Quasicriticality explains variability of human neural dynamics across life span.

Aging impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset-the largest aging cohort available-in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to aging and increased dynamical fluctuations of neuronal firing rates. Our findings suggest a platform to develop biomarkers of neurological health.

Spike-phase coupling as an order parameter in a leaky integrate-and-fire model.

It is known that the leaky integrate-and-fire neural model shows a transition from irregular to synchronous firing by increasing the coupling between the neurons. However, a quantitative characterization of this order-disorder transition, that is, the determination of the order of transition and also the critical exponents in the case of continuous transition, is not entirely known. In this work, we consider a network of N excitatory neurons with local connections, residing on a square lattice with periodic boundary conditions. The cooperation between neurons K plays the role of the control parameter that generates criticality when the critical cooperation strength K_{c} is adopted. We introduce the population-averaged voltage (PAV) as a representative value of the network's cooperative activity. Then, we show that the coupling between the timing of spikes and the phase of temporal fluctuations of PAV defined as m resorts to identify a Kuramoto order parameter. By increasing K, we find a continuous transition from irregular spiking to a phase-locked state at the critical point K_{c}. We deploy the finite-size scaling analysis to calculate the critical exponents of this transition. To explore the formal indicator of criticality, we study the neuronal avalanches profile at this critical point and find a scaling behavior with the exponents in a fair agreement with the experimental values both in vivo and in vitro.

Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering.

A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer's disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering, we found clusters displaying structural features of typical AD, cortically-driven atypical AD, limbic-predominant AD, and early-onset AD (EOAD). Among these clusters, EOAD subjects displayed marked cortical gray matter atrophy and atrophy of the precuneus. Furthermore, EOAD subjects had the highest progression rates as measured with ADAS slopes during the longitudinal follow-up of 36 months. Striking heterogeneities in brain atrophy patterns were observed with MCI subjects. We found clusters of stable MCI, clusters of diffuse brain atrophy with fast progression, and MCI subjects displaying similar atrophy patterns as the typical or atypical AD subjects. Bidirectional differences in structural phenotypes were found with MCI subjects involving the anterior cerebellum and the frontal cortex. The diversity of the MCI subjects suggests that the structural phenotypes of MCI subjects would deserve a more detailed investigation with a significantly larger cohort. Our results demonstrate that the hierarchical agglomerative clustering method is an efficient tool in dividing a cohort of subjects with gray matter atrophy into coherent clusters manifesting different structural phenotypes.

Thermosensitive in situ liposomal gels loaded with antimicrobial agent for oral care in critically ill patients.

Aim: A novel thermosensitive in situ gel loaded with meropenem (MP) liposomes was designed to improve retention in the oral cavity as a prophylactic measure to prevent ventilator-acquired pneumonia in critically ill patients. Methodology & results: Meropenem liposomes were incorporated into poloxamer 407 gels and gamma irradiated. Mean size of liposome was 247 nm, polydispersity index < 0.3 and zeta potential >-25 mV; properties remained unaltered even post sterilization. Permeation study revealed that 75.26% and 34% of MPs were released from MP in situ gel and MP in situ liposomal gel, respectively. The relation between viscosity (cp) and shear rate (1/s) indicate that in situ gels exhibited non-Newtonian behavior at 37°C. The study using Pseudomonas aeruginosa confirmed the antimicrobial activity of meropenem. Conclusion: Prolonged in situ residence, because of rapid gelation process enables an easy administration of meropenem as liposomal suspension in critically ill patients.

Retraction Note to: Stability of neuronal avalanches and long-range temporal correlations during the first year of life in human infant.

The authors have retracted this article Jannesari et al. (2019) because an incorrect version of the article was published in error. The manuscript has been republished as Jannesari et al. (2020). All authors agree to this retraction.

Stability of neuronal avalanches and long-range temporal correlations during the first year of life in human infants.

