Non-invasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), and Transcranial Ultrasound Stimulation (TUS), have gained prominence for their potential to modulate neural activity and enhance cognitive, emotional, and motor functions. Despite their promise, individual responses to NIBS vary significantly, influenced by unique neuroanatomical and functional characteristics. To address this variability, computational approaches—including advanced neuroimaging analyses, individualized brain modeling, and machine learning algorithms—are being developed to tailor NIBS protocols to each person's neural architecture and specific needs or symptoms. This personalized methodology aims to optimize stimulation parameters and to target specific brain regions, thereby improving the efficacy and reliability of therapeutic interventions. This Research Topic seeks to explore these computational and/or methodological strategies and their applications in personalizing NIBS for functional enhancement, aiming to bridge the gap between theoretical models and practical, individualized therapeutic solutions.
Despite the growing use of non-invasive brain stimulation (NIBS) for cognitive enhancement and neurorehabilitation, individual variability in response to stimulation remains a major challenge. Differences in brain structure, functional connectivity, and neuroplasticity contribute to inconsistent effects, limiting the widespread clinical application of NIBS. Current one-size-fits-all stimulation protocols often fail to account for these individual differences, reducing treatment efficacy and reliability.
To address this, computational approaches such as neuroimaging-informed modeling, machine learning, and real-time adaptive stimulation offer promising solutions for personalizing NIBS. By tailoring stimulation parameters to an individual’s unique neural architecture and specific symptoms, these methods can optimize outcomes and improve reproducibility. This Research Topic seeks contributions that explore novel methodological innovations, computational frameworks, individualized neurostimulation models, and clinical applications of personalized NIBS, bridging the gap between theoretical advancements and real-world applications in cognitive neuroscience and clinical practice.
This Research Topic focuses on advancing personalized non-invasive brain stimulation (NIBS) through computational approaches, aiming to enhance cognitive function and neurorehabilitation outcomes. We welcome review and research articles that address the following themes:
- Computational Models for Personalized NIBS - Individualized Neurostimulation Protocols - Real-Time and Closed-Loop Stimulation - Cognitive and Clinical Applications - Neuroplasticity and Network-Based Approaches - AI-driven optimization of stimulation parameters in NIBS - Deep learning and network-based models for predicting individual responses to NIBS - Multi-modal approaches integrating EEG, fMRI, and computational modeling for tailored NIBS interventions
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