative Symptom Scale. The study analyzed voxel-wise rsFC between 9 frontal “seed regions” and the entire striatum, with the intention to reduce potential biases introduced by predefining any single frontal or striatal region. SCZ showed significantly reduced rsFC between the striatum and the right medial and lateral orbitofrontal cortex (OFC), lateral prefrontal cortex, and rostral anterior cingulate cortex compared with HCs. Further, rsFC between the striatum and the right medial OFC was significantly associated with negative symptom severity. The involved striatal regions were primarily at the ventral putamen. Our results support reduced frontostriatal functional connectivity in SCZ and implicate striatal connectivity with the right medial OFC in negative symptoms. This task-independent resting functional magnetic resonance imaging study showed that medial OFC–striatum functional connectivity is reduced in SCZ and associated with severity of negative symptoms. This finding supports a significant association between frontostriatal connectivity and negative symptoms and thus may provide a potential circuitry-level biomarker to study the neurobiological mechanisms of negative symptoms.

Poster #M161 THE BRIEF NEGATIVE SYMPTOM SCALE: PSYCHOMETRIC PROPERTIES AND FACTOR ANALYSIS OF THE GERMAN TRANSLATION

2014 ◽  
Vol 153 ◽  
pp. S249
Author(s):  
Martin Bischof ◽  
Stefan Kaiser ◽  
Matthias N. Hartmann ◽  
Oliver Hager ◽  
Matthias Kirschner ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Psychometric Properties ◽  
Negative Symptom ◽  
Symptom Scale ◽  
German Translation

scholarly journals Negative Symptom Domains Are Associated With Verbal Learning in Adolescents With Early Onset Psychosis

2022 ◽  
Vol 12 ◽  
Author(s):  
Lynn Mørch-Johnsen ◽  
Runar Elle Smelror ◽  
Dimitrios Andreou ◽  
Claudia Barth ◽  
Cecilie Johannessen ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Negative Symptom ◽  
Early Onset ◽  
Negative Symptoms ◽  
Factor Model ◽  
Verbal Learning ◽  
Confirmatory Factor ◽  
Symptom Domains ◽  
Symptom Scores ◽  
Early Onset Psychosis

Background: Early-onset psychosis (EOP) is among the leading causes of disease burden in adolescents. Negative symptoms and cognitive deficits predicts poorer functional outcome. A better understanding of the association between negative symptoms and cognitive impairment may inform theories on underlying mechanisms and elucidate targets for development of new treatments. Two domains of negative symptoms have been described in adult patients with schizophrenia: apathy and diminished expression, however, the factorial structure of negative symptoms has not been investigated in EOP. We aimed to explore the factorial structure of negative symptoms and investigate associations between cognitive performance and negative symptom domains in adolescents with EOP. We hypothesized that (1) two negative symptom factors would be identifiable, and that (2) diminished expression would be more strongly associated with cognitive performance, similar to adult psychosis patients.Methods: Adolescent patients with non-affective EOP (n = 169) were included from three cohorts: Youth-TOP, Norway (n = 45), Early-Onset Study, Norway (n = 27) and Adolescent Schizophrenia Study, Mexico (n = 97). An exploratory factor analysis was performed to investigate the underlying structure of negative symptoms (measured with the Positive and Negative Syndrome Scale (PANSS)). Factor-models were further assessed using confirmatory factor analyses. Associations between negative symptom domains and six cognitive domains were assessed using multiple linear regression models controlling for age, sex and cohort. The neurocognitive domains from the MATRICS Consensus Cognitive Battery included: speed of processing, attention, working memory, verbal learning, visual learning, and reasoning and problem solving.Results: The exploratory factor analysis of PANSS negative symptoms suggested retaining only a single factor, but a forced two factor solution corroborated previously described factors of apathy and diminished expression in adult-onset schizophrenia. Results from confirmatory factor analysis indicated a better fit for the two-factor model than for the one-factor model. For both negative symptom domains, negative symptom scores were inversely associated with verbal learning scores.Conclusion: The results support the presence of two domains of negative symptoms in EOP; apathy and diminished expression. Future studies on negative symptoms in EOP should examine putative differential effects of these symptom domains. For both domains, negative symptom scores were significantly inversely associated with verbal learning.

scholarly journals S71. CLINICAL, BEHAVIOURAL AND NEURAL VALIDATION OF THE POSITIVE AND NEGATIVE SYMPTOM SCALE (PANSS) AMOTIVATION FACTOR

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S61-S61
Author(s):  
Mariia Kaliuzhna ◽  
Matthias Kirschner ◽  
Fabien Carruzzo ◽  
Matthias Hartmann ◽  
Bischof Martin ◽  
...  
Keyword(s):  
Factor Structure ◽  
Negative Symptom ◽  
Negative Symptoms ◽  
Symptom Assessment ◽  
Symptom Scale ◽  
New Approach ◽  
Reward Anticipation ◽  
Two Factors ◽  
Highly Correlated ◽  
Assessment Scales

