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.

Real‐time estimation of dynamic functional connectivity networks

“…Especially with regard to our second example, we demonstrate that rt-SINGLE is able to capture moment-to-moment fluctuations in the attentional state of subjects and could potentially be used to boost brain state decoding accuracy by providing additional information relating to functional connectivity. Finally, there is great potential to integrate this work with the recently proposed Automatic Neuroscientist framework of Lorenz et al [2016a]. Lorenz et al [2016a] combined real-time fMRI with machine learning techniques to optimize experimental conditions to maximize a given target brain state [Lorenz et al, , 2016a.…”

“…Finally, there is great potential to integrate this work with the recently proposed Automatic Neuroscientist framework of Lorenz et al [2016a]. Lorenz et al [2016a] combined real-time fMRI with machine learning techniques to optimize experimental conditions to maximize a given target brain state [Lorenz et al, , 2016a. While the target brain state in their proof-of-principle study was simply based on BOLD differences, our proposed method can be utilized to extend the Automatic Neuroscientist to target entire functional connectivity networks.…”

“…It therefore follows that the next frontier for rt-fMRI studies involves accurately estimating brain connectivity in real-time. So far there have been only a limited number of studies focusing on estimating functional connectivity in real-time and their primary focus has been on neurofeedback (Ruiz et al, 2014; Zilverstand et al,2014; Koush et al 2013; Koush et al 2016; Liew el al, 2015). A limitation of those studies however is that it only provides real-time neurofeedback based on changes in connectivity between a very small number of ROIs.…”

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

“…Further, this approach has been used recently in other fields, for example, for parameter tuning biomedical models (Ghassemi, Lehman, Snoek, & Nemati, 2014) and speech recognition models (Watanabe & Le Roux, 2014). It has also been used for fMRI study design (Lorenz et al, 2016) and optimization of game engagement (Khajah, Roads, Lindsey, Liu, & Mozer, 2016). Lee and Wagenmakers (2014) present a guide to Bayesian cognitive modeling with practical examples such as estimating coefficient of agreement in a decision-making task.…”

Phoneme-level processing in low-frequency cortical responses to speech explained by acoustic features

“…In our current approach, we treated the source position coordinates as a continuous parameter and navigated to optimal positions within the usually smooth and thus easily optimiseable space in the volumes. A similar approach has recently also been developed (Lorenz et al, 2016) and applied (Lorenz et al, 2018) for closed-loop applications in fMRI.…”