# A network analysis of catatonia symptoms across diagnoses.

Christophe Gauld, Paul Expert, Thomas Fovet, Ali Amad

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Christophe Gauld, Paul Expert, Thomas Fovet, Ali Amad. A network analysis of catatonia symptoms across diagnoses. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2024, Progress in Neuro-Psychopharmacology and Biological Psychiatry, 134, pp.111082. 10.1016/j.pnpbp.2024.111082. hal-04676359

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# A network analysis of catatonia symptoms across diagnoses

Christophe Gauld<sup>a,b</sup>, Paul Expert<sup>c</sup>, Thomas Fovet<sup>d</sup>, Ali Amad<sup>d,e,*</sup>

## ARTICLE INFO

### Keywords:

Symptom

Catatonia

Transdiagnostic

Network approach

Personalized medicine

## ABSTRACT

**Background:** Catatonia, as a transdiagnostic construct, manifests across various psychiatric and non-psychiatric conditions. Understanding how symptom variations impact the catatonia construct and differ across primary diagnoses (schizophrenia, bipolar disorder, unipolar depression, and neurological/metabolic/immunological condition) is essential to refine diagnostic and therapeutic approaches. This study aims to compare the symptom networks and centrality measures of these diagnoses.

**Methods:** We conducted a network analysis using Bush-Francis Catatonia Rating Scale (BFCRS) data from 118 patients, examining centrality measures and network comparisons across the four primary diagnostic groups.

**Results:** In the general catatonia network, the three most central symptoms identified were Excitement (1.462), Perseveration (1.456), and Impulsivity (1.332). While the overall structure of the catatonia networks did not show significant differences between diagnoses in terms of symptom connections and centrality, variations in centrality measures were observed among the different networks.

**Conclusions:** The study reinforces the notion of catatonia as an independent syndrome relatively to psychiatric or non-psychiatric diagnoses. However, the variation in centrality of symptoms across different primary diagnoses provides critical insights that could aid clinicians in tailoring diagnostic and therapeutic strategies. Future research should further explore these relationships and develop more refined approaches to managing catatonia.

## 1. Introduction

Catatonia is a neuropsychiatric clinical syndrome characterized by motor, behavioral, affective and neurovegetative manifestations. Although historically associated with schizophrenia, it has been increasingly recognized as an independent syndrome that co-occurs with a spectrum of psychiatric and non-psychiatric conditions (Fink et al., 2010; Taylor and Fink, 2003). However, such transdiagnostic challenges pose significant obstacles for the improvement of diagnostic, prognostic and therapeutic management.

A growing field of literature aims to explore whether the distribution of catatonia symptoms varies depending on the underlying primary psychiatric diagnosis, such as schizophrenia (Kleinhaus et al., 2012), bipolar disorder (Bräunig et al., 1998), unipolar depression, or other

conditions (e.g., neurological, metabolic or immunological conditions) (Starkstein et al., 1996). While some studies show no difference regarding symptoms distribution between diagnoses (e.g., between schizophrenia and mood disorders) (Grover et al., 2015), others suggest that at least the prevalence of symptoms and their intensity may vary across diverse psychiatric conditions (Aandi Subramaniyam et al., 2020; Bräunig et al., 1998; Cornic et al., 2009; Dawkins et al., 2022; Espi Forcen et al., 2022; Fink et al., 2010; Grover et al., 2019; Nath et al., 2021; Starkstein et al., 1996; Stuivenga and Morrens, 2014).

Moreover, although catatonia is more commonly associated with mood disorders (Grover et al., 2019), it can also be associated with schizophrenia or exhibit psychotic symptoms (Fink et al., 2010). A better understanding of these symptom-level differences would enable more precise diagnostic and therapeutic management of catatonia and

**Abbreviations:** BFCRS, Bush Francis Catatonia Rating Scale; CNIL, Commission Nationale de l'Informatique et des Libertés; DSM-5, Diagnostic and Statistical Manual of Mental Disorders, fifth edition; eBIC, extended Bayesian Information Criterion; gLASSO, graphical Least Absolute Shrinkage and Selection Operator; IRT, Item Response Theory; MGM, Mixed Graphical Models; NCT, NetworkComparisonTest; PCA, Principal Component Analysis.

* Corresponding author at: Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France.

E-mail address: ali.amad@outlook.com (A. Amad).

<footnote><sup>a</sup> Department of Child and Adolescent Psychopathology, CHU de Lyon, F-69000 Lyon, France</footnote>

<footnote><sup>b</sup> Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS & Université Claude Bernard Lyon 1, F-69000 Lyon, France</footnote>

<footnote><sup>c</sup> Global Business School for Health, University College London, London WC1E 6BT, United Kingdom</footnote>

<footnote><sup>d</sup> Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France</footnote>

<footnote><sup>e</sup> Department of neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK</footnote>
its underlying disorders. To our knowledge, no rigorous and systematic analysis encompassing all the symptoms of this construct has been conducted to date.

