Adaptive and Non-Invasive Neuromodulation in Psychiatry and Cognition: Mechanisms, AI-Guided Targeting, and Translation

Non-invasive brain stimulation (NIBS), including TMS, tDCS, and tACS, has become an established treatment option in psychiatry and a powerful tool for probing causal brain–behavior relationships. Yet most protocols remain open-loop and one-size-fits-all: stimulation parameters are fixed in advance and applied uniformly, which contributes to the well-documented variability in clinical and cognitive outcomes. Progress in brain-computer interfaces, real-time signal analysis, machine learning, and neurotechnology now permits the modulation of brain activity with unprecedented temporal and spatial precision, and allows stimulation to be adapted to the state of the individual brain. Translating these technical capacities into robust, personalized clinical and cognitive enhancement tools remains a formidable challenge, requiring deeper integration of mechanistic understanding, technological innovation, and clinical application. This Research Topic provides a platform for work that bridges these domains.This Research Topic brings together mechanistic, methodological, and clinical work on how brain stimulation can be made adaptive, targeted, and individualized. We seek to address questions such as: What network-level mechanisms support successful adaptive neuromodulation, and how do they differ between responders and non-responders? How can real-time signal analysis, brain-computer interfaces, and control algorithms be optimized to enhance intervention efficacy? Can artificial intelligence and interpretable machine-learning models, applied to neuroimaging, electrophysiological, or clinical data, predict who will respond to a given stimulation protocol and help select targets and parameters? What are the most promising targets and protocols for psychiatric conditions and for cognitive enhancement? And how can both successful and failed closed-loop paradigms inform our understanding of brain function? By assembling contributions from diverse methodological and conceptual vantage points, we hope to define both the current state and the future trajectory of adaptive neuromodulation as a unified field.We welcome submissions addressing, but not limited to:• Mechanisms and network-level effects of non-invasive and invasive brain stimulation in neuropsychiatric disorders (e.g., depression, addiction, schizophrenia) and cognitive domains (e.g., attention, memory, executive function).• Closed-loop and adaptive stimulation: state-dependent, EEG- and BCI-triggered, and control-theoretic protocols.• Artificial intelligence and interpretable machine learning for predicting treatment response, stratifying patients, and informing target and parameter selection.• Individualized targeting and dosing, including neuroimaging- and biomarker-guided protocols.• Methodological advances that enable more precise, personalized, or ecologically valid adaptive protocols.• Critical evaluations, meta-analysis, and frameworks that integrate mechanistic and applied perspectives.All contributions should be grounded in neurostimulation: computational, AI, or signal-analysis studies must connect to stimulation mechanisms, targeting, or outcomes. We welcome Original Research, Brief Research Reports, Clinical Trials, Methods, Systematic Reviews and meta-analyses, Reviews, Mini Reviews, Perspectives, and Opinions. Topic editors: Minmin Wang, Lu Zhang, Chanlin Yi, QING LI Source: https://www.frontiersin.org/research-topics/84685/adaptive-and-non-invasive-neuromodulation-in-psychiatry-and-cognition-mechanisms-ai-guided-targeting-and-translation