Ajay S. Pillai

Identification and validation of effective connectivity networks in functional magnetic resonance imaging using switching linear dynamic systems.

Dynamic connectivity networks identify directed interregional interactions between modeled brain regions in neuroimaging. However, problems arise when the regions involved in a task and their interconnections are not fully known a priori. Objective measures of model adequacy are necessary to validate such models. We present a connectivity formalism, the Switching Linear Dynamic System (SLDS), that is capable of identifying both Granger-Geweke and instantaneous connectivity that vary according to experimental conditions. SLDS explicitly models the task condition as a Markov random variable. The series of task conditions can be estimated from new data given an identified model providing a means to validate connectivity patterns. We use SLDS to model functional magnetic resonance imaging data from five regions during a finger alternation task. Using interregional connectivity alone, the identified model predicted the task condition vector from a different subject with a different task ordering with high accuracy. In addition, important regions excluded from a model can be identified by augmenting the model state space. A motor task model excluding primary motor cortices was augmented with a new neural state constrained by its connectivity with the included regions. The augmented variable time series, convolved with a hemodynamic kernel, was compared to all brain voxels. The right primary motor cortex was identified as the best region to add to the model. Our results suggest that the SLDS model framework is an effective means to address several problems with modeling connectivity including measuring overall model adequacy and identifying important regions missing from models.

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.

Using large-scale neural models to interpret connectivity measures of cortico-cortical dynamics at millisecond temporal resolution.

Over the last two decades numerous functional imaging studies have shown that higher order cognitive functions are crucially dependent on the formation of distributed, large-scale neuronal assemblies (neurocognitive networks), often for very short durations. This has fueled the development of a vast number of functional connectivity measures that attempt to capture the spatiotemporal evolution of neurocognitive networks. Unfortunately, interpreting the neural basis of goal directed behavior using connectivity measures on neuroimaging data are highly dependent on the assumptions underlying the development of the measure, the nature of the task, and the modality of the neuroimaging technique that was used. This paper has two main purposes. The first is to provide an overview of some of the different measures of functional/effective connectivity that deal with high temporal resolution neuroimaging data. We will include some results that come from a recent approach that we have developed to identify the formation and extinction of task-specific, large-scale neuronal assemblies from electrophysiological recordings at a ms-by-ms temporal resolution. The second purpose of this paper is to indicate how to partially validate the interpretations drawn from this (or any other) connectivity technique by using simulated data from large-scale, neurobiologically realistic models. Specifically, we applied our recently developed method to realistic simulations of MEG data during a delayed match-to-sample (DMS) task condition and a passive viewing of stimuli condition using a large-scale neural model of the ventral visual processing pathway. Simulated MEG data using simple head models were generated from sources placed in V1, V4, IT, and prefrontal cortex (PFC) for the passive viewing condition. The results show how closely the conclusions obtained from the functional connectivity method match with what actually occurred at the neuronal network level.

Decreased Modulation of EEG Oscillations in High-Functioning Autism during a Motor Control Task

Autism spectrum disorders (ASD) are thought to result in part from altered cortical excitatory-inhibitory balance; this pathophysiology may impact the generation of oscillations on electroencephalogram (EEG). We investigated premotor-parietal cortical physiology associated with praxis, which has strong theoretical and empirical associations with ASD symptomatology. Twenty five children with high-functioning ASD (HFA) and 33 controls performed a praxis task involving the pantomiming of tool use, while EEG was recorded. We assessed task-related modulation of signal power in alpha and beta frequency bands. Compared with controls, subjects with HFA showed 27% less left central (motor/premotor) beta (18–22 Hz) event-related desynchronization (ERD; p = 0.030), as well as 24% less left parietal alpha (7–13 Hz) ERD (p = 0.046). Within the HFA group, blunting of central ERD attenuation was associated with impairments in clinical measures of praxis imitation (r = −0.4; p = 0.04) and increased autism severity (r = 0.48; p = 0.016). The modulation of central beta activity is associated, among other things, with motor imagery, which may be necessary for imitation. Impaired imitation has been associated with core features of ASD. Altered modulation of oscillatory activity may be mechanistically involved in those aspects of motor network function that relate to the core symptoms of ASD.

Early sensory cortex is activated in the absence of explicit input during crossmodal item retrieval: evidence from MEG.

