The Spatiotemporal Neural Dynamics of Intersensory Attention Capture of Salient Stimuli: A Large-Scale Auditory-Visual Modeling Study.

The spatiotemporal dynamics of the neural mechanisms underlying endogenous (top-down) and exogenous (bottom-up) attention, and how attention is controlled or allocated in intersensory perception are not fully understood. We investigated these issues using a biologically realistic large-scale neural network model of visual-auditory object processing of short-term memory. We modeled and incorporated into our visual-auditory object-processing model the temporally changing neuronal mechanisms for the control of endogenous and exogenous attention. The model successfully performed various bimodal working memory tasks, and produced simulated behavioral and neural results that are consistent with experimental findings. Simulated fMRI data were generated that constitute predictions that human experiments could test. Furthermore, in our visual-auditory bimodality simulations, we found that increased working memory load in one modality would reduce the distraction from the other modality, and a possible network mediating this effect is proposed based on our model.

Quantifying Differences Between Passive and Task-Evoked Intrinsic Functional Connectivity in a Large-Scale Brain Simulation.

Establishing a connection between intrinsic and task-evoked brain activities is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from nontask neural elements while giving us the effects of task execution on nontask regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step toward elucidating the intrinsic versus task-evoked connection. In this study we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our large-scale neural modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of 10 subjects performing passive fixation (PF), passive viewing (PV), and delayed match-to-sample (DMS) tasks. We used the simulated blood oxygen level-dependent functional magnetic resonance imaging time series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain FC during task execution. Our results represent a step toward establishing a connection between intrinsic and task-related brain activities.

Simulating laminar neuroimaging data for a visual delayed match-to-sample task.

Invasive electrophysiological and neuroanatomical studies in nonhuman mammalian experimental preparations have helped elucidate the lamina (layer) dependence of neural computations and interregional connections. Noninvasive functional neuroimaging can, in principle, resolve cortical laminae (layers), and thus provide insight into human neural computations and interregional connections. However human neuroimaging data are noisy and difficult to interpret; biologically realistic simulations can aid experimental interpretation by relating the neuroimaging data to simulated neural activity. We illustrate the potential of laminar neuroimaging by upgrading an existing large-scale, multiregion neural model that simulates a visual delayed match-to-sample (DMS) task. The new laminar-based neural unit incorporates spiny stellate, pyramidal, and inhibitory neural populations which are divided among supragranular, granular, and infragranular laminae (layers). We simulated neural activity which is translated into local field potential-like data used to simulate conventional and laminar fMRI activity. We implemented the laminar connectivity schemes proposed by Felleman and Van Essen (Cerebral Cortex, 1991) for interregional connections. The hemodynamic model that we employ is a modified version of one due to Heinzle et al. (Neuroimage, 2016) that incorporates the effects of draining veins. We show that the laminar version of the model replicates the findings of the existing model. The laminar model shows the finer structure in fMRI activity and functional connectivity. Laminar differences in the magnitude of neural activities are a prominent finding; these are also visible in the simulated fMRI. We illustrate differences between task and control conditions in the fMRI signal, and demonstrate differences in interregional laminar functional connectivity that reflect the underlying connectivity scheme. These results indicate that multi-layer computational models can aid in interpreting layer-specific fMRI, and suggest that increased use of laminar fMRI could provide unique and fundamental insights to human neuroscience.

Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory.

Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex.

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were "non-task-specific" (NS) neurons that served as noise generators to "task-specific" neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.

Simulated neural dynamics of decision-making in an auditory delayed match-to-sample task.

An important goal of research on the cognitive neuroscience of decision-making is to produce a comprehensive model of behavior that flows from perception to action with all of the intermediate steps defined. To understand the mechanisms of perceptual decision-making for an auditory discrimination experiment, we connected a large-scale, neurobiologically realistic auditory pattern recognition model to a three-layer decision-making model and simulated an auditory delayed match-to-sample (DMS) task. In each trial of our simulated DMS task, pairs of stimuli were compared each stimulus being a sequence of three frequency-modulated tonal-contour segments, and a "match" or "nonmatch" button was pressed. The model's simulated response times and the different patterns of neural responses (transient, sustained, increasing) are consistent with experimental data and the simulated neurophysiological activity provides insights into the neural interactions from perception to action in the auditory DMS task.

Neural mechanisms of auditory discrimination of long-duration tonal patterns: a neural modeling and fMRI study.