More precisely, the identification of mutual relations between symptoms can allow better understanding of catatonia in patients presenting different underlying diagnoses, psychiatric or not. Network analyzes are well-suited for studying the symptoms of catatonia. They provide computational tools that allow for the visualization of symptom co-occurrence in patients and the identification of the strength of their relationships. Network analyses are essential in enhancing our understanding of the connections between symptoms, thereby elucidating the construct of catatonia itself. These analyses enable a detailed examination of symptom interrelationships, offering clinicians insights that are critical for refined diagnostic and therapeutic decisions. In practice, comparing the catatonia symptoms networks obtained for psychiatric and non-psychiatric disorders primary diagnoses (i.e., schizophrenia, bipolar disorder, unipolar depression and neurological / metabolic / immunological conditions) can thus help to understand the different relationships between catatonia symptoms across the different primary diagnoses.

This study aims to compare the symptom networks of catatonia across different psychiatric diagnoses (schizophrenia, bipolar disorder, unipolar depression, and neurological/metabolic/immunological conditions). The main objective is to highlight the differential relationships between symptoms and differences in symptom centrality, indicating their relative importance to each other.

## 2. Material and methods

### 2.1. Participants

Patients with catatonia were consecutively included in this cohort between July 2016 and July 2022. This observational, non-interventional study based on routinely collected data received approval from the Commission Nationale de l'Informatique et des Libertés (CNIL) and the Local Data Protection Service (DEC19-076).

All patients were admitted for full-time hospitalization at Fontan Hospital (the psychiatry ward of Lille University Medical Centre, Lille, France) and received a standardized, complete evaluation and treatment of the catatonic state by medical staff. The study was conducted in both a general service and a referral service. Our protocol collects data and administers the Bush Francis Catatonia Rating Scale (BFCRS) only when three DSM criteria are met. This approach differentiates between clinical criteria (DSM) and clinical rating scales (BFCRS), both of which are validated systems. Additionally, some authors recommend using multiple scales to obtain a more comprehensive phenotype for each patient (Hirjak et al., 2024). All catatonic patients were considered, regardless of their underlying etiological diagnosis. The diagnosis of catatonia was made during the initial assessment, by senior psychiatrists, according to Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) criteria. Physicians tasked with assessing patients received specialized training in recognizing catatonia's clinical signs, incorporating educational sessions with patient videos to improve their accuracy in identifying catatonic symptoms. The evaluation scales for each patient were scored by the same examiner throughout, providing consistency and dependability in the assessment. Furthermore, the evaluation and treatment of each patient were overseen at least once by a senior psychiatrist (AA) specializing in catatonia, bringing additional expertise and scrutiny to the evaluation process. Demographic and clinical data such as age, sex, primary psychiatric diagnosis according to the DSM-5 were collected for all participants.

Participants were divided in a “Schizophrenia” group, a “Bipolar disorder” group, a “Unipolar depression” group and a “Neurological / metabolic / immunological conditions” group. We have merged bipolar disorder type 1 and type 2 under the label of “Bipolar disorder”. The combination of neurological, metabolic and immunological etiologies

within the same condition is justified by their non-psychiatric nature.

An assessment of the severity of the catatonic syndrome using the 23-item BFCRS was performed prospectively, during their initial assessment (Bush et al., 1996). We provided the mean of the BFCRS for all groups and for each group separately.

We conducted a sensitivity prevalence analysis by calculating the average difference between patients analyzed with the BFCRS and with the DSM-5 criteria. This analysis involved calculating the average difference between the assessments of patients diagnosed using the BFCRS and those evaluated under the DSM-5 criteria, to determine the degree of concordance between the two diagnostic approaches.

All patients received a physical examination, laboratory tests, and structural brain MRI to rule out other potential causes before they could be considered for electroconvulsive therapy. Lorazepam was used as the initial treatment for catatonia in every case. All patients were treated according to current recommendations for catatonia (Beach et al., 2017; Rogers et al., 2023). According to the CNIL, consent is collected retrospectively with information on the use of data and the possibility of objecting.

Patients were chosen based on the DSM-5 threshold de la catatonia and a complementary quantitative evaluation with the BFCRS is subsequently carried out. We provided mean, median and standard deviation for age, number and percentages of males and females, and number and percentages of each primary diagnosis in the whole dataset. Despite the absence of the possibility of verifying whether the data was missing at random, to ensure methodological rigor, we conducted a listwise deletion (i.e., any individual is deleted if they are missing data on any variable). This method (rather than carrying out multiple imputations) was chosen because >90% of patients with at least one missing data had >80% missing data. Only data from participants over the age of 18 were included in the analysis.

### 2.2. Rating scales

One of the most widely used scales in the literature to collect catatonia symptoms is the BFCRS, the first operational and systematic clinical rating scale used for the quantifiable examination of catatonia (Bush et al., 1996). The BFCRS allows evaluating the severity score of catatonia. It is a validated 23-item scale, based on the observation of behaviors. The 23 items are detailed in Supplementary Material 1. The detailed description of the four modalities (0 to 3) of the BFCRS is formulated differently for each item. Increasing modalities are considered proxies of the intensity of catatonia (details of the scoring method are described in Supplementary Material 1). The total score is the result of the sum of the scores obtained for each of the 23 items.

