Assessing the encoding of stimulus attributes with rapid sequences of stimulus events

In a preceding paper (M. Eger and R. Eckhorn, J. Comput. Neurosci., 2002) we have published a three step method for the quantification of transinformation in multi-input and -output neuronal systems. Here we present an extension that applies to rapid series of transient stimuli and thus, fills the gap between the discrete and continuous stimulation paradigm. While the three step method potentially captures all stimulus aspects, the present approach quantifies the discriminability of selected attributes of discrete stimuli and thus, assesses their encoding. Based on simulated and recorded data we investigate the performance of the implemented algorithm. Our approach is illustrated by analyses of neuronal population activity from the visual cortex of the cat, evoked by electrical stimuli of the retina.     

Visual signals in an optomotor reflex: systems and information theoretic analysis

Compensatory optomotor reflexes were examined in crayfish (Procambarus clarkii) with oscillating sine wave gratings and step displacements of a single stripe. A capacitance transducer was used to measure the rotation of the eyestalk about its longitudinal axis. System studies reveal a spatial frequency response independent of velocity and stimulus amplitude and linear contrast sensitivity similar to that of neurons in the visual pathway. The reflex operates at low temporal frequencies (<0.002 Hz to 0.5 Hz) and exhibits a low-pass temporal frequency response with cut-off frequency of 0.1 Hz. Eyestalk rotation increases as a saturable function of the angular stimulus displacement. When compared to the oscillatory response, transient responses are faster, and they exhibit a lower gain for large stimulus displacements. These differences may reflect system nonlinearity and/or the presence of at least two classes of afferents in the visual pathway. Our metric for information transmission is the Kullback-Leibler (K-L) distance, which is inversely proportional to the probability of an error in distinguishing two stimuli. K-L distances are related to differences in responsiveness for variations in spatial frequency, contrast, and angular displacement. The results are interpreted in terms of the neural filters that shape the system response and the constraints that the K-L distances place on information transmission in the afferent visual pathway.     

Model-based approach for human kinematics reconstruction from markerless and marker-based motion analysis systems

Modeling tools related to the musculoskeletal system have been previously developed. However, the integration of the real underlying functional joint behavior is lacking and therefore available kinematic models do not reasonably replicate individual human motion. In order to improve our understanding of the relationships between muscle behavior, i.e. excursion and motion data, modeling tools must guarantee that the model of joint kinematics is correctly validated to ensure meaningful muscle behavior interpretation. This paper presents a model-based method that allows fusing accurate joint kinematic information with motion analysis data collected using either marker-based stereophotogrammetry (MBS) (i.e. bone displacement collected from reflective markers fixed on the subject's skin) or markerless single-camera (MLS) hardware. This paper describes a model-based approach (MBA) for human motion data reconstruction by a scalable registration method for combining joint physiological kinematics with limb segment poses. The presented results and kinematics analysis show that model-based MBS and MLS methods lead to physiologically-acceptable human kinematics. The proposed method is therefore available for further exploitation of the underlying model that can then be used for further modeling, the quality of which will depend on the underlying kinematic model.     

A network-based approach for resistance transmission in bacterial populations

Horizontal transfer of mobile genetic elements (conjugation) is an important mechanism whereby resistance is spread through bacterial populations. The aim of our work is to develop a mathematical model that quantitatively describes this process, and to use this model to optimize antimicrobial dosage regimens to minimize resistance development. The bacterial population is conceptualized as a compartmental mathematical model to describe changes in susceptible, resistant, and transconjugant bacteria over time. This model is combined with a compartmental pharmacokinetic model to explore the effect of different plasma drug concentration profiles. An agent-based simulation tool is used to account for resistance transfer occurring when two bacteria are adjacent or in close proximity. In addition, a non-linear programming optimal control problem is introduced to minimize bacterial populations as well as the drug dose. Simulation and optimization results suggest that the rapid death of susceptible individuals in the population is pivotal in minimizing the number of transconjugants in a population. This supports the use of potent antimicrobials that rapidly kill susceptible individuals and development of dosage regimens that maintain effective antimicrobial drug concentrations for as long as needed to kill off the susceptible population. Suggestions are made for experiments to test the hypotheses generated by these simulations.     

