scholarly journals Longitudinal symptomatic interactions in long-standing schizophrenia: a novel five-point analysis based on directed acyclic graphs

2021 ◽  
pp. 1-8
Author(s):  
Giusi Moffa ◽  
Jack Kuipers ◽  
Giuseppe Carrà ◽  
Cristina Crocamo ◽  
Elizabeth Kuipers ◽  
...  
Keyword(s):  
Network Models ◽  
Chronic Schizophrenia ◽  
Directed Acyclic Graphs ◽  
Psychotic Symptoms ◽  
Outpatient Services ◽  
Cross Sectional ◽  
Affective Symptoms ◽  
Acyclic Graphs ◽  
Affective Disturbance ◽  
The Uk

Abstract Background Recent network models propose that mutual interaction between symptoms has an important bearing on the onset of schizophrenic disorder. In particular, cross-sectional studies suggest that affective symptoms may influence the emergence of psychotic symptoms. However, longitudinal analysis offers a more compelling test for causation: the European Schizophrenia Cohort (EuroSC) provides data suitable for this purpose. We predicted that the persistence of psychotic symptoms would be driven by the continuing presence of affective disturbance. Methods EuroSC included 1208 patients randomly sampled from outpatient services in France, Germany and the UK. Initial measures of psychotic and affective symptoms were repeated four times at 6-month intervals, thereby furnishing five time-points. To examine interactions between symptoms both within and between time-slices, we adopted a novel technique for modelling longitudinal data in psychiatry. This was a form of Bayesian network analysis that involved learning dynamic directed acyclic graphs (DAGs). Results Our DAG analysis suggests that the main drivers of symptoms in this long-term sample were delusions and paranoid thinking. These led to affective disturbance, not vice versa as we initially predicted. The enduring relationship between symptoms was unaffected by whether patients were receiving first- or second-generation antipsychotic medication. Conclusions In this cohort of people with chronic schizophrenia treated with medication, symptoms were essentially stable over long periods. However, affective symptoms appeared driven by the persistence of delusions and persecutory thinking, a finding not previously reported. Although our findings as ever remain hostage to unmeasured confounders, these enduring psychotic symptoms might nevertheless be appropriate candidates for directly targeted psychological interventions.

scholarly journals Cross-Sectional Model-Building for Research on Subjective Well-Being: Gaining Clarity on Control Variables

2021 ◽  
Author(s):  
David Bartram
Keyword(s):  
Model Building ◽  
Well Being ◽  
Directed Acyclic Graphs ◽  
Subjective Well Being ◽  
Social Scientists ◽  
Cross Sectional ◽  
Acyclic Graphs ◽  
Standard Set ◽  
Substantial Bias ◽  
Parsimonious Model

AbstractHappiness/well-being researchers who use quantitative analysis often do not give persuasive reasons why particular variables should be included as controls in their cross-sectional models. One commonly sees notions of a “standard set” of controls, or the “usual suspects”, etc. These notions are not coherent and can lead to results that are significantly biased with respect to a genuine causal relationship.This article presents some core principles for making more effective decisions of that sort.  The contribution is to introduce a framework (the “causal revolution”, e.g. Pearl and Mackenzie 2018) unfamiliar to many social scientists (though well established in epidemiology) and to show how it can be put into practice for empirical analysis of causal questions.  In simplified form, the core principles are: control for confounding variables, and do not control for intervening variables or colliders.  A more comprehensive approach uses directed acyclic graphs (DAGs) to discern models that meet a minimum/efficient criterion for identification of causal effects.The article demonstrates this mode of analysis via a stylized investigation of the effect of unemployment on happiness.  Most researchers would include other determinants of happiness as controls for this purpose.  One such determinant is income—but income is an intervening variable in the path from unemployment to happiness, and including it leads to substantial bias.  Other commonly-used variables are simply unnecessary, e.g. religiosity and sex.  From this perspective, identifying the effect of unemployment on happiness requires controlling only for age and education; a small (parsimonious) model is evidently preferable to a more complex one in this instance.