Regarding prevalence, we provided the number and the percentage of non-zero values of each BFCRS symptom (considering the presence or absence of a symptom), as well as the mean and standard deviation of each BFCRS symptom (considering the score of each symptom on the Likert scale).

We finally studied the centrality of psychotic symptoms (Fink et al., 2010). In this way, two additional symptoms were collected: (i) the presence of delusional ideas and/or (ii) the presence of hallucinations from the DSM-5 criteria. This was done to explore if such correlations are found as highlighted by a “mythology” related to catatonia which would be almost systematically linked to psychosis (Abrams and Taylor, 1976; Fink et al., 2010; Hirjak et al., 2022; Pfuhlmann and Stöber, 2001).

### 2.3. Network estimation, computations and comparisons

The aim of this network analysis was to investigate the relationships and the centrality measures in the general catatonia network (all patients combined), and between four diagnosis groups, using the BFCRS items. In this way, such network analyzes are primarily exploratory and do not require theoretical testing of hypotheses, while allowing the
exploration of homogeneous and rare patient samples, as in this study (Borsboom et al., 2018a).

The four primary diagnoses were: schizophrenia, bipolar disorder, unipolar depression and neurological / metabolic / immunological conditions (with these five networks by adding the two psychotic symptoms presented in Supplementary Material 2).

This network study involved four steps: the estimation of the networks, the computation of centrality measures, the global and local network comparisons and the network robustness. We followed the network guidelines for the computational analysis of network properties in cross-sectional data (Burger et al., 2022).

### 2.3.1. Network estimations

The ten BFCRS networks (the general catatonia network, with and without psychotic symptoms, and the four specific catatonia networks, with and without psychotic symptoms), were estimated with a gaussian graphical model, the state-of-the-art network model used in psychopathology research for ordinal data. We applied a regularization technique called graphical Least Absolute Shrinkage and Selection Operator (gLASSO) (Friedman et al., 2008; Tibshirani, 1996). The final model was chosen by using the extended Bayesian Information Criterion (eBIC) (Epskamp et al., 2018b) (Supplementary Material 2 for details).

Networks were plotted using the Fruchterman-Reingold algorithm, where symptoms with higher degree are placed closer to each other (Fruchterman and Reingold, 1991). Predictability of a node compared to its direct neighbors was calculated based on models derived from Mixed Graphical Models (MGM) (Haslbeck and Waldorp, 2018) and graphically represented as a pie chart in the ring around each symptom. Predictability refers to how well a given node in the network can be predicted by the directly neighboring nodes. We provided the values of the most strongly correlated nodes of each computed network (Pearson's correlation coefficient *r*).

### 2.3.2. Centrality measures

Network analyses provide insight into which symptom(s) drive(s) differences, aka “central symptoms” (Boccaletti et al., 2006). In network analysis, the most central symptoms do not necessarily correspond to the most frequent symptoms of a group of patients.

Centrality measures are particularly important to clarify the relationship between symptoms, especially because nodes with a high centrality “drive” the overall structure of a network (Cramer et al., 2010). At the populational level, the removal of a central symptom will strongly modify the overall structure of the network with respect to the property it captures. Strength-type centrality is the most clinically relevant measure (see Supplementary Material 3 for details). The strength of a node computes the degree to which it is connected with all the other nodes of the network (Epskamp et al., 2012). By considering a rail network metaphor, a city has high strength (e.g., Paris in France) if it constitutes a transportation “hub” because of the importance of its direct connections with other cities. We provide the value of strength for each discussed symptom.

In this study, the centrality of a symptom is considered high relative to the centrality of other symptoms.

### 2.3.3. Global and local network comparisons

Network comparisons were conducted between the four BFCRS catatonia networks based on the four previously described primary diagnoses. Comparisons were performed at local and global levels.

At the local level, the centrality of the symptoms of the four catatonia networks were compared two by two. Since the aim is to observe centrality values two by two, no statistical measurement is carried out at this local level and the result comes only from the relative position of each centrality measure compared to the others in each network. Such comparisons between each pair of centrality measures could also lead to a problem of multiple comparison, and these local comparisons, although highly clinically relevant, should therefore be considered with

caution in terms of interpretation.

At the global level, the networks were systematically compared two by two (unlike in the local analysis, in which the symptoms themselves were compared two by two). To evaluate the differences between each network, two kinds of significance indices were calculated with a two-tailed permutation test of comparison (NetworkComparisonTest – NCT) (van Borkulo et al., 2022). First, the NCT analyzed the difference in terms of relationships between symptom connections (“Are all of the two-to-two connections between symptoms different between two networks?”) and provided a unique *p*-value for this analysis, referred to as “edge weights analysis”. Secondly, the NCT analyzed the difference in terms of centrality values (“Are the whole set of the centrality values of each symptom different between the symptoms of two networks?”) and provided another unique *p*-value for this analysis, referred to as “global strength analysis”. At this global level, the network comparison analysis therefore presented two results: the global comparison in terms of connections, and the global comparison in terms of centrality. A *p*-value <0.05 indicated a significant difference.

