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Article

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

1
Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Korea
2
Department of Psychiatry, Hanyang University Guri Hospital, Guri 11923, Korea
3
Department of Psychiatry, College of Medicine, Chosun University, Gwangju 61452, Korea
4
Department of Psychiatry, College of Medicine, Dong-A University, Busan 47392, Korea
5
Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
6
Department of Psychiatry, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
7
Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
8
Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0395, Japan
9
Department of Pharmacy, Taipei City Hospital, Fu Jen University, Taipei 24205, Taiwan
10
School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242062, Taiwan
11
Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
12
Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung & Chang Gung University School of Medicine, Taoyuan 83301, Taiwan
13
Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India
14
Department of Psychiatry, Pushpagiri Institute of Medical Sciences, Tiruvalla 689101, India
15
Department of Psychiatry, Faculty of Medicine, Hasanuddin University, Makassar 90245, Indonesia
16
Tunku Abdul Rahman Institute of Neuroscience, Kuala Lumpur Hospital, Kuala Lumpur 50586, Malaysia
17
Unit of Psychiatry, Department of Public Health and Medicinal Administration, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR 820006, China
18
Institute of Mental Health, Buangkok Green Medical Park, Singapore 539747, Singapore
19
Pakistan Psychiatric Research Centre, Fontain House, Lahore 1317, Pakistan
20
Department of Pharmacology, National University of Singapore, Singapore 119244, Singapore
21
Association for the Improvement of Mental Health Programmes, 1211 Geneva, Switzerland
22
Department of Social Welfare, School of Human Sciences, Seinan Gakuin University, Fukuoka 814-8511, Japan
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2022, 12(1), 33; https://doi.org/10.3390/jpm12010033
Received: 30 November 2021 / Revised: 25 December 2021 / Accepted: 29 December 2021 / Published: 3 January 2022
(This article belongs to the Special Issue Towards Precision Medicine in Psychosis)

Abstract

:
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.

1. Introduction

In psychiatric taxonomy, schizophrenia has been conceptualized as a unitary disease entity. However, the etiopathology, symptomatology, and clinical courses of schizophrenia are heterogeneous [1,2,3]. To explain the heterogeneity of the clinical features of schizophrenia, several models describing the etiology, pathophysiology, and illness have been proposed. Regarding the traditional ‘single common pathway’ construct, it is presumed that the interactions among multiple etiological factors produce neurobiological alterations, which lead to the broadly similar phenotypic expression of ‘single’ schizophrenia through the ‘final common pathophysiological process.’ Contrarily, in the ‘single schizophrenia with many domains’ model, it is presumed that the interactions among multiple etiological factors lead to the multiple concurrent pathophysiological dimension. Moreover, in the ‘many schizophrenias’ model, it is presumed that the interactions among multiple etiological factors lead to different types of the disease with a similar array of symptoms. Thus, precision medicine is required to disentangle the symptom heterogeneity of schizophrenia [2].
Regarding the symptom heterogeneity of schizophrenia, its definitions and boundaries have been changed based on the influences of available diagnostic methods and therapeutic modalities in the past century [3]. The concept of schizophrenia has evolved from Emil Kraepelin’s nosological principle to the description of schizophrenia in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as follows: Kraepelin [4] incorporated hebephrenia, catatonia, and paranoia into a single disease entity of dementia praecox (schizophrenia) since he had noticed that all the patients with hebephrenia, catatonia, and paranoia shared the similar clinical course and outcome, including adolescent or early adult onset, tendency towards deterioration, and an outcome of dementia. Moreover, he made a distinction of dementia praecox from folie circulaire (manic-depressive insanity), which had characteristic features, including episodicity, absence of deterioration, and a more favorable outcome [4,5]. Under the Kraepelinian nosological principle, Zerfahrenheit, which denoted the loss of internal or external connections of the chains of ideas or loss of rational ties between the associations, was proposed as a typical form of thought and language disorder in dementia praecox [5]. Whereas Kraepelin emphasized the importance of the longitudinal course and outcome in defining schizophrenia, both Eugen Bleuler and Kurt Schneider proposed specific symptom criteria [3]. Bleuler [6] stated that not delusion and hallucination but a set of symptoms including loosening of associations, blunted affect, ambivalence, and autism comprise the basic or fundamental symptoms of schizophrenia. Additionally, Schneider [7] defined the 11 first-rank symptoms as the basis of the definition of ‘nuclear’ schizophrenia. Since Bleulerian viewpoints broadly prevailed in the USA by the 1960s, ‘loss of ego boundaries’ was defined as the basic symptom of the DSM-II criteria for schizophrenia. Thus, DSM-II [8] provided the broadest definition of schizophrenia in a historical framework. Contrastingly, the narrowest definition of schizophrenia was included in DSM-III [9]. The boundaries of schizophrenia have been modestly expanded in subsequent editions of the DSM (DSM-III-R, DSM-IV, and DSM-IV-TR) as a reaction to its narrowest definition. It is presumed that the DSM concept of schizophrenia has been defined by an integration of Kraepelinian chronicity, Bleulerian negative symptoms, and Schneiderian positive symptoms [3]. However, this concept has been criticized in terms of the regression to pre-Kraepelinian nosology because its definition has narrowed down into a predominantly chronic delusional-hallucinatory syndrome with the disappearance of non-paranoid schizophrenia (hebephrenia) and oblivion of the constitutive ideas with respect to its psychopathological nature [10]. As a reaction to the Kraepelinian nosological principle, deconstructing schizophrenia or defining psychosis with dimensions and intermediated phenotypes have been proposed [11]. Thus, the DSM-5 concept for schizophrenia has been defined predominantly based on the categorical concept, with the help of the dimensional concept [12].
The current concept of schizophrenia is considered the discrete kind or fuzzy one partly inconsistent of the Kraepelinian nosological principle because its category boundary and essence are still arbitrary and unclear, respectively [13]. The current concept of schizophrenia is constituted not only of DSM symptoms (i.e., delusion, hallucinations, psychomotor disturbance, bizarre thoughts, and negative symptoms) but also non-DSM symptoms (i.e., impaired reality testing, impaired cognition, social withdrawal, depression, and anxiety). Herein, it is presumed that the symptom heterogeneity of schizophrenia is consistent not with the ‘disease essentialism paradigm’ but with the Wittgensteinian analogy of language game as described below [14]:
‘Consider for example the proceedings that we call games. I mean board-games, card-games, ball-games, Olympic games, and so on. What is common to them all?—Don’t say: ‘There must be something common, or they would not be called games’—but look and see whether there is anything common to all.—For if you look at them you will not see something that is common to all, but similarities, relationships, and a whole series of them at that. To repeat: don’t think, but look! … the concept game is a concept with blurred edges.—‘But is a blurred concept a concept at all?’—Is an indistinct photograph a picture of a person at all? Is it even always an advantage to replace an indistinct picture by a sharp one? Isn’t the indistinct one often exactly what we need? [15]’.
In other words, since the cases of schizophrenia are connected by ‘family resemblance’, but not by ‘essence’, the category of schizophrenia is regarded as an operational constitute but not a natural kind [14,16]. Thus, Thomas Insel’s ‘next-generation treatment for mental disorders’ [17,18] can be considered as an alternative approach to disentangle the symptom heterogeneity of schizophrenia. Consistent with the change in the theoretical construct from a chemical imbalance to dysfunctional circuitry, it has been suggested that clinical targets should be changed from the clinical diagnoses (e.g., psychosis, mood disorder, and anxiety disorder) to symptoms or endophenotypes (e.g., amotivation, attentional bias, executive function, anhedonia, social deficit, and working memory). Precision medicine includes tailoring treatments to a specific disease process and parsing the etiology or underlying disease mechanism-based heterogeneous syndrome. Understanding the distinct etiology of a disease process is required to split complex syndromes into etiologically homogeneous subtypes in terms of precision medicine targeting not broad-based but specific etiologies [19]. Regarding precision medicine, differences in the clinical features of the research sample may contribute to hampering the identification of clinically valid and reliable biomarkers of psychosis [20]. A ‘psychonectome’ has been proposed as a complex ensemble of dependencies between psychological constructs to formalize the idea of psychological constructs forming a dynamic network of mutually dependent elements [21]. Herein, it has been proposed that identifying a psychonectome for the symptom heterogeneity of schizophrenia can help establish precision medicine as an efficient intervention for psychosis. Notably, a network analysis may comprise a novel computational method to disentangle the symptom heterogeneity of schizophrenia [22]. Based on the determination of the variables contributing proportionally or disproportionally to the adaptive functioning of the network, the symptom heterogeneity of schizophrenia can be adequately evaluated within an estimated network structure [23]. Since centrality is defined as the overall connectivity of an individual symptom within a network structure, it is suggested that the central symptoms can contribute to the rapid activation of the interrelated symptoms within the network structure and comprise the potential therapeutic targets [24,25]. A network analysis is based on the idea that “symptoms are not outcome factors of an underlying disease; symptoms and the associations between them are the disease itself” [26]. This study aimed to estimate a network structure and identify a psychonectome from the symptom heterogeneity of schizophrenia, using data obtained from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics (REAP-AP) [27,28].