Semiparametric model-based inference in the presence of missing responses

We consider a semiparametric model that parameterizes the conditionaldensity of the response, given covariates, but allows the marginaldistribution of the covariates to be completely arbitrary. Responsesmay be missing. A likelihood-based imputation estimator anda semi-empirical-likelihood-based estimator for the parametervector describing the conditional density are defined and provedto be asymptotically normal. Semi-empirical loglikelihood functionsfor the parameter vector and the response mean are derived.It is shown that the two semi-empirical loglikelihood functionsare distributed asymptotically as weighted ² and scaled ², respectively.     

A new experimental animal for the study of age-related changes in serotonergic neuronal activity in the brain cortex

Calcium uptake by the cortical synaptosomes in a rodent (Fischer rat) and an insectivore shrew (Suncus murinus) was detected as a parameter reflecting molecular dysfunction of the aging brain. The change in calcium uptake by the cortical synaptosomes in both species was concomitant which showed less than half the capacity at 24 months old animals compared with those at 8 months old. On the other hand, 5-hydroxytryptamine binding and imipramine binding to the membrane fraction of aging rat brain cortex was not altered in terms of binding capacity along with aging, while, in Suncus, the binding of both serotonergic ligands declined with aging. In order to elucidate decreased serotonergic activity in human demented aged brain, together with declining activity in neurotransmitting systems detectable as a function of calcium uptake by the cortical synaptosomes, Suncus may be an appropriate animal model for studying physiological aging processes in the mammalian brain cortex.Special Issue Dedicated to Dr. Abel Lajtha.     

nAdder: A scale-space approach for the 3D analysis of neuronal traces

Tridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neuronal traces. In particular, means to characterize the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, measuring for each point of a curve the characteristic length where it is fully 3D as opposed to being embedded in a 2D plane or 1D line. The larger this metric is and the more complex the local 3D loops and turns of the curve are. Available through the GeNePy3D open-source Python quantitative geometry library (https://genepy3d.gitlab.io), this approach termed nAdder offers new means of describing and comparing axonal and dendritic arbors. We validate this metric on simulated and real traces. By reanalysing a published zebrafish larva whole brain dataset, we show its ability to characterize different population of commissural axons, distinguish afferent connections to a target region and differentiate portions of axons and dendrites according to their behavior, shedding new light on the stereotypical nature of neurites’ local geometry.     

Social transmission of maladaptive information in the guppy

Many animals are capable of learning from others, a processreferred to as social learning. There is little doubt that acapacity for social learning is an adaptation and that it typicallyresults in adaptive behavior. What is less clear is whetherthere are circumstances under which social learning can resultin the transmission of outdated, inappropriate, or maladaptiveinformation. Here we report an experimental study that investigatedthe social learning and transmission of maladaptive foraginginformation through small social groups of guppies, Poeciliareticulata. This experiment used a transmission chain designin which fish in small founder groups were trained to take eitheran energetically costly circuitous route to a feeder or a lesscostly short route, with trained founder members gradually replacedby untrained conspecifics. Three days after all the foundershad been removed, the behavioral traditions of groups of untrainedfish were still strongly influenced by their founder's behavior.Moreover, the rate at which untrained subjects that shoaledwith founder conspecifics trained to take the long route learnedto take the short route was significantly slower than for fishforaging alone. The results provide unequivocal evidence thatmaladaptive information can be socially transmitted throughanimal populations and imply that socially learned informationcan inhibit learning of the optimal behavior pattern.     

