dfpk: An R-package for Bayesian dose-finding designs using pharmacokinetics (PK) for phase I clinical trials.

BACKGROUND AND OBJECTIVE: Dose-finding, aiming at finding the maximum tolerated dose, and pharmacokinetics studies are the first in human studies in the development process of a new pharmacological treatment. In the literature, to date only few attempts have been made to combine pharmacokinetics and dose-finding and to our knowledge no software implementation is generally available. In previous papers, we proposed several Bayesian adaptive pharmacokinetics-based dose-finding designs in small populations. The objective of this work is to implement these dose-finding methods in an R package, called dfpk. METHODS: All methods were developed in a sequential Bayesian setting and Bayesian parameter estimation is carried out using the rstan package. All available pharmacokinetics and toxicity data are used to suggest the dose of the next cohort with a constraint regarding the probability of toxicity. Stopping rules are also considered for each method. The ggplot2 package is used to create summary plots of toxicities or concentration curves. RESULTS: For all implemented methods, dfpk provides a function (nextDose) to estimate the probability of efficacy and to suggest the dose to give to the next cohort, and a function to run trial simulations to design a trial (nsim). The sim.data function generates at each dose the toxicity value related to a pharmacokinetic measure of exposure, the AUC, with an underlying pharmacokinetic one compartmental model with linear absorption. It is included as an example since similar data-frames can be generated directly by the user and passed to nsim. CONCLUSION: The developed user-friendly R package dfpk, available on the CRAN repository, supports the design of innovative dose-finding studies using PK information.

Validation of a Hemodynamic Model for the Study of the Cerebral Venous Outflow System Using MR Imaging and Echo-Color Doppler Data.

BACKGROUND AND PURPOSE: A comprehensive parameter model was developed to investigate correlations between cerebral hemodynamics and alterations in the extracranial venous circulation due to posture changes and/or extracranial venous obstruction (stenosis). The purpose of this work was to validate the simulation results by using MR imaging and echo-color Doppler experimental blood flow data in humans. MATERIALS AND METHODS: To validate the model outcomes, we used supine average arterial and venous extracerebral blood flow, obtained by using phase-contrast MR imaging from 49 individuals with stenosis in the acquisition plane at the level of the disc between the second and third vertebrae of the left internal jugular vein, 20 with stenosis in the acquisition plane at the level of the disc between the fifth and sixth vertebrae of the right internal jugular vein, and 38 healthy controls without stenosis. Average data from a second group of 10 healthy volunteers screened with an echo-color Doppler technique were used to evaluate flow variations due to posture change. RESULTS: There was excellent agreement between experimental and simulated supine flows. Every simulated CBF fell inside the standard error from the corresponding average experimental value, as well as most of the simulated extracerebral arterial flow (extracranial blood flow from the head and face, measured at the level of the disc between second and third vertebrae) and venous flows. Simulations of average jugular and vertebral blood flow variations due to a change of posture from supine to upright also matched the experimental data. CONCLUSIONS: The good agreement between simulated and experimental results means that the model can correctly reproduce the main factors affecting the extracranial circulation and could be used to study other types of stenotic conditions not represented by the experimental data.

A simulation model to study the role of the extracranial venous drainage pathways in intracranial hemodynamics.

Alterations in the extracranial venous circulation due to posture changes, and/or extracranial venous obstructions in patients with vascular diseases, can have important implications on cerebral hemodynamics. A hemodynamic model for the study of cerebral venous outflow was developed to investigate the correlations between extracranial blood redistributions and changes in the intracranial environment. Flow data obtained with both magnetic resonance (MR) and Echo-Color Doppler (ECD) technique are used to validate the model. The very good agreement between simulated supine and upright flows and experimental results means that the model can correctly reproduce the main factors affecting the extracranial venous circulation.

A new hemodynamic model for the study of cerebral venous outflow.

We developed a mathematical model of the cerebral venous outflow for the simulation of the average blood flows and pressures in the main drainage vessels of the brain. The main features of the model are that it includes a validated model for the simulation of the intracranial circulation and it accounts for the dependence of the hydraulic properties of the jugular veins with respect to the gravity field, which makes it an useful tool for the study of the correlations between extracranial blood redistributions and changes in the intracranial environment. The model is able to simulate the average pressures and flows in different points of the jugular ducts, taking into account the amount of blood coming from the anastomotic connections; simulate how the blood redistribution due to change of posture affects flows and pressures in specific points of the system; and simulate redistributions due to stenotic patterns. Sensitivity analysis to check the robustness of the model was performed. The model reproduces average physiologic behavior of the jugular, vertebral, and cerebral ducts in terms of pressures and flows. In fact, jugular flow drops from ∼11.7 to ∼1.4 ml/s in the passage from supine to standing. At the same time, vertebral flow increases from 0.8 to 3.4 ml/s, while cerebral blood flow, venous sinuses pressure, and intracranial pressure are constant around the average value of 12.5 ml/s, 6 mmHg, and 10 mmHg, respectively. All these values are in agreement with literature data.

