Neuronal Networks In Brain Research Articles

A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.

The origins and development of aesthetics.

All people (and some other animals) have aesthetic responses to sensory stimulation, responses of emotional pleasure or displeasure. These emotions vary from one person and culture to another, yet they share a common mechanism. To survive, an adaptive animal (as opposed to a tropic animal) needs to become comfortable with normality and to have slight abnormalities draw attention to themselves. Walking through a jungle you need to notice a tiger from a single stripe: if you must wait to see the whole animal, you are unlikely to survive. In Homo sapiens, the brain's adaptive neurochemistry does this naturally, partly because the brain's neuronal networks are structured to react efficiently to fractal structures, structures that shape much of nature. In addition, previous associations may turn a slight variation from normal into feelings of either pleasure or danger. The details of these responses-what is normal and what variations feel like-will depend upon an individual's experience, but the mechanism is the same, no matter whether a person is tasting a wine, seeing a face or landscape, or hearing a song. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

The physiological role of TRP channels in sleep and circadian rhythm.

TRP channels, are non-specific cationic channels that are involved in multiple physiological processes that include salivation, cellular secretions, memory extinction and consolidation, temperature, pain, store-operated calcium entry, thermosensation and functionality of the nervous system. Here we choose to look at the evidence that decisively shows how TRP channels modulate human neuron plasticity as it relates to the molecular neurobiology of sleep/circadian rhythm. There are numerous model organisms of sleep and circadian rhythm that are the results of the absence or genetic manipulation of the non-specific cationic TRP channels. Drosophila and mice that have had their TRP channels genetically ablated or manipulated show strong evidence of changes in sleep duration, sleep activity, circadian rhythm and response to temperature, noxious odours and pattern of activity during both sleep and wakefulness along with cardiovascular and respiratory function during sleep. Indeed the role of TRP channels in regulating sleep and circadian rhythm is very interesting considering the parallel roles of TRP channels in thermoregulation and thermal response with concomitant responses in growth and degradation of neurites, peripheral nerves and neuronal brain networks. TRP channels provide evidence of an ability to create, regulate and modify our sleep and circadian rhythm in a wide array of physiological and pathophysiological conditions. In the current review, we summarize previous results and novel recent advances in the understanding of calcium ion entry via TRP channels in different sleep and circadian rhythm conditions. We discuss the role of TRP channels in sleep and circadian disorders.

Using graph theory to analyze human brain neuronal networks – a novel approach to investigate neuronal crosstalk.

Using graph theory to analyze human brain neuronal networks – a novel approach to investigate neuronal crosstalk.

Chaotic Behavior of the Basal Ganglia Cortical Thalamic Model for Absence Seizures: A Comprehensive Dynamical Analysis

Children frequently experience absence seizures, a form of seizure that is characterized by brief periods of unconsciousness and staring spells. While many studies have been conducted on absence seizures, there is still some uncertainty regarding the precise mechanisms causing absence seizures. The basal ganglia are believed to be essential in regulating thalamocortical network activity responsible for such seizures. Controlling or designing a treatment for this disorder requires an understanding of the contribution of the basal ganglia regions in the absence seizures. In this regard, efforts have been made to propose a mathematical model of brain neuronal substructures and their connections in the basal ganglia. The basal ganglia cortex-thalamus (BGCT) model is one of the most-studied mathematical models investigating absence seizures. However, this model has not been comprehensively studied from the viewpoint of dynamical behavior. Hence, to evaluate the BGCT model, this paper is devoted to studying a detailed and in-depth bifurcation analysis of the basal ganglia regions in the BGCT loop. Moreover, the 0–1 test for chaos is performed to confirm the results shown in the bifurcation diagrams. Our results suggest that the BGCT model can exhibit chaotic behavior in small regions of the coupling parameter, which is consistent with the complex nature of the brain neuronal network.

Sudden bio-mathematical self-similarity and the uniqueness of human mass societies: from T-patterns and T-strings to T-societies.

