Estimating a Separably Markov Random Field from Binary Observations.

A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause the loss of information pertinent to understanding the function of a neuron or circuit. We introduce a new method that can determine not only the trial-to-trial dynamics that accompany the learning of a contingency by a neuron, but also the latency of this learning with respect to the onset of a conditioned stimulus. The backbone of the method is a separable two-dimensional (2D) random field (RF) model of neural spike rasters, in which the joint conditional intensity function of a neuron over time and trials depends on two latent Markovian state sequences that evolve separately but in parallel. Classical tools to estimate state-space models cannot be applied readily to our 2D separable RF model. We develop efficient statistical and computational tools to estimate the parameters of the separable 2D RF model. We apply these to data collected from neurons in the prefrontal cortex in an experiment designed to characterize the neural underpinnings of the associative learning of fear in mice. Overall, the separable 2D RF model provides a detailed, interpretable characterization of the dynamics of neural spiking that accompany the learning of a contingency.

A separable two-dimensional random field model of binary response data from multi-day behavioral experiments.

BACKGROUND: The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease. NEW METHOD: We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning. RESULTS: We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels. COMPARISON WITH EXISTING METHOD(S): In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences. CONCLUSIONS: Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups.

Accurate High-speed 3D-Registration of EPI vNavs for Head Motion Correction.

Low-resolution, whole-head volumes can be acquired rapidly with EPI-based volumetric navigators (vNavs). vNavs interspersed in a longer scan are widely used for prospective motion correction in a variety of sequences. To further improve the accuracy and flexibility of vNavs, we present a novel registration algorithm, tailored specifically for the vNavs application. Accuracy of the algorithm is tested on navigator volumes acquired with human volunteers at three isotropic resolutions, 6.4mm, 8mm, and 10mm, using a series of field of view (FOV) rotations and translations to provide ground truth rigid "motion".

Effects of Resolution and Registration Algorithm on the Accuracy of EPI vNavs for Real Time Head Motion Correction in MRI.

Low-resolution, EPI-based Volumetric Navigators (vNavs) have been used as a prospective motion-correction system in a variety of MRI neuroimaging pulse sequences. The use of low-resolution volumes represents a trade-off between motion tracking accuracy and acquisition time. However, this means that registration must be accurate on the order of 0.2 voxels or less to be effective for motion correction. While vNavs have shown promising results in clinical and research use, the choice of navigator and registration algorithm have not previously been systematically evaluated. In this work we experimentally evaluate the accuracy of vNavs, and possible design choices for future improvements to the system, using real human data. We acquired navigator volumes at three isotropic resolutions (6.4 mm, 8 mm, and 10 mm) with known rotations and translations. The vNavs were then rigidly registered using trilinear, tricubic, and cubic B-spline interpolation. We demonstrate a novel refactoring of the cubic B-spline algorithm that stores pre-computed coefficients to reduce the per-interpolation time to be identical to tricubic interpolation. Our results show that increasing vNav resolution improves registration accuracy, and that cubic B-splines provide the highest registration accuracy at all vNav resolutions. Our results also suggest that the time required by vNavs may be reduced by imaging at 10 mm resolution, without substantial cost in registration accuracy.