Language perception comprises mechanisms of perception and discrimination of auditory stimuli. An important component of auditory perception and discrimination concerns auditory objects. Many interesting auditory objects in our environment are of relatively long duration; however, the temporal window of integration of auditory cortex neurons processing these objects is very limited. Thus, it is necessary to make active use of short-term memory in order to construct and temporarily store long-duration objects. We sought to understand the mechanisms by which the brain manipulates long-duration tonal patterns, temporarily stores the segments of those patterns, and integrates them into an auditory object. We extended a previously constructed model of auditory recognition of short-duration tonal patterns by expanding the prefrontal cortically-based short-term memory module of the previous model into a memory buffer with multiple short-term memory submodules and by adding a gating module. The gating module distributes the segments of the input pattern to separate locations of the extended prefrontal cortex in an orderly fashion, allowing a subsequent comparison of the stored segments against the segments of a second pattern. In addition to simulating behavioral data and electrical activity of neurons, our model also produces simulations of the blood oxygen level dependent (BOLD) signal as obtained in fMRI studies. The results of these simulations provided us with predictions that we tested in an fMRI experiment with normal volunteers. This fMRI experiment used the same task and similar stimuli to that of the model. We compared simulated data with experimental values. We found that two brain areas, the right precentral gyrus and the left medial frontal gyrus, correlated well with our simulations of the memory gating module. Other fMRI studies of auditory perception and discrimination have also found correlation of fMRI activation of those areas with similar tasks and thus provide further support to our findings.

Investigating the neural basis of the auditory continuity illusion.

In this study, we investigated one type of auditory perceptual grouping phenomena--the auditory continuity illusion (also called temporal induction). We employed a previously developed, neurobiologically realistic, large-scale neural network model of the auditory processing pathway in the cortex, ranging from the primary auditory cortex to the prefrontal cortex, and simulated temporal induction without changing any model parameters. The model processes tonal contour stimuli, composed of combinations of upward and downward FM sweeps and tones, in a delayed match-to-sample task. The local electrical activities of the neuronal units of the model simulated accurately the experimentally observed electrophysiological data, where available, and the model's simulated BOLD-fMRI data were quantitatively matched with experimental fMRI data. In the present simulations, intact stimuli were matched with fragmented versions (i.e., with inserted silent gaps). The ability of the model to match fragmented stimuli declined as the duration of the gaps increased. However, when simulated broadband noise was inserted into these gaps, the matching response was restored, indicating that a continuous stimulus was perceived. The electrical activities of the neuronal units of the model agreed with electrophysiological data, and the behavioral activity of the model matched human behavioral data. In the model, the predominant mechanism implementing temporal induction is the divergence of the feedforward connections along the auditory processing pathway in the temporal cortex. These simulation results not only attest to the robustness of the model, but further predict the primary role of the anatomical connectivity of the auditory processing areas in mediating the continuity illusion.

A neural network simulating human reach-grasp coordination by continuous updating of vector positioning commands.

We developed a neural network model to simulate temporal coordination of human reaching and grasping under variable initial grip apertures and perturbations of object size and object location/orientation. The proposed model computes reach-grasp trajectories by continuously updating vector positioning commands. The model hypotheses are (1) hand/wrist transport, grip aperture, and hand orientation control modules are coupled by a gating signal that fosters synchronous completion of the three sub-goals. (2) Coupling from transport and orientation velocities to aperture control causes maximum grip apertures that scale with these velocities and exceed object size. (3) Part of the aperture trajectory is attributable to an aperture-reducing passive biomechanical effect that is stronger for larger apertures. (4) Discrepancies between internal representations of targets partially inhibit the gating signal, leading to movement time increases that compensate for perturbations. Simulations of the model replicate key features of human reach-grasp kinematics observed under three experimental protocols. Our results indicate that no precomputation of component movement times is necessary for online temporal coordination of the components of reaching and grasping.

Adaptive force generation for precision-grip lifting by a spectral timing model of the cerebellum.

We modeled adaptive generation of precision grip forces during object lifting. The model presented adjusts reactive and anticipatory grip forces to a level just above that needed to stabilize lifted objects in the hand. The model obeys principles of cerebellar structure and function by using slip sensations as error signals to adapt phasic motor commands to tonic force generators associated with output synergies controlling grip aperture. The learned phasic commands are weight- and texture-dependent. Simulations of the new circuit model reproduce key aspects of experimental observations of force application. Over learning trials, the onset of grip force buildup comes to lead the load force buildup, and the rate-of-rise of grip force, but not load force, scales inversely with the friction of the object.