Changes in Resting-State Functional Connectivity Related to Freezing of Gait in Parkinson's Disease.

Freezing of gait (FOG) is a common motor symptom in Parkinson's disease (PD) thought to arise from the dysfunctional cortico-basal ganglia-thalamic circuity. The purpose of this study was to assess the changes in brain resting-state functional connectivity (rs-FC) of subcortical structures comprising the cortico-basal ganglia-thalamic circuity in individuals with PD with and without FOG. Resting-state functional magnetic resonance imaging was acquired in 27 individuals with idiopathic PD (14 with FOG and 13 without FOG). A seed-to-voxel analysis was performed with the seeds in the bilateral basal ganglia nuclei, thalamus, and pedunculopontine nucleus. Between-group differences in rs-FC revealed that the bilateral thalamus and globus pallidus external were significantly more connected with visual areas in PD with FOG compared to PD without FOG. In addition, PD with FOG had increased connectivity between the left putamen and retrosplenial cortex as well as with the cerebellum. Our findings suggest an increased connectivity at rest of subcortical and cortical regions involved in sensory and visuospatial processing that may be compensating for sensorimotor deficits in FOG. This increased connectivity may contribute to the hypothesized overload in the cortico-basal ganglia-thalamic circuity processing capacity, which may ultimately result in FOG occurrence.

Interhemispheric connectivity of primary sensory cortex is associated with motor impairment after stroke.

Neuroimaging-derived markers are used to model post-stroke impairment. Among these, lesion size, corticospinal-tract lesion-load (CST-LL) and resting-state functional-connectivity (rs-FC) have been correlated with impairment. It has been shown that the sensory cortex (S1) is associated with motor learning and is essential for post-stroke recovery; yet stroke-induced changes in S1 connectivity alone are yet to be investigated. We aim to determine whether interhemispheric rs-FC could be used to refine imaging models of stroke-related impairment. Subjects' post-stroke and age-matched controls underwent rs-fMRI. Stroke-related disability was correlated with lesion size, CST-LL and interhemispheric S1 and M1 rs-FC as independent seeds. Regression analyses were performed to assess the contribution of these markers in stroke-related deficits. Post-stroke subjects showed an asymmetrical pattern of rs-FC in which affected hemisphere S1 and M1 were mostly connected with ipsi-lesional regions. Correlations between rs-FC and stroke-severity were found. Adding rs-FC of S1 to the regression model of impairment decreased the variance 31% compared to lesion size only. After a stroke, S1 interhemispheric connectivity is decreased, with S1 only connected with ipsi-lesional regions. This asymmetry correlates with neurological and motor impairment. Furthermore, when combined with lesion anatomical measures, S1 connectivity might be an important marker in explaining stroke outcome.

A loop-based neural architecture for structured behavior encoding and decoding.

We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections. The two neural sets do the bulk of the loop's computations while the control unit specifies the timing and the conditions under which the computations implemented by the loop are to be performed. By functionally linking many such loops together, a neural network is obtained that may perform complex cognitive computations. To demonstrate the potential offered by such a system, we present two neural network simulations. The first illustrates the structure and dynamics of a single loop implementing a simple gating mechanism. The second simulation shows how connecting four loops in series can produce neural activity patterns that are sufficient to pass a simplified delayed-response task. We also show that this network reproduces electrophysiological measurements gathered in various regions of the brain of monkeys performing similar tasks. We also demonstrate connections between this type of neural network and recurrent or long short-term memory network models, and suggest ways to generalize them for future artificial intelligence research.

Mechanisms Gating the Flow of Information in the Cortex: What They Might Look Like and What Their Uses may be.

The notion of gating as a mechanism capable of controlling the flow of information from one set of neurons to another, has been studied in many regions of the central nervous system. In the nucleus accumbens, where evidence is especially clear, gating seems to rely on the action of bistable neurons, i.e., of neurons that oscillate between a quiescent "down" state and a firing "up" state, and that act as AND-gates relative to their entries. Independently from these observations, a growing body of evidence now indicates that bistable neurons are also quite abundant in the cortex, although their exact functions in the dynamics of the brain remain to be determined. Here, we propose that at least some of these bistable cortical neurons are part of circuits devoted to gating information flow within the cortex. We also suggest that currently available structural, electrophysiological, and imaging data support the existence of at least three different types of gating architectures. The first architecture involves gating directly by the cortex itself. The second architecture features circuits spanning the cortex and the thalamus. The third architecture extends itself through the cortex, the basal ganglia, and the thalamus. These propositions highlight the variety of mechanisms that could regulate the passage of action potentials between cortical neurons sets. They also suggest that gating mechanisms require larger-scale neural circuitry to control the state of the gates themselves, in order to fit in the overall wiring of the brain and complement its dynamics.

From a representation of behavior to the concept of cognitive syntax: a theoretical framework.

Before we do anything, our brain must first construct a neural correlate of the various mental operations needed. Imaging and recording techniques have vastly improved our understanding of this process by providing detailed insight into how different regions of the brain contribute to behavior. However, exactly how these regions collaborate with each other to form the brain-scale activity necessary to generate even the simplest task remains elusive. Here we present a neural network model based on the hypothesis of a modular organization of brain activity, where basic neural functions useful to the current task are recruited and integrated into actual behavior. At the heart of this mechanism are regulating structures that restrain activity from flowing freely between the different cortical areas involved, releasing it instead in a controlled fashion designed to produce the different mental operations required by the task at hand. The resulting dynamics enables the network to perform the delayed-matching to sample and delayed-pair association tasks. The model suggests that brain activity coding for elementary tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia. We also argue that such an integration process might take place through an iterative process, by piecing together previously validated behavioral chunks, while creating new ones under the guidance of a partially innate cognitive syntax.

A model for integrating elementary neural functions into delayed-response behavior.

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

Acquisition and performance of delayed-response tasks: a neural network model.

We study the time evolution of a neural network model as it learns the three stages of a visual delayed-matching-to-sample (DMS) task: identification of the sample, retention during delay, and matching of sample and target, ignoring distractors. We introduce a neurobiologically plausible, uncommitted architecture, comprising an "executive" subnetwork gating connections to and from a "working" layer. The network learns DMS by reinforcement: reward-dependent synaptic plasticity generates task-dependent behaviour. During learning, working layer cells exhibit stimulus specialization and increased tuning of their firing. The emergence of top-down activity is observed, reproducing aspects of prefrontal cortex control on activity in the visual areas of inferior temporal cortex. We observe a lability of neural systems during learning, with a tendency to encode spurious associations. Executive areas are instrumental during learning to prevent such associations; they are also fundamental for the "mature" network to keep passing DMS. In the mature model, the working layer functions as a short-term memory. The mature system is remarkably robust against cell damage and its performance degrades gracefully as damage increases. The model underlines that executive systems, which regulate the flow of information between working memory and sensory areas, are required for passing tests such as DMS. At the behavioural level, the model makes testable predictions about the errors expected from subjects learning the DMS.