Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks
Daniela Jardim Pereira, João Pereira, Alexandre Sayal, Sofia Morais, António Macedo, Bruno Direito, Miguel Castelo-Branco
Network Neuroscience September 15, 2023 DOI: 10.1162/netn_a_00338 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational neuroimaging study Peer reviewed |
|---|---|
| Topics | Default mode network |
| Citations | 5 |
| Key findings | Neurofeedback success was positively correlated with intrinsic default mode network functional connectivity and negatively correlated with DLPFC-precuneus connectivity during the 2-back condition, while salience network structural connectivity was the main structural contributor. Classification models based on functional and structural connectivity achieved 77% and 86% accuracy, respectively. The authors argue that dynamic switching among the default mode, salience, and central executive networks is key to neurofeedback success. |
Abstract
Abstract Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.
Comparable studies
Other experimental studies on default mode network, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Functional connectivity of default mode network components: Correlation, anticorrelation, and causality Healthy subjects | 2008 | Observational study using resting-state fMRI and seed correlation analysis | |
| Patterns of Brain Activity Supporting Autobiographical Memory, Prospection, and Theory of Mind, and Their Relationship to the Default Mode Network Healthy subjects | 2009 | Experimental study using fMRI | |
| Dynamic reconfiguration of the default mode network during narrative comprehension Human subjects | 2016 | Experimental study | |
| Broadband Cortical Desynchronization Underlies the Human Psychedelic State Healthy human participants | 2013 | Experimental study | |
| The brain on art: intense aesthetic experience activates the default mode network Human observers | 2012 | Observational study with fMRI and behavioral analysis |