Network Connectivity Strength Research Articles

Resting-state functional connectivity in women with Major Depressive Disorder

ObjectiveLimited research has focused on whole-brain functional connectivity in a well-characterized sample of subjects with current Major Depressive Disorder (MDD). We aimed to investigate resting-state functional connectivity and the extent to which this is correlated with depression severity in unmedicated depressed subjects without comorbidities. MethodsWe utilized Independent Component Analysis (ICA) to investigate whole-brain functional connectivity in a sample of healthy controls (n = 26) and unmedicated subjects diagnosed only with current MDD (n = 20). Correlations were calculated between network connectivity strength and depression severity. ResultsDepressed subjects demonstrated significantly decreased connectivity in the right frontoparietal (p = 0.03), left frontoparietal (p = 0.01), and language (p = 0.02) networks compared to healthy control subjects. ConclusionWe found abnormal resting-state functional connectivity not previously reported in MDD. Decreased connectivity in the frontoparietal and language networks may represent depression-related difficulties in attention, cognitive control, goal-directed cognition, and language. Findings from this study may further elucidate functional connectivity as a diagnostic marker of depression severity.

Reduced left executive control network functional connectivity is associated with alcohol use disorders.

Altered functional connectivity in critical networks has been associated with chronic alcohol abuse. In turn, changes in connectivity in executive control networks (ECNs) may undermine the ability to control alcohol consumption. It was hypothesized that network connectivity would be reduced in individuals with problematic alcohol use (ALC) compared with controls and that diminished network connectivity would be associated with greater failure to control drinking. Resting-state functional magnetic resonance imaging was analyzed to identify 14 previously identified intrinsic connectivity networks (ICNs) using a priori regions of interest in cases ranging from binge drinkers to those with severe alcohol use disorder, as well as control subjects. Analyses tested for differences in network connectivity strength between 255 ALC cases and 87 age- and gender-matched controls. Further, structural equation analysis, using 383 ALC cases, tested whether functional connectivity strength mediated the relationship between years of regular drinking and alcohol problems. The age- and gender-matched analysis showed that ALC had significantly lower network connectivity strength than controls in the left executive control (LECN), basal ganglia, and primary visual networks. For all ALC, LECN connectivity strength is negatively correlated with failed control and alcohol disorder severity. Edges connecting parietal regions with dorsolateral prefrontal, middle frontal, and temporal regions within the LECN drove these relationships. A positive association between years of drinking and severity of alcohol problems was mediated by reduced ECN connectivity. This study reports relationships between network strength and problematic alcohol use, suggesting that chronic drinking negatively impacts brain connectivity, specifically in the LECN. Altered functional connectivity, related to chronic alcohol abuse, may contribute to the etiology of alcohol dependence and relapse.

Strain-rate sensitivity of glasses

We report on the loading-rate dependence of localized plastic deformation in inorganic covalent, metallic, ionic and superionic glasses. For this, the strain-rate sensitivity is determined through instrumented nanoindentation in a load-controlled strain-rate jump test. Through relating the strain-rate sensitivity to the reduced temperature, the packing density, the network dimensionality and the average single bond strength of the system, a qualitative mechanistic description of the strain-mediating process is possible. A strong variability of strain-rate sensitivity is obtained only at intermediate values of packing density, network connectivity or bond strength, when other parameters such as chemical composition and specific structural arrangement are dominating the deformation process. On the other side, for high bond strength and connectivity or for high packing density, the strain-rate sensitivity of the considered glasses is always low, which is also confirmed through the dependence of strain-rate sensitivity on Poisson ratio. Here, only for glasses with a Poisson ratio of ~0.3–0.4 we observe a wide variability of the loading-rate dependence of local deformation. For higher or lower Poisson ratio, the observed dependence is always low: when the limiting factor in deformation is primarily network connectivity and bond strength or packing density, respectively, once an activation barrier is overcome, deformation is only weakly loading-rate-dependent. This is regardless of the height of the activation barrier. When approaching the glass transition temperature, high strain-rate sensitivity is observed only in glasses where non-Newtonian flow is expected also in the corresponding liquid.

