Neural Organization Research Articles

Analysis of Brain Structure and Neural Organization in Dystrophin-Deficient Model Mice with Magnetic Resonance Imaging at 7 T

Background: Dystrophin strengthens muscle cells; however, in muscular dystrophy, dystrophin is deficient due to an abnormal sugar chain. This abnormality occurs in skeletal muscle and in brain tissue. Objective: This study aimed to non-invasively analyze the neural organization of the brain in muscular dystrophy. We used a mouse model of muscular dystrophy to study whether changes in brain structure and neurodegeneration following dystrophin deficiency can be assessed by 7T magnetic resonance imaging. Methods: C57BL/10-mdx (X chromosome-linked muscular dystrophy) mice were used as the dystrophic mouse model and healthy mice were used as controls. Ventricular enlargement is one of the most common brain malformations in dystrophin-deficient patients. Therefore, we examined whether ventricular enlargement was observed in C57BL/10-mdx using transverse-relaxation weighted images. Brain parenchyma analysis was performed using diffusion MRI with diffusion tensor images and neurite orientation dispersion and density imaging. Parenchymal degeneration was assessed in terms of directional diffusion, nerve fiber diffusion, and dendritic scattering density. Results: For the volume of brain ventricles analyzed by T2WI, the average size was 1.5 times larger in mdx mice compared to control mice. In the brain parenchyma, a significant difference (p < 0.05) was observed in parameters indicating disturbances in the direction of nerve fibers and dendritic scattering density in the white matter region. Conclusion: Our results show that changes in brain structure due to dystrophin deficiency can be assessed in detail without tissue destruction by combining diffusion tensor images and neurite orientation dispersion and density imaging analyses.

Computer-Implemented Articulatory Models for Speech Production: A Review.

Modeling speech production and speech articulation is still an evolving research topic. Some current core questions are: What is the underlying (neural) organization for controlling speech articulation? How to model speech articulators like lips and tongue and their movements in an efficient but also biologically realistic way? How to develop high-quality articulatory-acoustic models leading to high-quality articulatory speech synthesis? Thus, on the one hand computer-modeling will help us to unfold underlying biological as well as acoustic-articulatory concepts of speech production and on the other hand further modeling efforts will help us to reach the goal of high-quality articulatory-acoustic speech synthesis based on more detailed knowledge on vocal tract acoustics and speech articulation. Currently, articulatory models are not able to reach the quality level of corpus-based speech synthesis. Moreover, biomechanical and neuromuscular based approaches are complex and still not usable for sentence-level speech synthesis. This paper lists many computer-implemented articulatory models and provides criteria for dividing articulatory models in different categories. A recent major research question, i.e., how to control articulatory models in a neurobiologically adequate manner is discussed in detail. It can be concluded that there is a strong need to further developing articulatory-acoustic models in order to test quantitative neurobiologically based control concepts for speech articulation as well as to uncover the remaining details in human articulatory and acoustic signal generation. Furthermore, these efforts may help us to approach the goal of establishing high-quality articulatory-acoustic as well as neurobiologically grounded speech synthesis.

How Does the Central Nervous System for Posture and Locomotion Cope With Damage-Induced Neural Asymmetry?

In most vertebrates, posture and locomotion are achieved by a biomechanical apparatus whose effectors are symmetrically positioned around the main body axis. Logically, motor commands to these effectors are intrinsically adapted to such anatomical symmetry, and the underlying sensory-motor neural networks are correspondingly arranged during central nervous system (CNS) development. However, many developmental and/or life accidents may alter such neural organization and acutely generate asymmetries in motor operation that are often at least partially compensated for over time. First, we briefly present the basic sensory-motor organization of posturo-locomotor networks in vertebrates. Next, we review some aspects of neural plasticity that is implemented in response to unilateral central injury or asymmetrical sensory deprivation in order to substantially restore symmetry in the control of posturo-locomotor functions. Data are finally discussed in the context of CNS structure-function relationship.

