Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia

Carolyn B McNabb; Roger J Tait; Meghan E McIlwain; Valerie M Anderson; John Suckling; Robert R Kydd; Bruce R Russell

doi:10.1016/j.schres.2017.10.015

Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia

Schizophr Res. 2018 May:195:160-167. doi: 10.1016/j.schres.2017.10.015. Epub 2017 Oct 16.

Authors

Carolyn B McNabb¹, Roger J Tait², Meghan E McIlwain¹, Valerie M Anderson¹, John Suckling², Robert R Kydd³, Bruce R Russell⁴

Affiliations

¹ School of Pharmacy, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand.
² Department of Psychiatry, University of Cambridge, Cambridge and Peterborough Foundation NHS Trust, Herchel Smith Buidling for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge CB2 0SZ, United Kingdom; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Downing Street, Cambridge CB2 3EB, United Kingdom.
³ Department of Psychological Medicine, University of Auckland, Auckland City Hospital, 2 Park Road, Grafton, Auckland 1023, New Zealand.
⁴ School of Pharmacy, University of Otago, PO Box 56, Dunedin 9054, New Zealand. Electronic address: bruce.russell@otago.ac.uk.

PMID: 29042073
DOI: 10.1016/j.schres.2017.10.015

Abstract

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.

Keywords: Clozapine; Magnetic resonance imaging; Network based statistics; Schizophrenia; Treatment resistance; Treatment response.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Analysis of Variance
Antipsychotic Agents / therapeutic use
Biomarkers / metabolism
Clozapine / therapeutic use
Female
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging / methods*
Male
Middle Aged
Nerve Net / diagnostic imaging*
Nerve Net / drug effects
Nerve Net / pathology
Oxygen / blood
Rest
Schizophrenia / diagnostic imaging*
Schizophrenia / drug therapy
Schizophrenia / metabolism*
Young Adult

Substances

Antipsychotic Agents
Biomarkers
Clozapine
Oxygen