Change in network connectivity during fictive-gasping generation in hypoxia: prevention by a metabolic intermediate.

The neuronal circuit in charge of generating the respiratory rhythms, localized in the pre-Bötzinger complex (preBötC), is configured to produce fictive-eupnea during normoxia and reconfigures to produce fictive-gasping during hypoxic conditions in vitro. The mechanisms involved in such reconfiguration have been extensively investigated by cell-focused studies, but the actual changes at the network level remain elusive. Since a failure to generate gasping has been linked to Sudden Infant Death Syndrome (SIDS), the study of gasping generation and pharmacological approaches to promote it may have clinical relevance. Here, we study the changes in network dynamics and circuit reconfiguration that occur during the transition to fictive-gasping generation in the brainstem slice preparation by recording the preBötC with multi-electrode arrays and assessing correlated firing among respiratory neurons or clusters of respiratory neurons (multiunits). We studied whether the respiratory network reconfiguration in hypoxia involves changes in either the number of active respiratory elements, the number of functional connections among elements, or the strength of these connections. Moreover, we tested the influence of isocitrate, a Krebs cycle intermediate that has recently been shown to promote breathing, on the configuration of the preBötC circuit during normoxia and on its reconfiguration during hypoxia. We found that, in contrast to previous suggestions based on cell-focused studies, the number and the overall activity of respiratory neurons change only slightly during hypoxia. However, hypoxia induces a reduction in the strength of functional connectivity within the circuit without reducing the number of connections. Isocitrate prevented this reduction during hypoxia while increasing the strength of network connectivity. In conclusion, we provide an overview of the configuration of the respiratory network under control conditions and how it is reconfigured during fictive-gasping. Additionally, our data support the use of isocitrate to favor respiratory rhythm generation under normoxia and to prevent some of the changes in the respiratory network under hypoxic conditions.

Exploring the relationship of functional network connectivity to latent trajectories of alcohol use and risky sex.

Alcohol use is a major risk factor associated with unprotected sexual behavior, leading to higher risk of sexually transmitted infections (STI) including the human immunodeficiency virus (HIV). Emerging largely cross-sectional data suggest functional network connectivity strength is associated with problematic alcohol use, and as evidence supports a relationship between risky sexual behaviors and alcohol use, we hypothesized that functional connectivity might be associated with both categories of risk behavior. As part of a sexual risk reduction intervention study, juvenile justice-involved adolescents (N = 239) underwent a baseline functional magnetic resonance imaging scan and completed questionnaires about their alcohol use and risky sexual behavior at 3-month intervals over 12 months of follow up. To test both cross-sectional and longitudinal relationships between alcohol use and sexual risk behaviors, we estimated a parallel process latent growth model that simultaneously modeled the trajectories of alcohol use and sexual risk behavior. Functional connectivity strength was included as an exogenous variable to evaluate its relationship with level of risk and change in risk over time in both behaviors. Associations were found between baseline alcohol use and risky sex, and between longitudinal trajectories of alcohol use and risky sex. Network functional connectivity strength of the dorsal default mode network was associated with initial and longitudinal alcohol use, which may suggest that self-awareness of the effects of alcohol could serve as a useful target to decrease subsequent risky sexual behavior in adolescence.

Variability in respiratory rhythm generation: in vitro and in silico models

The variability inherent in rhythmic physiological patterns may arise from both stochastic and deterministic sources. Though variability is disruptive when in excess, it may also be functional in evolved systems. Here we focus on the neural control of respiration which is critical for survival in many animals. The sources of respiratory variability, as well as its possible function, are unknown. A fundamental component of respiratory pattern generation is the preBotzinger complex (preBotC) which is part of the ventral respiratory column located in the brainstem. Here we use in vitro and in silico models of the preBotC to study the variability of respiratory rate. The in vitro model exhibits a bounded range of variability in which the upper and lower limits are functions of the respiratory rate. The in silico model consists of two types of neurons: intrinsically bursting “pacemaker” cells and tonically spiking cells that relay chemosensory and mechanosensory feedback. When fitted to the experimental data, the in silico model shows the existence of both stochastic and deterministic sources for variability in the respiratory rate. The upper and lower limits of variability seen experimentally are reproduced in silico using a range of network connection strengths. Simulations demonstrate that stochastic spiking in sensory relay neurons may be utilized to stabilize changes in variability when the respiratory rate changes due to physiological demand.