Finally, regarding the under/oversampling difficulty, due to potential risk of loss of power with unequal sample sizes, we subsampled the larger dataset to the size of the smaller dataset five times and we performed a bootstrap with 2000 replications for each comparison (van Borkulo et al., 2022). We present the average of the results without adjusting the *p*-values for multiple testing.

### 2.3.4. Network robustness

To verify that the number of subjects was adequate to perform such a network analysis, we analyzed the robustness of the network by a bootstrap analysis (N = 2000 iterations) (Borsboom et al., 2018b; Epskamp et al., 2018a). The robustness of the results is tested on a centrality measure, the strength (i.e., we check the stability of edges and centrality measures). We used a case-dropping subset assess the stability of this centrality measure, providing a centrality stability correlation coefficient (i.e., we computed how well the order of centralities was retained after observing only a subset of the data): the CS-coefficient, i. e., the maximum proportion of participants that can be dropped while maintaining 95% probability that the correlation between centrality metrics from the full data set and the subset data are at least 0.70. Based on a simulation study (Epskamp et al., 2018b), a minimum CS-coefficient of 0.25 is recommended. Results are presented in Supplementary Material 3.

All analyses and graphical visualizations were performed with R software (4.1.3). Supplementary Material 3 gives technical details on the packages used and on this symptom network computational approach.

## 3. Results

### 3.1. Demographic characteristics and prevalence

After processing the missing data (N<sub>missing</sub> = 35 [22.9%]), 118 participants (N = 153–35) were included in the study and analyzed. The mean age was 53.2 years, with a median of 57.1 and a standard deviation of 17.8. There were 83 females (70.3%) and 35 males (29.7%). Regarding the primary diagnosis, there were 37 individuals with schizophrenia (31.3%), 25 individuals with bipolar disorder (21.2%), 31 individuals with unipolar depression (26.3%) and 25 individuals with a neurological or metabolic or immunological condition (21.2%). Table 1 provides the prevalence of different catatonia symptoms found in the five BFCRS networks.

The global mean of the sum of each catatonia patient, based on the BFCRS, is at 18.6. The mean of the BFCRS is at: 16.4 for the schizophrenia group, 21.3 for the bipolar disorder group, 18.3 for the unipolar depression group and 19.6 for the neurological or metabolic or immunological condition group.

Regarding sensitivity prevalence analysis, this overall mean is not significantly different from the mean and variability calculated with the
<caption>**Table 1**</caption>

Prevalence of the BFCRS symptoms (N = 118), ordered by the number of symptoms. Means and standard deviations of each BFCRS symptom (considering the BFCRS Likert scale) for the general catatonia network; number and percentage of non-zero values of each BFCRS symptom (considering the presence or absence of a symptom) for the general catatonia network and the four networks based on the four primary diagnoses. An asterisk was added after the BFCRS symptoms which corresponded to the DSM-5 criteria.

