The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 129; pp. 320 - 334
• Main Authors: Lorenz, Romy, Monti, Ricardo Pio, Violante, Inês R., Anagnostopoulos, Christoforos, Faisal, Aldo A., Montana, Giovanni, Leech, Robert
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2016; Elsevier Limited; Academic Press
• Subjects: Adult; Algorithms; Automation; Bayes Theorem; Bayesian optimization; Brain; Brain - physiology; Brain Mapping - methods; Brain-Computer Interfaces; Brain–computer interface; Closed-loop; Cognition & reasoning; Design; Experimental design; Experiments; Female; Full Length; Humans; Image Processing, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Imaging - methods; Male; Methods; Neurosciences - methods; Optimization algorithms; Optimization techniques; Physiology; Real-time fMRI; Studies; Real-time fMRI; Bayesian optimization; Experimental design; Closed-loop; Machine learning; Brain–computer interface
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2016.01.032

Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals. •Mapping between cognitive task and neural processes is often not straightforward.•Current fMRI approach is not sufficient for holistic investigation of this mapping.•We propose a novel framework combining real-time fMRI and optimization techniques.•We validate our closed-loop method and find it to be highly efficient and accurate.•This framework is generalizable to numerous innovative applications.