New Competence Creation in Multinational Company Subunits: The Role of International Knowledge

AbstractIt has been demonstrated that more creative innovative activities of subunits of multinational companies (MNCs) rely upon the munificence of local knowledge in the host country. Here, we argue that the strength of international business network connections of a host location influences the potential accessibility of international knowledge and hence tends to widen the domain of new knowledge search that can be successfully undertaken by anMNCsubunit. This in turn increases the likelihood of anMNCsubunit building new areas of competencies, that is, in fields of competencies which are relatively new to theMNC. Specifically, we suggest that the relevant international business network connections are those involving local actors in theMNCsubunit's relevant industry. Moreover, geographical proximity betweenMNCsubunits and their parent company has an inverse U‐shaped relationship with new competence creation by subunits. Using a balanced panel data set on innovative activities conducted abroad by 194 of the world's largest industrial firms from 1975 to 1995, we find support for our expectations.

Fronto-Parietal Connectivity Is a Non-Static Phenomenon with Characteristic Changes during Unconsciousness

BackgroundIt has been previously shown that loss of consciousness is associated with a breakdown of dominating fronto-parietal feedback connectivity as assessed by electroencephalogram (EEG) recordings. Structure and strength of network connectivity may change over time. Aim of the current study is to investigate cortico-cortical connectivity at different time intervals during consciousness and unconsciousness. For this purpose, EEG symbolic transfer entropy (STEn) was calculated to indicate cortico-cortical information transfer at different transfer times.MethodsThe study was performed in 15 male volunteers. 29-channel EEG was recorded during consciousness and propofol-induced unconsciousness. EEG data were analyzed by STEn, which quantifies intensity and directionality of the mutual information flow between two EEG channels. STEn was computed over fronto-parietal channel pair combinations (10 s length, 0.5–45 Hz total bandwidth) to analyze changes of intercortical directional connectivity. Feedback (fronto → parietal) and feedforward (parieto → frontal) connectivity was calculated for transfer times from 25 ms to 250 ms in 5 ms steps. Transfer times leading to maximum directed interaction were identified to detect changes of cortical information transfer (directional connectivity) induced by unconsciousness (p<0.05).ResultsThe current analyses show that fronto-parietal connectivity is a non-static phenomenon. Maximum detected interaction occurs at decreased transfer times during propofol-induced unconsciousness (feedback interaction: 60 ms to 40 ms, p = 0.002; feedforward interaction: 65 ms to 45 ms, p = 0.001). Strength of maximum feedback interaction decreases during unconsciousness (p = 0.026), while no effect of propofol was observed on feedforward interaction. During both consciousness and unconsciousness, intensity of fronto-parietal interaction fluctuates with increasing transfer times.ConclusionNon-stationarity of directional connectivity may play a functional role for cortical network communication as it shows characteristic changes during propofol-induced unconsciousness.

Disruption of brain connectivity in acute stroke patients with early impairment in consciousness

Impairment in consciousness is common in acute stroke patients and is correlated with the clinical outcome after stroke. The underlying mechanism is not completely understood, with little known about brain activity and connectivity changes in acute stroke patients having impaired consciousness. In this study, we investigated changes in regional brain activity and brain networks of consciousness impaired stroke patients, as well as the amplitude of spontaneous low frequency fluctuation (ALFF) of each time series. Regional homogeneity (ReHo) of each voxel was measured, and resting state network analysis was consequently conducted. Results from this study demonstrate that, compared to normal subjects, the intensities of ALFF and ReHo, as well as the strength of the default mode network (DMN) connectivity, were significantly decreased in the precuneus and posterior cingulate cortex regions among stroke patients with impaired consciousness. Furthermore, the strength of the DMN was highly correlated with differences in the Glasgow Coma Scale (GCS) scores between the onset time and the scanning time. Results from this study suggest that the resting state fMRI is a feasible tool for the evaluation of acute stroke patients with an early impairment of consciousness. The detailed mechanisms, implications of these brain activities and networks exhibiting changes will require further investigation.

