An MSCA Doctoral Network developing active nanomaterials for magnetic sensing and stimulation of deep brain structures. The research centres on minimally invasive approaches and closed-loop control, linking materials science with neural measurement and modulation.
Selected · signature pending01/11/2026–31/10/2030research grant
European Commission
NEUROMAGIC / Active nanoMaterials for closed-looP minimaLly-Invasive magneTic sensing and stimUlation of DEep brain structures
NEUROMAGIC is set to pioneer a groundbreaking approach in the realm of bidirectional brain-machine interfaces (BBMIs) by creating Smart Anisotropic Magnetic Nanomaterials (SAMNs) for wireless, precise, and cell-specific neuromodulation in deep brain regions. This innovative technology circumvents the need for invasive electrode implants, by establishing a minimally-invasive and highly effective communication channel with neurons. By exploiting the unique properties of SAMNs, engineered through the doping of anisotropic magnetite nanoparticles with divalent transition metals, NEUROMAGIC will to significantly enhance vortex magnetization, facilitating the conversion of magnetic fields into mechanical torques that can trigger neuronal excitation via calcium influx, thereby enabling precise monitoring and modulation of neural activity. We will employ advanced polymer and biomolecule functionalization techniques to endow SAMNs with dual capabilities: target specific neuronal populations and read out neural activity through calcium signaling. This will be achieved by integrating a calcium-specific protein for direct MRI-based detection of neural activity and by functionalizing SAMNs with genetic material and antibodies to achieve cell-type specificity. NEUROMAGIC will leverage an Integrated Computational Materials Engineering (ICME) framework, incorporating text-mining, NLP, and predictive modeling, to optimize the magnetic properties of SAMNs, ensuring their effective passage through the blood-brain barrier and proximity to neuron membranes. A novel controller system based on reinforcement learning will enable real-time, adaptive neuromodulation tailored to individual physiological responses. This approach promises to revolutionize the field of BBMIs, offering a versatile platform for both research and therapeutic applications, with the potential to transform the treatment landscape for a range of neurological and psychiatric disorders.