Looking for “fNIRS Signature” in Autism Spectrum: A Systematic Review Starting From Preschoolers

Accumulating evidence suggests that functional Near-Infrared Spectroscopy (fNIRS) can provide an essential bridge between our current understanding of neural circuit organization and cortical activity in the developing brain. Indeed, fNIRS allows studying brain functions through the measurement of neurovascular coupling that links neural activity to subsequent changes in cerebral blood flow and hemoglobin oxygenation levels. While the literature offers a multitude of fNIRS applications to typical development, only recently this tool has been extended to the study of neurodevelopmental disorders (NDDs). The exponential rise of scientific publications on this topic during the last years reflects the interest to identify a “fNIRS signature” as a biomarker of high translational value to support both early clinical diagnosis and treatment outcome. The purpose of this systematic review is to describe the updating clinical applications of fNIRS in NDDs, with a specific focus on preschool population. Starting from this rationale, a systematic search was conducted for relevant studies in different scientific databases (Pubmed, Scopus, and Web of Science) resulting in 13 published articles. In these studies, fNIRS was applied in individuals with Autism Spectrum Disorder (ASD) or infants at high risk of developing ASD. Both functional connectivity in resting-state conditions and task-evoked brain activation using multiple experimental paradigms were used in the selected investigations, suggesting that fNIRS might be considered a promising method for identifying early quantitative biomarkers in the autism field.

Structural brain network topological alterations in stuttering adults.

Persistent developmental stuttering is a speech disorder that primarily affects normal speech fluency but encompasses a complex set of symptoms ranging from reduced sensorimotor integration to socioemotional challenges. Here, we investigated the whole-brain structural connectome and its topological alterations in adults who stutter. Diffusion-weighted imaging data of 33 subjects (13 adults who stutter and 20 fluent speakers) were obtained along with a stuttering severity evaluation. The structural brain network properties were analysed using network-based statistics and graph theoretical measures particularly focussing on community structure, network hubs and controllability. Bayesian power estimation was used to assess the reliability of the structural connectivity differences by examining the effect size. The analysis revealed reliable and wide-spread decreases in connectivity for adults who stutter in regions associated with sensorimotor, cognitive, emotional and memory-related functions. The community detection algorithms revealed different subnetworks for fluent speakers and adults who stutter, indicating considerable network adaptation in adults who stutter. Average and modal controllability differed between groups in a subnetwork encompassing frontal brain regions and parts of the basal ganglia. The results revealed extensive structural network alterations and substantial adaptation in neural architecture in adults who stutter well beyond the sensorimotor network. These findings highlight the impact of the neurodevelopmental effects of persistent stuttering on neural organization and the importance of examining the full structural connectome and the network alterations that underscore the behavioural phenotype.

Role of Artificial Intelligence Based Applications Used for Space Technologies

Artificial Intelligence (AI) contains all strategies that empower computers to imitate intelligence, for instance, computers that examine information or the frameworks inserted in an independent vehicle. Typically, AI frameworks are educated by people, an intervention that includes composing a terrible parcel of perplexing computer code. AI can likewise be accomplished through Machine Learning (ML). ML is a method of 'preparing' a basic calculation to turn out to be more intricate. In ML, machines process data likewise to people by creating artificial neural organizations. The best AI executions dependent on Deep learning (DL) are infrequently utilized in the space business today, as the models created inside the neural organization are not intelligible and have been difficult to recreate up to this point. Expected utilizations of AI are additionally being entirely examined in satellite tasks, specifically to help the activity of enormous satellite groups of stars, including relative situating, correspondence, and end-of-life the board. ML frameworks examine the colossal measure of information that comes from each space mission. The information from certain Mars meanderers is being sent utilizing AI, and these wanderers have even been helped how to explore without anyone else. Its advancement has progressed significantly throughout the most recent few decades, yet the convoluted models and constructions important for ML should be improved before it very well may be broadly helpful. AI likewise right now comes up short on the dependability and flexibility needed in new programming; these characteristics should be improved before it assumes control over the space business.