<table><thead><tr><th rowspan="2">BFCRS symptoms</th><th rowspan="2">Means and standard deviations of BFCRS symptoms (All patients; N = 118)</th><th colspan="5">Number and percentage of BFCRS symptoms</th></tr><tr><th>All patients (N = 118)</th><th>Bipolar disorders (N = 25)</th><th>Unipolar depression (N = 31)</th><th>Neurological or metabolic or immunological condition (N = 25)</th><th>Schizophrenia (N = 37)</th></tr></thead><tbody><tr><td>Immobility/stupor*</td><td>1.34 [0.96]</td><td>95 (80.5%)</td><td>17 (68%)</td><td>26 (83.9%)</td><td>21 (84%)</td><td>31 (83.8%)</td></tr><tr><td>Staring</td><td>1.20 [0.92]</td><td>89 (75.4%)</td><td>20 (80%)</td><td>23 (74.2%)</td><td>18 (72%)</td><td>28 (75.7%)</td></tr><tr><td>Mutism*</td><td>1.46 [1.17]</td><td>82 (69.5%)</td><td>15 (60%)</td><td>22 (71.0%)</td><td>22 (88%)</td><td>23 (62.2%)</td></tr><tr><td>Posturing/ catalepsy*</td><td>1.28 [1.12]</td><td>79 (66.9%)</td><td>17 (68%)</td><td>22 (71.0%)</td><td>16 (64%)</td><td>24 (64.9%)</td></tr><tr><td>Rigidity</td><td>1.14 [1.04]</td><td>75 (63.6%)</td><td>18 (72%)</td><td>22 (71.0%)</td><td>15 (60%)</td><td>20 (54.0%)</td></tr><tr><td>Negativism*</td><td>1.18 [1.20]</td><td>67 (56.8%)</td><td>17 (68%)</td><td>15 (48.4%)</td><td>17 (68%)</td><td>18 (48.6%)</td></tr><tr><td>Withdrawal</td><td>1.18 [1.18]</td><td>67 (56.8%)</td><td>15 (60%)</td><td>16 (51.6%)</td><td>18 (72%)</td><td>18 (48.6%)</td></tr><tr><td>Waxy flexibility*</td><td>1.42 [1.50]</td><td>56 (47.5%)</td><td>14 (56%)</td><td>14 (45.2%)</td><td>14 (56%)</td><td>14 (37.8%)</td></tr><tr><td>Stereotypy*</td><td>0.76 [1.04]</td><td>48 (40.7%)</td><td>12 (48%)</td><td>10 (32.3%)</td><td>10 (40%)</td><td>16 (43.2%)</td></tr><tr><td>Automatic obedience</td><td>0.69 [0.98]</td><td>47 (39.8%)</td><td>16 (64%)</td><td>10 (32.2%)</td><td>8 (32%)</td><td>13 (35.1%)</td></tr><tr><td>Ambitendency</td><td>1.04 [1.43]</td><td>41 (34.7%)</td><td>12 (48%)</td><td>7 (22.6%)</td><td>8 (32%)</td><td>14 (37.8%)</td></tr><tr><td>Perseveration</td><td>0.94 [1.40]</td><td>37 (31.4%)</td><td>12 (48%)</td><td>11 (35.5%)</td><td>5 (20%)</td><td>9 (24.3%)</td></tr><tr><td>Gegenhalten</td><td>0.84 [1.35]</td><td>33 (28.0%)</td><td>10 (40%)</td><td>8 (25.8%)</td><td>7 (28%)</td><td>8 (21.6%)</td></tr><tr><td>Verbigeration</td><td>0.51 [0.93]</td><td>32 (27.1%)</td><td>9 (36%)</td><td>11 (35.5%)</td><td>6 (24%)</td><td>6 (16.2%)</td></tr><tr><td>Excitement*</td><td>0.41 [0.80]</td><td>30 (25.4%)</td><td>9 (36%)</td><td>6 (19.3%)</td><td>6 (24%)</td><td>9 (24.3%)</td></tr><tr><td>Grimacing*</td><td>0.43 [0.86]</td><td>29 (24.6%)</td><td>6 (24%)</td><td>7 (22.6%)</td><td>7 (28%)</td><td>9 (24.3%)</td></tr><tr><td>Echopraxia/ echolalia*</td><td>0.32 [0.64]</td><td>28 (23.7%)</td><td>9 (36%)</td><td>8 (25.8%)</td><td>3 (12%)</td><td>8 (21.6%)</td></tr><tr><td>Mannerisms*</td><td>0.42 [0.84]</td><td>28 (23.7%)</td><td>8 (32%)</td><td>7 (22.6%)</td><td>4 (16%)</td><td>9 (24.3%)</td></tr><tr><td>Autonomic abnormality</td><td>0.31 [0.64]</td><td>28 (23.7%)</td><td>8 (32%)</td><td>2 (6.4%)</td><td>4 (16%)</td><td>14 (37.8%)</td></tr><tr><td>Grasp reflex</td><td>0.69 [1.27]</td><td>27 (22.9%)</td><td>6 (24%)</td><td>8 (25.8%)</td><td>7 (28%)</td><td>6 (16.2%)</td></tr><tr><td>Impulsivity</td><td>0.32 [0.69]</td><td>24 (20.3%)</td><td>5 (20%)</td><td>5 (16.1%)</td><td>6 (24%)</td><td>8 (21.6%)</td></tr><tr><td>Mitgehen</td><td>0.48 [1.11]</td><td>19 (16.1%)</td><td>1 (4%)</td><td>4 (12.9%)</td><td>8 (32%)</td><td>6 (16.2%)</td></tr><tr><td>Combativeness</td><td>0.24 [0.66]</td><td>16 (13.6%)</td><td>4 (16%)</td><td>2 (6.4%)</td><td>5 (20%)</td><td>5 (13.5%)</td></tr></tbody></table>

DSM-5 items (mean: 4.39; standard deviation: 1.79).

### 3.1.1. Catatonia network analysis

We present here: the values of the most strongly correlated nodes of each computed network (Pearson correlation coefficient *r*), relative to

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** General catatonia network and its strength centrality (N = 118). A. Graphical representation of the general catatonia network. Blue connections represent the positive associations between symptoms and orange connections the negative associations. The thickness of the line represents the level of correlation between two symptoms. Predictability of a node is depicted as a gray pie chart in the rings around nodes: the area in the outer ring of nodes represents the percentage of variance of the node that is explained by its neighboring nodes. B. Centrality measures of the general catatonia network, ordered by strength (i.e., the highest values of strength are located furthest to the right of the plot and the lowest values furthest to the left). Z-scores are used to plot standardized strength centrality values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
the other correlations of each network (network comparisons at the local level), centrality measures, predictability, and network comparisons at the global level.

### 3.1.2. Network estimation and centrality

Each of the five BFCRS catatonia networks (the general catatonia network and the four catatonia networks based on psychiatric and non-psychiatric diagnoses) contain 23 nodes and 253 connections. Supplementary Material 1 (networks and centrality measures) gives the five BFCRS catatonia networks with psychotic symptoms, with 25 nodes and 300 connections. Fig. 1 presents the resulting catatonia general network and its strength centrality. To avoid providing details of the results for all nodes, in the remainder of this section, we provide some clinically relevant examples of strongly connected nodes. By analyzing the network, we can extract four nodes strongly directly correlated to the most central node, "Excitement": "Combativeness" ($r = 0.66$), "Impulsivity" ($r = 0.62$), "Perseveration" ($r = 0.54$) and "Verbigeration" ($r = 0.49$).

