Treatment outcome is associated with pre-treatment connectome measures across psychiatric disorders - evidence for connectomic reserve?

Abstract

Predicting treatment efficacy in psychiatric disorders remains challenging, despite the availability of effective interventions. Previous studies suggest a link between pre-treatment brain network characteristics and treatment efficacy in individual disorders, but cross-disorder investigations are lacking. We analyzed pre-treatment MRI data from 177 individuals (113 females) with either obsessive-compulsive disorder (OCD) or post-traumatic stress disorder with comorbid personality disorders (PTSD) that received different non-pharmacological treatments. Using diffusion and resting-state MRI, we constructed structural, functional, and multilayer connectomes and calculated network measures for network integration (e.g. global efficiency, eccentricity), segregation (modularity) and their balance (small-worldness). We assessed the relationship between these pre-treatment network measures, and treatment improvement using mixed-model and Bayesian analyses. We also compared psychiatric cases with healthy controls and investigated associations between clinical response and treatment-induced changes in network measures. Across disorders and treatments, psychiatric cases showed a 41.6 ± 29.6 % symptom improvement (62 % response rate) after treatment. They also showed pre-treatment differences in functional and multilayer network topology compared to healthy controls. Symptom improvement was associated with pre-treatment functional (P = 0.04) and structural small-worldness (P = 0.01), and multilayer eccentricity (P = 0.01), while responders had higher functional modularity (P = 0.02). Results were robust across trials and treatments, when adjusting for medication status and showed high credibility in Bayesian analyses. Network change associations with treatment response were only modest. These results show that pre-treatment connectome characteristics are related to treatment response, regardless of treatment and psychiatric disorder, and suggest that individual differences in intrinsic features of the human connectome underlie amenability to treatment.

Keywords: Connectome; Neuroimaging; Obsessive-compulsive disorder; Posttraumatic stress disorder; Psychotherapy; Transdiagnostic.

Fig. 1

Case-control differences and associations with treatment outcome of pre-treatment global network measure. Violin plots show the distribution of the functional (a–d), structural (e–h) and multilayer (i–j) network measures. There were significant between-group differences in all functional network measures and the average multilayer eccentricity. Scatterplots show the associations between pre-treatment network measures and percentage symptom improvement, with positive values representing improvement after treatment. individual dots are colored according to whether someone was a responder (green) or non-responder (red). Percentage symptom improvement was significantly associated with functional (d) and structural (h) small-worldness and multilayer average eccentricity (i). Responders and non-responders also showed significant differences in functional modularity (b) and average participation coefficient (c). * significant association between percentage symptom improvement and the pre-treatment network measures. # significant difference in pre-treatment network measure between treatment responders and non-responders. abbreviations: Eglob = global efficiency, Q = modularity, PC = average participation coefficient, Ecc = average eccentricity, EC = average eigenvector centrality. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Bayesian posterior distribution plots of the association between structural and functional participation coefficient of eight brain systems and percentage change (a,b) or response status (c,d). The posterior distribution communicates the credibility of an effect. Positive posterior probabilities (P+) are shown next to each distribution and color coded. The values between brackets indicate the range of P+ values across leave-one-treatment-out, or leave-one-trial-out folds to show the robustness of the results. P+ values ≥0.90 indicate moderate to very high credibility for a positive effect, P+ ≤0.10 indicate moderate to very high credibility for a negative effect. The meaning of the direction of effects are shown next to the red zero-effect line. There was credible evidence for (a) negative associations between functional participation coefficient and percentage symptom improvement, and (b) lower functional participation coefficient in responders compared with non-responders across all eight systems. With the exception of a higher structural participation coefficient of the ventral attention network and subcortical structures in responders compared with non-responders (d), there was no credible evidence for associations between treatment outcome and structural participation coefficient. abbreviations: PC = participation coefficient, DMN = default mode network, FPN = frontoparietal network, SMN = somatomotor network, DAN = dorsal attention network, VAN = ventral attention network, LIM = limbic network, VIS = visual network. SUBC = subcortical structures. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Bayesian posterior distribution plots of the associations between multilayer eigenvector centrality and eccentricity of eight brain systems and percentage change (a,b) or response status (c,d). See the legend of Fig. 2 for an explanation of the posterior distribution plots. There was credible evidence for (a) negative associations between multilayer eccentricity and percentage symptom improvement across all eight systems, but (b) no credible evidence for differences between responders and non-responders. There was also no credible evidence for an association between (c) symptom improvement and multilayer eigenvector centrality. (d) Multilayer eigenvector centrality was higher in responders compared with non-responders in all eight networks but only the dorsal and ventral attention network as well as frontoparietal and limbic network showed a P+ > 0.90. Abbreviations: DMN = default mode network, FPN = frontoparietal network, SMN = somatomotor network, DAN = dorsal attention network, VAN = ventral attention network, LIM = limbic network, VIS = visual network. SUBC = subcortical structures.

Fig. 4

Schematic representation of the association between network topology and amenability to treatment. The architecture of the connectome is governed by the fundamental principles that under normal circumstances are in balance: segregation, i.e. tight local connections between brain areas that form a specialized subsystem, and integration, i.e. exchange between subsystems with long range connections. Deviation from the healthy variation in balance between these forces is associated with network dysfunction and the emergence of brain-related disorders as reviewed by (van den Heuvel and Sporns, 2019). The results of the current study suggest that (further) deviation from the norm is also associated with decreased amenability to non-pharmacological treatment for psychiatric disorders.