Brain bases of English morphological processing: A comparison between Chinese-English, Spanish-English bilingual, and English monolingual children.

How do early bilingual experiences influence children's neural architecture for word processing? Dual language acquisition can yield common influences that may be shared across different bilingual groups, as well as language-specific influences stemming from a given language pairing. To investigate these effects, we examined bilingual English speakers of Chinese or Spanish, and English monolinguals, all raised in the US (N=152, ages 5-10). Children completed an English morphological word processing task during fNIRS neuroimaging. The findings revealed both language-specific and shared bilingual effects. The language-specific effects were that Chinese and Spanish bilinguals showed principled differences in their neural organization for English lexical morphology. The common bilingual effects shared by the two groups were that in both bilingual groups, increased home language proficiency was associated with stronger left superior temporal gyrus (STG) activation when processing the English word structures that are most dissimilar from the home language. The findings inform theories of language and brain development during the key periods of neural reorganization for learning to read by illuminating experience-based plasticity in linguistically diverse learners.

Performance evaluation of artificial neural networks in sustainable modelling biodiesel synthesis

Biodiesel is a characteristic and inexhaustible homegrown fuel removed from creature fats or vegetable oil and liquor through a transesterification response. The exploration work means to assess the exhibition of biodiesel blend. In this paper, biodiesel was displayed and improved by utilizing a hereditary calculation and Artificial Neural Network (ANN). In AI, hereditary calculations and counterfeit neural organizations assume a significant part in displaying biodiesel blend. To upgrade an excellent arrangement hereditary calculation was created. The mix of ANN and Genetic Algorithm gives the ideal condition as the temperature of methanol molar proportion, impetus fixation. It tentatively decides the exhibition trademark like the Coefficient of determination and Absolute Average deviation (AAD). It predicts the Fatty Acid Methyl Ester (FAME) model productively than Response Surface Methodology (RSM). The exhibition examination is reenacted and hypothetical outcomes are recorded then it is contrasted with constant information to decide the exactness of ANN.

Returning to basic principles to develop more effective treatments for central nervous system disorders.

Development of new treatments for diseases of the central nervous system (CNS) is stalled. Of candidate drugs developed through costly preclinical research, 93% fail clinical trials. Hoped-for improvements in diagnosis or treatment from decades of positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) imaging have yet to materialize. To understand what we are doing wrong, I begin with recognition that all aspects of life, including the brain and mind, are physical phenomena consistent with processes described by physicists. Two processes, emergence and entropy, are of particular relevance in complex arrangements of matter that constitute life in general and the brain in particular. The human brain functions through dynamically reconfiguring and hierarchically organized neural functional systems with emergent properties of cognition, emotion, and conscious experience. These systems are shaped and maintained by negentropic environmental input transformed by sensory receptors into neural signals that trigger epigenetic neuroplastic processes. CNS diseases produce clinical disorders by disrupting these systems. As researchers seek appropriate levels of system organization at which to characterize and treat illness, focus has been on medications that impact processes at lower levels or transcranial electric or magnetic stimulation that impact broad contiguous swaths of tissue. Neither align with the brain’s neurosystem organization and therefore lack specificity necessary to be effective and to limit side effects. Digital neurotherapies (DNTs), in contrast, align with neurosystem organization and achieve the needed specificity using the same input pathways and neuroplastic processes that created the neural systems organization to repair it. The omission of DNTs from major systems-based initiatives represents powerful residua of dualist thinking. Interventions based on perceptual and cognitive processes are not thought of as being as physical as drugs or electric or magnetic stimulation through the skull. In fact, they are examples of the most basic processes that create and support life itself.