2. Materials and Methods

2.1. Study Overview and Participants

As described elsewhere, the aims of the REAP-AP [27,28], which was one of the largest international research collaborations in Asian countries or special administrative areas, were to examine patterns of psychotropic drug use and their related clinical characteristics, as well as to explore ways of improving psychotropic drug use in Asian patients with schizophrenia. In total, 3744 consecutive patients with schizophrenia were enrolled by the 4th REAP-AP between March and June 2016, from 71 survey centers in 15 Asian countries and areas (Bangladesh, China, Hong Kong, India, Indonesia, Japan, South Korea, Malaysia, Myanmar, Pakistan, Singapore, Sri Lanka, Taiwan, Thailand, and Vietnam). The study protocol and informed consent forms were approved by the institutional review boards of Taipei City Hospital, Taipei, Taiwan (receipt number: TCHIRB-10412128-E) and other hospitals participating in the survey. All the study participants signed informed consent forms prior to participation. Since short or long case report forms could be used depending on the resources available to the participating countries or special administrative areas, the 18-item Brief Psychiatric Rating Scale (BPRS) [29] was used. Thus, in this study, we used only data from the participants who met the following inclusion criteria: (i) diagnosis of schizophrenia, based on DSM-5 [12], by clinical psychiatrists at survey centers, (ii) medication with antipsychotics, coded as the F05A under the Anatomical Therapeutic Chemical (ATC) classification system [30], (iii) age ≥18 and ≤80 years, and (iv) availability of the complete 18-item BPRS [29]. Furthermore, we excluded data from participants who met the following exclusion criteria: (i) comorbidity of organic mental disorders, bipolar disorders, or intellectual disorder; and (ii) comorbid seizure disorders, other neurological diseases, and severe physical disease. Finally, 1438 patients with schizophrenia were enrolled from five Asian countries, including India, Indonesia, Japan, Malaysia, and Taiwan.

2.2. Brief Psychiatric Rating Scale

We used the 18-item BPRS to evaluate the psychiatric symptoms of the participants. All the BPRS items were scored on a 7-point Likert scale from ‘not present’ (1) to ‘very severe’ (7). Its psychometric properties included reliability and validity in addition to others [29,31,32]. Its English version was commonly used by clinical psychiatrists and study coordinators at the survey centers because the study participants were enrolled from five Asian countries with different languages. Based on the DSM-5 criteria for schizophrenia, the BPRS items were divided into DSM symptoms (i.e., emotional withdrawal (EMO), conceptual disorganization (CON), mannerism and posturing (MAN), suspiciousness (SUS), hallucinatory behavior (HAL), motor retardation (MOT), unusual thought content (THO), and blunted affect (BLU)) and non-DSM symptoms (i.e., somatic concern (SOM), anxiety (ANX), guilty feelings (GUI), tension (TEN), grandiosity (GRA), depressive mood (DEP), hostility (HOS), uncooperativeness (UNC), excitement (EXC), and disorientation (DIS)).

2.3. Operational Classification of Psychotropic Drugs

Using the ATC classification system [30], psychotropic drugs were classified into antipsychotics (N05A), mood stabilizers (antiepileptics and lithium; N03A and N05AN), antidepressants (N06A), anxiolytics (N05B), hypnotics (N05C), and antiparkinsonian drugs (N04). Although lithium and clonazepam were defined as antipsychotics and antiepileptics, respectively, under the ATC classification system, they were classified as a mood stabilizer and a hypnotic, respectively, on the basis of conventional grouping. High-dose antipsychotics were operationally defined as either a chlorpromazine equivalent corresponding to a cumulative dose of ≥1000 mg/day [33] or a ratio of prescribed daily dose to defined daily dose ≥1.5 [34].

