Table showing the quantitative results of the cluster-to-component matching procedure.

<p>Table shows the network community structures (NCS) that were most similar to the confirmatory 5, 6, 7, 8, 9, and 10 principal component structure (PCS) of the CPRS dataset. Component structure: the component structure that was matched against the candidate network community structures obtained from the incremental pruning procedure (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112734#s2" target="_blank">Materials and Methods</a>). Principal Component: the number of the principal component from this component structure. % mismatch per cluster: the percentage of items in a network cluster of the most similar NCS that did not match the item content of its corresponding principal component. % overall mismatch: the percentage of items in the entire NCS that did not match its corresponding PCS. ABS(r): the absolute value of the correlation coefficient at which the optimal match with a NCS was found. p: the corresponding p value. Nrnodes: number of nodes left in the NCS at this threshold (some nodes dropped off the network due to incremental pruning, see text).</p><p>Table showing the quantitative results of the cluster-to-component matching procedure.</p

A Network View on Psychiatric Disorders: Network Clusters of Symptoms as Elementary Syndromes of Psychopathology

<div><p>Introduction</p><p>The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders.</p><p>Aim</p><p>To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry.</p><p>Methods</p><p>192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique.</p><p>Results</p><p>In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "DEPRESSION", "MANIA", “ANXIETY”, "PSYCHOSIS", "RETARDATION", and "BEHAVIORAL DISORGANIZATION". Network metrics were used to quantify the continuities between the elementary syndromes.</p><p>Conclusion</p><p>We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a ‘Psychopathology Web’. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.</p></div

Results of optimizing the network community structure of the CPRS dataset with respect to its principal component structure.

<p>Results of the NCS-to-PCS matching procedure for 6 different components structures of the CPRS dataset. X-axis shows the correlation coefficient r as a threshold for significance of a link in the network graph (as r increases to the right, more links are pruned from the network). Y-axis shows dissimilarity (mismatch) scores. Dark blue: 5-PCS, dark red: 6-PCS, green: 7-PCS, purple: 8-PCS, turquoise: 9-PCS, orange: 10-PCS. Mismatch scores collapse at r = 0.27 (p = 1.67E-04), indicating an optimal threshold for the Psychopathology Web and a corresponding six-cluster solution. For details, see text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112734#pone-0112734-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112734#pone-0112734-t002" target="_blank">2</a>.</p

Table comparing the component memberships and network cluster memberships of items of the CPRS for the optimal 6-network cluster structure and corresponding 6-component structure.

<p>It01 etc: item 1 of the CPRS. Left: matching template of the 6-component structure consisting of 55 nodes. Items were assigned to a single component using a forced-choice filter based on the highest component loadings. Right: the 6-cluster network structure showing an optimal match with the matching template. 1 =  member of this component or cluster, blank  =  not a member. Items that dropped off the network during the incremental pruning procedure or that were discarded from further analyses (it35 and it55, no scores) are shown in [brackets]. Items that were allocated differently by PCA and NCD are shown in <b>bold.</b> See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112734#pone-0112734-g003" target="_blank">Figure 3</a>.</p><p>Table comparing the component memberships and network cluster memberships of items of the CPRS for the optimal 6-network cluster structure and corresponding 6-component structure.</p

The Psychopathology Web.

<p>Network graph of the correlational relationships between 55 items (symptoms) of the CPRS, which form a 6-cluster structure. Node  =  CPRS item (symptom), link  =  significant correlation. The threshold for the significance of network links has been optimized using the procedure described above. Red links: positive correlations. Blue links: negative correlations. The thickness of the links reflects the strength of their corresponding correlation coefficient (weight). It01 etc: item number of the CPRS. Nodes are positioned according to the Hagel-Koren Fast Multiscale layout algorithm. The color of the nodes shows their network cluster membership. Yellow: ANXIETY, Light Blue: DEPRESSION. Orange: MANIA, Green: PSYCHOSIS, Grey: RETARDATION, Brown: BEHAVIORAL DISORGANIZATION. NCD and PCA differ with respect to the placement of items 03, 04, 25, 27, and 28. These mismatches occur at the boundaries between the DEPRESSION cluster and the PSYCHOSIS cluster, and between the DEPRESSION cluster and the ANXIETY cluster and can be interpreted as ‘border disputes’ between NCD and PCA. Spheres: bridge symptoms. Closed diamonds: core symptoms. Node size denotes betweenness centrality score of the node (a measure of its involvement in connecting the various parts of the Psychopathology Web through shortest paths). Smaller and larger loops can be observed that run within and between the various network clusters. See text for further details.</p

Scree-plot (A) and pruning plot (B) of the CPRS dataset.