Regarding centrality, the four most central symptoms in terms of strength are: "Excitement" (1.462), "Perseveration" (1.456), "Impulsivity" (1.332), and "Immobility/stupor" (1.179).

Fig. 2 presents the four catatonia networks based on the four primary diagnoses (schizophrenia, bipolar disorder, unipolar depression and neurological / metabolic / immunological conditions), and their centrality measures (strength). The node-by-node correlations can be directly inferred from the networks. Especially, for the bipolar disorder network, we found four nodes strongly connected with the symptom of "Mitgehen": "Mutism" ($r = -0.97$), "Autonomic abnormality" ($r = -0.93$), "Verbigeration" ($r = 1$) and "Grimacing" ($r = -0.91$). For the

schizophrenia network, we found four nodes strongly connected with the symptom of "Combativeness": "Echopraxia/echolalia" ($r = -0.95$), "Excitement" ($r = 0.90$), "Staring" ($r = -0.82$) and "Immobility/stupor" ($r = -0.80$). For the unipolar depression network, we found four nodes also strongly connected with the symptom of "Combativeness": "Rigidity" ($r = 1$), "Waxy flexibility" ($r = 0.98$), "Stereotypy" ($r = -0.94$), and "Grimacing" ($r = -0.93$). Finally, for the neurological / metabolic / immunological condition network, we found four nodes strongly connected with the symptom of "Mannerism": "Immobility/stupor" ($r = -0.92$), "Perseverations" ($r = 0.86$), "Excitement" ($r = 0.90$) and "Withdrawal" ($r = -0.68$).

### 3.1.3. Predictability

Regarding predictability (i.e., when a node is predicted by all the nodes directly connected to it using an MGM, see methods section), in all the networks, the most central symptoms have a predictability of >50%, with however eight nodes presenting a predictability of zero ("Excitement", "Posturing/Catalepsy", "Echopraxia/echolalia", "Mannerism", "Mitgehen", "Ambitendency", "Grasp reflex" and "Autonomic abnormality").

### 3.1.4. Centrality and network comparisons at the local level

Although we can infer from the graphical presentation the previously described node-by-node correlations, other computations about the symptom influences should be studied systematically according to centrality measures. At the local level, between each catatonia network, the comparison of these centrality measures allows to show relative differences of these measures. In this way, Fig. 3 presents the centrality measures of the four catatonia networks. For the schizophrenia and

<figure><img src="image_5.png" /><figcaption>A. Bipolar disorder</figcaption></figure>

1: Excitement

2: Immobility/stupor

3: Mutism

4: Staring

5: Posturing/catalepsy

6: Grimacing

7: Echopraxia/echolalia

8: Stereotypy

9: Mannerisms

10: Verbigeration

11: Rigidity

12: Negativism

13: Waxy flexibility

14: Withdrawal

15: Impulsivity

16: Automatic obedience

17: Mitgehen

18: Gegenhalten

19: Ambitendency

20: Grasp reflex

21: Perseveration

22: Combativeness

23: Autonomic abnormality

<figure><img src="image_6.png" /><figcaption>C. Unipolar depression</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>B. Schizophrenia</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>D. Neurological/metabolic/immunological conditions</figcaption></figure>

Fig. 2. Graphical representation of the four catatonia networks based on the four primary diagnoses: schizophrenia, bipolar disorder, unipolar depression and neurological / metabolic / immunological conditions. Blue connections represent the positive associations between symptoms and orange connections the negative associations. The thickness of the line represents the level of correlation between two symptoms. Predictability of a node is depicted as a pie chart in the rings around nodes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
<figure><img src="image_9.png" /></figure>

A. Bipolar disorder

<figure><img src="image_10.png" /></figure>

B. Schizophrenia

<figure><img src="image_11.png" /></figure>

C. Unipolar depression

<figure><img src="image_12.png" /></figure>

D. Neurological/Metabolic/Immunological condition

Fig. 3. Strength centrality of the four catatonia networks, ordered by decreasing strength. The highest values of strength are located furthest to the right of the plot and the lowest values furthest to the left. Z-scores are used to plot standardized values.

unipolar depression networks, the most central symptom is the "Combativeness" (respectively at 1.990 and at 2.616), which is the second most central symptom in the bipolar disorder network (1.279). The most central symptom is the "Mitgehen" (2.035) for the bipolar disorder network and the "Mannerism" (2.044) for the neurological/metabolic/immunological condition network. The symptom of

"Excitement" is among the 4 most central symptoms of the three networks of bipolar disorder (1.175), schizophrenia (1.065) and neurological / metabolic / immunological conditions (1.622). "Perseveration" is a symptom whose centrality is relatively high for the schizophrenia network (1.430); in the same way, "Autonomic abnormality" is relatively specifically high for the unipolar depression network (1.980).
As presented in the Method part, we also carried out all of these steps to explore the centrality and predictability of two psychotic symptoms (delusional ideas and hallucinations), within the five networks, provided in Supplementary Material 2. Interestingly, these two symptoms are among the least central in the general network of catatonia. However, delusional ideas constitute the 6th most central symptom in the bipolar disorder network and the 12th most central symptom in the schizophrenia network.