With the explosive growth of human knowledge especially in the twenteeth century with even greater facilitation of access to knowledge, the world of even relatively recent great thinkers becomes daunting as seen from a modern viewpoint. Recently, humans ignored the existence of the complex intracellular world of cell organs, giant information molecules such as DNA, societies of specialized worker molecules (proteins), and generally the surprising nanoscale world visible to humanity since only a few decades ago. Moreover, computational power and video technology were inaccessible to all scientists from, for example, Aristotle to Freud, so new views and ideas seem to be expected about phenomena at all scales including nano and human. Some have arrived very recently. Thus urgently needed knowledge about the biology of animal and human behavior received the first Nobel Prize as late as 1973, in Physiology and Medicine, shared by Karl von Frisch, Konrad Lorenz, and Niko Tinbergen. Lorenz's Nobel lecture was entitled "Analogy as a Source of Knowledge" which did not mention self-analogy (self-similarity) as none of the species studied were part of others and knowledge of the nanoscale phenomena at the heart of this article had barely become available. The views and empirical findings presented in this article depend on such recent intracellular nanoscale insights and the development of a set of mathematical patterns, called T-system, of which only two are considered, the self-similar (i.e., parts having a structure similar to the whole) T-pattern and the derived T-string, a T-patterned material string (here, polymer or text). Specially developed algorithms implemented in the THEMETM software for T-pattern detection and analysis (TPA) allowed the detection of interaction T-patterns in humans, animals, and brain neuronal networks, showing self-similarity between animal interaction patterns and neuronal interaction patterns in their brains. TPA of DNA and text also showed unique self-similarity between modern human literate mass societies and the protein societies of their body cells, both with Giant Extra-Individual Purely Informational T-strings (GEIPIT; genomes or textomes) defining the behavioral potentials of their specialized citizens. This kind of society is here called T-society and only exists in humans and proteins, while the self-similarity between them only exists in human T-societies.

Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning

The effectiveness, robustness, and flexibility of memory and learning constitute the very essence of human natural intelligence, cognition, and consciousness. However, currently accepted views on these subjects have, to date, been put forth without any basis on a true physical theory of how the brain communicates internally via its electrical signals. This lack of a solid theoretical framework has implications not only for our understanding of how the brain works, but also for wide range of computational models developed from the standard orthodox view of brain neuronal organization and brain network derived functioning based on the Hodgkin–Huxley ad-hoc circuit analogies that have produced a multitude of Artificial, Recurrent, Convolution, Spiking, etc., Neural Networks (ARCSe NNs) that have in turn led to the standard algorithms that form the basis of artificial intelligence (AI) and machine learning (ML) methods. Our hypothesis, based upon our recently developed physical model of weakly evanescent brain wave propagation (WETCOW) is that, contrary to the current orthodox model that brain neurons just integrate and fire under accompaniment of slow leaking, they can instead perform much more sophisticated tasks of efficient coherent synchronization/desynchronization guided by the collective influence of propagating nonlinear near critical brain waves, the waves that currently assumed to be nothing but inconsequential subthreshold noise. In this paper we highlight the learning and memory capabilities of our WETCOW framework and then apply it to the specific application of AI/ML and Neural Networks. We demonstrate that the learning inspired by these critically synchronized brain waves is shallow, yet its timing and accuracy outperforms deep ARCSe counterparts on standard test datasets. These results have implications for both our understanding of brain function and for the wide range of AI/ML applications.

Fundamental Neurochemistry Review: GABAA receptor neurotransmission and epilepsy: Principles, disease mechanisms and pharmacotherapy.

Epilepsy is a common neurological disorder associated with alterations of excitation-inhibition balance within brain neuronal networks. GABAA receptor neurotransmission is the most prevalent form of inhibitory neurotransmission and is strongly implicated in both the pathophysiology and treatment of epilepsy, serving as a primary target for antiseizure medications for over a century. It is now established that GABA exerts a multifaceted influence through an array of GABAA receptor subtypes that extends far beyond simply negating excitatory activity. As the role of GABAA neurotransmission within inhibitory circuits is elaborated, this will enable the development of precision therapies that correct the network dysfunction underlying epileptic pathology.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.

