Innovations In Clinical Neuroscience

NOV-DEC 2017

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

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65 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 Phase 1 visits; however, there were differences in the closeness centrality (p<0.09) and degree centrality (p<0.01) of symptoms in the treatment-responsive group at the baseline and end of Phase I visits. In other words, in contrast with the treatment-responsive group, the microscopic network properties of the treatment-resistant group did not change significantly from baseline to the end of phase I. Moreover, the between-group comparisons indicate that there were significant differences in closeness and degree centrality values of the treatment-resistant and treatment-responsive groups before and after treatment (Table 3). DISCUSSION The network view considers psychiatric disorders as complex systems consisting of many interacting components (symptoms) with some emergent behavior (i.e., resistance/ responsiveness to the treatment). Hence, within this perspective, the psychiatric disorders are more than just the sum of the symptom intensities. For example, even though there might not be a mean change on a certain symptom (i.e., PANSS item) as a result of treatment, this symptom could still play a critical role in the overall responsiveness of the patient to treatments because of its interdependency with other important symptoms. In this regard, conventional univariate statistical analysis cannot fully explain the treatment responsiveness of patients to antipsychotic medications. Hence, we used analytical tools of network science to explore the sensitivity of the PANSS in detecting treatment effects. Specifically, we examined changes in symptom networks following antipsychotic administration in treatment- resistant and -responsive patients with psychosis who participated in the CATIE trial. Overall, our results suggest that the PANSS is highly sensitive in detecting treatment effects from a network perspective. Given the rapid advances in applying network analysis to examine psychopathology and treatment effects, 10,11 these findings are of considerable importance. The PANSS might be ideal not only for detecting whether an antipsychotic treatment is effective (i.e., via considering mean changes in symptom levels relative to baseline and placebo using traditional univariate statistics) but also for determining why it is effective from a network perspective (i.e., by determining whether a treatment increases or decreases global connectivity within an individual's symptom network and identifying which symptoms are most central to treatment response). There were also important implications for understanding how patients become treatment-resistant versus -responsive from a network perspective. Results of the macroscopic, mesoscopic, and microscopic level analyses revealed a consistent pattern of findings. Treatment-resistant patients demonstrated randomly fluctuating associations among symptoms regardless of whether they were receiving antipsychotic treatment. In contrast, treatment-responsive patients displayed an increase in network connectivity following treatment, such that improvement of certain key symptoms or clusters of symptoms (communities) precipitated improvements in global symptom presentation. Interestingly, the most central symptoms following antipsychotic administration in treatment-responsive patients were Blunted Affect, Excitement, and Poor Rapport. This finding suggests that when antipsychotics do have beneficial effects on these specific symptoms, this leads to a spreading effect and improvement in positive (i.e., hallucinations and delusions) and other symptoms as well (e.g., disorganization). In a sense, the positive symptoms that typically bring patients in for treatment might be FIGURE 3. Network representation of microscopic properties. Clockwise from top-left: treatment-resistant/closeness centrality; treatment-resistant/degree centrality; treatment-responsive/degree centrality; and treatment-responsive/ closeness centrality. These graphs represent network representation of closeness and degree centralities at baseline and 18 months follow-up for treatment resistant and treatment responsive groups. Node colors represent the difference of centralities between baseline and end of phase 1; the node sizes represent the absolute value of changes in centralities from baseline to 18 months follow-up. The closeness centrality represents how much a particular node in the network is accessible to the other nodes in the network. Nodes with high closeness centrality can quickly access other nodes in the network. The degree centrality is the sum of the weights of edges connected to a node and represents the level of connectivity of a node in the network pos _ p1 = Delusions; pos _ p2 = Conceptual Organization; pos _ p3 = Hallucinatory Behavior; pos _ p4 = Excitement; pos _ p5 = Grandiosity; pos _ p6 = Suspiciousness/Persecution; pos _ p7 = Hostility; neg _ n1 = Blunted Affect; neg _ n2 = Emotional Withdrawal; neg _ n3 = Poor Rapport; neg _ n4 = Passive/Apathetic Social Withdrawal; neg _ n5 = Difficulty in Abstract Thinking; neg _ n6 = Lack of Spontaneity and Flow of Conversation; neg _ n7 = Stereotyped Thinking; gps _ g1 = Somatic Concern; gps _ g2 = Anxiety; gps _ g3 = Guilt Feelings; gps _ g4 = Tension; gps _ g5 = Mannerisms and Posturing; gps _ g6 = Depression; gps _ g7 = Motor Retardation; gps _ g8 = Uncooperativeness; gps _ g9 = Unusual Thought Content; gps _ g10 = Disorientation; gps _ g11 = Poor Attention; gps _ g12 = Lack of Judgement and Insight; gps _ g13 = Disturbance of Volition gps _ g14 = Poor Impulse Control; gps _ g15 = Preoccupation; gps _ g16 = Active Social Avoidance.

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