### 3.1.5. Network comparisons at the global level

At the global level, two results of the network comparison test are given: results comparing the connections between symptoms ("edge weights") and results comparing centrality measures ("global strength") (p ~ 1). At this global level, when all the networks are compared two by two, there is no difference in terms of two-to-two symptom connections ("edge weights"), nor between the centrality measures ("global strength").

### 3.1.6. Network robustness

Robustness, based on the stability of strength, was judged at the limit of acceptability (near to 0.5) for the general catatonia network, as presented in Supplementary Material 4. Especially, the CS-coefficient for strength is lower than recommended (see limitation part). The restricted sample did not allow for a sensitivity analysis.

## 4. Discussion

At the global level, network comparisons between primary diagnoses of catatonia do not reveal significant differences. However, a more detailed local analysis shows that the most central symptoms differ across the catatonia networks. While the first finding supports the idea that catatonia is an irreducible and entirely distinctive entity, the centrality differences, though interpreted cautiously, could have important clinical, diagnostic, and therapeutic implications.

The transdiagnostic nature of catatonia requires a refined analysis at the symptom level. The relative distribution of symptoms with respect to each other can be studied through measures of centrality when the relationships between symptoms are represented by networks. In the general catatonia network, the nodes related to excitement, perseveration and impulsivity constitute the three most central nodes. These three symptoms refer respectively to the three traditional affective, psycho-behavioral and motor dimensions of catatonia (Fink et al., 2010; Hirjak et al., 2022). Moreover, excitement and perseveration refer to an increase in psychomotor activity and impulsivity belongs to an abnormal activity. These differences reinforce the idea that catatonia, having its most central symptoms belonging to different dimensions, is highly heterogeneous and that a comprehensive panel of varied questions (as present in the BFCRS) is necessary. As expected, given that centrality measures are not necessarily the most frequent measures, these three symptoms do not correspond to the symptoms most frequently found in our study (immobility/stupor [80.5%], staring [75.4%] and mutism [69.5%]), a result qualitatively consistent with previous prevalence studies (e.g., staring [74%], immobility/stupor [59%] and mutism [50%] are the most frequent symptoms in Wilson et al) (Wilson et al., 2015).

Importantly, these centrality results are consistent with catatonia models based on Item Response Theory (IRT), aiming to show the level of discrimination of each BFCRS item. For instance, Wilson et al. describe that the first two most discriminating symptoms are excitement (0.8) and impulsivity (0.77) (Wilson et al., 2015). The three most central symptoms retrieved in our study are also among the most discriminating on the BFCRS in the Wong et al. study (Wong et al., 2007), with information weighted fits (INFIT – quantifying the disparity between clinical ratings and the expression of the underlying latent catatonia phenotype) of 1.02 for excitement, 1.00 for perseveration and 1.16 for impulsivity [normal range for INFIT: 0.7 to 1.3] (Wong et al., 2007). In Wong et al.,

the only other three items with an INFIT as high were combativeness (1.04), grasp reflex (1.05), and stereotypy, grimacing, and mannerisms (1.06). While this study did not explicitly focus on the underlying primary diagnosis, it is particularly noteworthy that combativeness emerges as the most central symptom in the schizophrenia and unipolar depression networks (strengths at respectively 1.990 and 2.616), and the second most central symptom of the bipolar disorder network (1.279). Similarly, mannerism has an important INFIT (1.06) in Wong et al. work and is the most central symptom of the neurological/metabolic/immunological conditions network (2.044) in our study, further strengthening the hypothesis of specific central symptoms depending on psychiatric and/or non-psychiatric disorders. More generally, this consistency between methods adapted to the study of symptoms, their relationships and their hierarchy supports the observation of "limited psychometric evidence" (Walther et al., 2019). The lack of precision at the semiological level leads us to continue to speculate on how conceptualize catatonia (e.g., as a motor syndrome, a paralysis of will, a fear syndrome, a result of immune dysregulation – (Walther et al., 2019)). This research thus responds to this need to better understand the underlying structure of catatonia, particularly in its association with different psychiatric conditions (Serrat et al., 2022; Wilson et al., 2015).

By retaining only the first most central specific symptom for each of the four networks studied, the symptom of combativeness is the most specifically central in catatonia occurring on a unipolar depression (2.616), the symptom of perseveration the most specifically central in catatonia occurring in schizophrenia (1.430), the symptom of mitgehen the most specifically central in catatonia occurring in bipolar disorder (2.035) and the symptom of mannerism the most specifically central in catatonia occurring in neurological/metabolic/immunological condition (2.044). Finally, although the symptom of excitement is not the most specifically central of these four networks, it appears among the 4 most central symptoms in 3 disorders (bipolar disorder, schizophrenia and neurological / metabolic / immunological conditions). Although to be considered with great caution, these results may help guide clinicians in their clinical diagnosis, considering the central symptoms of catatonia as relevant phenomenological markers for these phenotypic specificities.

