Real‐time fMRI using brain‐state classification

Abstract

Abstract:We have implemented a real-time functional magnetic resonance imaging system based on multivariate classification. This approach is distinctly different from spatially localized real-time implementations, since it does not require prior assumptions about functional localization and individual performance strategies, and has the ability to provide feedback based on intuitive translations of brain state rather than localized fluctuations. Thus this approach provides the capability for a new class of experimental designs in which real-time feedback control of the stimulus is possible-rather than using a fixed paradigm, experiments can adaptively evolve as subjects receive brain-state feedback. In this report, we describe our implementation and characterize its performance capabilities. We observed $80% classification accuracy using whole brain, block-design, motor data. Within both left and right motor task conditions, important differences exist between the initial transient period produced by task switching (changing between rapid left or right index finger button presses) and the subsequent stable period during sustained activity. Further analysis revealed that very high accuracy is achievable during stable task periods, and that the responsiveness of the classifier to changes in task condition can be much faster than signal time-to-peak rates. Finally, we demonstrate the versatility of this implementation with respect to behavioral task, suggesting that our results are applicable across a spectrum of cognitive domains. Beyond basic research, this technology can complement electroencephalography-based brain computer interface research, and has potential applications in the areas of biofeedback rehabilitation, lie detection, learning studies, virtual reality-based training, and enhanced conscious awareness.

Nonlinear neurobiological probability weighting functions for aversive outcomes

“…Recent approaches of utilizing fMRI data to predict an individual's choices have met with varying success rates. In general, approaches that utilize distributed patterns of activation do better than approaches relying on individual brain regions (Hampton and O'Doherty, 2007;Knutson et al, 2007;LaConte et al, 2006). The results of the regression analysis of our choice data indicate that there is a strong relationship between passively evoked activations and subsequent decisions about the same stimuli.…”

Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification

“…It did not run in real-time during the experiment but was used offline for validation. It is worth to mention that classification with ordinary linear regression performs as well as the far more advanced support vector machines (SVM) approach used by Laconte et al [5]. 2 or π 2 the pendulum is restarted and the angle is set to zero, this happened 9 times in this real-time phase.…”

“…In the future we would like to improve the detection speed of the system. One way to do this is to train the classifier on the transitions between the different states instead of the states them self, as mentioned in [5]. This can be done by looking back at the signal a number of seconds to learn what the different transitions look like, to earlier detect a change of activity.…”

“…[4] and [5]. Laconte et al uses a similar setup in [5] but the arrow that they control can not be considered to be a dynamical system that changes by itself. The dynamical properties of the inverted pendulum make our setup a more challenging problem.…”

“…Laconte et al [5] were one of the first to perform real-time classification of brain activity. Ohlsson et al [6] use real-time fMRI to determine where you are looking.…”