2.4. Statistical Analyses

Using the R-package qgraph [35], a network structure, which consisted of both nodes (corresponding to symptoms) and edges (corresponding to associations among symptoms), was estimated for 18 items of the BPRS. All the BPRS items were considered to be ordered-categorical variables ranging in a value from 1–7. Due to the cross-sectional characteristic of our data, the network structure was estimated in a unidirectional manner. Network analyses were based on polychoric correlations. Using the least absolute shrinkage and selection operator (LASSO) [36], false-positive edges were controlled, and very small edges were set exactly to zero. Using the graphical LASSO (GLASSO) procedure, since the edges were defined as partial correlation coefficients, the average edge was defined based on the relationship level between two symptoms controlling for all other relationships within the network. Using the shrinkage parameter, the extended Bayesian Information Criterion [37] was minimized, and the underlying network structures were accurately recovered [25]. We used the Frutchterman-Reingold algorithm [38] to place stronger connected nodes closer together with an estimated network structure. Moreover, using a modularity-based community-detecting algorithm, we investigated whether nodes were clustered together within the estimated network structure. We used the spin-glass community algorithm [39] to test whether the number and weighted strength of edges within a cluster exceeded those within another cluster in terms of communities within the network (weights = null, vertex = null, parupdate = false, gamma = 0.5, start temperature = 1, stop temperature = 0.01, cooling factor = 0.99, spins = 17).
Regarding the node statistics, the centrality of all the BPRS items was estimated as follows [40]: Node strength centrality, which was a common and stable metric, denoted the sum of all associations of a given node with all the other nodes. Betweenness centrality denoted the shortest length of a path connecting any two nodes. Closeness centrality denoted the measure of how close a symptom was to all other symptoms. Since node strength centrality was substantially correlated with betweenness centrality and closeness centrality, the most central symptoms within the network structure of all the BPRS items were estimated based on the node strength centrality, betweenness centrality, and closeness centrality. Using a permutation test [41], the node statistics across different symptom groups (i.e., DSM and non-DSM symptoms) were compared. By assigning symptoms randomly to the two groups 100,000 times, the difference between the groups at each time was estimated. If the difference between the two groups was observed within 2.5% on either side of the distribution, the test significance was set at p < 0.05. Using a correlation stability coefficient (CS-coefficient), the centrality stability was operationally defined since the CS-coefficient denotes the maximum proportion of cases that can be eliminated to obtain a 95% probability that the ranking correlation between the original network and case-subset network would amount to a very large effect (0.7) [42]. Thus, solely interpreting centrality indices with a CS-coefficient >0.25, but preferentially >0.5, was recommended [43]. Using 95% nonparametric bootstrap confidence intervals (1000 bootstrap samples) of differences between each pair of centrality indices, significant differences between centrality indices were identified.

3. Results

3.1. General Description of the Study Participants

As shown in Table 1, the cohort consisted of Indian (n = 400, 27.8%), Indonesian (n = 261, 18.2%), Japanese (n = 98, 6.8%), Malaysian (n = 299, 20.8%), and Taiwanese (n = 380, 26.4%) participants. Approximately half of them were male (n = 830, 57.7%) and had a duration of illness >10 years (n = 788, 54.8%), and 46.2% of the cohort were inpatients (n = 664). Additionally, approximately one-third of them had a duration of untreated psychosis >1 year (n = 422, 29.4%). The mean age was 39.9 (standard deviation [SD] = 12.5) years. Regarding the patterns of psychotropic drug use, approximately one-third were treated with antipsychotic polypharmacy (n = 536, 37.3%) and adjunctive antiparkinsonian drugs (n = 580, 38.9%). Table 2 lists the response frequency distributions of BPRS items.

3.2. Edge Statistics

As shown in Figure 1, the construction of a network of the 18 BPRS items revealed that 102 (66.7%) out of 153 possible edges were estimated to be >0. Several interconnections including SOM–ANX (weight = 0.463), EMO–BLU (weight = 0.406), HOS–UNC (weight = 0.397), ANX–TEN (weight = 0.352), GUI–DEP (weight = 0.342), HOS–SUS (weight = 0.329), and others were revealed within the network. The 18 BPRS items were organized into three meaningful clusters by the community-detection analysis. Cluster A consisted of CON–MAN–GRA–HOS–SUS–HAL–UNC–THO–EXC–DIS, cluster B consisted of EMO–MOT–BLU, and cluster C consisted of SOM–ANX–GUI–TEN–DEP.

3.3. Node Statistics

As shown in Figure 1 and Figure 2, regarding the inspection of the node strength centrality of BPRS items, MOT was the most centrally situated BPRS item within the network, followed by DEP, THO, EXC, ANX, and HOS. Contrastingly, HAL was the most poorly interconnected BPRS item within the network, followed by SOM, TEN, DIS, EMO, and UNC. Node strength centrality revealed an interpretable level of CS-coefficient (0.361), whereas betweenness centrality and closeness centrality revealed low levels of CS-coefficients (0.128 and 0.206). As shown in Figure 3, there were no fundamental differences between DSM and non-DSM symptoms regarding the difference tests analyzing node strength centrality (p = 0.814), betweenness centrality (p = 0.831), and closeness centrality (p = 0.758).