Finally, regarding the integration of the two psychotic symptoms (delusional ideas and hallucinations), as expected, these are non-central in the general catatonia network since they do not belong to the construct of catatonia. This is an important result, reinforcing the current structure of catatonia not containing such symptoms. However, the relative importance of centrality of delusional ideas in the schizophrenia and bipolar disorders networks, compared to the other two, opens interesting avenues to specifically explore these symptoms within these two particular phenotypes.

Based on the results of comparisons between networks, we can try advancing the debates considering either the existence of several distinct syndromes of catatonia, or phenomenologically fluid boundaries according to different disorders (Caroff, 2004; Carroll et al., 2007). Global comparisons between networks (regarding the whole set of connections and centrality measures) does not show any differences between psychiatric or non-psychiatric disorders. This result reinforces the idea that catatonia constitutes, at a nosological level, a unified category independent of other disorders (Kahlbaum, 1874). However, local comparisons (regarding the two-to-two symptom relationships) highlight relevant differences. In other words, at the level of the symptoms, the relationships of mutual influence are different, allowing to affirm the existence of clinical specificities according to the implied disorders in which this syndrome occurs. Such a symptom network analysis shows that the most central symptoms of catatonia are different according to the primary diagnoses.

These results should be interpreted with caution regarding the potential refinement of catatonia phenotypes. Identifying differences in symptom centrality, according to different diagnoses, does not mean that these symptoms can be used to distinguish subtypes of catatonia.
Complementary analyses, based on clusters or on latent variables (e.g., principal component analysis – PCA), would be needed to provide such inferences. Considering this precaution, however, the contribution of symptom networks allows to move away from the century-old debate seeking to define the latent structure of catatonia (as done statistically in IRT or PCA studies, or biologically in neuroscientific studies). Focusing solely on the structure of symptoms supports the view of catatonia as a unified diagnostic category, independent of any underlying latent variables. This consideration constitutes an important goal to identify diagnostic, prognostic and therapeutic target symptoms for future studies. Consistent with the Research Domain Criteria project launched by the National Institute of Mental Health (Cuthbert and Insel, 2013; Ferrante et al., 2019), the further analyses of mutual influence relations between catatonia symptoms could help to support a construct more readily tied to underlying neurobiological mechanisms.

Several limitations should be acknowledged. First, the generalization of the results according to the four psychiatric and non-psychiatric diagnoses should be particularly cautious. The samples corresponding to these primary diagnoses are relatively low, which can limit the robustness and stability of networks. The collection is monocentric, without pediatric or geriatric data. Furthermore, the cases of catatonia presented in this cohort are relatively severe. Recruitment in both a general service and a referral service could also limit the representativeness of all catatonia s. In this way, the robustness of the analysis remains very moderate, and the interpretation of the results should be particularly cautious. However, it should be emphasized that the difficulty of prospectively collecting such samples of patients with catatonia could suggest that these exploratory results will be useful for the clinical and research communities for future studies. Secondly, although network analyzes are a powerful way of analyzing original datasets, the clinical interpretation of centrality results should also be done carefully (Bringmann et al., 2019), especially because network analyses highlight the mutual interaction between symptoms at a population level, but not at an individual level. Likewise, on a methodological level, we chose to compare centrality measures relatively to each other, an interpretation which allows reliable assumptions for the clinical practice despite the absence of a measure of statistical significance. Finally, our analyzes depend on contingent theoretical frameworks, nosological concepts, and rating criteria used to diagnose catatonia, which may evolve across time (Fink et al., 2010; Hirjak et al., 2022). A growing field of literature illustrates the limitations of such cross-sectional networks (e.g., see (Neal et al., 2023a, 2023b)).

## 5. Conclusions

In conclusion, this network analysis supports discussions about the structure of catatonia symptoms. While caution is necessary when generalizing the findings of this first network study on catatonia, the results indicate that the structure of catatonia networks varies across different psychiatric and non-psychiatric diagnoses. Such results should be interpreted cautiously in view of the wide literature discussing the structure of catatonia construct, and additional network studies should be carried out to deepen the relations between the most central catatonia symptoms. However, our findings align with other studies that have examined the roles or positions of catatonia symptoms as core markers of various disorders and conditions. This alignment suggests that the identified central symptoms could be important targets for diagnosing and treating catatonia. Future research could build on these preliminary results by developing networks that incorporate longitudinal data and integrate multilevel biological markers.

## Author agreement

All the authors approved the version to be published and took public responsibility for appropriate portions of the content.

## Financial disclosure

None.

## Ethical statement

This study received approval from the Commission Nationale de l'Informatique et des Libertés (CNIL) and the Local Data Protection Service (DEC19-076).

## CRediT authorship contribution statement

Gauld Christophe: Writing – review & editing, Writing – original draft, Methodology, Conceptualization. Paul Expert: Writing – review & editing, Validation, Methodology, Conceptualization. Thomas Fovet: Writing – review & editing, Validation, Methodology, Conceptualization. Ali Amad: Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Conceptualization.

## Declaration of competing interest

The Authors have declared that there are no conflicts of interest in relation to the subject of this study.

## Data availability

Data will be made available on request.

## Acknowledgments

Any.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pnpbp.2024.111082.

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