Facial emotion recognition and its relationship to symptomatic, subjective, and functional outcomes in outpatients with chronic schizophrenia

2009 ◽  
Vol 24 (1) ◽  
pp. 27-32 ◽  
Author(s):  
Alex Hofer ◽  
Cord Benecke ◽  
Monika Edlinger ◽  
Regina Huber ◽  
Georg Kemmler ◽  
...  
Keyword(s):  
Psychosocial Functioning ◽  
Chronic Schizophrenia ◽  
Cross Sectional Study ◽  
Affect Recognition ◽  
Global Functioning ◽  
Cross Sectional ◽  
Affective Symptoms ◽  
Relationship Of ◽  
The Relationship

AbstractOutcome in schizophrenia is multidimensional and consists of clinical and psychosocial domains. Difficulties in affect recognition are a hallmark of schizophrenia, but there is little research investigating the consequences of this deficit on patients’ psychosocial status. This cross-sectional study examined the relationship of facial affect recognition and treatment outcomes in terms of psychopathology, quality of life (QOL), and psychosocial functioning.We investigated 40 regular attendees of a specialized schizophrenia outpatient clinic who had been stable both from a symptomatic and a medication perspective for a minimum of 6 months and 40 healthy volunteers who were chosen to match patients in age, sex, and education. Affect recognition was positively associated with patients’ level of education and negatively with increasing age. Deficits in this area corresponded to the severity of negative and affective symptoms as well as to poor work and global functioning. These findings suggest that affect recognition is an important aspect of psychosocial functioning in stable outpatients with schizophrenia.

scholarly journals CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

2014 ◽  
Author(s):  
Daniele Ramazzotti ◽  
Giulio Caravagna ◽  
Loes Olde Loohuis ◽  
Alex Graudenzi ◽  
Ilya Korsunsky ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Directed Acyclic Graphs ◽  
The Cancer Genome Atlas ◽  
Scoring Method ◽  
Cross Sectional ◽  
Progression Model ◽  
Acyclic Graphs ◽  
Cancer Genome Atlas ◽  
Atypical Chronic Myeloid Leukemia ◽  
Insight Into

We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. Motivation: Several cancer-related genomic data have become available (e.g., The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer ?progression? models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of ?selectivity? relations, where a mutation in a gene A ?selects? for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. Results: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy, and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events.

scholarly journals Modelling cancer progression using Mutual Hazard Networks

2019 ◽  
Vol 36 (1) ◽  
pp. 241-249 ◽  
Author(s):  
Rudolf Schill ◽  
Stefan Solbrig ◽  
Tilo Wettig ◽  
Rainer Spang
Keyword(s):  
Cancer Progression ◽  
Learning Algorithm ◽  
Directed Acyclic Graphs ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Cross Sectional ◽  
Acyclic Graphs ◽  
Cancer Genome Atlas ◽  
Occurrence State ◽  
Occurrence Patterns

Abstract Motivation Cancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurrence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap. Results Here we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data. MHN model events by their spontaneous rate of fixation and by multiplicative effects they exert on the rates of successive events. MHN compared favourably to acyclic models in cross-validated model fit on four datasets tested. In application to the glioblastoma dataset from The Cancer Genome Atlas, MHN proposed a novel interaction in line with consecutive biopsies: IDH1 mutations are early events that promote subsequent fixation of TP53 mutations. Availability and implementation Implementation and data are available at https://github.com/RudiSchill/MHN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals From Speech Illusions to Onset of Psychotic Disorder: Applying Network Analysis to an Experimental Measure of Aberrant Experiences

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Lindy-Lou Boyette ◽  
Adela-Maria Isvoranu ◽  
Frederike Schirmbeck ◽  
Eva Velthorst ◽  
Claudia J P Simons ◽  
...  
Keyword(s):  
Psychotic Disorder ◽  
Psychotic Disorders ◽  
Psychotic Symptoms ◽  
Positive Symptoms ◽  
Clinical High Risk ◽  
Cross Sectional ◽  
Affective Symptoms ◽  
The Eu