Integrated Multi-Omics Analysis Reveals the Effect of Maternal Gestational Diabetes on Fetal Mouse Hippocampi.

Growing evidence suggests that adverse intrauterine environments could affect the long-term health of offspring. Recent evidence indicates that gestational diabetes mellitus (GDM) is associated with neurocognitive changes in offspring. However, the mechanism remains unclear. Using a GDM mouse model, we collected hippocampi, the structure critical to cognitive processes, for electron microscopy, methylome and transcriptome analyses. Reduced representation bisulfite sequencing (RRBS) and RNA-seq in the GDM fetal hippocampi showed altered methylated modification and differentially expressed genes enriched in common pathways involved in neural synapse organization and signal transmission. We further collected fetal mice brains for metabolome analysis and found that in GDM fetal brains, the metabolites displayed significant changes, in addition to directly inducing cognitive dysfunction, some of which are important to methylation status such as betaine, fumaric acid, L-methionine, succinic acid, 5-methyltetrahydrofolic acid, and S-adenosylmethionine (SAM). These results suggest that GDM affects metabolites in fetal mice brains and further affects hippocampal DNA methylation and gene regulation involved in cognition, which is a potential mechanism for the adverse neurocognitive effects of GDM in offspring.

Orienting to fear under transient focal disruption of the human amygdala.

Responding to threat is under strong survival pressure, promoting the evolution of systems highly optimized for the task. Though the amygdala is implicated in 'detecting' threat, its role in the action that immediately follows-'orienting'-remains unclear. Critical to mounting a targeted response, such early action requires speed, accuracy, and resilience optimally achieved through conserved, parsimonious, dedicated systems, insured against neural loss by a parallelized functional organization. These characteristics tend to conceal the underlying substrate not only from correlative methods but also from focal disruption over time scales long enough for compensatory adaptation to take place. In a study of six patients with intracranial electrodes temporarily implanted for the clinical evaluation of focal epilepsy, we investigated gaze orienting to fear during focal, transient, unilateral direct electrical disruption of the amygdala. We showed that the amygdala is necessary for rapid gaze shifts towards faces presented in the contralateral hemifield regardless of their emotional expression, establishing its functional lateralization. Behaviourally dissociating the location of presented fear from the direction of the response, we implicated the amygdala not only in detecting contralateral faces, but also in automatically orienting specifically towards fearful ones. This salience-specific role was demonstrated within a drift-diffusion model of action to manifest as an orientation bias towards the location of potential threat. Pixel-wise analysis of target facial morphology revealed scleral exposure as its primary driver, and induced gamma oscillations-obtained from intracranial local field potentials-as its time-locked electrophysiological correlate. The amygdala is here reconceptualized as a functionally lateralized instrument of early action, reconciling previous conflicting accounts confined to detection, and revealing a neural organisation analogous to the superior colliculus, with which it is phylogenetically kin. Greater clarity on its role has the potential to guide therapeutic resection, still frequently complicated by impairments of cognition and behaviour related to threat, and inform novel focal stimulation techniques for the management of neuropsychiatric conditions.

Auditory brainstem response in unilateral tinnitus patients: does symmetrical hearing thresholds and within-subject comparison affect responses?

