Offline effects of closed-loop neuromodulation of the default mode network
Poster No:
68
Submission Type:
Abstract Submission
Authors:
Cameron Higgins1, Navin Cooray2, Brendan Harris3, Justin Chan4, Adriano Henrique de Matos Moffa4, Stevan Nikolin5
Institutions:
1University of Oxford, Rozelle, NSW, 2CSIRO, Sydney, New South Wales, 3The University of Sydney, Sydney, New South Wales, 4UNSW, Sydney, NSW, 5UNSW, Sydney, New South Wales
First Author:
Co-Author(s):
Introduction:
Hyperconnectivity in the brain's default mode network (DMN) is hypothesised as a mechanism underlying major depressive disorder (MDD)(1). Normalisation of this hyperconnectivity pattern is thought to be the mechanism of action of Transcranial Magnetic Stimulation (TMS) when used for treating MDD (2). But despite robust evidence of clinical efficacy, clinical adoption has remained sluggish with clinicians often expressing concerns about how long an effect is maintained offline after cessation of treatment. Neurofeedback that is integrated into TMS treatment may offer a potential maintenance therapy to maintain the offline effects of TMS for longer following cessation of treatment. Recent findings showing that motor evoked potentials are on average 30% larger when delivered during periods of sensorimotor network activation (3) may indicate that neural responses to TMS pulses are amplified during activation of the target network. We hypothesised that TMS efficacy may be enhanced by timing TMS pulses to coincide with periods of DMN activation, and that this same signal could be used in neurofeedback to achieve a similar physiological effect. We report below the interim results of a sham-controlled, triple-blinded preregistered study assessing the offline effects of DMN targeting using both TMS and audio-visual stimulation (AVS) neurofeedback.
Methods:
Our study protocol was preregistered prior to starting data collection (4). We applied a within-subject crossover design in healthy volunteers, with participants randomly assigned to receive either active TMS, sham TMS, active AVS or sham AVS over 4 separate sessions with concurrent EEG. We utilised commercially produced software for real-time DMN detection from EEG signals, which has been validated for this purpose (5). In active conditions, stimulation was delivered exclusively during periods of inferred DMN activation; in sham conditions stimulation was delivered at random times independent of the detected DMN signal (Figure 1A). TMS in both active and sham conditions was delivered over the dorsomedial prefrontal cortex (a key node of the DMN) in theta burst trains. AVS in both active and sham conditions comprised a red screen and an audio tone. Each session comprised 6 recording blocks (Figure 1B), with each session administering exactly one of the four interventions. Results were analysed by a mixed effects repeated measures model (MRMM), with primary outcome being the change in DMN activation from pre to post resting state scans, in active vs sham conditions. A power analysis led to planned recruitment of N=56 participants given significance threshold of p<0.05, with an interim analysis conducted after N=28 participants supporting immediate conclusion if tests met a Pocock stopping boundary of p<0.031.
Results:
We report the interim analysis results conducted after recruitment of half (N=28) of the planned total subjects. The primary outcome measure of the change in offline DMN activity provided insufficient evidence (p=0.051) of a difference between active and sham conditions to meet the stopping rule and conclude the study early. There nonetheless appears to be a strong trend, which may indicate an effect size stronger than hypothesised when initially powering the study. Post hoc analyses of the online blocks (i.e. whilst the intervention was applied confirmed strong (p<0.001) DMN inhibition achieved in both modalities. Furthermore the degree of online DMN modulation in individual participants correlated strongly with offline effects in those participants (rho=0.44, p<0.001), suggesting a direct relationship.
Conclusions:
We have developed an intervention that modulates activity in the brain's default mode network when applied. At this interim analysis point there is insufficient evidence to conclude that inhibition of the default mode network is sustained following cessation of the intervention, and the study will proceed to its planned recruitment endpoint of 56 participants.
Brain Stimulation:
Non-invasive Magnetic/TMS
TMS 1
Non-Invasive Stimulation Methods Other 2
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Keywords:
Electroencephaolography (EEG)
Transcranial Magnetic Stimulation (TMS)
1|2Indicates the priority used for review
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2. Liston, C., Chen, A. C., Zebley, B. D., Drysdale, A. T., Gordon, R., Leuchter, B., ... & Dubin, M. J. (2014). Default mode network mechanisms of transcranial magnetic stimulation in depression. Biological psychiatry, 76(7), 517-526.
3. Marzetti, L., Basti, A., Guidotti, R., Baldassarre, A., Metsomaa, J., Zrenner, C., ... & Pizzella, V. (2024). Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study. Biomedicines, 12(5), 955.
4. Cooray, N., Gohil, C., Harris, B., Frost, S., & Higgins, C. (2024). Default Mode Network Detection using EEG in Real-time. medRxiv, 2024-04.
5. Cooray, N., Moffa, A., Higgins, C., Harris, B., Chan, J., & Nikolin, S. (2023). Disrupting the default mode network through real-time closed loop neurofeedback.