<p>A. Scree-plot of the exploratory principal component analysis of the CPRS dataset, suggesting a 10-component structure. Defining a cut-off for the total number of components to extract was complicated by the lack of a clear bend in the plot. Hence, 5 additional alternative component structures were matched to the total array of possible network community structures of the dataset, to allow identification of an optimal solution. B. Incremental pruning plot of network community structure analyses showing the number of clusters in the Psychopathology Web as a function of the correlation coefficient that defines the threshold for significance of the links in the network. Neighborless nodes (isolates) are removed from the calculation and do not count as clusters.</p

Network metrics of individual symptoms of the Psychopathology Web.

<p>Item: item of the CPRS. Cluster: name of the network cluster to which the symptom belongs (one of 6 network clusters identified in the CPRS dataset). Bridge or core: specifies whether the symptom is a bridge symptom or a core symptom. Ext. clust.: number of external clusters that the (bridge) symptom connects with. Ln_w_D: logtransformed and weighted degree. Ln_w_BS: logtransformed and weighted betweenness centrality. Ln_w_CS: logtransformed and weighted closeness centrality.</p><p>Network metrics of individual symptoms of the Psychopathology Web.</p

The Personality Web at facet level: network graph of correlational relationships between the 30 facets of the NEO-PI-R.

<p>The community structure of this graph has an overall best fit with the confirmatory 5-FS, occurring at r>0.271, p<4.89 E-09. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t002" target="_blank">Table 2</a> for significances and correlation coefficients. Node = facet, link = significant correlation. Red links: positive correlations. Blue links: negative correlations. The thickness of the links represents the strength of the correlation. For further information, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t004" target="_blank">Table 4B</a>. n = neuroticism, e = extraversion, o = openness, a = agreeableness, c = conscientiousness. Numbers refer to facet number. Nodes are positioned in clusters according to their factor membership (standard 5-FS). The color of nodes denotes their network cluster membership. Only two facets show a mismatch with the standard 5-FS (n5 and n2). Both mismatches involve the neuroticism dimension depicted below in red. These facets have strong correlations with facets from the conscientiousness cluster (blue) and the agreeableness cluster (green), as can be observed by the thickness of the corresponding links. As a result, n5 is “drawn” into the conscientiousness cluster and n2 into the agreeableness cluster.</p

Overall network metrics for the ‘winning’ network graphs described in this paper.

<p><b>A.</b> Facet level network graph matching with the confirmatory 5-FS of the present dataset, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-g003" target="_blank">Figure 3</a>.</p><p><b>B.</b> Item level network graph matching with the standard 5-FS of the NEO-PI-R standard dataset, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-g005" target="_blank">Figure 5</a>.</p><p><b>C.</b> Cluster level network graph generated from correlations between cluster scores, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-g006" target="_blank">Figure 6</a>. The facet- and item level network graphs display a small-world topology.</p

Table showing the quantitative results of the cluster-to-factor matching procedure at facet level.

<p>Standard, confirmatory, exploratory: type of factor analysis. N, E, O, A, C: NEUROTICISM, EXTRAVERSION, OPENNESS, AGREEABLENESS, CONSCIENTIOUSNESS. F1–F6: factors of the corresponding factor analysis. F1–F6 correspond to N, E, O, A,C, and a 6th factor that is the product of the exploratory factor analysis of the current dataset. Best overall match: best match between overall cluster contents and overall FS. r and p: r and p values at which the best overall match is found. Best cluster-to-factor match: best match between individual factors and network clusters, which may occur at different r and p-values per cluster. % mismatch denotes the percentage of normalized mismatch between factor and network cluster contents. Note that FSs and NCSs at facet level generally show high degrees of correspondence (96.2%), with a best match occurring with the confirmatory 5-FS at r = 0.271, p = 4.89E-09.</p