Network transmission inference: Host behavior and parasite life cycle make social networks meaningful in disease ecology

The process of disease transmission is determined by the interaction of host susceptibility and exposure to parasite infectious stages. Host behavior is an important determinant of the likelihood of exposure to infectious stages but is difficult to measure and often assumed to be homogenous in models of disease spread. We evaluated the importance of precisely defining host contact when using networks that estimate exposure and predict infection prevalence in a replicated, empirical system. In particular, we hypothesized that infection patterns would be predicted only by a contact network that is defined according to host behavior and parasite life cycle. Two competing host contact criteria were used to construct networks defined by parasite life cycle and social contacts. First, parasite-defined contacts were based on shared space with a time delay corresponding to the environmental development time of nematode parasites with a direct fecal-oral life cycle. Second, social contacts were defined by shared space in the same time period. To quantify the competing networks of exposure and infection, we sampled natural populations of the eastern chipmunk (Tamias striatus) and infection of their gastrointestinal helminth community using replicated longitudinal capture-mark-recapture techniques. We predicted that (1) infection with parasites with direct fecal-oral life cycles would be explained by the time delay contact network, but not the social contact network; (2) infection with parasites with trophic life cycles (via a mobile intermediate host; thus, spatially decoupling transmission from host contact) would not be explained by either contact network. The prevalence of fecal-oral life cycle nematode parasites was strongly correlated to the number and strength of network connections from the parasite-defined network (including the time delay), while the prevalence of trophic life cycle parasites was not correlated with any network metrics. We concluded that incorporating the parasite life cycle, relative to the way that exposure is measured, is key to inferring transmission and can be empirically quantified using network techniques. In addition, appropriately defining and measuring contacts according the life history of the parasite and relevant behaviors of the host is a crucial step in applying network analyses to empirical systems.

An abnormal resting-state functional brain network indicates progression towards Alzheimer's disease.

Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer's disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations (normal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer's disease) using the Alzheimer's Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer's disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the resting-state functional network gradually increased, while clustering coefficients gradually decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In addition, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and Alzheimer's disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventually lead to diffuse brain injury and other cognitive impairments.

Diffusion tensor tractography reveals disrupted structural connectivity in childhood absence epilepsy

The structural connection patterns of the human brain are the underlying bases for functional connectivity. Although abnormal functional connectivity has been uncovered in childhood absence epilepsy (CAE) in previous electroencephalography and functional magnetic resonance imaging studies, little is known regarding the structural connectivity in CAE. We hypothesized that the structural connectivity would be disrupted in response to the decreased brain function in CAE. Diffusion tensor imaging tractography was utilized to map the white matter (WM) structural network, composed of 90 cortical and sub-cortical regions, in 18 CAE and 18 age- and gender-matched healthy controls. Graph theoretical methods were applied to investigate the alterations in the topological and nodal properties of the networks in these patients. Both the CAE and the controls showed small-world properties in their WM networks. However, the network connection strength, absolute clustering coefficient, and global/local efficiency were significantly decreased, but characteristic path length was significantly increased in the CAE compared with the controls. Significantly decreased WM connections, nodal properties, and impaired sub-networks were found in the sub-cortical structures, orbitofrontal area, and limbic cortex in the CAE. Moreover, network connection strength, local efficiency, and nodal features in some regions were significantly negatively correlated with the duration of epilepsy. The present study demonstrated, for the first time, the disrupted topological organization of WM networks in CAE. The decreased connectivity and efficiency in the orbitofrontal and sub-cortical regions may serve as anatomical evidence to support the functional abnormalities related to the epileptic discharges observed in CAE. Moreover, the orbitofrontal sub-network may play a key role in CAE. These findings open up new avenues toward the understanding of absence epilepsy.

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.