Switching behavior of the gamma power in the neuronal network modulated by the astrocytes

Understanding the dynamical neural gamma oscillation during brain state switch has been accepted as a critical step to explain attention and related human behaviors. Recent experimental studies have found a specific pulse of gamma power traces, coexisting with a pulse of functional magnetic resonance imaging with energy consumption by utilizing intracranial electroencephalography and neuroimaging. This phenomenon implies that the gamma oscillation is crucial in the success of the attention switching process after the attention task termination. However, until now no experimental evidence exists that explains how this type of gamma oscillation occurs and participates in the attention switching process. In this paper, the neuron-astrocyte-capillary network model was established, by utilizing the mathematical models of neurons and astrocytes that could explain the micro-scale behavior of neurons and investigated the formation process of this type of gamma oscillation. Our simulation results showed that the astrocyte calcium activity was pivotal in the occurrence of gamma oscillation after the attention task termination, and properly explained the underlying mechanisms of the dynamical switching of brain neuronal networks.

Analysis of Comprehensive Artificial Neural Network Computer Media Aided Construction of Economic Forecasting Model

As a complex system engineering, economic forecasting involves many fields and disciplines, and its accuracy largely depends on the selected forecasting method and forecasting model. The construction of the traditional economic prediction model mainly depends on the theories of economic statistics and metrology, which cannot meet the prediction needs of the new era. ANN model has a more flexible nonlinear quality and learning mode so that it can effectively adapt to the prediction scenario of a nonlinear economic system. ANN is an abstract model of the human brain neuronal network based on the information-processing perspective and composed of different networks to achieve a specific representation of logical strategies. ANN’s self-learning function, associative storage function, and the ability to find optimal solutions at high speed have led to its in-depth application and research in an increasing number of fields. In addition, ANN research work has continued to advance, has made great progress in many fields represented by economic forecasting, has successfully solved many practical problems, showing a more intelligent adaptation in the field of economics, and therefore has an important research value. Based on this, this paper first analyzes the economic system prediction, then studies the ANN medium model and learning algorithm, and finally verifies the effectiveness of the ANN economic prediction model integrating computer medium.

How Are Nerve Cells And Artificial Intelligence Similar?

In the field of artificial intelligence, which has a large influence on our lives today, the term “neural network” has been gaining popularity. It usually refers to a dense web of simple units, each of which may be in one of two states (on/off) and impacts of the state of other units connected to it. What exactly is the relationship between the way a nerve cell acts and the way artificial intelligence works? How is the action of an artificial neural network similar to a network of neurons in the human brain? This article tries to answer these questions. We will summarize how thinking happens, provide a short description of how a nerve cell works, describe the similarities between a nerve cell and a basic unit of a logical system, and finally show how connecting several such units is the basis for artificial intelligence.

Abstract- Poster

Background:Neurocognitive abilities are a catch-all term that refers to cognitive talents that facilitate perception, reasoning, action, and emotion generation and are believed to be associated with specific brain neuronal pathways and networks. Because It is considered that abilities operate as a mediator between ideas and actions, they may be especially well adapted to explaining the path of suicidal thoughts to acts of suicide. Objectives: To compare the pattern of neuro cognition in single self-harm attempters and multiple self-harm attempters in adult population.Methods:A cross-sectional study conducted at Department of Psychiatry and Drug De-addiction Centre on Participants with self-harm attempt seeking consultation from Psychiatry Department and Drug De-addiction Centre either directly or after referral. After institutional ethics clearance participants consent taken and a semi-structured proforma was filled. Socio-demographic and clinical data were obtained CTMT used for assessing neurocoginition then appropriate statistics applied Manova and ANOVA.Result:The mean value for taril time is less in single self harm attempters then multiple self harm attempters for eg the time score of trail 1 in SSA was 51.1sec where as 60.2 sec in MSA.Composite index high means good performance.Among from singleharm attempters The mean CI score was (32.83) compared with Multi harm attempters (29.01).Conclusion:A significant comparative difference was observed in selective attention, cognitive flexibility and inhibition in relation to the multiple self harm attempters and single self harm attempters.This adds to the evidence of frontal lobe dysfunction Therefore A larger study with a bigger sample is needed to replicate cut offs for this test in the Indian population so that these cut offs can be generalized

Resting-State Functional Magnetic Resonance Imaging for Surgical Neuro-Oncology Planning: Towards a Standardization in Clinical Settings.