A dynamic model for tuberculosis transmission and optimal treatment strategies in South Korea

We have developed a dynamic model for tuberculosis (TB) transmission in South Korea using a SEIR model with the time-dependent parameters. South Korea ranked the highest TB incidence among members of the Organization for Economic Cooperation and Development (OECD) in 2005 yr. The observed data from the Korea Center for Disease Control and Prevention (KCDC) shows a certain rise of active-TB incidence individuals after 2001 yr. Because of this sudden jump, we have considered two different periods for best fitting the model: prior to 2001 yr and posterior to 2001 yr. The least-squares fitting has been used for estimating model parameters to the observed data of active-TB incidence. Our model agrees well with the observed data. In this work, we also propose optimal treatment strategies of TB model in South Korea for the future. We have considered three control mechanisms representing distancing, case finding and case holding efforts. Optimal control programs have been proposed in various scenarios, in order to minimize the number of exposed and infectious individuals and the cost of implementing the control treatment.     

A model for the neuronal substrate of dead reckoning and memory in arthropods: a comparative computational and behavioral study

Returning to the point of departure after exploring the environment is a key capability for most animals. In the absence of landmarks, this task will be solved by integrating direction and distance traveled over time. This is referred to as path integration or dead reckoning. An important question is how the nervous systems of navigating animals such as the 1 mm³ brain of ants can integrate local information in order to make global decision. In this article we propose a neurobiologically plausible system of storing and retrieving direction and distance information. The path memory of our model builds on the well established concept of population codes, moreover our system does not rely on trigonometric functions or other complex non-linear operations such as multiplication, but only uses biologically plausible operations such as integration and thresholding. We test our model in two paradigms; in the first paradigm the system receives input from a simulated compass, in the second paradigm, the model is tested against behavioral data recorded from 17 ants. We were able to show that our path memory system was able to reliably encode and compute the angle of the vector pointing to the start location, and that the system stores the total length of the trajectory in a dependable way. From the structure and behavior of our model, we derive testable predictions both at the level of observable behavior as well as on the anatomy and physiology of its underlying neuronal substrate.     

A two‐phase growth strategy in cultured neuronal networks as reflected by the distribution of neurite branching angles

Neurite outgrowth and branching patterns are instrumental in dictating the wiring diagram of developing neuronal networks. We study the self‐organization of single cultured neurons into complex networks focusing on factors governing the branching of a neurite into its daughter branches. Neurite branching angles of insect ganglion neurons in vitro were comparatively measured in two neuronal categories: neurons in dense cultures that bifurcated under the presence of extrinsic (cellular environment) cues versus neurons in practical isolation that developed their neurites following predominantly intrinsic cues. Our experimental results were complemented by theoretical modeling and computer simulations. A preferred regime of branching angles was found in isolated neurons. A model based on biophysical constraints predicted a preferred bifurcation angle that was consistent with this range shown by our real neurons. In order to examine the origin of the preferred regime of angles we constructed simulations of neurite outgrowth in a developing network and compared the simulated developing neurons with our experimental results. We tested cost functions for neuronal growth that would be optimized at a specific regime of angles. Our results suggest two phases in the process of neuronal development. In the first, reflected by our isolated neurons, neurons are tuned to make first contact with a target cell as soon as possible, to minimize the time of growth. After contact is made, that is, after neuronal interconnections are formed, a second branching strategy is adopted, favoring higher efficiency in neurite length and volume. The two‐phase development theory is discussed in relation to previous results. © 2004 Wiley Periodicals, Inc. J Neurobiol, 2005     

Natural selection. VI. Partitioning the information in fitness and characters by path analysis

Three steps aid in the analysis of selection. First, describe phenotypes by their component causes. Components include genes, maternal effects, symbionts and any other predictors of phenotype that are of interest. Second, describe fitness by its component causes, such as an individual's phenotype, its neighbours’ phenotypes, resource availability and so on. Third, put the predictors of phenotype and fitness into an exact equation for evolutionary change, providing a complete expression of selection and other evolutionary processes. The complete expression separates the distinct causal roles of the various hypothesized components of phenotypes and fitness. Traditionally, those components are given by the covariance, variance and regression terms of evolutionary models. I show how to interpret those statistical expressions with respect to information theory. The resulting interpretation allows one to read the fundamental equations of selection and evolution as sentences that express how various causes lead to the accumulation of information by selection and the decay of information by other evolutionary processes. The interpretation in terms of information leads to a deeper understanding of selection and heritability, and a clearer sense of how to formulate causal hypotheses about evolutionary process. Kin selection appears as a particular type of causal analysis that partitions social effects into meaningful components.     