Abstract Background Negative symptoms of schizophrenia are suggested to map onto two distinct factors – amotivation and diminished expression, which relate to different aspects of behaviour and neural activity. Most research in patients with schizophrenia is conducted with broad symptom assessment scales, such as the PANSS, for which factor solutions allowing the distinction between amotivation and diminished expression have only recently been reported. We aimed to establish whether the PANSS factor structure corresponds to the well-established two-factor structure of the Brief Negative Symptom Scale (BNSS) and whether it allows distinguishing specific behavioural and neuronal correlates of amotivation. Methods In study 1 (N=120) we examined the correlations between the PANSS factors and the BNSS factors. In study 2 (N=31) we examined whether PANSS amotivation is specifically associated with reduced willingness to work for reward in an effort-based decision making task. In study 3 (N=43) we investigated whether PANSS amotivation is specifically correlated with reduced ventral striatal activation during reward anticipation using functional magnetic resonance imaging. Results On the clinical level, the PANSS amotivation and diminished expression were highly correlated with their BNSS counterparts. On the behavioural level, PANSS amotivation factor but not the diminished expression factor was specifically associated with reduced willingness to invest effort to obtain a reward. On the neural level, PANSS amotivation was specifically associated with ventral striatal activation during reward anticipation. Discussion Our data confirm that the two domains of negative symptoms can be measured with the PANSS and are linked to specific aspects of behaviour and brain function. To our knowledge, this is the first study employing behavioural and neural measures to validate a new approach to clinical measurement of negative symptoms. Our results warrant a re-analysis of previous work that used the PANSS to further substantiate the distinction between the two factors in behavioural and neuroimaging studies.

Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data

2021 ◽  
pp. 2142002
Author(s):  
Giuseppe Agapito ◽  
Marianna Milano ◽  
Mario Cannataro
Keyword(s):  
Network Analysis ◽  
Real Data ◽  
Detection Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Italian Regions ◽  
Initial Dataset ◽  
Parallel Network ◽  
Community Detection Algorithm ◽  
Similarity Matrices

A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0[Formula: see text]MB, 180.0[Formula: see text]MB, and 360.0[Formula: see text]MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.

The second-generation assessment scales: Brief negative symptom scale and clinical assessment interview for negative symptoms

2016 ◽  
Vol 33 (S1) ◽  
pp. S70-S70
Author(s):  
A. Mucci ◽  
S. Galderisi
Keyword(s):  
Negative Symptom ◽  
Clinical Assessment ◽  
Negative Symptoms ◽  
Assessment Instruments ◽  
Symptom Scale ◽  
Assessment Interview ◽  
Competing Interest ◽  
Functional Outcome Measures ◽  
Internal Experience ◽  
Assessment Scales

The construct of negative symptoms has undergone significant changes since the introduction of first generation assessment scales, such as the Scale for the Assessment of Negative Symptoms or the Positive and Negative Syndrome Scale. Blunted affect, Alogia, Asociality, Anhedonia and Avolition are largely recognized as valid domains of the negative symptoms construct.Among the new assessment instruments, both the Brief Negative Symptom Scale (BNSS) and the Clinical Assessment Interview for Negative Symptoms (CAINS) are considered adequate in their coverage of the negative symptoms domains. They include the assessment of both behavior and internal experience for Anhedonia, Asociality and Avolition to avoid overlap with functional outcome measures, as well as consummatory and anticipatory components of anhedonia with an emphasis on the internal experience of pleasure.Strengths and limitations of these new assessment instruments will be reviewed in the light of some existing challenges, such as the distinction between primary and secondary negative symptoms and development of innovative treatments.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Network analysis and systemic FX settlement risk

2017 ◽  
Vol 34 (3-4) ◽  
Author(s):  
José Henry León-Janampa
Keyword(s):  
Network Analysis ◽  
Financial Institutions ◽  
Detection Algorithm ◽  
Network Centrality ◽  
Settlement System ◽  
Centrality Metrics ◽  
Community Detection Algorithm ◽  
Fx Trading ◽  
Law Degree ◽  
Network Metrics

AbstractA proposal for applying network analysis to a foreign exchange (FX) settlement system is considered. In particular, network centrality metrics are used to analyse payments of financial institutions which settle through CLS Bank (CLS). Network centrality metrics provide a way to study settlement members’ connectivity, obtain a sense of their payments evolution with time, and measure their network topology variability. The analysis shows that although the continuous link settlement (CLS) network structure can be approximated with a power law degree distribution for many trade days, this is not always the case. A network community detection algorithm is applied to the FX settlement network to explore relationships between communities and to detect classification patterns in the FX trading net payments. A metric called SinkRank is used to build a ranking of the most systemic settlement risk important financial institutions trading on the FX system, and to understand how the metric depends on network’s connectivity. Since network metrics do not fully explain the dynamics of the settlement process, the CLS’ settlement system is simulated to measure the contagion of unsettled trades and its spread among network members. The effect of settlement failure and contagion on the settlement members is also explored.

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