RESUMEN
The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the "higher-order" information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure-function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses.
RESUMEN
The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.
RESUMEN
BACKGROUND: Because there can be no delay in providing identification wristbands to newborns, some hospitals assign newborns temporary first names such as Babyboy or Babygirl. These nondistinct naming conventions result in a large number of patients with similar identifiers in NICUs. To determine the level of risk associated with nondistinct naming conventions, we performed an intervention study to evaluate if assigning distinct first names at birth would result in a reduction in wrong-patient errors. METHODS: We conducted a 2-year before/after implementation study to examine the effect of a distinct naming convention that incorporates the mother's first name into the newborn's first name (eg, Wendysgirl) on the incidence of wrong-patient errors. We used the Retract-and-Reorder (RAR) tool, an established, automated tool for detecting the outcome of wrong-patient electronic orders. The RAR tool identifies orders placed on a patient that are retracted within 10 minutes and then placed by the same clinician on a different patient within the next 10 minutes. RESULTS: The reduction in RAR events post- versus preintervention was 36.3%. After accounting for clusters of orders within order sessions, the odds ratio of an RAR event post- versus preintervention was 0.64 (95% confidence interval: 0.42-0.97). CONCLUSIONS: The study results suggest that nondistinct naming conventions are associated with an increased risk of wrong-patient errors and that this risk can be mitigated by changing to a more distinct naming convention.