Resting-state functional magnetic resonance imaging (rest-f-MRI) is a neuroimaging technique that has demonstrated its potential in providing new insights into brain physiology. rest-f-MRI can provide useful information in pre-surgical mapping aimed to balancing long-term survival by maximizing the extent of resection of brain neoplasms, while preserving the patient’s functional connectivity. Rest-fMRI may replace or can be complementary to task-driven fMRI (t-fMRI), particularly in patients unable to cooperate with the task paradigm, such as children or sedated, paretic, aphasic patients. Although rest-fMRI is still under standardization, this technique has been demonstrated to be feasible and valuable in the routine clinical setting for neurosurgical planning, along with intraoperative electrocortical mapping. In the literature, there is growing evidence that rest-fMRI can provide valuable information for the depiction of glioma-related functional brain network impairment. Accordingly, rest-fMRI could allow a tailored glioma surgery improving the surgeon’s ability to increase the extent of resection (EOR), and simultaneously minimize the risk of damage of eloquent brain structures and neuronal networks responsible for the integrity of executive functions. In this article, we present a review of the literature and illustrate the feasibility of rest-fMRI in the clinical setting for presurgical mapping of eloquent networks in patients affected by brain tumors, before and after tumor resection.

Frontal lobe hypometabolism associated with Sudden Unexpected Death in Epilepsy (SUDEP) risk: An objective PET study

ObjectiveAbnormalities of brain structures and neuronal networks have been identified in MRI studies of patients with Sudden Unexpected Death in Epilepsy (SUDEP) as well as in those at elevated risk. The goal of this study was to identify common patterns of objectively detected brain glucose metabolic abnormalities associated with SUDEP patients and those at high SUDEP risk. MethodsPatients with refractory epilepsy (n = 78, age: 16–61 years, 44 females), who underwent comprehensive presurgical evaluation, were assessed for their risk of SUDEP using the revised SUDEP-7 inventory. From the 57 patients with low SUDEP risk, 35 were selected to match their demographic and clinical characteristics to those with high SUDEP risk (n = 21). [18F]fluoro-deoxy-glucose positron emission tomography (FDG-PET) abnormalities were evaluated in the high- and low-SUDEP risk subgroups compared to FDG-PET scans of a healthy adult control group using statistical parametric mapping (SPM). Individual FDG-PET scans of 4 additional patients, who died from SUDEP, were also analyzed by SPM. ResultsMean SUDEP-7 score was 6.1 in the high and 2.7 in the low SUDEP risk group. MRI showed no lesion in 36 patients (64%). Statistical parametric mapping analysis of the high SUDEP risk subgroup showed bilateral medial frontal and inferior frontal hypometabolism as a common pattern. The low-risk group showed no specific common metabolic abnormalities on SPM group analysis. Individual PET scans of all 4 patients who died from SUDEP also showed bilateral frontal lobe hypometabolism. ConclusionsThese data show that bilateral frontal lobe involvement on FDG-PET, especially the medial and inferior frontal cortex, may be a common metabolic pattern associated with high SUDEP risk and SUDEP itself, in patients with refractory focal epilepsy.

The Role of Parvalbumin Interneurons in Neurotransmitter Balance and Neurological Disease.

While great progress has been made in the understanding of neurological illnesses, the pathologies, and etiologies that give rise to these diseases still remain an enigma, thus, also making treatments for them more challenging. For effective and individualized treatment, it is beneficial to identify the underlying mechanisms that govern the associated cognitive and behavioral processes that go awry in neurological disorders. Parvalbumin fast-spiking interneurons (Pv-FSI) are GABAergic cells that are only a small fraction of the brain's neuronal network, but manifest unique cellular and molecular properties that drastically influence the downstream effects on signaling and ultimately change cognitive behaviors. Proper brain functioning relies heavily on neuronal communication which Pv-FSI regulates, excitatory-inhibitory balances and GABAergic disinhibition between circuitries. This review highlights the depth of Pv-FSI involvement in the cortex, hippocampus, and striatum, as it pertains to expression, neurotransmission, role in neurological disorders, and dysfunction, as well as cognitive behavior and reward-seeking. Recent research has indicated that Pv-FSI play pivotal roles in the molecular pathophysiology and cognitive-behavioral deficits that are core features of many psychiatric disorders, such as schizophrenia, autism spectrum disorders, Alzheimer's disease, and drug addiction. This suggests that Pv-FSI could be viable targets for treatment of these disorders and thus calls for further examination of the undeniable impact Pv-FSI have on the brain and cognitive behavior.

