Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia

McNabb, Carolyn B. and Tait, Roger and McIlwain, Meghan E. and Anderson, Valerie M. and Suckling, John and Kydd, Robert R. and Russell, Bruce R. (2017) Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia. Schizophrenia Review. ISSN 0920-9964

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Schizophrenia may develop from disruptions in functional connectivity regulated by neurotransmitters such as dopamine and acetylcholine. The modulatory effects of these neurotransmitters might explain how antipsychotics attenuate symptoms of schizophrenia and account for the variable response to antipsychotics observed in clinical practice. Based on the putative mechanisms of antipsychotics and evidence of disrupted connectivity in schizophrenia, we hypothesised that functional network connectivity, as assessed using network-based statistics, would exhibit differences between treatment response subtypes of schizophrenia and healthy controls. Resting-state functional MRI data were obtained from 17 healthy controls as well as individuals with schizophrenia who responded well to first-line atypical antipsychotics (first-line responders; FLR, n = 18), had failed at least two trials of antipsychotics but responded to clozapine (treatment-resistant schizophrenia; TRS, n = 18), or failed at least two trials of antipsychotics and a trial of clozapine (ultra-treatment-resistant schizophrenia; UTRS, n = 16). Data were pre-processed using the Advanced Normalization Toolkit and BrainWavelet Toolbox. Network connectivity was assessed using the Network-Based Statistics toolbox in Matlab. ANOVA revealed a significant difference in functional connectivity between groups that extended between cerebellar and parietal regions to the frontal cortex (p < 0.05). Post-hoc t-tests revealed weaker network connectivity in individuals with UTRS compared with healthy controls but no other differences between groups. Results demonstrated distinct differences in functional connectivity between individuals with UTRS and healthy controls. Future work must determine whether these changes occur prior to the onset of treatment and if they can be used to predict resistance to antipsychotics during first-episode psychosis.

Item Type: Article
Identification Number:
16 October 2017Published Online
9 October 2017Accepted
Uncontrolled Keywords: Schizophrenia Treatment resistance Treatment response Magnetic resonance imaging Network based statistics Clozapine
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Ian Mcdonald
Date Deposited: 02 Nov 2017 09:26
Last Modified: 22 Mar 2023 12:01

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