During infancy, the human brain rapidly expands in size and complexity as neural networks mature and new information is incorporated at an accelerating pace. Recently, it was shown that single-electrode EEG in preterms at birth exhibits scale-invariant intermittent bursts. Yet, it is currently not known whether the normal infant brain, in particular, the cortex, maintains a distinct dynamical state during development that is characterized by scale-invariant spatial as well as temporal aspects. Here we employ dense-array EEG recordings acquired from the same infants at 6 and 12 months of age to characterize brain activity during an auditory odd-ball task. We show that suprathreshold events organize as spatiotemporal clusters whose size and duration are power-law distributed, the hallmark of neuronal avalanches. Time series of local suprathreshold EEG events display significant long-range temporal correlations (LRTCs). No differences were found between 6 and 12 months, demonstrating stability of avalanche dynamics and LRTCs during the first year after birth. These findings demonstrate that the infant brain is characterized by distinct spatiotemporal dynamical aspects that are in line with expectations of a critical cortical state. We suggest that critical state dynamics, which theory and experiments have shown to be beneficial for numerous aspects of information processing, are maintained by the infant brain to process an increasingly complex environment during development.

Stability of neuronal avalanches and long-range temporal correlations during the first year of life in human infant.

During infancy, the human brain rapidly expands in size and complexity as neural networks mature and new information is incorporated at an accelerating pace. Recently, it was shown that single electrode EEG in preterms at birth exhibits scale-invariant intermittent bursts. Yet, it is currently not known whether the normal infant brain, in particular, the cortex maintains a distinct dynamical state during development that is characterized by scale-invariant spatial as well as temporal aspects. Here we employ dense-array EEG recordings acquired from the same infants at 6 and 12 months of age to characterize brain activity during an auditory oddball task. We show that suprathreshold events organize as spatiotemporal clusters whose size and duration are power-law distributed, the hallmark of neuronal avalanches. Time series of local suprathreshold EEG events display significant long-range temporal correlations (LRTCs). No differences were found between 6 and 12 months, demonstrating stability of avalanche dynamics and LRTCs during the first year after birth. These findings demonstrate that the infant brain is characterized by distinct spatiotemporal dynamical aspects that are in line with expectations of a critical cortical state. We suggest that critical state dynamics, which theory and experiments have shown to be beneficial for numerous aspects of information processing, are maintained by the infant brain to process an increasingly complex environment during development.

Role-play versus lecture methods in community health volunteers.

BACKGROUND: Considering the key role of health volunteers in promoting community's health, their effective training is of particular importance. Training can be more effective through cooperative and learner-centered methods. Role-play is among the cooperative methods with numerous advantages. Considering the positive impact of training via various methods, we aimed to compare training through role-play and lecture on health volunteers' health knowledge in selected comprehensive health centers of Shiraz, Iran. METHODS: This quasi-experimental study was conducted on all health volunteers in four comprehensive health centers selected via random cluster sampling during the second half of 2017. The participants were divided into intervention and control groups and took part in the pretest based on the book entitled "Promotion of Breastfeeding". Based on the pretest results, health volunteers trained the participants in three educational sessions. The two groups were evaluated again immediately and two months after the intervention. RESULTS: The mean age of the participants was 49.97 ±â€¯8.1 and 46.52 ±â€¯10.74 years in intervention and control groups, respectively. Most participants were married (94.8%) and had diplomas (92.1%).A significant difference was seen between both groups in knowledge scores at the three time points (P < 0.001). A significant difference was found between the two groups regarding knowledge scores immediately and two months after the intervention (P < 0.001), indicating the effectiveness of training through role-play. CONCLUSION: The advantages of role-play, including development of communication skills and active listening, resulted in the learners' enthusiasm and motivation. This method was accompanied with higher educational output as well as longer knowledge persistence. Role-play increased cooperation and group discussions performed after the role-play promoted the transfer emotional experiences.

The Conference of the Birds: An Old Artistic Concept Making Sense in Modern Sciences.