In recent studies, cochlear synaptopathy has been suggested as a potential pathophysiology mechanism for tinnitus, which occurs in individuals with normal hearing thresholds. Auditory Brainstem Response (ABR) is a noninvasive method frequently used in the literature to evaluate cochlear synaptopathy in tinnitus patients. However, possible factors such as high-frequency pure-tone hearing thresholds, age, gender, and head characteristics that may affect ABR were not considered sufficiently in previous studies. Therefore, the present study aims to evaluate tinnitus ears and non-tinnitus ears with ABR in unilateral chronic tinnitus patients with symmetrical hearing. Twenty unilateral chronic tinnitus patients having normal pure-tone average with symmetrical hearing thresholds was included in the study. Subjects were evaluated with 0.25-16kHz pure-tone audiometry, Tinnitus Handicap Inventory (THI) and ABR were administered. All ears were evaluated monaurally using click stimuli at 80dB nHL, alternating polarity (21.1 rate/s, 2000 sweeps). Wave I amplitude of the ABR and the ratio of III/I, V/I, and V/III wave amplitudes from tinnitus ears was higher than non-tinnitus ears. At the same time, there was a positive correlation between THI and V-I and V-III interpeak latency range, and a negative correlation between V/III wave amplitude ratio. ABR can be used as an evaluation method to provide evidence that the neural organizations of individuals with chronic tinnitus differ in certain regions in their auditory pathways. The correlation between THI and ABR findings suggests that there may be a connection between tinnitus distress and the neural organization of the auditory system.

Nematic Templated Complex Nanofiber Structures by Projection Display.

Oriented arrays of nanofibers are ubiquitous in nature and have been widely used in recreation of the biological functions such as bone and muscle tissue regenerations. However, it remains a challenge to produce nanofiber arrays with a complex organization by using current fabrication techniques such as electrospinning and extrusion. In this work, we propose a method to fabricate the complex organization of nanofiber structures templated by a spatially varying ordered liquid crystal host, which follows the pattern produced by a maskless projection display system. By programming the synchronization of the rotated polarizer and projected segments with different shapes, various configurations of nanofiber organization ranging from a single to two-dimensional lattice of arbitrary topological defects are created in a deterministic manner. The nanofiber arrays can effectively guide and promote neurite outgrowth. The application of nanofibers with arced profiles and topological defects on neural tissue organization is also demonstrated. This finding, combined with the versatility and programmability of nanofiber structures, suggests that they will help solve challenges in nerve repair, neural regeneration, and other related tissue engineering fields.

Neural organization of the third optic neuropil, the lobula, in the highly visual semiterrestrial crab Neohelice granulata.

The visual neuropils (lamina, medulla, and lobula complex) of malacostracan crustaceans and hexapods have many organizational principles, cell types, and functional properties in common. Information about the cellular elements that compose the crustacean lobula is scarce especially when focusing on small columnar cells. Semiterrestrial crabs possess a highly developed visual system and display conspicuous visually guided behaviors. In particular, Neohelice granulata has been previously used to describe the cellular components of the first two optic neuropils using Golgi impregnation technique. Here, we present a comprehensive description of individual elements composing the third optic neuropil, the lobula, of that same species. We characterized a wide variety of elements (140 types) including input terminals and lobula columnar, centrifugal, and input columnar elements. Results reveal a very dense and complex neuropil. We found a frequently impregnated input element (suggesting a supernumerary cartridge representation) that arborizes in the third layer of the lobula and that presents four variants each with ramifications organized following one of the four cardinal axes suggesting a role in directional processing. We also describe input elements with two neurites branching in the third layer, probably connecting with the medulla and lobula plate. These facts suggest that this layer is involved in the directional motion detection pathway in crabs. We analyze and discuss our findings considering the similarities and differences found between the layered organization and components of this crustacean lobula and the lobula of insects.

Analyzing Modeled Torque Profiles to Understand Scale-Dependent Active Muscle Responses in the Hip Joint.

Animal locomotion is influenced by a combination of constituent joint torques (e.g., due to limb inertia and passive viscoelasticity), which determine the necessary muscular response to move the limb. Across animal size-scales, the relative contributions of these constituent joint torques affect the muscular response in different ways. We used a multi-muscle biomechanical model to analyze how passive torque components change due to an animal’s size-scale during locomotion. By changing the size-scale of the model, we characterized emergent muscular responses at the hip as a result of the changing constituent torque profile. Specifically, we found that activation phases between extensor and flexor torques to be opposite between small and large sizes for the same kinematic motion. These results suggest general principles of how animal size affects neural control strategies. Our modeled torque profiles show a strong agreement with documented hindlimb torque during locomotion and can provide insights into the neural organization and muscle activation behavior of animals whose motion has not been extensively documented.

Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila.

Behavioral flexibility is critical to survival. Animals must adapt their behavioral responses based on changes in the environmental context, internal state, or experience. Studies in Drosophila melanogaster have provided insight into the neural circuit mechanisms underlying behavioral flexibility. Here we discuss how Drosophila behavior is modulated by internal and behavioral state, environmental context, and learning. We describe general principles of neural circuit organization and modulation that underlie behavioral flexibility, principles that are likely to extend to other species.

Tourist Behaviour Analysis Based on Digital Pattern of Life

The tourist industry has been quickly becoming in the course of the last years and IT innovations have had an extraordinary effect on tourists. Vacationer conduct examination has been the subject of various research considers as of late. This paper presents the advanced example of life idea which rearranges the vacationer conduct models' development and use. The computerized example of life characterizes the general ideas of vacationer conduct, interfaces the tourist and the advanced world and permits us to follow conduct changes over the long haul. A writing survey of the present status of the examination in chose fields is performed for recognizing the current issues. The contextual investigations of conduct examination dependent on arrangement, grouping and time series occasions conduct models are shown. An ontological methodology also, fake neural organizations are utilized during conduct model development, preparing and assessment. The accumulated outcomes can be utilized by brilliant the travel industry administration engineers and business partners.

Image Verification and Emotion Detection using Effective Modelling Techniques

The feelings expressed on the face reflect the manner of thinking and provide useful insights of happenings inside the brain. Face Detection enables us to identify a face. Recognizing the facial expressions for different emotions is to familiarize the machine with human like capacity to perceive and identify human feelings, which involves classifying the given input images of the face into one of the seven classes which is achieved by building a multi class classifier. The proposed methodology is based on convolutional neural organizations and works on 48x48 pixel-based grayscale images. The proposed model is tested on various images and gives the best accuracy when compared with existing functionalities. It detects faces in images, recognizes them and identifies emotions and shows improved performance because of data augmentation. The model is experimented with varying depths and pooling layers. The best results are obtained sequential model of six layers of Convolutional Neural Network and softmax activation function applied to last layer. The approach works for real time data taken from videos or photos.

Neuromorphic Memristive Chips: Design and Technology

Memristive neuromorphic chips exploit a prospective class of novel functional materials (memristors) to deploy a new architecture of spiking neural networks for developing basic blocks of brain-like systems. Memristor-based neuromorphic hardware solutions for multi-agent systems are considered as challenges in frontier areas of chip design for fast and energy-efficient computing. As functional materials, metal oxide thin films with resistive switching and memory effects (memristive structures) are recognized as a potential elemental base for new components of neuromorphic engineering, enabling a combination of both data storage and processing in a single unit. A key design issue in this case is a hardware defined functionality of neural networks. The gradient change of resistive properties of memristive elements and its non-volatile memory behavior ensure the possibility of spiking neural network organization with unsupervised learning through hardware implementation of basic synaptic mechanisms, such as Hebb's learning rules including spike — timing dependent plasticity, long-term potentiation and depression. This paper provides an overview of scientific researches carrying out at Saint Petersburg Electrotechnical University "LETI" since 2014 in the field of novel electronic components for neuromorphic hardware solutions of brain-like chip design. Among the most promising concepts developed by ETU "LETI" are: the design of metal-insulator-metal structures exhibiting multilevel resistive switching (gradient tuning of resistive properties and bipolar resistive switching are combined together in a sin¬gle memristive element) for further use as artificial synaptic devices in neuromorphic chips; computing schemes for spatio-temporal pattern recognition based on spiking neural network architecture implementation; breadboard models of analogue circuits of hardware implementation of neuromorphic blocks for brain-like system developing.

Detecting Credit Card Fraud using Machine Learning

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.