Proactive control deficit in schizophrenia is underpinned by reduced alpha and beta power in DLPFC
Poster No:
490
Submission Type:
Abstract Submission
Authors:
Matthew Hughes1,2, Alexandra Gaillard1, Philip Sumner1, Sean Carruthers1, Susan Rossell1, Pat Michie3, Will Woods1,2
Institutions:
1Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, VIC, 2Australian National Imaging Facility, Brisbane, QLD, Australia, 3Priority Research Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW
First Author:
Matthew Hughes, PhD
Centre for Mental Health and Brain Sciences, Swinburne University of Technology|Australian National Imaging Facility
Hawthorn, VIC|Brisbane, QLD, Australia
Centre for Mental Health and Brain Sciences, Swinburne University of Technology|Australian National Imaging Facility
Hawthorn, VIC|Brisbane, QLD, Australia
Co-Author(s):
Alexandra Gaillard
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Philip Sumner, PhD
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Sean Carruthers, PhD
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Susan Rossell
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Centre for Mental Health and Brain Sciences, Swinburne University of Technology
Hawthorn, VIC
Pat Michie, PhD
Priority Research Centre for Brain and Mental Health Research, University of Newcastle
Callaghan, NSW
Priority Research Centre for Brain and Mental Health Research, University of Newcastle
Callaghan, NSW
Will Woods
Centre for Mental Health and Brain Sciences, Swinburne University of Technology|Australian National Imaging Facility
Hawthorn, VIC|Brisbane, QLD, Australia
Centre for Mental Health and Brain Sciences, Swinburne University of Technology|Australian National Imaging Facility
Hawthorn, VIC|Brisbane, QLD, Australia
Introduction:
Cognitive impairment is a core feature of schizophrenia (Kahn & Keefe, 2013). While schizophrenia patients (SZ) perform poorly across diverse cognitive domains, it is widely thought that impaired cognitive control underlies these deficits. Specifically, SZ are thought have an impaired capacity for 'proactive control' (Barch & Ceaser, 2012), which refers to maintenance of goal-relevant information and biasing of cognitive resources in anticipation of a cognitively demanding event. The key locus of SZ dysfunction is thought to be left dorsolateral prefrontal cortex (DLPFC). Here we assess proactive control in SZ using the stopsignal paradigm where participants attempt to inhibit reaction-time (RT) responses on occasional trials when a stopsignal cue follows a go cue (the stopsignal-task). Numerous studies have shown that greater proactive control over go-task responding is associated with slower, more accurate go-task RTs (goRTs), and inhibitory performance (Verbruggen et al., 2019). For each SZ and age/sex matched healthy control (HC) participant, we extracted source-localised neuro-oscillatory power observed during a fixation period that immediately preceded go cues and correlated this activity with corresponding goRTs. Hence, variation in goRT indexes proactive control. We hypothesised that neuro-oscillatory power in left DLPFC would exhibit a greater positive correlation with goRT in HC compared to SZ.
Methods:
Participants performed a stopsignal paradigm with 560 trials (160 stopsignal-task), in which every go-task stimulus was preceded by a 500ms fixation cross, while undergoing MEG recording (MEGIN Triux). Participants also underwent a T1 MRI scan that was used for co-registration and source-localization purposes. MEG and T1 MRI data were co-registered, then environmental noise was removed from MEG data using the temporal extension to signal source separation algorithm of Maxfilter, then sensor noise was suppressed using oversampled temporal projection (OTP). Sensors still containing gross artifacts were removed ('bad sensors'). Remaining sensor data were high-pass filtered (>1 Hz) then independent component analysis (Fast-ICA) was to remove cardiac, eyeblink and muscle artefacts (Barbati et al., 2004). Epochs from -300-0 ms prior to correct go-task stimulus onsets (i.e., beginning 200 ms after fixation cross onset) were created to capture proactive control processes. Epoch co-variance and noise covariance estimates were computed and de-ranked. Then a linearly constrained minimum variance (LCMV) beamformer was reconstructed on a scanning grid with 2 mm resolution (Brookes et al., 2008), which was used to spatially filter the data in source space. These epochs were correlated (Kendall's Tau, τ) with goRTs from the corresponding trial to yield a τ correlation co-efficient map that was transformed to a z-score map. Z-score maps were entered into an independent-samples t-test in a preliminary analysis of SZ (N=19) and HC (N=18). Statistical inferences were made using the false discovery rate (FDR; q<.05).
Results:
SZ exhibited significantly lower positive correlations between goRTs and pre-stimulus go-task activity in left DLPFC (BA9) in both alpha and beta frequency bands, each of which exhibited broader network reductions. Significantly reduced alpha-band correlations in SZ were also observed in bilateral parietal cortices, while beta-band reductions were further observed within a left lateralised network including the pre-supplementary motor area, anterior cingulate cortex, precuneus and dorsal thalamus.
Conclusions:
Diminished proactive control in SZ was associated with reduced alpha- and beta-band neuro-oscillatory power within DLPFC and associated networks. This work directly relates proactive control in a behavioural context to a neurophysiological context and underscores the hypotheses that impaired proactive control in SZ is associated with left DLPFC dysfunction.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Cognition
MEG
Psychiatric Disorders
Schizophrenia
Other - Cognitive control
1|2Indicates the priority used for review
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Provide references using APA citation style.
Barch, D. M., & Ceaser, A. (2012). Cognition in schizophrenia: Core psychological and neural mechanisms. Trends in Cognitive Sciences, 16(1), 27–34.
Brookes, M. J., Vrba, J., Robinson, S. E., Stevenson, C. M., Peters, A. M., Barnes, G. R., Hillebrand, A., & Morris, P. G. (2008). Optimising experimental design for MEG beamformer imaging. Neuroimage, 39(4), 1788–1802.
Kahn, R. S., & Keefe, R. S. (2013). Schizophrenia is a cognitive illness: Time for a change in focus. JAMA Psychiatry, 70(10), 1107–1112.
Verbruggen, F., Aron, A. R., Band, G. P., Beste, C., Bissett, P. G., Brockett, A. T., Brown, J. W., Chamberlain, S. R., Chambers, C. D., & Colonius, H. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. Elife, 8, e46323.