scholarly journals Network Analysis-Based Disentanglement of the Symptom Heterogeneity in Asian Patients with Schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics

2022 ◽  
Vol 12 (1) ◽  
pp. 33
Author(s):  
Joonho Choi ◽  
Hyung-Jun Yoon ◽  
Jae Hong Park ◽  
Yukako Nakagami ◽  
Chika Kubota ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Network Structure ◽  
Rating Scale ◽  
Language Game ◽  
Depressive Mood ◽  
Detection Algorithm ◽  
Prescription Patterns ◽  
Asian Patients ◽  
Motor Retardation ◽  
Symptom Heterogeneity

The symptom heterogeneity of schizophrenia is consistent with Wittgenstein’s analogy of a language game. From the perspective of precision medicine, this study aimed to estimate the symptom presentation and identify the psychonectome in Asian patients, using data obtained from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics. We constructed a network structure of the Brief Psychiatric Rating Scale (BPRS) items in 1438 Asian patients with schizophrenia. Furthermore, all the BPRS items were considered to be an ordered categorical variable ranging in value from 1–7. Motor retardation was situated most centrally within the BPRS network structure, followed by depressive mood and unusual thought content. Contrastingly, hallucinatory behavior was situated least centrally within the network structure. Using a community detection algorithm, the BPRS items were organized into positive, negative, and general symptom clusters. Overall, DSM symptoms were not more central than non-DSM symptoms within the symptom network of Asian patients with schizophrenia. Thus, motor retardation, which results from the unmet needs associated with current antipsychotic medications for schizophrenia, may be a tailored treatment target for Asian patients with schizophrenia. Based on these findings, targeting non-dopamine systems (glutamate, γ-aminobutyric acid) may represent an effective strategy with respect to precision medicine for psychosis.

scholarly journals The psychopathological factors of refractory schizophrenia

2005 ◽  
Vol 27 (2) ◽  
pp. 108-112 ◽  
Author(s):  
Tânia Maria Alves ◽  
Júlio César Rodrigues Pereira ◽  
Hélio Elkis
Keyword(s):  
Rating Scale ◽  
Clinical Manifestations ◽  
Depressive Mood ◽  
Symptom Clusters ◽  
Treatment Resistant ◽  
Brief Psychiatric Rating Scale ◽  
Motor Retardation ◽  
Psychiatric Rating ◽  
Psychiatric Rating Scale ◽  
Assessment Scales

OBJECTIVES: The heterogeneity of clinical manifestations in schizophrenia has lead to the study of symptom clusters through psychopathological assessment scales. The objective of this study was to elucidate clusters of symptoms in patients with refractory schizophrenia which may also help to assess the patients' therapeutical response. METHODS: Ninety-six treatment resistant patients were evaluated by the anchored version Brief Psychiatric Rating Scale (BPRS-A) as translated into Portuguese. The inter-rater reliability was 0.80. The 18 items of the BPRS-A were subjected to exploratory factor analysis with Varimax rotation. RESULTS: Four factors were obtained: Negative/Disorganization, composed by emotional withdrawal, disorientation, blunted affect, mannerisms/posturing, and conceptual disorganization; Excitement, composed of excitement, hostility, tension, grandiosity, and uncooperativeness, grouped variables that evoke brain excitement or a manic-like syndrome; Positive, composed of unusual thought content, suspiciousness, and hallucinatory behavior; and Depressive, composed of depressive mood, guilt feelings, and motor retardation, clearly related to depressive syndrome. CONCLUSIONS: The study reproduced the four factors described in the literature, either in refractory or non-refractory patients. The BPRS-A allowed the distinction of psychopathological factors, which are important in the evaluation of treatment response of patients with schizophrenia.

Psychological networks in clinical populations: investigating the consequences of Berkson's bias

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I. Fried ◽  
Sacha Epskamp
Keyword(s):  
Network Structure ◽  
Binary Data ◽  
Ordinal Data ◽  
Graphical Model ◽  
Rating Scale ◽  
Network Models ◽  
Simulation Studies ◽  
Potential Threat ◽  
Sum Score ◽  
Research Populations

Abstract Background In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. Methods In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. Results The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Conclusion Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

scholarly journals Psychological Networks in Clinical Populations: A tutorial on the consequences of Berkson's Bias

2019 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I Fried ◽  
Sacha Epskamp
Keyword(s):  
Network Structure ◽  
Binary Data ◽  
Ordinal Data ◽  
Graphical Model ◽  
Rating Scale ◽  
Network Models ◽  
Simulation Studies ◽  
Potential Threat ◽  
Sum Score ◽  
Research Populations

In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson’s bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson’s bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2,807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson’s bias literature, selection reduced recovery rates by inducing negative connections between the items. Our findings provide evidence that Berkson’s bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson’s bias and their pitfalls.

scholarly journals The Cortisol Awakening Response in Patients with Poststroke Depression Is Blunted and Negatively Correlated with Depressive Mood