4. Discussion

In summary, motor retardation (MOT), depressive mood (DEP), and unusual thought content (THO) were estimated as the top three central symptoms within the network structure, followed by excitement (EXC), anxiety (ANX), and hostility (HOS). Contrarily, hallucinatory behavior (HAL) was the most poorly interconnected BPRS item within the network structure. Furthermore, overall, DSM symptoms were not more central than non-DSM symptoms within the BPRS items network of Asian patients with schizophrenia. Moreover, 18 BPRS items were organized into three meaningful symptom clusters, including the positive (CON–MAN–GRA–HOS–SUS–HAL–UNC–THO–EXC–DIS), negative (EMO–MOT–BLU), and general symptom clusters (SOM–ANX–GUI–TEN–DEP). Finally, SOM–ANX, EMO–BLU, CON–THO, HOS–UNC, and ANX–TEN interconnections were the top five strongest associations within the networks, followed by MOT–BLU, HOS–SUS, and MAN–EXC interconnections.
It has been presumed that central symptoms may comprise the pharmacological therapeutic targets because central symptoms can contribute to the rapid activation of intertwined symptoms within the network [24,25]. It has been proposed that psychomotor retardation and its biochemical modulation are regarded as the paradigmatic example of a dimensional approach in the Research Domain Criteria. It has been known that psychomotor retardation is neurobiologically underpinned by three mechanisms, including (i) the modulation of substantia nigra-based subcortical–cortical motor circuit primarily by the non-motor subcortical raphe nucleus via the basal ganglia, (ii) modulation of the motor network by non-motor cortical networks such as default-mode and sensory networks, and (iii) shaping the regional distribution of neural activity within the motor cortex by global cortical activity. Moreover, it has been suggested that the operation of psychomotor mechanisms can be performed in a dimensional and transdiagnostic manner, not based on the diagnostic category but on the levels of psychomotor activity [44]. According to findings from previous neuroimaging studies, it has been proposed that abnormalities in the sensorimotor domain are related to the dysfunction of the cerebello-thalamo-cortico-cerebellar network [45]. Additionally, it has been reported that psychomotor slowing is positively related to negative symptoms and mania, regardless of the diagnostic category, among individuals with schizophrenia, schizoaffective disorder, bipolar disorder, and others [46]. Thus, it can be proposed that motor retardation may be the dimensionally defined core constitute of the psychonectome underpinned by the neural network (i.e., the cerebello-thalamo-cortico-cerebellar network) in Asian patients with schizophrenia. It has been speculated that motor retardation may be consistent with the unmet needs of antipsychotic medications for schizophrenia [47]. Thus, from the perspective of precision medicine, targeting the non-dopaminergic systems (glutamate and γ-aminobutyric acid) should be considered [48]. In addition to these networks, the pulvinar nucleus of the thalamus is an important neural structure that can be involved in cognitive, sensory, and motor deficits in patients with schizophrenia. The pulvinar nucleus is the largest nucleus in the thalamus and is mutually connected to several cortical and subcortical regions, including the prefrontal cortex, sensory cortex, superior colliculus, and amygdala [49]. Thus, the pulvinar nucleus plays an important role in normal multisensory processing, emotional response, and decision making, which are significantly impaired in patients with schizophrenia [50,51,52]. A positive relationship has been reported between performance on working memory and activation in the pulvinar nucleus and other structures [53]. Herein, structural and functional abnormalities of the pulvinar nucleus may be a neurobiological underpinning for the motor retardation-centered symptom networks observed in our study. Moreover, it has been reported that the measure of depressive mood is related to the neurobiological dysfunction for reward prediction in transnosological samples, including patients with schizophrenia, alcohol dependence, major depression, bipolar disorder, and attention-deficit/hyperactivity disorder [54]. Further, unusual thought content, in addition to conceptual disorganization and difficulty in abstract thinking, is regarded as a symptom combination predictor for treatment-resistant schizophrenia in clinical practice [55]. Hallucinations comprise one of the hallmark symptoms of schizophrenia and an important treatment target. [56,57] It has also been reported that antipsychotics are a rapid and efficient intervention for hallucinations. Notably, the differential antihallucinatory effects of olanzapine, amisulpride, and aripiprazole have been demonstrated previously [58]. Since the duration of illness in approximately half of the study participants was >10 years in our study, it can be speculated that the hallucinatory behaviors may be the most interconnected BPRS item within the estimated network structure. Since DSM symptoms were not more central than non-DSM symptoms within the BPRS items network, it can be speculated that not only DSM symptoms but also non-DSM symptoms may be considered as therapeutic targets. Moreover, the organization of BPRS items into positive, negative, and general symptom clusters may help match the specific treatment to the relevant symptom cluster.
Our study has several limitations. First, the inter-rater reliability to assess clinical characteristics including the 18-item BPRS was not measured. Second, since our data were collected in a cross-sectional manner, the networks were unidirectionally estimated. However, the differentiation between out-degree centrality and in-degree centrality can be allowed for longitudinal studies. Third, the duration of illness of approximately half of the participants was >10 years. It cannot be excluded that the chronic features of schizophrenia can potentially influence the network structure of the BPRS items. Fourth, the floor and ceiling effects on the network structure cannot be excluded. However, the Pearson correlation coefficient (r) between the standard deviation and node strength centrality of the BPRS items was negligible (−0.12). A potential modified 18-item BPRS, which has been proposed by Sawamura and colleagues [59], can be used in further network analysis studies to overcome these floor and ceiling effects.

5. Conclusions

Despite the limitations, our findings can help estimate the network structure of the BPRS items to disentangle the heterogeneity of symptom presentation in Asian patients with schizophrenia. Notably, our findings indicate that motor retardation, which is underpinned by non-dopaminergic systems (glutamate, γ-aminobutyric acid), may be an important therapeutic target for individuals with schizophrenia.

Author Contributions

Conceptualization, C.H.T., N.S. (Norman Sartorius), S.K., N.S. (Naotaka Shinfuku), Y.C.P. and S.-C.P.; methodology, C.H.T., N.S. (Norman Sartorius), S.K., N.S. (Naotaka Shinfuku), Y.C.P. and S.-C.P.; software, J.C. and S.-C.P.; validation, J.C. and S.-C.P.; formal analysis, J.C. and S.-C.P.; investigation, S.-C.P., J.C., H.-J.Y., J.H.P., Y.N., C.K., T.I., T.A.K., S.-Y.Y., S.-K.L., M.-Y.C., A.A., S.G., R.A.K., A.J.T., K.Y.C., Y.-T.X., K.S., A.J., C.H.T., N.S. (Norman Sartorius), S.K., N.S. (Naotaka Shinfuku), Y.C.P. and S.-C.P.; resources, all authors; data curation, all authors; writing---original draft preparation, J.C. and S.-C.P.; writing---review and editing, all authors; visualization, J.C. and S.-C.P.; supervision, J.C. and S.-C.P.; project administration, all authors; funding acquisition, S.-C.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by a grant, the Schizophrenia Research Grant for Psychiatrists (2020), from the Korean Society for Schizophrenia Research.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Taipei City Hospital, Taipei, Taiwan (receipt number: TCHIRB-10412128-E).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data sharing not applicable.

