title: The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI.
abstract: 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 10 min. 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 6.3 min. 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.
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London W12 0NN, UK; Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
authors.forename: Romy
authors.initials: R
authors.lastname: Lorenz
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, UK.
authors.forename: Ricardo Pio
authors.initials: RP
authors.lastname: Monti
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London W12 0NN, UK.
authors.forename: Inês R
authors.initials: IR
authors.lastname: Violante
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, UK.
authors.forename: Christoforos
authors.initials: C
authors.lastname: Anagnostopoulos
authors.affiliation: Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
authors.forename: Aldo A
authors.initials: AA
authors.lastname: Faisal
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, UK; Department of Biomedical Engineering, King's College London, London SE1 7EH, UK.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Montana
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London W12 0NN, UK. Electronic address: r.leech@imperial.ac.uk.
authors.forename: Robert
authors.initials: R
authors.lastname: Leech
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009488:Neurosciences / Q000379:methods
keywords: Bayesian optimization; Brain–computer interface; Closed-loop; Experimental design; Machine learning; Real-time fMRI