Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems

Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a "node" in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an "instantaneous" connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis.

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.

Effective connectivity: influence, causality and biophysical modeling

This is the final paper in a Comments and Controversies series dedicated to "The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution". We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener-Akaike-Granger-Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.

Neural correlates of blink suppression and the buildup of a natural bodily urge

Neuroimaging studies have elucidated some of the underlying physiology of spontaneous and voluntary eye blinking; however, the neural networks involved in eye blink suppression remain poorly understood. Here we investigated blink suppression by analyzing fMRI data in a block design and event-related manner, and employed a novel hypothetical time-varying neural response model to detect brain activations associated with the buildup of urge. Blinks were found to activate visual cortices while our block design analysis revealed activations limited to the middle occipital gyri and deactivations in medial occipital, posterior cingulate and precuneus areas. Our model for urge, however, revealed a widespread network of activations including right greater than left insular cortex, right ventrolateral prefrontal cortex, middle cingulate cortex, and bilateral temporo-parietal cortices, primary and secondary face motor regions, and visual cortices. Subsequent inspection of BOLD time-series in an extensive ROI analysis showed that activity in the bilateral insular cortex, right ventrolateral prefrontal cortex, and bilateral STG and MTG showed strong correlations with our hypothetical model for urge suggesting these areas play a prominent role in the buildup of urge. The involvement of the insular cortex in particular, along with its function in interoceptive processing, helps support a key role for this structure in the buildup of urge during blink suppression. The right ventrolateral prefrontal cortex findings in conjunction with its known involvement in inhibitory control suggest a role for this structure in maintaining volitional suppression of an increasing sense of urge. The consistency of our urge model findings with prior studies investigating the suppression of blinking and other bodily urges, thoughts, and behaviors suggests that a similar investigative approach may have utility in fMRI studies of disorders associated with abnormal urge suppression such as Tourette syndrome and obsessive-compulsive disorder.