Disrupted cortico-cerebellar connectivity in older adults

Healthy aging is marked by declines in a variety of cognitive and motor abilities. A better understanding of the aging brain may aid in elucidating the neural substrates of these behavioral effects. Investigations of resting state functional brain connectivity have provided insights into pathology, and to some degree, healthy aging. Given the role of the cerebellum in both motor and cognitive behaviors, as well as its known volumetric declines with age, investigating cerebellar networks may shed light on the neural bases of age-related functional declines. We mapped the resting state networks of the lobules of the right hemisphere and the vermis of the cerebellum in a group of healthy older adults and compared them to those of young adults. We report disrupted cortico-cerebellar resting state network connectivity in older adults. These results remain even when controlling for cerebellar volume, signal-to-noise ratio, and signal-to-fluctuation noise ratio. Specifically, there was consistent disruption of cerebellar connectivity with both the striatum and the medial temporal lobe. Associations between connectivity strength and both sensorimotor and cognitive task performances indicate that cerebellar engagement with the default mode network and striatal pathways is associated with better performance for older adults. These results extend our understanding of the resting state networks of the aging brain to include cortico-cerebellar networks, and indicate that age differences in network connectivity strength are important for behavior.

Tactile stimulation and hemispheric asymmetries modulate auditory perception and neural responses in primary auditory cortex

Although multisensory integration has been an important area of recent research, most studies focused on audiovisual integration. Importantly, however, the combination of audition and touch can guide our behavior as effectively which we studied here using psychophysics and functional magnetic resonance imaging (fMRI). We tested whether task-irrelevant tactile stimuli would enhance auditory detection, and whether hemispheric asymmetries would modulate these audiotactile benefits using lateralized sounds. Spatially aligned task-irrelevant tactile stimuli could occur either synchronously or asynchronously with the sounds. Auditory detection was enhanced by non-informative synchronous and asynchronous tactile stimuli, if presented on the left side. Elevated fMRI-signals to left-sided synchronous bimodal stimulation were found in primary auditory cortex (A1). Adjacent regions (planum temporale, PT) expressed enhanced BOLD-responses for synchronous and asynchronous left-sided bimodal conditions. Additional connectivity analyses seeded in right-hemispheric A1 and PT for both bimodal conditions showed enhanced connectivity with right-hemispheric thalamic, somatosensory and multisensory areas that scaled with subjects' performance. Our results indicate that functional asymmetries interact with audiotactile interplay which can be observed for left-lateralized stimulation in the right hemisphere. There, audiotactile interplay recruits a functional network of unisensory cortices, and the strength of these functional network connections is directly related to subjects' perceptual sensitivity.

The impact of serotonin transporter genotype on default network connectivity in children and adolescents with autism spectrum disorders

Compared to healthy controls, individuals with autism spectrum disorders (ASD) have weaker posterior–anterior connectivity that strengthens less with age within the default network, a set of brain structures connected in the absence of a task and likely involved in social function. The serotonin transporter-linked polymorphic region (5-HTTLPR) genotypes that result in lowered serotonin transporter expression are associated with social impairment in ASD. Additionally, in healthy controls, low expressing 5-HTTLPR genotypes are associated with weaker default network connectivity. However, in ASD, the effect of 5-HTTLPR on the default network is unknown. We hypothesized that 5-HTTLPR's influence on posterior–anterior default network connectivity strength as well as on age-related changes in connectivity differs in the ASD group versus controls. Youth with ASD and healthy controls, ages 8–19, underwent a resting fMRI acquisition. Connectivity was calculated by correlating the posterior hub of the default network with all voxels. Triallelic genotype was assessed via PCR and Sanger sequencing. A genotype-by-diagnosis interaction significantly predicted posterior–anterior connectivity, such that low expressing genotypes (S/S, S/LG, LG/LG) were associated with stronger connectivity than high expressing genotypes (LA/LA, S/LA, LA/LG) in the ASD group, but the converse was true for controls. Also, youth with ASD and low expressing genotypes had greater age-related increases in connectivity values compared to those with high expressing genotypes and controls in either genotype group. Our findings suggest that the cascade of events from genetic variation to brain function differs in ASD. Also, low expressing genotypes may represent a subtype within ASD.