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Oh Jeong Kwon ◽  
Munsoo Kim ◽  
Ho Sub Lee ◽  
Kang-keyng Sung ◽  
Sangkwan Lee
Keyword(s):  
Rating Scale ◽  
Area Under The Curve ◽  
Significant Negative Correlation ◽  
Depressive Mood ◽  
Cortisol Awakening Response ◽  
Poststroke Depression ◽  
Stroke Outcomes ◽  
Underlying Mechanisms ◽  
Pituitary Adrenal Axis

It is important to reduce poststroke depression (PSD) to improve the stroke outcomes and quality of life in stroke patients, but the underlying mechanisms of PSD are not completely understood. As many studies implicate dysregulation of hypothalamic-pituitary-adrenal axis in the etiology of major depression and stroke, we compared the cortisol awakening response (CAR) of 28 admitted PSD patients with that of 23 age-matched caregiver controls. Saliva samples for cortisol measurement were collected immediately, 15, 30, and 45 min after awakening for two consecutive days. Depressive mood status in PSD patients was determined with Beck Depression Inventory and Hamilton Depression Rating Scale. Salivary cortisol levels of PSD patients did not rise significantly at any sampling time, showing a somewhat flat curve. Caregiver controls showed significantly higher CAR at 15 and 30 min after awakening compared to PSD patients even though the two groups did not differ at awakening or 45 min after awakening. Area-under-the-curve analysis revealed a significant negative correlation between the CAR and the degree of depression in PSD patients. Thus, our findings suggest that poststroke depression is closely related with dysfunctional HPA axis indicated by blunted CAR.

Treatment with Olanzapine, Risperidone or Typical Antipsychotic Drugs in Asian Patients with Schizophrenia

2006 ◽  
Vol 40 (5) ◽  
pp. 437-445 ◽  
Author(s):  
Chul Lee ◽  
Kuang-Hsien Wu ◽  
Hussain Habil ◽  
Yulia Dyachkova ◽  
Phil Lee
Keyword(s):  
Rating Scale ◽  
Severity Of Illness ◽  
Typical Antipsychotic ◽  
Asian Patients ◽  
Olanzapine Treatment ◽  
Menstrual Disturbance ◽  
Symptom Scores ◽  
Report Data ◽  
Outcomes Study ◽  
Typical Treatment

Objective: To examine clinical outcomes in Asian patients with schizophrenia receiving monotherapy with olanzapine, risperidone or typical antipsychotics in naturalistic settings. Method: In this report, data from the first 12 months of the prospective, observational, 3-year Intercontinental Schizophrenia Outpatient Health Outcomes study are presented for patients from participating Asian countries (Korea, Taiwan and Malaysia) who were started on, or switched to, monotherapy with olanzapine (n = 484), risperidone (n = 287) or a typical antipsychotic drug (n = 127) at baseline. Results: At 12 months, overall reduction in the score of Clinical Global Impressions-Severity of Illness rating scale was greatest with olanzapine (p < 0.001 vs typical agents), followed by risperidone (p = 0.007 vs typical agents) treatment. Olanzapine treatment was found to have significantly better effects than typical agents on negative and depressive symptom scores, and significantly greater improvements than risperidone on negative and cognitive symptoms. The occurrence of extrapyramidal symptoms was least likely with olanzapine (p < 0.001 vs typical agents, and p = 0.012 vs risperidone), while the estimated odds of tardive dyskinesia were greatest in the typical treatment group (p = 0.046 vs olanzapine, and p = 0.082 vs risperidone). Mean weight increase was greater for olanzapine-treated patients compared with the other agents (p = 0.030 vs typical agents and p < 0.001 vs risperidone). The risk of menstrual disturbance was relatively high with risperidone when compared with olanzapine treatment (p < 0.001). Conclusions: The results of this observational study indicate that, in Asian patients with schizophrenia, olanzapine may offer benefits when compared with typical agents or risperidone. However, the significantly greater odds of weight gain should be considered in the clinical management of olanzapine-treated patients.

scholarly journals Assessment of menopausal symptoms using modified menopause rating scale (MRS) in women of Northern India

2018 ◽  
Vol 7 (3) ◽  
pp. 947 ◽  
Author(s):  
Fareha Khatoon ◽  
Parul Sinha ◽  
Sana Shahid ◽  
Uma Gupta
Keyword(s):  
Formal Education ◽  
Rating Scale ◽  
Sleep Problems ◽  
Depressive Mood ◽  
Cross Sectional Study ◽  
Menopausal Symptoms ◽  
Common Symptom ◽  
Cross Sectional ◽  
Postmenopausal Symptoms ◽  
Menopause Rating Scale