Effect of Citrus bergamia juice on human neuroblastoma cells in vitro and in metastatic xenograft models.

Neuroblastoma is the most common extracranial pediatric solid tumor with poor prognosis in children with disseminated stage of disease. A number of studies show that molecules largely distributed in commonly consumed fruits and vegetables may have anti-tumor activity. In this study we evaluate the effect of Citrus bergamia (bergamot) juice (BJ) in vitro and in a spontaneous metastatic neuroblastoma SCID mouse model. Qualitative and quantitative characterizations of BJ flavonoid fractions were performed by RP-HPLC/PDA/MS. We show that BJ significantly affects SK-N-SH and LAN-1 cell proliferation in vitro, but fails to reduce primary tumor weight in vivo. Moreover, BJ reduced cell adhesiveness and invasion of LAN-1 and SK-N-SH cells in vitro and the number of pulmonary metastases under consideration of the number of tumor cells in the blood in mice inoculated with LAN-1 cells in vivo. These effects without any apparent sign of systemic toxicity confirm the potential clinical interest of BJ and lay the basis for further investigation in cancer.

A thalamo-cortical neural mass model for the simulation of brain rhythms during sleep.

Cortico-thalamic interactions are known to play a pivotal role in many brain phenomena, including sleep, attention, memory consolidation and rhythm generation. Hence, simple mathematical models that can simulate the dialogue between the cortex and the thalamus, at a mesoscopic level, have a great cognitive value. In the present work we describe a neural mass model of a cortico-thalamic module, based on neurophysiological mechanisms. The model includes two thalamic populations (a thalamo-cortical relay cell population, TCR, and its related thalamic reticular nucleus, TRN), and a cortical column consisting of four connected populations (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow and fast kinetics). Moreover, thalamic neurons exhibit two firing modes: bursting and tonic. Finally, cortical synapses among pyramidal neurons incorporate a disfacilitation mechanism following prolonged activity. Simulations show that the model is able to mimic the different patterns of rhythmic activity in cortical and thalamic neurons (beta and alpha waves, spindles, delta waves, K-complexes, slow sleep waves) and their progressive changes from wakefulness to deep sleep, by just acting on modulatory inputs. Moreover, simulations performed by providing short sensory inputs to the TCR show that brain rhythms during sleep preserve the cortex from external perturbations, still allowing a high cortical activity necessary to drive synaptic plasticity and memory consolidation. In perspective, the present model may be used within larger cortico-thalamic networks, to gain a deeper understanding of mechanisms beneath synaptic changes during sleep, to investigate the specific role of brain rhythms, and to explore cortical synchronization achieved via thalamic influences.

Hebbian mechanisms help explain development of multisensory integration in the superior colliculus: a neural network model.

The superior colliculus (SC) integrates relevant sensory information (visual, auditory, somatosensory) from several cortical and subcortical structures, to program orientation responses to external events. However, this capacity is not present at birth, and it is acquired only through interactions with cross-modal events during maturation. Mathematical models provide a quantitative framework, valuable in helping to clarify the specific neural mechanisms underlying the maturation of the multisensory integration in the SC. We extended a neural network model of the adult SC (Cuppini et al., Front Integr Neurosci 4:1-15, 2010) to describe the development of this phenomenon starting from an immature state, based on known or suspected anatomy and physiology, in which: (1) AES afferents are present but weak, (2) Responses are driven from non-AES afferents, and (3) The visual inputs have a marginal spatial tuning. Sensory experience was modeled by repeatedly presenting modality-specific and cross-modal stimuli. Synapses in the network were modified by simple Hebbian learning rules. As a consequence of this exposure, (1) Receptive fields shrink and come into spatial register, and (2) SC neurons gained the adult characteristic integrative properties: enhancement, depression, and inverse effectiveness. Importantly, the unique architecture of the model guided the development so that integration became dependent on the relationship between the cortical input and the SC. Manipulations of the statistics of the experience during the development changed the integrative profiles of the neurons, and results matched well with the results of physiological studies.

A dynamical pattern recognition model of γ activity in auditory cortex.

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75-150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain.

A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.

Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs.

Transient respiratory response to hypercapnia: analysis via a cardiopulmonary simulation model.