Crossmodal associations form a fundamental aspect of our daily lives. In this study we investigated the neural correlates of crossmodal association in early sensory cortices using magnetoencephalography (MEG). We used a paired associate recognition paradigm in which subjects were tested after multiple training sessions over a span of four weeks. Subjects had to learn 12 abstract, nonlinguistic, pairs of auditory and visual objects that consisted of crossmodal (visual-auditory, VA; auditory-visual, AV) and unimodal (visual-visual, VV; auditory-auditory, AA) paired items. Visual objects included abstract, non-nameable, fractal-like images, and auditory objects included abstract tone sequences. During scanning, subjects were shown the first item of a pair (S1), followed by a delay, then the simultaneous presentation of a visual and auditory stimulus (S2). Subjects were instructed to indicate whether either of the S2 stimuli contained the correct paired associate of S1. Synthetic aperture magnetometry (SAMspm), a minimum variance beamformer, was then used to assess source power differences between the crossmodal conditions and their corresponding unimodal conditions (i.e., AV-AA and VA-VV) in the beta (15-30 Hz) and low gamma frequencies (31-54 Hz) during the S1 period. We found greater power during S1 in the corresponding modality-specific association areas for crossmodal compared with unimodal stimuli. Thus, even in the absence of explicit sensory input, the retrieval of well-learned, crossmodal pairs activate sensory areas associated with the corresponding modality. These findings support theories which posit that modality-specific regions of cortex are involved in the storage and retrieval of sensory-specific items from long-term memory.

Building Neurocognitive Networks with a Distributed Functional Architecture

In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.

Temporal microstructure of cortical networks (TMCN) underlying task-related differences

Neuro-electromagnetic recording techniques (EEG, MEG, iEEG) provide high temporal resolution data to study the dynamics of neurocognitive networks: large scale neural assemblies involved in task-specific information processing. How does a neurocognitive network reorganize spatiotemporally on the order of a few milliseconds to process specific aspects of the task? At what times do networks segregate for task processing, and at what time scales does integration of information occur via changes in functional connectivity? Here, we propose a data analysis framework-Temporal microstructure of cortical networks (TMCN)-that answers these questions for EEG/MEG recordings in the signal space. Method validation is established on simulated MEG data from a delayed-match to-sample (DMS) task. We then provide an example application on MEG recordings during a paired associate task (modified from the simpler DMS paradigm) designed to study modality specific long term memory recall. Our analysis identified the times at which network segregation occurs for processing the memory recall of an auditory object paired to a visual stimulus (visual-auditory) in comparison to an analogous visual-visual pair. Across all subjects, onset times for first network divergence appeared within a range of 0.08-0.47 s after initial visual stimulus onset. This indicates that visual-visual and visual auditory memory recollection involves equivalent network components without any additional recruitment during an initial period of the sensory processing stage which is then followed by recruitment of additional network components for modality specific memory recollection. Therefore, we propose TMCN as a viable computational tool for extracting network timing in various cognitive tasks.

Intraoperative neurophysiology in deep brain surgery for psychogenic dystonia.

Psychogenic dystonia is a challenging entity to diagnose and treat because little is known about its pathophysiology. We describe two cases of psychogenic dystonia who underwent deep brain stimulation when thought to have organic dystonia. The intraoperative microelectrode recordings in globus pallidus internus were retrospectively compared with those of five patients with known DYT1 dystonia using spontaneous discharge parameters of rate and bursting, as well as movement‐related discharges. Our data suggest that simple intraoperative neurophysiology measures in single subjects do not differentiate psychogenic dystonia from DYT1 dystonia.

Sequence Effect in Parkinson’s Disease Is Related to Motor Energetic Cost

Bradykinesia is the most disabling motor symptom of Parkinson’s disease (PD). The sequence effect (SE), a feature of bradykinesia, refers to the rapid decrement in amplitude and speed of repetitive movements (e.g., gait, handwriting) and is a major cause of morbidity in PD. Previous research has revealed mixed results regarding the role of dopaminergic treatment in the SE. However, external cueing has been shown to improve it. In this study, we aimed to characterize the SE systematically and relate this phenomenon to the energetic cost of movement within the context of cost–benefit framework of motor control. We used a dynamic isometric motor task with auditory pacing to assess the SE in motor output during a 15-s task segment in PD patients and matched controls. All participants performed the task with both hands, and without and with visual feedback (VF). Patients were also tested in “on”- and “off”-dopaminergic states. Patients in the “off” state did not show higher SE compared to controls, partly due to large variance in their performance. However, patients in the “on” state and in the absence of VF showed significantly higher SE compared to controls. Patients expended higher total motor energy compared to controls in all conditions and regardless of their medication status. In this experimental situation, the SE in PD is associated with the cumulative energetic cost of movement. Dopaminergic treatment, critical for internal triggering of movement, fails to maintain the motor vigor across responses. The high motor cost may be related to failure to incorporate limbic/motivational cues into the motor plan. VF may facilitate performance by shifting the driving of movement from internal to external or, alternatively, by functioning as a motivational cue.