Experimentally induced low back pain influences brain networks activity

Purpose of this study was to answer the question whether the recognized patterns of brain activity are likely to change under the influence of experimentally induced low back pain (LBP), and also to determine the functional networks of the brain engaged in this process. Twenty healthy subjects (8 women) participated. An experimental design was applied with repeated measurements of the blood oxygen level-dependent (BOLD) signal from the brain during two different voluntary contractions of the abdominal muscles without and with experimentally induced LBP. Brain areas showing significant differences in activity were identified and ascribed to the three functional neuronal brain networks: default mode network (DMN), pain matrix (PM), and sensorimotor (SM) areas. After implementation of the experimental painful stimulus the overall level of brain activity appears to be higher. No higher brain deactivations are seen in painful conditions and no higher activations in pain-free conditions. During isolated-type of muscular contraction a slight tendency to DMN deactivation may be observed.

A modular brain-on-a-chip for modelling epileptic seizures with functionally connected human neuronal networks

Epilepsies are a group of neurological disorders characterised by recurrent epileptic seizures. Seizures, defined as abnormal transient discharges of neuronal activity, can affect the entire brain circuitry or remain more focal in the specific brain regions and neuronal networks. Human pluripotent stem cell (hPSC)-derived neurons are a promising option for modelling epilepsies, but as such, they do not model groups of connected neuronal networks or focal seizures. Our solution is a Modular Platform for Epilepsy Modelling In Vitro (MEMO), a lab-on-chip device, in which three hPSC-derived networks are separated by a novel microfluidic cell culture device that allows controlled network-to-network axonal connections through microtunnels. In this study, we show that the neuronal networks formed a functional circuitry that was successfully cultured in MEMO for up to 98 days. The spontaneous neuronal network activities were monitored with an integrated custom-made microelectrode array (MEA). The networks developed spontaneous burst activity that was synchronous both within and between the axonally connected networks, i.e. mimicking both local and circuitry functionality of the brain. A convulsant, kainic acid, increased bursts only in the specifically treated networks. The activity reduction by an anticonvulsant, phenytoin, was also localised to treated networks. Therefore, modelling focal seizures in human neuronal networks is now possible with the developed chip.

Prenatal stress-related alterations in synaptic transmission and 5-HT7 receptor-mediated effects in the rat dorsal raphe nucleus are ameliorated by the 5-HT7 receptor antagonist SB 269970.

Early life adversity exerts a detrimental influence on developing brain neuronal networks and its consequences may include mental health disorders. In rats, prenatal stress may lead to anxiety and depressive-like behavior in the offspring. Several lines of evidence implicated an involvement of prenatal stress in alterations of the brain serotonergic system functions, but the effects of prenatal stress on its core, the dorsal raphe nucleus (DRN), still remain incompletely understood. The present study was aimed at finding whether prenatal stress induces modifications in the glutamatergic and GABAergic inputs to DRN projection cells and whether it affects DRN 5-HT7 receptors, which modulate activity of these synapses. Prenatal stress resulted in an increase in basal frequency of spontaneous excitatory postsynaptic currents (sEPSCs) and in a decrease in basal frequency of spontaneous inhibitory postsynaptic currents (sIPSCs) recorded from putative projection neurons in DRN slices ex vivo. While there were no changes in the excitability of DRN projection neurons, the 5-HT7 receptor-mediated reduction in the sEPSC frequency and rise in the sIPSC frequency, seen in control rats, were largely absent in slices obtained from prenatally stressed rats. Repeated administration of SB 269970, a 5-HT7 receptor antagonist, resulted in a reversal of prenatal stress-induced alterations in 5-HT7 receptor-mediated effects on the sEPSC/sIPSC frequency. Moreover, the treatment reversed prenatal stress-induced alterations in basal excitatory transmission and partially reversed the effect of stress on basal inhibitory transmission in the DRN.