Innovations In Clinical Neuroscience

NOV-DEC 2017

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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60 ICNS INNOVATIONS IN CLINICAL NEUROSCIENCE November-December 2017 • Volume 14 • Number 11–12 O R I G I N A L R E S E A R C H of symptoms is similar to symptom cluster analysis, in which a cluster of symptoms is identified using statistical analyses. 28 However, symptom cluster analysis only focuses on the interdependency of symptoms within one cluster, whereas using mesoscopic measures, we can also analyze the interaction of cluster of symptoms with each other. Finally, the microscopic properties, such as degree centrality and closeness centrality, provide information about the role of each symptom and are useful for identifying the most influential symptoms in the symptoms interaction network. A summary of definitions and equations for all network measures is provided in Table 1. One example of utilizing network analysis to examine psychiatric disorders is the study by Cramer et al, 11 where the authors examined the vulnerability of patients to develop major depressive disorder (MDD) using symptom interaction networks, and suggested, based on their findings, that the individuals with more interconnected symptoms network are more vulnerable to developing MDD. 11 Another example is the study by Borkulo et al 12 in which they investigated the association between the symptom network structure of MDD patients with recovery from depression. According to their results, a more densely connected symptoms network is an indication of poor prognosis. Moreover, they identified fatigue/ loss of energy, feeling guilty, and psychomotor retardation as important symptoms in the persistent MDD network, which could be potentially the target of clinical intervention. 12 Finally, Beard et al 13 also used network view to examine the relationship between anxiety and depression disorder. Their findings suggest that the strength of connection between depression disorder and anxiety disorders is higher than the strength of connection between depression-anxiety symptoms. Moreover, they identified the presence of a sad mood and worry as the most central symptoms in the network. 13 According to these previous studies, antipsychotic treatments do not have a localized effect; rather, they achieve their goals via targeting a few central symptoms that have a spreading influence on a global symptom network. 10–13 Although network-level changes in symptom dynamics have yet to be examined using the PANSS, the measure is ideally suited for this purpose given its wide TABLE 1. Summary of network measures for weighted networks T YPE MEASURE DEFINITION INTUITION EQUATION STUDY Macroscopic Density Average network degree To what extent nodes of the network are interconnected Rubinov et al. 16 Average shortest path length Average shortest path length between all nodes Level of information efficiency in the network Rubinov et al. 16 Average clustering coefficient Overall clustering in the network To what extent nodes tend to cluster together Watts et al. 17 Modularity Partitioning networks into a collection of discrete modules, each performing a specific task To what extent nodes can be separated into distinct groups Blondel et al. 18 Mesoscopic Community detection A problem of finding maximal modularity in the network To find the optimal community structure of nodes in the network Blondel et al. 18 Microscopic Degree centrality Sum of the edge weights connected to a node Level of connectivity of a node in the network Barrat et al. 19 Closeness centrality Distance of a node to all other nodes in the network How quickly it reaches other nodes Freeman et al. 20 i, j, u, and v = node (symptom) index; N = total number of nodes; w i j , w iu , w iv , w uv = weight between nodes i and j, i and u, i and v, u and v; d i,j = 1/w i j = distance between nodes i and j; k i and k j = degrees of nodes i and j; c i and c j = the communities that nodes i and j belong to; ; the The δ-function δ(c i ,c j ) = 1 if c i = c j and 0 if c i ≠ c j .

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