Acknowledgments

The authors are grateful to the REAP-AP colleagues. We would also like to thank Jinseob Kim for his valuable help with the network analysis.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Tandon, R.; Keshavan, M.S.; Nasrallah, H.A. Schizophrenia, “just the facts”: What we know in 2008. Part 1: Overview. Schizophr. Res. 2008, 100, 4–19. [Google Scholar] [CrossRef]
  2. Tandon, R.; Keshavan, M.S.; Nasrallah, H.A. Schizophrenia, “just the facts”: What we know in 2008. 2. Epidemiology and etiology. Schizophr. Res. 2008, 102, 1–18. [Google Scholar] [CrossRef] [PubMed]
  3. Tandon, R.; Nasrallah, H.A.; Keshavan, M.S. Schizophrenia, “just the facts.” 4. Clinical features and conceptualization. Schizophr. Res. 2009, 110, 1–23. [Google Scholar] [CrossRef]
  4. Kraepelin, E. Dementia Praecox and Hebephrenia, 1919; Robertson, G., Ed.; Krieger: New York, NY, USA, 1971. [Google Scholar]
  5. Kraepelin, E. Psychiatry—A Textbook for Students and Doctors, 1899; Acanet: Seoul, Korea, 2021. [Google Scholar]
  6. Bleuler, E. Dementia Praecox or the Groups of Schizophrenia, 1911; International University Press: New York, NY, USA, 1950. [Google Scholar]
  7. Schneider, K. Clinical Psychopathology, 1946; Grune and Stratton: New York, NY, USA, 1959. [Google Scholar]
  8. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 2nd ed.; (DSM-II); American Psychiatric Publishing, Inc.: Washington, DC, USA, 1968. [Google Scholar]
  9. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 3rd ed.; (DSM-III); American Psychiatric Publishing, Inc.: Washington, DC, USA, 1980. [Google Scholar]
  10. Parnas, I. Philosophical Issues in Psychiatry II: Nosology; Oxford University Press: Oxford, UK, 2012. [Google Scholar]
  11. Tammiga, C.A.; Sirovatka, P.J.; Regier, D.A.; van Os, J. Deconstructing Psychosis:Refining the Research Agenda for DSM-V; American Psychiatric Publishing, Inc.: Washington, DC, USA, 2009. [Google Scholar]
  12. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; (DSM-5); American Psychiatric Publishing, Inc.: Washington, DC, USA, 2013. [Google Scholar]
  13. Kincaid, H.; Sullivan, J.A. Classifying Psychopathology: Mental Kinds and Natural Kinds; The MIT Press: New York, NY, USA, 2014. [Google Scholar]
  14. Jeong, S.H. “Does schizophrenia exist?”: A number of related questions in psychiatric nosology. Korean J. Schizophr. Res. 2008, 11, 78–88. [Google Scholar]
  15. Wittgenstein, L. Philosophical Investigations. (German Text, with a Revised English Translation, Trans GEM Anscombe), 3rd ed.; Blackwell: Oxford, UK, 2001. [Google Scholar]
  16. Rosenman, S.; Nasti, J. Psychiatric diagnoses are not mental processes: Wittgenstein on conceptual confusion. Aust. N. Z. J. Psychiatry 2012, 46, 1046–1052. [Google Scholar] [CrossRef]
  17. Insel, T.R. Next-generation treatments for mental disorders. Sci. Transl. Med. 2012, 4, 155psc19. [Google Scholar] [CrossRef]
  18. Insel, T.R.; Cuthbert, B.N. Endophenotypes: Bridging genomic complexity and disorder heterogeneity. Biol. Psychiatry 2009, 66, 988–989. [Google Scholar] [CrossRef]
  19. Posner, J. The role of precision medicine in child psychiatry: What can we expect and when? J. Am. Acad. Child Adolesc. Psychiatry 2018, 57, 813–817. [Google Scholar] [CrossRef] [PubMed]
  20. Tognin, S.; van Hell, H.H.; Merritt, K.; Winter-van Rossum, I.; Bossong, M.G.; Kempton, M.J.; Modinos, G.; Fusar-Poli, P.; Mechelli, A.; Dazzan, P.; et al. Towards precision medicine in psychosis: Benefits and challenges of multimodal multicenter studies–PSYSCAN: Translating neuroimaging findings from research into clinical practice. Schizophr. Bull. 2020, 46, 432–441. [Google Scholar] [CrossRef]
  21. Roca, P.; Diez, G.G.; Castellanos, N.; Vazquez, C. Does mindfulness change mind? A novel psychonectome perspective based on network analysis. PLoS ONE 2019, 14, e0219793. [Google Scholar] [CrossRef] [PubMed][Green Version]
  22. Saxe, G.N. Network psychiatry: Computational methods to understand the complexity of psychiatric disorders. J. Am. Acad. Child. Adolesc. Psychiatry 2017, 56, 639–641. [Google Scholar] [CrossRef]
  23. Barabasi, A.L. Network medicine form obesity to the “diseasome”. N. Engl. J. Med. 2017, 357, 404–407. [Google Scholar] [CrossRef][Green Version]
  24. Armour, C.; Fried, E.I.; Deserno, M.K.; Tsai, J.; Pietrzak, R.H. A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. J. Affect. Disord. 2017, 45, 49–59. [Google Scholar] [CrossRef]
  25. Van Borkulo, C.; Boschloo, L.; Borsboom, D.; Penninx, B.W.J.H.; Waldorp, L.J.; Schoevers, R.A. Association of symptom network structure with the course of depression. JAMA Psychiatry 2015, 72, 1219–1226. [Google Scholar] [CrossRef] [PubMed]
  26. McNally, R.J.; Robinaugh, D.J.; Wu, G.W.; Wang, L.; Deserno, M.K.; Borsboom, D. Mental disorders as causal systems a network approach to posttraumatic stress disorder. Clin. Psychol. Sci. 2015, 23, 836–849. [Google Scholar] [CrossRef][Green Version]
  27. Park, Y.C.; Lee, M.S.; Si, T.M.; Chiu, H.F.K.; Kanba, S.; Chong, M.-Y.; Tripathi, A.; Udomratn, P.; Chee, K.Y.; Tanra, A.J.; et al. Psychotropic Drug-Prescribing Correlates of Disorganized Speech in Asians with Schizophrenia: The REAP-AP Study. Saudi Pharm. J. 2019, 27, 246–253. [Google Scholar] [CrossRef]
  28. Park, Y.C.; Kanba, S.; Chong, M.-Y.; Tripathi, A.; Kallivayalil, R.A.; Avasthi, A.; Grover, S.; Chee, K.Y.; Tanra, A.J.; Maramis, M.M.; et al. To Use the Brief Psychiatric Rating Scale to Detect Disorganized Speech in Schizophrenia: Findings from the REAP-AP Study. Kaohsiung J. Med. Sci. 2018, 34, 113–119. [Google Scholar] [CrossRef] [PubMed]
  29. Overall, J.E.; Gorham, D.R. The brief psychiatric rating scale. Psychol. Rep. 1962, 10, 779–812. [Google Scholar] [CrossRef]
  30. World Health Organization. Anatomical Therapeutic Chemical (ATC) Classification System. Available online: https://www.whocc.no/atc/structure_and_principles/ (accessed on 18 January 2021).
  31. Leucht, S.; Kane, J.M.; Kissling, W.; Hamann, J.; Etschel, E.; Engel, R. Clinical implications of brief psychiatric rating scale score. Br. J. Psychiatry 2005, 187, 366–371. [Google Scholar] [CrossRef]
  32. Hedlund, J.L.; View, B.W. The brief psychiatric rating scale: A comprehensive review. J. Oper. Psychiatr. 1980, 11, 48–65. [Google Scholar]
  33. Sim, K.; Su, A.; Leong, I.Y.; Yip, K.; Chong, M.Y.; Fujii, S.; Yang, S.; Ungvari, G.S.; Si, T.; Chung, E.K.; et al. High dose antipsychotic use in schizophrenia: Findings of the REAP (Research on East Asian Psychotropic Prescriptions) study. Pharmacopsychiatry 2004, 37, 175–179. [Google Scholar] [CrossRef]
  34. Tihonen, J.; Mittendorfer-Rutz, E.; Tornianen, M.; Alexanderson, K.; Tanskanen, A. Mortality and cumulative exposure to antipsychotics, antidepressants, and benzodiazepine in patients with schizophrenia: An observational follow-up study. Am. J. Psychiatry 2016, 173, 600–606. [Google Scholar] [CrossRef] [PubMed]
