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Developing non-invasive brain-computer interfaces with optical modalities, creating real time decoding and signal processing BCI software, building AI models to decode human intention and speech

Electrical stimulation therapies represent nonpharmacologic treatment options for chronic pain management. However, we do not understand how these therapies work and this knowledge gap continues to limit the success of these technologies. Therefore, our research group implements a patient-specific approach that integrates detailed clinical mechanistic testing with computational models. We believe that this systematic approach will improve our scientific understanding of neurostimulation for chronic pain and provide scientific guidance to individualize and optimize several components of these neurostimulation technologies.

How does the brain represent, process, and regulate pain and affective experiences? The goal of our lab research is to answer this question to better understand pain and emotions, thereby promoting the physical and psychological well-being of people who suffer from pain and emotional distress. Specific aims include 1) understanding the brain mechanisms of pain and emotion dynamics (mechanisms), 2) developing integrated brain models of human affective experiences and clinical outcomes (biomarkers), 3) translating basic and computational neuroscience findings into clinical applications (translation), and 4) building a life- and neuroscience-inspired artificial intelligence (AI) to better understand pain, affect, and human intelligence.

Although some AI models look or act like a brain, they do not necessarily think like a brain. My research focuses on understanding how the brain learns and makes inferences from the machine learning perspective. To address this question, I have put together ideas from AI and computational neuroscience. The approach is two-fold: 1) “Brain↦AI” aimed at understanding how the brain learns from a machine learning standpoint, and 2) “AI↦Brain” aimed at understanding why such neural processes occur.

Flavia Mancini is an MRC Career Development Award fellow and head of a multidisciplinary research group, called the Nox Lab, at the Department of Engineering, University of Cambridge. The Nox Lab includes a mix of computational neuroscientists, information and biomedical engineers, united by a shared passion for the development of open-source computational methods to understand brain function and improve human health. Their work is motivated by neuroscience questions relating to how neural activity generates perception and behaviour, mostly in humans. They use a combination of neuroimaging, physiological, behavioural and computational methods for the processing of neural signals and behavioural/clinical data.

The Nox Lab’s current work has a primary application to chronic pain. They take an innovative information engineering approach to understanding the neural processing and regulation of pain. Nox Lab’s research is split into a basic research line, aiming to understand the computational and neural mechanisms of pain inference, learning and control, and a translational research line in which they translate this knowledge into digital and neurotechnology tools for precision medicine, pain prevention and treatment.

My research focuses on the neurobiology of decision-making and learning. I use multimodal neuroimaging and neurostimulation methods to uncover the roles of multiple areas in the brain, predominantly the prefrontal cortex. Recently, I have shown that transcranial ultrasound neuromodulation can safely change neural activity in precise parts of the brain, both in non-human primates and humans. I am now working towards bringing this technology forward and apply it to mental health challenges.