Moliadze, V. | bioRxiv Science

bioRxiv2026

A Real-Time Functional Localization Method Based on Dynamic Connectivity for Coupled Brain Regions

In the rapidly growing field of dynamic connectivity (dyC)-based real-time functional magnetic resonance imaging (rtfMRI) neurofeedback training, participants receive feedback on the dynamical coupling of brain nodes based on real-time dynamic connectivity measurements between multiple regions-of-interest. Given the fundamental role of regions-of-interest in this context, their signal-to-noise-ratio is critical. In closely related activity-based rtfMRI neurofeedback, region-of-interest selection is guided by functional localization methods informed by the BOLD-signal during a functional localizer experiment conducted prior to neurofeedback. However, methods that account for dynamic coupling of multiple brain regions remain lacking. Here, we develop a method based on dynamic connectivity between two brain areas that aligns with conventional activity-based localization approaches and is applicable for rtfMRI-NF. The proposed pipeline adapts processing steps from activity-based methods and was successfully implemented in a dyC-informed neurofeedback study. We observed a significant increase in dyC signal-to-noise-ratio during the localizer experiment and the first three blocks of neurofeedback training. Importantly, dyC values also correlated with anxiodepressive symptom levels, serving as proof of sensitivity, as changes in dyC reflect not only task-related neural dynamics but also individual differences in anxiodepressive traits. Our findings demonstrate that functional localization methods can be extended to dynamic connectivity, improving rtfMRI feedback accuracy, and with evidence to serve as sensitive measure for emotional states. This approach may enable precise targeting of coupled brain regions in neurofeedback and holds potential for personalized clinical applications, with broader implications beyond neurofeedback discussed.

neuroscience↗

bioRxiv2022

Stratification of responses to tDCS intervention in a healthy paediatric population based on resting-state EEG profiles

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of applications in both the clinical and cognitive psychology domains. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to timely and costly treatments with ineffective results if this variability is not taken into account. We propose the usage of electroencephalography (EEG) for the analysis and prediction of individual responses to tDCS. In this context the application of machine learning can be of enormous help. We analysed resting-state EEG activity to identify subgroups of participants with an homogeneous electrophysiological profile and their response to different tDCS interventions. The study described herein, which focuses on healthy controls, was conducted within a clinical trial for the development of treatments based on tDCS for age-matched children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). We have studied a randomized, double-blind, sham-controlled tDCS intervention in 56 healthy children and adolescents aged 10-17, applied in 2 parallel groups over 2 target regions, namely left Dorsolateral Prefrontal Cortex (lDLPFC) and right Inferior Frontal Gyrus (rIFG). Cognitive behavioural tasks were used to both activate particular brain areas during the stimulation and to assess the impact of the intervention afterwards. We have implemented an unsupervised learning approach to stratify participants based on their resting-state EEG spectral features before the tDCS application. We have then applied a correlational analysis to identify EEG profiles associated with tDCS subject response to the specific stimulation sites and the presence or not of concurrent tasks during the intervention. In the results we found specific digital electrophysiological profiles that can be associated to a positive response, whereas subjects with other profiles respond negatively or do not respond to the intervention. Findings suggest that unsupervised machine learning procedures, when associated with proper visualization features, can be successfully used to interpret and eventually to predict responses of individuals to tDCS treatment.

neuroscience↗