Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states

Amphetamine Exerts Dose-Dependent Changes in Prefrontal Cortex Attractor Dynamics during Working Memory

Modulation of neural activity by monoamine neurotransmitters is thought to play an essential role in shaping computational neurodynamics in the neocortex, especially in prefrontal regions. Computational theories propose that monoamines may exert bidirectional (concentration-dependent) effects on cognition by altering prefrontal cortical attractor dynamics according to an inverted U-shaped function. To date, this hypothesis has not been addressed directly, in part because of the absence of appropriate statistical methods required to assess attractor-like behavior in vivo. The present study used a combination of advanced multivariate statistical, time series analysis, and machine learning methods to assess dynamic changes in network activity from multiple single-unit recordings from the medial prefrontal cortex (mPFC) of rats while the animals performed a foraging task guided by working memory after pretreatment with different doses of d-amphetamine (AMPH), which increases monoamine efflux in the mPFC. A dose-dependent, bidirectional effect of AMPH on neural dynamics in the mPFC was observed. Specifically, a 1.0 mg/kg dose of AMPH accentuated separation between task-epoch-specific population states and convergence toward these states. In contrast, a 3.3 mg/kg dose diminished separation and convergence toward task-epoch-specific population states, which was paralleled by deficits in cognitive performance. These results support the computationally derived hypothesis that moderate increases in monoamine efflux would enhance attractor stability, whereas high frontal monoamine levels would severely diminish it. Furthermore, they are consistent with the proposed inverted U-shaped and concentration-dependent modulation of cortical efficiency by monoamines

Nonstationary Stochastic Resonance in a Single Neuron-Like System

Stochastic resonance holds much promise for the detection of weak signals in the presence of relatively loud noise. Following the discovery of nondynamical and of aperiodic stochastic resonance, it was recently shown that the phenomenon can manifest itself even in the presence of nonstationary signals. This was found in a composite system of differentiated trigger mechanisms mounted in parallel, which suggests that it could be realized in some elementary neural networks or nonlinear electronic circuits. Here, we find that even an individual trigger system may be able to detect weak nonstationary signals using stochastic resonance. The very simple modification to the trigger mechanism that makes this possible is reminiscent of some aspects of actual neuron physics. Stochastic resonance may thus become relevant to more types of biological or electronic systems injected with an ever broader class of realistic signals.Comment: Plain Latex, 7 figure

A Two-stage Approach for Rapid Assessment of the Proportion Achieving Viral Suppression Using Routine Clinical Data

Background: Improving viral suppression among people with HIV reduces morbidity, mortality, and transmission. Accordingly, monitoring the proportion of patients with a suppressed viral load is important to optimizing HIV care and treatment programs. But viral load data are often incomplete in clinical records. We illustrate a two-stage approach to estimate the proportion of treated people with HIV who have a suppressed viral load in the Dominican Republic. Methods: Routinely collected data on viral load and patient characteristics were recorded in a national database, but 74% of patients on treatment at the time of the study did not have a recent viral load measurement. We recruited a subset of these patients for a rapid assessment that obtained additional viral load measurements. We combined results from the rapid assessment and main database using a two-stage weighting approach and compared results to estimates obtained using standard approaches to account for missing data. Results: Of patients with recent routinely collected viral load data, 60% had a suppressed viral load. Results were similar after applying standard approaches to account for missing data. Using the two-stage approach, we estimated that 77% (95% confidence interval [CI] = 74, 80) of those on treatment had a suppressed viral load. Conclusions: When assessing the proportion of people on treatment with a suppressed viral load using routinely collected data, applying standard approaches to handle missing data may be inadequate. In these settings, augmenting routinely collected data with data collected through sampling-based approaches could allow more accurate and efficient monitoring of HIV treatment program effectiveness