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Record W4282929732 · doi:10.3389/fnhum.2022.838614

Combined Aerobic Exercise and Neurofeedback Lead to Improved Task-Relevant Intrinsic Network Synchrony

2022· article· en· W4282929732 on OpenAlexafffund
Saurabh Bhaskar Shaw, Yarden Levy, Allison Mizzi, Gabrielle Herman, Margaret C. McKinnon, Jennifer J. Heisz, Suzanna Becker

Bibliographic record

VenueFrontiers in Human Neuroscience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonHomewood Research InstituteVector InstituteWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMcMaster University
KeywordsNeurofeedbackMindfulnessMoodElectroencephalographyPsychological interventionAerobic exercisePsychologyDefault mode networkPhysical medicine and rehabilitationFunctional magnetic resonance imagingMedicineClinical psychologyNeurosciencePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Lifestyle interventions such as exercise and mindfulness training have the potential to ameliorate mental health symptoms and restore dysregulated intrinsic connectivity network (ICN) dynamics, seen in many psychopathologies. Multiple lifestyle interventions, in combination, may interact synergistically for enhanced benefits. While the impacts of lifestyle interventions on subjective measures of mood are well-documented, their impacts on ICN dynamics are not well-established. In this study, we assessed the validity of EEG-derived measures of ICN dynamics as potential markers of mood disorders, by tracking ICN dynamics and mood symptoms through the course of a longitudinal exercise intervention. Specifically, we investigated the separate and combined effects of aerobic exercise and mindfulness-like neurofeedback training on task-linked ICN dynamics of the default mode network (DMN), central executive network (CEN), and salience network (SN). Participants were assigned pseudo-randomly into four experimental conditions-Control, Running, Neurofeedback, and Combined, performing the corresponding intervention for 16 sessions across 8 weeks. Intervention-linked changes in ICN dynamics were studied using EEG-based neuroimaging scans before and after the 8-week intervention, during which participants performed multiple blocks of autobiographical memory recall (AM) and working memory (WM) trials, designed to activate the DMN and CEN, respectively, and to activate the SN in conjunction with the task-appropriate network. The EEG-based features for classification of the three core networks had been identified in our prior research from simultaneously recorded EEG and fMRI during the same AM and WM tasks. We categorized participants as "responders" or "non-responders" based on whether the exercise intervention increased their aerobic capacity (VO2-max) (Running/Combined group), and/or neurofeedback increased the percentage time spent in the calm mindfulness state (Neurofeedback/Combined group). In responders, compared to each intervention alone, the combined exercise-neurofeedback intervention resulted in a more healthy CEN-SN synchrony pattern. Interestingly, non-responders to neurofeedback exhibited a maladaptive pattern of persistent, task-inappropriate DMN-SN synchrony which we speculate could be linked to depressive rumination. Furthermore, the CEN-SN synchrony at baseline predicted NFB response with up to 80% accuracy, demonstrating the potential utility of such network-based biomarkers in personalizing intervention plans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.239
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes2
Has abstractyes

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