Reducing default mode network connectivity with mindfulness-based fMRI neurofeedback: a pilot study among adolescents with affective disorder history

Abstract

Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed mindfulness-based fMRI neurofeedback (mbNF) for adolescents that aims to reduce default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and each participant’s DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks associated with the emergence and persistence of depressive symptoms during adolescence.

Default Mode Network Detection using EEG in Real-time

“…The real-time DMN detection algorithms tested are implemented through proprietary software produced by Resonait Medical Technologies Pty Ltd, fully outlined in [14]. The established method for inferring the DMN states has been the subject of numerous publications [7], [10], [15], [16]. It was applied to the LEMON dataset using the open source OSL-Dynamics toolbox [17].…”

“…Normalisation of DMN connectivity is theorised to be the mechanism of action of transcranial magnetic stimulation (TMS), a brain stimulation therapy that has proven effective in treating depression [6]. Neurofeedback methods implemented using functional magnetic resonance (fMRI) imaging to target DMN connectivity patterns have also been shown to reduce depressive symptoms in patients [7], [8].…”

“…Methods that detect DMN activity have been widely implemented in fMRI, but have also been demonstrated in noninvasive neurophysiology modalities such as EEG [9]. Whereas DMN-neurofeedback methods implemented in fMRI have been effective in reducing depressive symptoms [7], [8], these methods face significant economic barriers to becoming a scalable therapy. EEG based DMN-Neurofeedback has the potential to achieve comparable results whilst presenting drastically fewer practical and economic barriers to patient adoption.…”

Incorporation of Brain Connectomics for Stereotactic Radiosurgery Treatment Planning

“…The result is a patient-specific parcellation and white matter fiber map for several well-known connectome networks, including the default mode network (DMN), 10,11 optic radiation (OR), 12 and central executive network (CEN). 13-15 The network data were in-turn converted to a set of “burn-in” images where the voxel data containing network parcellation or fiber tracts were set to the maximum voxel value for the data set. One series of burn-in images was created for each connectome network.…”

Preliminary findings on long‐term effects of fMRI neurofeedback training on functional networks involved in sustained attention

“…Several studies have shown the feasibility of volitional control of DMN activity using functional magnetic resonance imaging (fMRI)-neurofeedback, either defined as a single region (Garrison et al, 2013;Harmelech et al, 2015;G. Zhang et al, 2013), multiple regions (McDonald et al, 2017), or through relative DMN activity, compared to other largescale networks (Kim et al, 2019;J. Zhang et al, 2023).…”