In recent years, our group has developed a comprehensive cardiopulmonary (CP) model that comprises the heart, systemic and pulmonary circulations, lung mechanics and gas exchange, tissue metabolism, and cardiovascular and respiratory control mechanisms. In this paper, we analyze the response of the model to hypercapnic conditions and hence focus on the chemoreflex control mechanism. Particularly, we have enhanced the peripheral chemoreceptor model in order to better reflect respiratory control physiology. Using the CO(2) fraction in the inspired air as input to the CP model, we were able to analyze the transient response of the system to CO(2) step input at different levels, in terms of alveolar gas partial pressures, tidal volume, minute ventilation and respiratory frequency. Model predictions were tested against experimental data from human subjects [1]. Results show good agreement for all the variables under study during the transient phases and low root mean square errors at steady state. This indicates the potential for the model to be used as a valid tool for clinical practice and medical research, providing a complementary way to experience-based clinical decisions.

A semantic model to study neural organization of language in bilingualism.

A neural network model of object semantic representation is used to simulate learning of new words from a foreign language. The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation. Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization. Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule. In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase. Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one. A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons. Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language. Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se. In this case, a conflict between words can occur, requiring a higher-level decision mechanism.

A neural mass model to simulate different rhythms in a cortical region.

An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, GABA(A, slow) and GABA(A,fast) respectively. A new aspect, not present in previous versions, consists in the inclusion of a self-loop among GABA(A,fast) interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma (gamma) band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward GABA(A,fast) interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of gamma rhythms and to provide better understanding of cortical EEG spectra.

A neural network model of semantic memory linking feature-based object representation and words.

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators.

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.

Time-varying cortical connectivity by adaptive multivariate estimators applied to a combined foot-lips movement.

In this paper we propose the use of an adaptive multivariate approach to define time-varying multivariate estimators based on the Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC). DTF and PDC are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. Time-varying DTF and PDC were obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Such estimators are able to follow rapid changes in the connectivity between cortical areas during an experimental task. We provide an application to the cortical estimations obtained from high resolution EEG data, recorded from a group of healthy subject during a combined foot-lips movement, and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected, one constant across the task and the other evolving during the preparation of the joint movement.

Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings.

OBJECTIVE: The present work aimed to evaluate the performance of an automatic slow eye movement (SEM) detector in overnight and 24-h electro-oculograms (EOG) including all sleep stages (1, 2, 3, 4, REM) and wakefulness. METHODS: Ten overnight and five 24-h EOG recordings acquired in healthy subjects were inspected by three experts to score SEMs. Computerized EOG analysis to detect SEMs was performed on 30-s epochs using an algorithm based on EOG wavelet transform, recently developed by our group and initially validated by considering only pre-sleep wakefulness, stages 1 and 2. RESULTS: The validation procedure showed the algorithm could identify epochs containing SEM activity (concordance index k=0.62, 80.7% sensitivity, 63% selectivity). In particular, the experts and the algorithm identified SEM epochs mainly in pre-sleep wakefulness, stage 1, stage 2 and REM sleep. In addition, the algorithm yielded consistent indications as to the duration and position of SEM events within the epoch. CONCLUSIONS: The study confirmed SEM activity at physiological sleep onset (pre-sleep wakefulness, stage 1 and stage 2), and also identified SEMs in REM sleep. The algorithm proved reliable even in the stages not used for its training. SIGNIFICANCE: The study may enhance our understanding of SEM meaning and function. The algorithm is a reliable tool for automatic SEM detection, overcoming the inconsistency of manual scoring and reducing the time taken by experts.

A mathematical model for the prediction of solute kinetics, osmolarity and fluid volume changes during hemodiafiltration with on-line regeneration of ultrafiltrate (HFR).

Hemodiafiltration with on-line regeneration of ultrafiltrate (HFR) is a technique indicated for the treatment of dialysis patients affected by inflammatory syndrome and malnutrition. In the present work, a mathematical model, which describes intradialytic fluid and solute kinetics during standard diffusive dialysis, has been adapted to analyze solutes and fluid dynamics during HFR. The model is an improved version of our previous ones, and represents a good compromise between simplicity and reliability. It considers the intradialytic kinetics of sodium, potassium and urea, and two body fluid compartments: intracellular and extracellular. Moreover, the model includes simple equations to predict the intradialytic time pattern of osmolarity. The model has been experimentally validated by using 9 HFR sessions on 9 patients (one per each patient), comparing the time course of plasma solutes and osmolarity measured every 30 minutes during HFR, with those predicted by the model. Predictions are performed a priori, i.e., without any parameter adjustment, but just starting from knowledge of a few quantities (plasma sodium, potassium, urea, osmolarity and body weight) at the beginning of the session. The average deviations between model and real data (sodium: 1.9 mEq/L; potassium: 0.32 mEq/L; urea: 1.04 mmol/L; osmolarity: 5.02 mosm/L) are of the same order as measurement errors and similar to those obtained using our previous models in standard and profiled hemodialysis. Moreover, the prediction on sodium concentration only scarcely worsens (from 1.9 to 2.02 mEq/L) if default values are used for the initial value of other solutes in blood (i.e., if the algorithm uses only initial body weight and initial sodium concentration in plasma). The results confirm the good predictive capacity of the model in HFR, and suggest its possible innovative use to optimize sodium balance in HFR, from knowledge of only the sodium concentration in the ultrafiltrate.