  35. Epskamp, S.; Cramer, A.O.J.; Waldorp, L.J.; Schmittman, V.D.; Borsboom, D. Qgraph: Network visualizations of relationships in psychometric data. J. Stat. Softw. 2012, 48, 1–18. [Google Scholar] [CrossRef][Green Version]
  36. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 1996, 58, 267–288. [Google Scholar] [CrossRef]
  37. Chen, J.; Chen, Z. Extended Bayesian information criteria for model selection with large model speaces. Biometrika 2008, 95, 759–771. [Google Scholar] [CrossRef][Green Version]
  38. Reichardt, J.; Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 2006, 74, 016110. [Google Scholar] [CrossRef][Green Version]
  39. Csardi, G.; Nepusz, T. The igraph software package for complex network research. Inter J. Complex Syst. 2006, 1695, 1–9. [Google Scholar]
  40. Opsahl, T.; Agneessens, F.; Skvoretz, J. Node centrality in weighted networks: Generalizing degrees and shortest paths. Soc. Netw. 2010, 32, 245–251. [Google Scholar] [CrossRef]
  41. Fried, E.I.; Epskamp, S.; Nesse, R.M.; Tuerlinckx, F.; Borsboom, D. What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. J. Affect. Disord. 2016, 189, 314–329. [Google Scholar] [CrossRef]
  42. Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Revised Edition); Academic Press: New York, NY, USA, 1977. [Google Scholar]
  43. Epskamp, S.; Borsboom, D.; Fried, E.I. Estimating psychological networks and their accuracy: A tutorial paper. Behav. Res. Methods 2018, 50, 195–212. [Google Scholar] [CrossRef] [PubMed][Green Version]
  44. Northoff, G.; Hirjak, D.; Wolf, R.C.; Magioncalda, P.; Martino, M. All roads lead to the motor cortex: Psychomotor mechanisms and their biochemical modulation in psychiatric disorders. Mol. Psychiatry 2021, 26, 92–102. [Google Scholar] [CrossRef]
  45. Hirjak, D.; Meyer-Lindenberg, A.; Sambataro, F.; Fritze, S.; Kukovic, J.; Kubera, K.K.; Wolf, R.C. Progress in sensorimotor neuroscience of schizophrenia spectrum disorders: Lessens learned and future directions. Prog. Neuropsychopharmacol. Biol. Psychiatry 2021, 111, 110370. [Google Scholar] [CrossRef] [PubMed]
  46. Kent, J.S.; Disner, S.G.; van Voorhis, A.C.; Urosevic, S.; Caligiuri, M.P.; Sponheim, S.R. Exploring the relationship of transdiagnostic mood and psychosis symptom domains with mood dysfunction. Neuropsychobiology 2020, 79, 301–312. [Google Scholar] [CrossRef]
  47. Kim, Y.-K.; Choi, J.; Park, S.-C. A novel bio-psychosocial-behavioral treatment model in schizophrenia. Int. J. Mol. Sci. 2017, 18, 734. [Google Scholar] [CrossRef]
  48. Keshavan, M.S.; Lawler, A.N.; Nasrallah, H.A.; Tandon, R. New drug developments in psychosis: Challenges, opportunities and strategies. Prog. Neurobiol. 2017, 152, 3–30. [Google Scholar] [CrossRef][Green Version]
  49. Zhou, N.; Masterson, S.P.; Damron, J.K.; Guido, W.; Bickford, M. The mouse pulvinar nucleus links the lateral extrastriate cortex, striatum, and amygdala. J. Neurosci. 2018, 38, 347–362. [Google Scholar] [CrossRef][Green Version]
  50. Chou, X.-L.; Fang, Q.; Yan, L.; Zhong, W.; Peng, B.; Li, H.; Wei, J.; Tao, H.W.; Zhang, L.I. Contextual and cross-modality modulation of auditory cortical processing through pulvinar mediated suppression. Elife 2020, 9, e54157. [Google Scholar] [CrossRef] [PubMed]
  51. Fang, Q.; Chou, X.-L.; Peng, B.; Zhong, W.; Zhang, L.I.; Tao, H.W. A differential circuit via retiono-colliculo-pulvinar pathway enhances feature selectively in visual cortex through surround suppression. Neuron 2020, 105, 355–369. [Google Scholar] [CrossRef]
  52. Ibrahim, L.A.; Mesik, L.; Ji, X.-Y.; Fang, Q.; Li, H.-F.; Li, Y.-T.; Zingg, B.; Zhang, L.I.; Tao, H.W. Cross-modality sharpening of visual cortical processing through layer-1-mediated inhibition and disinhibition. Neuron 2016, 89, 1031–1045. [Google Scholar] [CrossRef] [PubMed][Green Version]
  53. Andrews, J.; Wang, L.; Csernansky, J.G.; Gado, M.H.; Barch, D.M. Abnormalities of thalamic activation and cognition in schizophrenia. Am. J. Psychiatry 2006, 163, 463–469. [Google Scholar] [CrossRef] [PubMed]
  54. Hagele, C.; Schlagenhauf, F.; Rapp, M.; Sterzer, P.; Beck, A.; Bermpohl, F.; Stoy, M.; Strohle, A.; Wittchen, H.U.; Dolan, R.J.; et al. Dimensional psychiatry: Reward dysfunction and depressive mood across psychiatric disorders. Psychopharmacology 2015, 232, 331–341. [Google Scholar] [CrossRef][Green Version]
  55. Ortiz, B.B.; Higuch, C.H.; Noto, C.; Joyce, D.W.; Correll, C.U.; Bressan, R.A.; Gadelha, A. A symptom combination predicting treatment-resistant schizophrenia—A strategy for real-world clinical practice. Schizophr. Res. 2020, 218, 195–200. [Google Scholar] [CrossRef] [PubMed]
  56. McCarthy-Jones, S.; Smailes, D.; Corvin, A.; Gill, M.; Morris, D.W.; Dinan, T.G.; Murphy, K.C.; Neil, F.A.O.; Waddington, J.L.; Austrailian Schizophrenia Research Bank; et al. Occurrence and co-occurrence of hallucinations by modality in schizophrenia-spectrum disorders. Psychiatry Res. 2017, 252, 154–160. [Google Scholar] [CrossRef] [PubMed][Green Version]
  57. Dugre, J.R.; West, M.L. Disentangling compliance with command hallucinations in schizophrenia spectrum disorders. Schizophr. Bull. 2019, 212, 33–39. [Google Scholar]
  58. Sinkeviciute, I.; Hugdahl, K.; Bartz-Johannessen, C.; Kroken, R.A.; Loberg, E.-M.; Kjelby, E.; Rettenbacher, M.A.; Joa, I.; Reitan, S.K.; Alisauskiene, R.; et al. Differential effectiveness of atypical antipsychotics on hallucinations: A pragmatic randomized controlled trial. J. Clin. Psychopharmacol. 2021, 41, 389–396. [Google Scholar] [CrossRef]
  59. Sawamur, J.; Morishita, S.; Ishigooka, J. A modification of the relative weightings of symptoms utilizing a logistic function to enhance the linearity of the Brief Psychiatric Rating Scale: A retrospective analysis. J. Behav. Brain Sci. 2012, 2, 225–238. [Google Scholar] [CrossRef][Green Version]
Figure 1. Network structure of the Brief Psychiatric Rating Scale items in Asian patients with schizophrenia (n = 1438; CS-coefficient = 0.361). Green lines represent positive associations, whereas red lines represent negative associations between the connecting nodes. The thickness of the lines represents the strength of the edges. Abbreviations: ANX, anxiety; BLU, blunted affect; CON, conceptual disorganization; DEP, depressive mood; DIS, disorientation; EMO, emotional withdrawal; EXC, excitement; GRA, grandiosity; GUI, guilt feelings; HAL, hallucinatory behavior; HOS, hostility; MAN, mannerism and posturing; MOT, motor retardation; SOM, somatic concern; SUS, suspiciousness; THO, unusual thought content; UNC, uncooperativeness.