Abstract Aberrant perceptional experiences are a potential early marker of psychosis development. Earlier studies have found experimentally assessed speech illusions to be associated with positive symptoms in patients with psychotic disorders, but findings for attenuated symptoms in individuals without psychotic disorders have been inconsistent. Also, the role of affect is unclear. The aim of this study was to use the network approach to investigate how speech illusions relate to individual symptoms and onset of a psychotic disorder. We estimated a network model based on data from 289 Clinical High-Risk (CHR) subjects, participating in the EU-GEI project. The network structure depicts statistical associations between (affective and all) speech illusions, cross-sectional individual attenuated positive and affective symptoms, and transition to psychotic disorder after conditioning on all other variables in the network. Speech illusions were assessed with the White Noise Task, symptoms with the BPRS and transition during 24-month follow-up with the CAARMS. Affective, not all, speech illusions were found to be directly, albeit weakly, associated with hallucinatory experiences. Hallucinatory experiences, in turn, were associated with delusional ideation. Bizarre behavior was the only symptom in the network steadily predictive of transition. Affective symptoms were highly interrelated, with depression showing the highest overall strength of connections to and predictability by other symptoms. Both speech illusions and transition showed low overall predictability by symptoms. Our findings suggest that experimentally assessed speech illusions are not a mere consequence of psychotic symptoms or disorder, but that their single assessment is likely not useful for assessing transition risk.

scholarly journals Association between stroke and psychosis across four nationally representative epidemiological studies

2021 ◽  
Author(s):  
Vaughan Bell ◽  
William Tamayo-Agudelo ◽  
Grace Revill ◽  
David Okai ◽  
Norman Poole
Keyword(s):  
Epidemiological Studies ◽  
Psychotic Symptoms ◽  
Antipsychotic Treatment ◽  
Cross Sectional ◽  
Middle Income ◽  
Treatment Pathways ◽  
The Us ◽  
Nationally Representative ◽  
The Uk ◽  
Adjusted Association

Background: Both stroke and psychosis are independently associated with high levels of disability. However, psychosis in the context of stroke has received remarkably little interest from clinicians and researchers. To date there are currently no population studies on their joint prevalence and association. Methods: We estimated the prevalence of i) probable psychosis in stroke and, ii) stroke in probable psychosis using four nationally representative cross-sectional psychiatric epidemiological studies: two from high-income countries (United Kingdom and United States) and two from middle-income countries (Chile and Colombia) and, subsequently, a combined dataset from all four countries. We also tested the statistical association between stroke and psychosis using single and multi-level regression models to estimate the unadjusted association between stroke and psychosis, and the association adjusted for potential demographic confounders. Results: The prevalence of probable psychosis in stroke ranged from 1.05% [95% CIs 0.03 - 5.73] in Chile, to 13.92% [95% CIs 7.16 - 23.55] in Colombia, with the prevalence from the combined countries dataset estimated at 3.81% [95% CIs 2.34 - 5.82]. Stroke in probable psychosis ranged from 2.18% [95% CIs 1.09 - 3.86] in Colombia, to 16.67% [95% CIs 6.37 - 32.81] in the US, with the combined countries prevalence estimated at 3.15% [95% CIs 1.94 - 4.83]. Estimates for the adjusted association between stroke and probable psychosis ranged from an OR = 1.11 [95% CIs 0.15 - 8.26] in the UK to an OR = 6.22 in the US [95% CIs 2.52 - 15.35] with the adjusted association from the combined dataset estimated at OR = 3.32 [95% CIs 2.05 - 5.38]. Larger prevalences and associations were associated with larger confidence intervals and we suggest the smaller estimates are likely to be more accurate. We also examined the association between stroke and paranoia, hallucinated voices, and thought passivity delusion, and although we found significant variation in the reliability and strength of association across countries, all three psychotic symptoms were associated with stroke in the unadjusted and adjusted analyses in the combined countries dataset. Conclusions: There are high rates of association between psychosis and stroke, meaning there is likely a high clinical need group who are under-researched and may be poorly served by existing services. Notably, stroke is a known risk factor for psychosis, and psychosis and antipsychotic treatment for psychosis are known risk factors for stroke, meaning causality is likely to be bidirectional and treatment pathways should be integrated across traditional service boundaries.