Background: Menopause is defined as complete cessation of menses for twelve months or more. It is a normal physiological change experienced by middle aged women and some of the menopausal symptoms experienced by these women can be severe enough to affect their normal daily activities.Methods: An observational cross sectional study was carried out in the Department of Obstetrics and Gynecology, Era’s Lucknow Medical College and hospital, Lucknow for a period of one year.300 patients who had attained menopause were analyzed. Menopausal symptoms were assessed using Modified Menopause Rating Scale (MRS).Results: Majority of patients attained menopause at the age of 50-54 years and the calculated mean age came to be 50.33±5.26. The most common symptom reported was joint and muscular discomfort (87%), depressive mood (70%), heart discomfort (60.3%), physical and mental exhaustion (60%), sleep problems (56%). The most classical symptom of menopause i. e. hot flushes was reported in 53.3%. Prevalence of other symptoms in decreasing order were irritability (46.6%), anxiety (40.3%), bladder problem (26%), dryness of vagina (23%), sexual problems (20%). The menopausal symptoms were found to be more prevalent in women of lower socio economic strata and those who had no formal education and this difference was found to be statistically significant.Conclusions: There is a high burden of postmenopausal symptoms which have shown an increasing trend with advancement of age. This calls for establishment of specific health intervention for postmenopausal women through the existing health centres by having geriatric clinics.

Network Analysis of Depressive Symptomatology in Elderly Patients with Major Depressive Disorder

2021 ◽  
Vol 17 ◽  
Author(s):  
Seon-Cheol Park
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Symptoms ◽  
Network Analysis ◽  
Depressive Disorder ◽  
Elderly Patients ◽  
Rating Scale ◽  
Depressive Symptomatology ◽  
Depressive Mood ◽  
Major Depressive ◽  
Using Data

Background: A novel psychopathological approach is the application of network analysis, as it is proposed that symptoms and their interconnections constitute a disease itself, rather than simply being components or outcome factors of disease. Objective: Using data from the Clinical Research Center for Depression (CRESCEND) Study, this study examined depressive symptoms in elderly patients with major depressive disorder using a network analysis approach. Methods: Among 135 elderly patients with major depressive disorder who were recruited from the CRESCEND study, we created a network based on individual items from the Hamilton Depression Rating Scale (HAMD), with the nodes being each item (symptom) and the edges being the strength of the association between the items (interconnection). By calculating measures of centrality of each of the nodes, we were able to determine which depressive symptoms were most central (influential) in the network. Results: The insight item was completely unconnected with other items and it was excluded in terms of network analysis. Thus, a network analysis of the 16 HAMD items estimated that the anxiety psychic item was the most central domain, followed by insomnia (middle of the night), depressive mood, and insomnia (early hours of the morning) items. On the contrary, the retardation item was the most poorly interconnected with the network. Conclusion: We suggest that our study makes a significant contribution to the literature because we have found that anxiety, depressed mood, and insomnia are most central to the network, indicating that they are the most influential symptoms in major depression in elderly individuals.

scholarly journals Longitudinal diffusion imaging across the C9orf72 clinical spectrum

2017 ◽  
Vol 89 (1) ◽  
pp. 53-60 ◽  
Author(s):  
Mary Kay Floeter ◽  
Laura E Danielian ◽  
Laura E Braun ◽  
Tianxia Wu
Keyword(s):  
White Matter ◽  
Rating Scales ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
Longitudinal Diffusion ◽  
Mutation Carriers ◽  
Cognitive Behavioural ◽  
Symptom Heterogeneity

IntroductionDiscrepancies between diffusion tensor imaging (DTI) findings and functional rating scales in amyotrophic lateral sclerosis (ALS) may be due to symptom heterogeneity, particularly coexisting cognitive-behavioural dysfunction affecting non-motor regions of the brain. Carriers of expansion mutations in the C9orf72 gene, whose motor and cognitive-behavioural symptoms span a range from ALS to frontotemporal dementia, present an opportunity to evaluate the relationship between symptom heterogeneity and DTI changes.MethodsTwenty-eight C9orf72 mutation carriers with varied cognitive and motor symptoms underwent clinical evaluation and DTI imaging. Twenty returned for two or more follow-up evaluations. Each evaluation included motor, executive and behavioural scales and disease staging using the King’s college staging system.ResultsWidespread reduction of white matter integrity occurred in C9orf72 mutation carriers compared with 28 controls. The ALS Functional Rating Scale (ALSFRS-R) and King’s stage correlated with DTI measures of the corticospinal tract and mid-callosum. Cognitive and behavioural scores correlated with diffusion measures of frontal white matter. King’s stage, but not ALSFRS-R, correlated with anterior callosum DTI measures. Over a 6-month follow-up, DTI changes spread from anterior to posterior, and from deep to superficial subcortical white matter. In C9orf72 carriers with ALS or ALS-FTD, changes in corticospinal tractography measures correlated with changes in ALSFRS-R.ConclusionDiscrepancies between DTI findings and clinical measures of disease severity in ALS may partly be accounted for by cognitive-behavioural deficits affecting extramotor white matter tracts. Both ALSFRS-R and King’s stage correlated with corticospinal DTI measures. Group-level DTI changes could be detected over 6 months.

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