Figure 1. Network structure of the Brief Psychiatric Rating Scale items in Asian patients with schizophrenia (n = 1438; CS-coefficient = 0.361). Green lines represent positive associations, whereas red lines represent negative associations between the connecting nodes. The thickness of the lines represents the strength of the edges. Abbreviations: ANX, anxiety; BLU, blunted affect; CON, conceptual disorganization; DEP, depressive mood; DIS, disorientation; EMO, emotional withdrawal; EXC, excitement; GRA, grandiosity; GUI, guilt feelings; HAL, hallucinatory behavior; HOS, hostility; MAN, mannerism and posturing; MOT, motor retardation; SOM, somatic concern; SUS, suspiciousness; THO, unusual thought content; UNC, uncooperativeness.
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Figure 2. Node statistics including node strength centrality, closeness centrality, and betweenness centrality (n = 1438). Abbreviations: ANX, anxiety; BLU, blunted affect; CON, conceptual disorganization; DEP, depressive mood; DIS, disorientation; EMO, emotional withdrawal; EXC, excitement; GRA, grandiosity; GUI, guilt feelings; HAL, hallucinatory behavior; HOS, hostility; MAN, mannerism and posturing; MOT, motor retardation; SOM, somatic concern; SUS, suspiciousness; THO, unusual thought content; UNC, uncooperativeness.
Figure 2. Node statistics including node strength centrality, closeness centrality, and betweenness centrality (n = 1438). Abbreviations: ANX, anxiety; BLU, blunted affect; CON, conceptual disorganization; DEP, depressive mood; DIS, disorientation; EMO, emotional withdrawal; EXC, excitement; GRA, grandiosity; GUI, guilt feelings; HAL, hallucinatory behavior; HOS, hostility; MAN, mannerism and posturing; MOT, motor retardation; SOM, somatic concern; SUS, suspiciousness; THO, unusual thought content; UNC, uncooperativeness.
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Figure 3. Comparison of node statistics of DSM symptoms and non-DSM symptoms within the network structure of BPRS items in Asian patients with schizophrenia (n = 1438; Difference test p-value = 0.814). Green lines represent positive associations, whereas red lines represent negative associations between the connecting nodes. The thickness of the lines represents the strength of the edges. Abbreviations: ANX, anxiety; BLU, blunted affect; CON, conceptual disorganization; DEP, depressive mood; DIS, disorientation; EMO, emotional withdrawal; EXC, excitement; GRA, grandiosity; GUI, guilt feelings; HAL, hallucinatory behavior; HOS, hostility; MAN, mannerism and posturing; MOT, motor retardation; SOM, somatic concern; SUS, suspiciousness; THO, unusual thought content; UNC, uncooperativeness.
Figure 3. Comparison of node statistics of DSM symptoms and non-DSM symptoms within the network structure of BPRS items in Asian patients with schizophrenia (n = 1438; Difference test p-value = 0.814). Green lines represent positive associations, whereas red lines represent negative associations between the connecting nodes. The thickness of the lines represents the strength of the edges. Abbreviations: ANX, anxiety; BLU, blunted affect; CON, conceptual disorganization; DEP, depressive mood; DIS, disorientation; EMO, emotional withdrawal; EXC, excitement; GRA, grandiosity; GUI, guilt feelings; HAL, hallucinatory behavior; HOS, hostility; MAN, mannerism and posturing; MOT, motor retardation; SOM, somatic concern; SUS, suspiciousness; THO, unusual thought content; UNC, uncooperativeness.
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Table 1. General description of the study participants (n = 1438).
Table 1. General description of the study participants (n = 1438).
Continuous VariableMeanSD
Age (years)39.912.5
Chlorpromazine equivalent dose (mg/day)501.5396.5
Discrete variablen%
Sex
Male83057.7
Female60842.3
Country
India40027.8
Indonesia26118.2
Japan986.8
Malaysia29920.8
Taiwan38026.4
Duration of illness
<3 months523.6
3–6 months382.6
6–12 months463.2
1–5 years27419.1
5–10 years24016.7
10–20 years42929.8
>20 years35925.0
Duration of untreated psychosis
<3 months52436.4
3–12 months49234.2
1–5 years27018.8
>5 years15210.6
Inpatient66446.2
Unemployed23716.5
Antipsychotic polypharmacy53637.3
Adjunctive mood stabilizer1429.9
Adjunctive antidepressant1299.0
Adjunctive antiparkinsonian56038.9
High dose antipsychotic 16111.2
Long-acting injectable antipsychotic30521.2
Clozapine29320.4
Electroconvulsive therapy422.9
Cannabis use (lifetime)1198.3
Cumulative dose of ≥1000 mg/day chlorpromazine equivalent or a ratio of prescribed daily dose (PDD) to the defined daily dose (DDD) ≥1.5. SD, standard deviation.
Table 2. Mean (SD) and % score of the Brief Psychiatric Rating Scale items (n = 1438).
Table 2. Mean (SD) and % score of the Brief Psychiatric Rating Scale items (n = 1438).
ItemsAbbreviationMean (SD)1234567
n (%)n (%)n (%)n (%)n (%)n (%)n (%)
Somatic concernSOM1.8 (1.2)840 (58.4)258 (17.9)199 (13.8)87 (6.1)37 (2.6)13 (0.9)4 (0.3)
AnxietyANX2.1 (1.2)596 (41.4)348 (24.2)290 (20.2)143 (9.9)44 (3.1)16 (1.1)1 (0.1)
Emotional withdrawalEMO2.5 (1.5)511 (35.5)274 (19.1)294 (20.4)221 (15.4)85 (5.9)42 (2.9)11 (0.8)
Conceptual disorganizationCON2.4 (1.5)599 (41.7)251 (17.5)265 (18.4)176 (12.2)102 (7.1)32 (2.2)13 (0.9)
Guilt feelingsGUI1.5 (0.9)1016 (70.7)237 (16.5)140 (9.7)32 (2.2)7 (0.5)4 (0.3)2 (0.1)
TensionTEN2.0 (1.1)656 (45.6)336 (23.4)286 (19.9)121 (8.4)31 (2.2)8 (0.6)0 (0.0)
Mannerism and posturingMAN1.5 (1.0)1090 (75.8)148 (10.3)117 (8.1)56 (3.9)18 (1.3)9 (0.6)0 (0.0)
GrandiosityGRA1.5 (1.0)1107 (77.0)140 (9.7)97 (6.7)63 (4.4)14 (1.0)12 (0.8)5 (0.3)
Depressive moodDEP1.8 (1.1)804 (55.9)301 (20.9)233 (16.2)74 (5.1)14 (1.0)9 (0.6)3 (0.2)
HostilityHOS1.9 (1.3)807 (56.1)256 (17.8)169 (11.8)136 (9.5)47 (3.3)15 (1.0)8 (0.6)
SuspiciousnessSUS2.4 (1.5)587 (40.8)248 (17.2)269 (18.7)201 (14.0)81 (5.6)41 (2.9)11 (0.8)
Hallucinatory behaviorHAL2.6 (1.6)530 (36.9)234 (16.3)259 (18.0)196 (13.6)118 (8.2)80 (5.6)21 (1.5)
Motor retardationMOT1.7 (1.2)898 (62.4)244 (17.0)158 (11.0)82 (5.7)37 (2.6)15 (1.0)4 (0.3)
UncooperativenessUNC1.9 (1.2)814 (56.6)273 (19.0)182 (12.7)103 (7.2)38 (2.6)22 (1.5)6 (0.4)
Unusual thought contentTHO2.5 (1.6)578 (40.2)239 (17.5)251 (17.5)181 (12.6)118 (8.2)54 (3.8)17 (1.2)
Blunted affectBLU2.3 (1.4)572 (39.8)285 (19.8)285 (19.8)162 (11.3)84 (5.8)43 (3.0)7 (0.5)
ExcitementEXC1.6 (1.2)1001 (69.6)184 (12.8)113 (7.9)77 (5.4)49 (3.4)13 (0.9)1 (0.1)
DisorientationDIS1.4 (0.8)1144 (79.6)162 (11.3)87 (6.1)30 (2.1)7 (0.5)3 (0.2)5 (0.3)
BPRS, Brief Psychiatric Rating Scale; SD, standard deviation.
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Choi, J.; Yoon, H.-J.; Park, J.H.; Nakagami, Y.; Kubota, C.; Inada, T.; Kato, T.A.; Yang, S.-Y.; Lin, S.-K.; Chong, M.-Y.; Avasthi, A.; Grover, S.; Kallivayalil, R.A.; Tanra, A.J.; Chee, K.Y.; Xiang, Y.-T.; Sim, K.; Javed, A.; Tan, C.H.; Sartorius, N.; Kanba, S.; Shinfuku, N.; Park, Y.C.; Park, S.-C. Network Analysis-Based Disentanglement of the Symptom Heterogeneity in Asian Patients with Schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics. J. Pers. Med. 2022, 12, 33. https://doi.org/10.3390/jpm12010033