Design of a data model for developing laboratory information management and analysis systems for protein production

Data management has emerged as one of the central issues in the high-throughput processes of taking a protein target sequence through to a protein sample. To simplify this task, and following extensive consultation with the international structural genomics community, we describe here a model of the data related to protein production. The model is suitable for both large and small facilities for use in tracking samples, experiments, and results through the many procedures involved. The model is described in Unified Modeling Language (UML). In addition, we present relational database schemas derived from the UML. These relational schemas are already in use in a number of data management projects.     

A systematic quality assurance framework for the upgrade of radiation oncology information systems

In spite of its importance, no systematic and comprehensive quality assurance (QA) program for radiation oncology information systems (ROIS) to verify clinical and treatment data integrity and mitigate against data errors/corruption and/or data loss risks is available. Based on data organization, format and purpose, data in ROISs falls into five different categories: (1) the ROIS relational database and associated files; (2) the ROIS DICOM data stream; (3) treatment machine beam data and machine configuration data; (4) electronic medical record (EMR) documents; and (5) user-generated clinical and treatment reports from the ROIS. For each data category, this framework proposes a corresponding data QA strategy to very data integrity. This approach verified every bit of data in the ROIS, including billions of data records in the ROIS SQL database, tens of millions of ROIS database-associated files, tens of thousands of DICOM data files for a group of selected patients, almost half a million EMR documents, and tens of thousands of machine configuration files and beam data files. The framework has been validated through intentional modifications with test patient data. Despite the ‘big data’ nature of ROIS, the multiprocess and multithread nature of our QA tools enabled the whole ROIS data QA process to be completed within hours without clinical interruptions. The QA framework suggested in this study proved to be robust, efficient and comprehensive without labor-intensive manual checks and has been implemented for our routine ROIS QA and ROIS upgrades.     

Inhibition of unfolded protein response prevents post‐anesthesia neuronal hyperactivity and synapse loss in aged mice

Delirium is the most common postoperative complication in older patients after prolonged anesthesia and surgery and is associated with accelerated cognitive decline and dementia. The neuronal pathogenesis of postoperative delirium is largely unknown. The unfolded protein response (UPR) is an adaptive reaction of cells to perturbations in endoplasmic reticulum function. Dysregulation of UPR has been implicated in a variety of diseases including Alzheimer''s disease and related dementias. However, whether UPR plays a role in anesthesia‐induced cognitive impairment remains unexplored. By performing in vivo calcium imaging in the mouse frontal cortex, we showed that exposure of aged mice to the inhalational anesthetic sevoflurane for 2 hours resulted in a marked elevation of neuronal activity during recovery, which lasted for at least 24 hours after the end of exposure. Concomitantly, sevoflurane anesthesia caused a prolonged increase in phosphorylation of PERK and eIF2α, the markers of UPR activation. Genetic deletion or pharmacological inhibition of PERK prevented neuronal hyperactivity and memory impairment induced by sevoflurane. Moreover, we showed that PERK suppression also reversed various molecular and synaptic changes induced by sevoflurane anesthesia, including alterations of synaptic NMDA receptors, tau protein phosphorylation, and dendritic spine loss. Together, these findings suggest that sevoflurane anesthesia causes abnormal UPR in the aged brain, which contributes to neuronal hyperactivity, synapse loss and cognitive decline in aged mice.