Enabling Unobtrusive Real-World Monitoring of Brain-Networks with Wearable Neurotechnology and Multimodal Machine Learning

Descripción del proyecto

Tecnología ponible para obtener imágenes del cerebro reales

Comprender cómo funcionan las redes cerebrales en situaciones reales podría revolucionar los tratamientos de los trastornos cerebrales. Sin embargo, las actuales herramientas, como la resonancia magnética funcional, se limitan a entornos de laboratorio controlados, y el electroencefalograma móvil carece de capacidad para captar plenamente las redes cerebrales. El proyecto INTEGRAL, financiado por el Consejo Europeo de Investigación, pretende resolver este problema desarrollando una plataforma portátil que combina tomografía óptica difusa de alta densidad, electroencefalogramas y sensores fisiológicos. El sistema híbrido permitirá obtener imágenes cerebrales de forma continua y discreta en entornos cotidianos. Gracias a los avances en «hardware» y análisis del aprendizaje automático, el equipo de INTEGRAL promete ofrecer nuevos conocimientos sobre la función cerebral, transformando la investigación en neurotecnología y sus aplicaciones en ámbitos como la salud digital y la neurociencia.

Objetivo

Measuring and linking brain network activity to human physiology and behavior in natural everyday situations promises profound new insights into healthy brain function and disorders. However, the absence of suitable mobile neurotechnology presents a significant roadblock. Functional magnetic resonance imaging (fMRI) has greatly advanced our understanding of brain function and networks, but it is limited to single-snapshot experiments in constrained lab settings. Electroencephalography (EEG), while mobile, cannot directly be linked to brain networks captured by fMRI. To overcome these roadblocks and to advance neuro-inspired treatments and discoveries to natural environments, a hybrid wearable platform is required that combines innovations in hardware and analysis methods to enable continuous and stable measurements of brain network activity maps in the everyday world. Advancing high-density diffuse optical tomography (HD-DOT) can provide such a suitable alternative to fMRI. With a unique systems engineering concept, INTEGRAL aims to miniaturize and integrate DOT, EEG, and physiological sensors with advanced multimodal machine learning to improve spatio-temporal contrast in mobile brain-imaging. To this end, Objective 1 (Instruments) will develop hardware for unobtrusive and continuous wearable brain-body imaging with HD-DOT-EEG. Objective 2 (Experiments) will collect extensive multimodal data for measuring brain networks while controlling for environmental and physiological artifacts. Objective 3 (Analysis) will enable estimation of brain network activity with multimodal sensor fusion and machine learning and Objective 4 (Integration) will provide validation of robust brain-networks imaging in ecologically valid everyday world environments. If successful, this new platform will provide unprecedented opportunities to study brain function with global impact on neurotechnology applications and research from Neuroscience of the Everyday World to digital health.