Timing of herbage allocation in strip grazing: Effects on grazing pattern and performance of beef heifers.

The timing of grazing bouts (GB) determines how cattle allot time to meet their nutritional needs. Net photosynthesis and evapotranspirational losses increase herbage nonstructural carbohydrate and DM concentrations, which may lead to longer and more intense GB at dusk. Hence, linking the grazing pattern, plant phenology, and herbage allocation time emerges as an option to manipulate the GB and nutrient intake. The objectives of this work were to analyze grazing behavior and performance of beef heifers when herbage allocation was at 0700 each morning (MHA) or at 1500 each afternoon (AHA). Two pairs of experiments were conducted during the winter and spring examining behavior and performance. Measurements were grazing, rumination, and idling times during daylight hours, and their patterns, as well as bite rate, ADG, change in BCS, and daily herbage DMI. In the behavioral experiments, 8 heifers strip-grazed annual ryegrass (Lolium multiflorum Lam.). The grazing, rumination, and idling times as well as bite rate were measured and also analyzed per time of day. In the performance experiments, 48 beef heifers strip-grazed annual ryegrass in 2 groups according to treatments. Daily DMI, ADG, and changes in BCS were analyzed. The AHA increased daily idling time (P < 0.01) and decreased grazing time (P < 0.01). The AHA concentrated grazing time in the evening, when bite rate was greater (P < 0.01). The daylight rumination time varied by time of day (P < 0.01), but total daylight rumination time did not differ (P = 0.11). With AHA, rumination time and idling time were concentrated in the morning and afternoon. In the performance experiment during the winter, there was a treatment x week effect (P < 0.01) for ADG and change in BCS. Beginning in wk 4, heifers in AHA gained 150 g of BW and 0.0145 points of BCS more than those in MHA (P < 0.05) per day. In the spring, AHA increased ADG by 549 g and 0.0145 points of BCS more than those in MHA (P < 0.05) per day during the entire 6 wk. The herbage DMI (kg/d) did not differ in winter (AHA, 5.0 vs. MHA, 4.5) or spring (AHA, 5.6 vs. MHA, 5.0). These results suggest that timing of herbage allocation alters grazing, rumination, and idling patterns; AHA leads to longer and more intense GB when herbage has greater quality, which improves cattle performance.

Assessment of effective connectivity among cortical regions based on a neural mass model.

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this work is to use a neural mass model to assess the effect of various connectivity patterns in the power spectral density (PSD) of cortical EEG, and investigate the possibility to derive connectivity circuits from real EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations. The present study suggests that the model can be used as a simulation tool, able to produce reliable intracortical EEG signals. Moreover, it can be used to look for simple connectivity circuits, able to explain the main features of observed cortical PSD. These results may open new prospective in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Object segmentation and reconstruction via an oscillatory neural network: interaction among learning, memory, topological organization and gamma-band synchronization.

Synchronization of neuronal activity in the gamma-band has been shown to play an important role in higher cognitive functions, by grouping together the necessary information in different cortical areas to achieve a coherent perception. In the present work, we used a neural network of Wilson-Cowan oscillators to analyze the problem of binding and segmentation of high-level objects. Binding is achieved by implementing in the network the similarity and prior knowledge Gestalt rules. Similarity law is realized via topological maps within the network. Prior knowledge originates by means of a Hebbian rule of synaptic change; objects are memorized in the network with different strengths. Segmentation is realized via a global inhibitor which allows desynchronisation among multiple objects avoiding interference. Simulation results performed with a 40x40 neural grid, using three simultaneous input objects, show that the network is able to recognize and segment objects in several different conditions (different degrees of incompleteness or distortion of input patterns), exhibiting the higher reconstruction performances the higher the strength of object memory. The presented model represents an integrated approach for investigating the relationships among learning, memory, topological organization and gamma-band synchronization.