scholarly journals Links between psychotic and neurotic symptoms in the general population: an analysis of longitudinal British National Survey data using Directed Acyclic Graphs

2018 ◽  
Vol 49 (3) ◽  
pp. 388-395 ◽  
Author(s):  
J. Kuipers ◽  
G. Moffa ◽  
E. Kuipers ◽  
D. Freeman ◽  
P. Bebbington
Keyword(s):  
Sleep Problems ◽  
Psychiatric Morbidity ◽  
Dynamic Bayesian Network ◽  
Epidemiological Studies ◽  
Directed Acyclic Graphs ◽  
The Other ◽  
Dependence Structure ◽  
Affective Symptoms ◽  
Acyclic Graphs ◽  
Multiple Variables

AbstractBackgroundNon-psychotic affective symptoms are important components of psychotic syndromes. They are frequent and are now thought to influence the emergence of paranoia and hallucinations. Evidence supporting this model of psychosis comes from recent cross-fertilising epidemiological and intervention studies. Epidemiological studies identify plausible targets for intervention but must be interpreted cautiously. Nevertheless, causal inference can be strengthened substantially using modern statistical methods.MethodsDirected Acyclic Graphs were used in a dynamic Bayesian network approach to learn the overall dependence structure of chosen variables. DAG-based inference identifies the most likely directional links between multiple variables, thereby locating them in a putative causal cascade. We used initial and 18-month follow-up data from the 2000 British National Psychiatric Morbidity survey (N = 8580 and N = 2406).ResultsWe analysed persecutory ideation, hallucinations, a range of affective symptoms and the effects of cannabis and problematic alcohol use. Worry was central to the links between symptoms, with plausible direct effects on insomnia, depressed mood and generalised anxiety, and recent cannabis use. Worry linked the other affective phenomena with paranoia. Hallucinations were connected only to worry and persecutory ideation. General anxiety, worry, sleep problems, and persecutory ideation were strongly self-predicting. Worry and persecutory ideation were connected over the 18-month interval in an apparent feedback loop.ConclusionsThese results have implications for understanding dynamic processes in psychosis and for targeting psychological interventions. The reciprocal influence of worry and paranoia implies that treating either symptom is likely to ameliorate the other.

scholarly journals Cancer progression models and fitness landscapes: a many-to-many relationship

2017 ◽  
Author(s):  
Ramon Diaz-Uriarte
Keyword(s):  
Cancer Progression ◽  
Fitness Landscape ◽  
Simulated Data ◽  
Directed Acyclic Graphs ◽  
Fitness Landscapes ◽  
Gene Interactions ◽  
Cross Sectional ◽  
Data Set ◽  
Large Variability ◽  
Acyclic Graphs

AbstractThe identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to try to identify these constraints, and return Directed Acyclic Graphs (DAGs) of genes. On the other hand, fitness landscapes, which map genotypes to fitness, contain all possible paths of tumor progression. Thus, we expect a correspondence between DAGs from CPMs and the fitness landscapes where evolution happened. But many fitness landscapes —e.g., those with reciprocal sign epistasis— cannot be represented by CPMs. Using simulated data under 500 fitness landscapes, I show that CPMs’ performance (prediction of genotypes that can exist) degrades with reciprocal sign epistasis. There is large variability in the DAGs inferred from each landscape, which is also affected by mutation rate, detection regime, and fitness landscape features, in ways that depend on CPM method. And the same DAG is often observed in very different landscapes, which differ in more than 50% of their accessible genotypes. Using a pancreatic data set, I show that this many-to-many relationship affects the analysis of empirical data. Fitness landscapes that are widely different from each other can, when evolutionary processes run repeatedly on them, both produce data similar to the empirically observed one, and lead to DAGs that are very different among themselves. Because reciprocal sign epistasis can be common in cancer, these results question the use and interpretation of CPMs.

