CyberNeuro: A Privacy-Preserving Agentic Workbench for Cohort-Scale Brain Imaging and Clinical Data Analysis

Despite tremendous success in neuroimaging methodology, making large-scale, high-dimensional datasets ready for AI/ML applications remains a critical operational bottleneck. Conventional workflows require extensive manual effort across metadata curation, pipeline execution, post-processing quality control, and data management, a burden that disproportionately excludes laboratories with limited manpower and computational infrastructure. To address this real-world barrier, there is an urgent need for scalable, cost-effective computational platforms that democratize advanced neuroimaging analytics and accelerate discoveries in mental health and clinical translation. Capitalizing on multi-agent LLM breakthroughs, we introduce CyberNeuro, an agentic workbench with a tailored local LLM-model (called ‘WandaMind’) for automated neuroimaging and health-data analysis. Driven by four dedicated agents (Planner, Validator, Dispatcher, and Reporter) communicating via a secure MCP bridge and a pinned execution layer, CyberNeuro enables researchers to execute complex workflows using natural language; in the evaluated local configuration, all workflows completed with process-level network access denied. On the held-out QC-62 suite, WandaMind raises overall accuracy from 40% to 73% over its base model, mainly through safer abstention and fewer inappropriate tool calls; tool-selection accuracy is unchanged and argument validity drops slightly. Beyond automated metrics, the platform integrates a human-in-the-loop verification panel, which currently supports quality control of raw T1w images. On an end-to-end DICOM-to-BIDS suite of ten cohorts, CyberNeuro completed all tasks using about 10.6% of NeuroClaw’s per-volume token count with WandaMind and 61.7% with a cloud backend. The QC-62 benchmark and implementation details are provided in the appendix.