How Laminar Frontal Cortex and Basal Ganglia Circuits Interact to Control Planned and Reactive Saccades

The basal ganglia and frontal cortex together allow animals to learn adaptive responses that acquire rewards when prepotent reflexive responses are insufficient. Anatomical studies show a rich pattern of interactions between the basal ganglia and distinct frontal cortical layers. Analysis of the laminar circuitry of the frontal cortex, together with its interactions with the basal ganglia, motor thalamus, superior colliculus, and inferotemporal and parietal cortices, provides new insight into how these brain regions interact to learn and perform complexly conditioned behaviors. A neural model whose cortical component represents the frontal eye fields captures these interacting circuits. Simulations of the neural model illustrate how it provides a functional explanation of the dynamics of 17 physiologically identified cell types found in these areas. The model predicts how action planning or priming (in cortical layers III and VI) is dissociated from execution (in layer V), how a cue may serve either as a movement target or as a discriminative cue to move elsewhere, and how the basal ganglia help choose among competing actions. The model simulates neurophysiological, anatomical, and behavioral data about how monkeys perform saccadic eye movement tasks, including fixation; single saccade, overlap, gap, and memory-guided saccades; anti-saccades; and parallel search among distractors.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-l-0409, N00014-92-J-1309, N00014-95-1-0657); National Science Foundation (IRI-97-20333)

Selection of cortical dynamics for motor behaviour by the basal ganglia

The basal ganglia and cortex are strongly implicated in the control of motor preparation and execution. Re-entrant loops between these two brain areas are thought to determine the selection of motor repertoires for instrumental action. The nature of neural encoding and processing in the motor cortex as well as the way in which selection by the basal ganglia acts on them is currently debated. The classic view of the motor cortex implementing a direct mapping of information from perception to muscular responses is challenged by proposals viewing it as a set of dynamical systems controlling muscles. Consequently, the common idea that a competition between relatively segregated cortico-striato-nigro-thalamo-cortical channels selects patterns of activity in the motor cortex is no more suf?cient to explain how action selection works. Here, we contribute to develop the dynamical view of the basal ganglia-cortical system by proposing a computational model in which a thalamo-cortical dynamical neural reservoir is modulated by disinhibitory selection of the basal ganglia guided by top-down information, so that it responds with different dynamics to the same bottom-up input. The model shows how different motor trajectories can so be produced by controlling the same set of joint actuators. Furthermore, the model shows how the basal ganglia might modulate cortical dynamics by preserving coarse-grained spatiotemporal information throughout cortico-cortical pathways

Involvement of the cortico-basal ganglia-thalamocortical loop in developmental stuttering

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.R01 DC007683 - NIDCD NIH HHS; R01 DC011277 - NIDCD NIH HHSPublished versio

THE LEFT HEMISPHERE’S STRUCTURAL CONNECTIVITY FOR THE INFERIOR FRONTAL GYRUS, STRIATUM, AND THALAMUS, AND INTRA-THALAMIC TOPOGRAPHY

The neuroanatomy of language cognition has an extensive history of scientific interest and inquiry. Over a century of behavioral lesion studies and decades of functional neuroimaging research have established the left hemisphere’s inferior frontal gyrus (IFG) as a critical region for speech and language processing. This region’s subcortical projections are thought to be instrumental for supporting and integrating the cognitive functions of the language network. However, only a subset of these projections have been shown to exist in humans, and structural evidence of pars orbitalis’ subcortical circuitry has been limited to non-human primates. This thesis demonstrates direct, intra-structural connectivity of each of the left IFG’s gyral regions with the thalamus and the putamen in humans, using high-angular, deterministic tractography. Novel processing and analysis methods elucidated evidence of predominantly segregated cortical circuits within the thalamus, and suggested the presence of parallel circuits for motor/language integration along the length of the putamen

A working model on large-scale spatio-temporal organization of brain functioning and its implications for bipolar disorder

A working model on large-scale spatio-temporal organization of brain functioning and its implications for bipolar disorde

How sleep deprivation degrades task performance: combining experimental analysis with simulations of adenosinergic effects of basal ganglia and cortical circuits

Thesis (Ph.D.)--Boston UniversityHumans configure themselves into "neural machines" to perform optimally on distinct tasks, and they excel at maintaining such configurations for brief episodes. The neural configuration needed for peak performance, however, is subject to perturbations on multiple time scales. This thesis reports new empirical analyses and computational modeling to advance understanding of the variations in reaction time (RT) on simple RT tasks that are associated with the duration of the preceding inter-stimulus interval (order of seconds); the time-on-task duration (order of minutes); and sleep deprivation duration (order of hours to days). Responses from the psychomotor vigilance task (PVT), including anticipations (false alarms), normal RTs, and very long RTs (lapses in attention), were analyzed to discover the effects of: the 1 - 9 second inter-stimulus interval (ISI); the 10-minute task session; up to 50 hours of sleep deprivation (SD); and wake-promoting agents, caffeine and modafinil. Normal RTs and lapses in attention were negatively correlated with ISI length, whereas anticipations were positively correlated. Anticipations, normal RTs, and lapses increased as time-on-task increased, and during SD. Both caffeine and modafinil reduced lapses and anticipations during SD and decreased RT variability. A simple neural network model incorporating both a time-dependent inhibitory process and a time-dependent excitatory process was developed. The model robustly simulated the ISI effect on behavior. The SD effects were reproducible with two parameter adjustments. Informed modeling of drug effects required greater neurobiological detail. In the basal ganglia (BG), adenosine accumulation during SD has two notable effects: it antagonizes dopamine to reduce BG responsiveness to incoming cortical signals, and it reduces cholinergic transmission to parietal and prefrontal cortices, thus reducing attention to visual signals. A detailed computational model of interactions between BG and cortex during PVT was developed to simulate effects of adenosine and their amelioration by caffeine. The model simulates drug, ISI and SD effects on anticipations, RTs, and lapses. This model can be used to describe the effects of SD over a wide range of tasks requiring planned and reactive movements, and can predict and model effects of pharmacological agents acting on the adenosinergic, cholinergic and dopaminergic systems

Cortical Models for Movement Control

Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)

Biologically Plausible Cortical Hierarchical-Classifier Circuit Extensions in Spiking Neurons

Hierarchical categorization inter-leaved with sequence recognition of incoming stimuli in the mammalian brain is theorized to be performed by circuits composed of the thalamus and the six-layer cortex. Using these circuits, the cortex is thought to learn a ‘brain grammar’ composed of recursive sequences of categories. A thalamo-cortical, hierarchical classification and sequence learning “Core” circuit implemented as a linear matrix simulation and was published by Rodriguez, Whitson & Granger in 2004. In the brain, these functions are implemented by cortical and thalamic circuits composed of recurrently-connected, spiking neurons. The Neural Engineering Framework (NEF) (Eliasmith & Anderson, 2003) allows for the construction of large-scale biologically plausible neural networks. Existing NEF models of the basal-ganglia and the thalamus exist but to the best of our knowledge there does not exist an integrated, spiking-neuron, cortical-thalamic-Core network model. We construct a more biologically-plausible version of the hierarchical-classification function of the Core circuit using leaky-integrate-and-fire neurons which performs progressive visual classification of static image sequences relying on the neural activity levels to trigger the progressive classification of the stimulus. We proceed by implementing a recurrent NEF model of the cortical-thalamic Core circuit and then test the resulting model on the hierarchical categorization of images