AMA Style

Choi J, Yoon H-J, Park JH, Nakagami Y, Kubota C, Inada T, Kato TA, Yang S-Y, Lin S-K, Chong M-Y, Avasthi A, Grover S, Kallivayalil RA, Tanra AJ, Chee KY, Xiang Y-T, Sim K, Javed A, Tan CH, Sartorius N, Kanba S, Shinfuku N, Park YC, Park S-C. Network Analysis-Based Disentanglement of the Symptom Heterogeneity in Asian Patients with Schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics. Journal of Personalized Medicine. 2022; 12(1):33. https://doi.org/10.3390/jpm12010033

Chicago/Turabian Style

Choi, Joonho, Hyung-Jun Yoon, Jae Hong Park, Yukako Nakagami, Chika Kubota, Toshiya Inada, Takahiro A. Kato, Shu-Yu Yang, Sih-Ku Lin, Mian-Yoon Chong, Ajit Avasthi, Sandeep Grover, Roy Abraham Kallivayalil, Andi Jaylangkara Tanra, Kok Yoon Chee, Yu-Tao Xiang, Kang Sim, Afzal Javed, Chay Hoon Tan, Norman Sartorius, Shigenobu Kanba, Naotaka Shinfuku, Yong Chon Park, and Seon-Cheol Park. 2022. "Network Analysis-Based Disentanglement of the Symptom Heterogeneity in Asian Patients with Schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics" Journal of Personalized Medicine 12, no. 1: 33. https://doi.org/10.3390/jpm12010033

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