scholarly journals Mental well-being of the general population: direct and indirect effects of socioeconomic, relational and health factors

2021 ◽  
Author(s):  
Natalia Soldevila-Domenech ◽  
Carlos G. Forero ◽  
Itxaso Alayo ◽  
Jordina Capella ◽  
Joan Colom ◽  
...  
Keyword(s):  
Social Support ◽  
Well Being ◽  
Cross Sectional Study ◽  
Directed Acyclic Graphs ◽  
Gender Effects ◽  
Noticeable Effect ◽  
Cross Sectional ◽  
Acyclic Graphs ◽  
Health Factors ◽  
Mental Well Being

Abstract Purpose The aim of this study was to analyse the association between individual mental well-being and social, economic, lifestyle and health factors. Methods Cross-sectional study on a representative sample of 13,632 participants (> 15y/o) from the Catalan Health Interview Survey 2013–2016 editions. Mental well-being was assessed with the Warwick–Edinburg Mental Well-being Scale (WEMWBS). Linear regressions were fitted to associate well-being and sociodemographic, relational, lifestyle and health variables according to minimally sufficient adjustment sets identified using directed acyclic graphs. Predictors entered the model in blocks of variable types and analysed individually. Direct and total effects were estimated. Results Health factors significantly contributed to mental well-being variance. Presence of a mental disorder and self-reported health had the largest effect size (eta2 = 13.4% and 16.3%). The higher individual impact from a variable came from social support (β = − 12.8, SE = 0.48, eta2 = 6.3%). A noticeable effect gradient (eta2 = 4.2%) from low to high mental well-being emerged according to economic difficulties (from β = 1.59, SE = 0.33 for moderate difficulties to β = 6.02 SE = 0.55 for no difficulties). Younger age (β = 5.21, SE = 0.26, eta2 = 3.4%) and being men (β = 1.32, SE = 0.15, eta2 = 0.6%) were associated with better mental well-being. Direct gender effects were negligible. Conclusions This study highlights health and social support as the most associated factors with individual mental well-being over socioeconomic factors. Interventions and policies aimed to these factors for health promotion would improve population mental well-being.

scholarly journals Biases in GWAS – the dog that did not bark

2019 ◽  
Author(s):  
C M Schooling
Keyword(s):  
Selection Bias ◽  
Scientific Discovery ◽  
Association Studies ◽  
Directed Acyclic Graphs ◽  
Genome Wide Association Studies ◽  
Acyclic Graphs ◽  
Genome Wide ◽  
The Uk ◽  
Type 2 Error ◽  
Disease Specific

AbstractBackgroundGenome wide association studies (GWAS) of specific diseases are central to scientific discovery. Bias from inevitably recruiting only survivors of genetic make-up and disease specific competing risk has not been comprehensively considered.MethodsWe identified sources of bias using directed acyclic graphs, and tested for them in the UK Biobank GWAS by making comparisons across the survival distribution, proxied by age at recruitment.ResultsAssociations of genetic variants with some diseases depended on their effect on survival. Variants associated with common harmful diseases had weaker or reversed associations with subsequent diseases that shared causes.ConclusionGenetic studies of diseases that involve surviving other common diseases are open to selection bias that can generate systematic type 2 error. GWAS ignoring such selection bias are most suitable for monogenetic diseases. Genetic effects on age at recruitment may indicate potential bias in disease-specific GWAS and relevance to population health.

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