In this article, we will discuss scientific aspects of an old Persian story, Simorgh, in the book of The Conference of the Birds. The story is fulfilled with artistic and philosophical metaphors that make sense in two hot topics of the contemporary modern sciences i.e. cognitive science and complexity science. The poet addresses some humanity's bygone concerns and fundamental questions about self, the quality that shapes a person's uniqueness, and essential existence. The sophisticated language used in the poem contains allusions, symbols, and implications that are interpreted in five main topics. We think that the story deserves to be the touchstone for questions on the nature of the mind, including the profound question of humanity's search for self and meaning of life.

Neuronal avalanches: Where temporal complexity and criticality meet.

The model of the current paper is an extension of a previous publication, wherein we have used the leaky integrate-and-fire model on a regular lattice with periodic boundary conditions, and introduced the temporal complexity as a genuine signature of criticality. In that work, the power-law distribution of neural avalanches was a manifestation of supercriticality rather than criticality. Here, however, we show that the continuous solution of the model and replacing the stochastic noise with a Gaussian zero-mean noise leads to the coincidence of power-law display of temporal complexity, and spatiotemporal patterns of neural avalanches at the critical point. We conclude that the source of inconsistency may be a numerical artifact originated by the discrete description of the model which may imply a slow numerical convergence of the avalanche distribution compared to temporal complexity.

New Perspectives on Spontaneous Brain Activity: Dynamic Networks and Energy Matter.

Spontaneous brain activity has received increasing attention as demonstrated by the exponential rise in the number of published article on this topic over the last 30 years. Such "intrinsic" brain activity, generated in the absence of an explicit task, is frequently associated with resting-state or default-mode networks (DMN)s. The focus on characterizing spontaneous brain activity promises to shed new light on questions concerning the structural and functional architecture of the brain and how they are related to "mind". However, many critical questions have yet to be addressed. In this review, we focus on a scarcely explored area, specifically the energetic requirements and constraints of spontaneous activity, taking into account both thermodynamical and informational perspectives. We argue that the "classical" definitions of spontaneous activity do not take into account an important feature, that is, the critical thermodynamic energetic differences between spontaneous and evoked brain activity. Spontaneous brain activity is associated with slower oscillations compared with evoked, task-related activity, hence it exhibits lower levels of enthalpy and "free-energy" (i.e., the energy that can be converted to do work), thus supporting noteworthy thermodynamic energetic differences between spontaneous and evoked brain activity. Increased spike frequency during evoked activity has a significant metabolic cost, consequently, brain functions traditionally associated with spontaneous activity, such as mind wandering, require less energy that other nervous activities. We also review recent empirical observations in neuroscience, in order to capture how spontaneous brain dynamics and mental function can be embedded in a non-linear dynamical framework, which considers nervous activity in terms of phase spaces, particle trajectories, random walks, attractors and/or paths at the edge of the chaos. This takes us from the thermodynamic free-energy, to the realm of "variational free-energy", a theoretical construct pertaining to probability and information theory which allows explanation of unexplored features of spontaneous brain activity.

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

OBJECTIVES: This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. METHODS: Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. RESULTS: Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. CONCLUSIONS: The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. SIGNIFICANCE: The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation.

Cooperation in neural systems: bridging complexity and periodicity.

Inverse power law distributions are generally interpreted as a manifestation of complexity, and waiting time distributions with power index µ<2 reflect the occurrence of ergodicity-breaking renewal events. In this paper we show how to combine these properties with the apparently foreign clocklike nature of biological processes. We use a two-dimensional regular network of leaky integrate-and-fire neurons, each of which is linked to its four nearest neighbors, to show that both complexity and periodicity are generated by locality breakdown: Links of increasing strength have the effect of turning local interactions into long-range interactions, thereby generating time complexity followed by time periodicity. Increasing the density of neuron firings reduces the influence of periodicity, thus creating a cooperation-induced renewal condition that is distinctly non-Poissonian.