Age-Related Changes in Task Related Functional Network Connectivity

Aging has a multi-faceted impact on brain structure, brain function and cognitive task performance, but the interaction of these different age-related changes is largely unexplored. We hypothesize that age-related structural changes alter the functional connectivity within the brain, resulting in altered task performance during cognitive challenges. In this neuroimaging study, we used independent components analysis to identify spatial patterns of coordinated functional activity involved in the performance of a verbal delayed item recognition task from 75 healthy young and 37 healthy old adults. Strength of functional connectivity between spatial components was assessed for age group differences and related to speeded task performance. We then assessed whether age-related differences in global brain volume were associated with age-related differences in functional network connectivity. Both age groups used a series of spatial components during the verbal working memory task and the strength and distribution of functional network connectivity between these components differed across the age groups. Poorer task performance, i.e. slower speed with increasing memory load, in the old adults was associated with decreases in functional network connectivity between components comprised of the supplementary motor area and the middle cingulate and between the precuneus and the middle/superior frontal cortex. Advancing age also led to decreased brain volume; however, there was no evidence to support the hypothesis that age-related alterations in functional network connectivity were the result of global brain volume changes. These results suggest that age-related differences in the coordination of neural activity between brain regions partially underlie differences in cognitive performance.

Noniterative Convex Optimization Methods for Network Component Analysis

This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.

RHEOLOGICAL PROPERTIES OF BATTER DOUGH: EFFECT OF EGG LEVEL

ABSTRACT In this study a rheological characterization of batter dough for pudding production is presented. Small amplitude oscillation tests in linear viscoelastic range were performed at different temperatures (from 10 to 76C), and rheological characterization was completed also measuring apparent viscosity, as a function of shear rate and batter surface tension. A physical model based on weak gel behavior was used to asset the mechanical behavior of the material by measuring the three‐dimensional network connectivity and interaction strength. This approach allowed the effect of egg level to be investigated, correlating the changes in mechanical properties to the egg amount in the recipe. It was found that egg affects the rheological response of the material and its level may be adjusted in order to change the mechanical properties at low temperatures (compensating raw ingredients variation). In addition, at high temperature (after baking) some differences arise in network properties and the final structure of the proof may vary as a consequence. PRACTICAL APPLICATIONSBatter is a widely diffused “liquid‐like” dough, used to produce numerous baked cereal goods. Batter formulation is quite simple and, in addition to flour, egg proteins are added to guarantee the final proof texture. In addition, in some applications raising agents are not used and water is responsible for proof expansion during baking. Therefore a fine control of the rheological properties is requested in order to guarantee bubbles growth and gas retention within a weakly structured system. At industrial level, the control is performed by acting on the protein content (i.e., egg level) aiming at keeping the optimal rheological behavior even in the presence of raw material variability. The rheological characterization of batter at different egg levels may be helpful in order to better understand the effect of protein amount variation on the batter structure as a function of temperature.

Dissociable Network Properties of Anesthetic State Transitions

It is still unknown whether anesthetic state transitions are continuous or binary. Mathematical graph theory is one method by which to assess whether brain networks change gradually or abruptly upon anesthetic induction and emergence. Twenty healthy males were anesthetized with an induction dose of propofol, with continuous measurement of 21-channel electroencephalogram at baseline, during anesthesia, and during recovery. From these electroencephalographic data a "genuine network" was reconstructed based on the surrogate data method. The effects of topologic structure and connection strength on information transfer through the network were measured independently across different states. Loss of consciousness was consistently associated with a disruption of network topology. However, recovery of consciousness was associated with complex patterns of altered connection strength after the initial topologic structure had slowly recovered. In one group of subjects, there was a precipitous increase of connection strength that was associated with reduced variability of emergence time. Analysis of regional effects on brain networks demonstrated that the parietal network was significantly disrupted, whereas the frontal network was minimally affected. By dissociating the effects of network structure and connection strength, both continuous and discrete elements of anesthetic state transitions were identified. The study also supports a critical role of parietal networks as a target of general anesthetics.