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Neural activation patterns and connectivity in visual attention during number and non-number processing: An ERP study using Ishihara pseudoisochromatic plates

Al-Marri F, Reza F, Begum T, Hitam WHW, Jin GK, Xiang J

J Integr Neurosci, 2018 Aug 15;17(3):257-269.
PMID: 30338955 DOI: 10.31083/JIN-170058

Visual cognitive function is important in the construction of executive function in daily life. Perception of visual number form (e.g. Arabic digits) and numerosity (numeric magnitude) is of interest to cognitive neuroscientists. Neural correlates and the functional measurement of number representations are complex events when their semantic categories are assimilated together with concepts of shape and color. Color perception can be processed further to modulate visual cognition. The Ishihara pseudoisochromatic plates are one of the best and most common screening tools for basic red-green color vision testing. However, there has been little study of visual cognitive function assessment using such pseudoisochromatic plates. 25 healthy normal trichromat volunteers were recruited and studied using a 128-sensor net to record event-related electroencephalogram. Subjects were asked to respond by pressing numbered buttons when they saw the number and non-number plates of the Ishihara color vision test. Amplitudes and latencies of N100 and P300 event related potential components were analyzed from 19 electrode sites in the international 10-20 system. A brain topographic map, cortical activation patterns, and Granger causation (effective connectivity) were analyzed from 128 electrode sites. No significant differences between N100 event related potential components for either stimulus indicates early selective attention processing was similar for number and non-number plate stimuli, but non-number plate stimuli evoked significantly higher amplitudes, longer latencies of the P300 event related potential component with a slower reaction time compared to number plate stimuli imply the allocation of attentional load was more in non-number plate processing. A different pattern of the asymmetric scalp voltage map was noticed for P300 components with a higher intensity in the left hemisphere for number plate tasks and higher intensity in the right hemisphere for non-number plate tasks. Asymmetric cortical activation and connectivity patterns revealed that number recognition occurred in the occipital and left frontal areas where as the consequence was limited to the occipital area during the non-number plate processing. Finally, results demonstrated that the visual recognition of numbers dissociates from the recognition of non-numbers at the level of defined neural networks. Number recognition was not only a process of visual perception and attention, but was also related to a higher level of cognitive function, that of language.
Neural activation patterns and connectivity in visual attention during Number and Non-number processing: An ERP study using the Ishihara pseudoisochromatic plates

Al-Marri F, Reza F, Begum T, Hitam WHW, Jin GK, Xiang J

J Integr Neurosci, 2017 Oct 25.
PMID: 29081422 DOI: 10.3233/JIN-170058

Visual cognitive function is important to build up executive function in daily life. Perception of visual Number form (e.g., Arabic digit) and numerosity (magnitude of the Number) is of interest to cognitive neuroscientists. Neural correlates and the functional measurement of Number representations are complex occurrences when their semantic categories are assimilated with other concepts of shape and colour. Colour perception can be processed further to modulate visual cognition. The Ishihara pseudoisochromatic plates are one of the best and most common screening tools for basic red-green colour vision testing. However, there is a lack of study of visual cognitive function assessment using these pseudoisochromatic plates. We recruited 25 healthy normal trichromat volunteers and extended these studies using a 128-sensor net to record event-related EEG. Subjects were asked to respond by pressing Numbered buttons when they saw the Number and Non-number plates of the Ishihara colour vision test. Amplitudes and latencies of N100 and P300 event related potential (ERP) components were analysed from 19 electrode sites in the international 10-20 system. A brain topographic map, cortical activation patterns and Granger causation (effective connectivity) were analysed from 128 electrode sites. No major significant differences between N100 ERP components in either stimulus indicate early selective attention processing was similar for Number and Non-number plate stimuli, but Non-number plate stimuli evoked significantly higher amplitudes, longer latencies of the P300 ERP component with a slower reaction time compared to Number plate stimuli imply the allocation of attentional load was more in Non-number plate processing. A different pattern of asymmetric scalp voltage map was noticed for P300 components with a higher intensity in the left hemisphere for Number plate tasks and higher intensity in the right hemisphere for Non-number plate tasks. Asymmetric cortical activation and connectivity patterns revealed that Number recognition occurred in the occipital and left frontal areas where as the consequence was limited to the occipital area during the Non-number plate processing. Finally, the results displayed that the visual recognition of Numbers dissociates from the recognition of Non-numbers at the level of defined neural networks. Number recognition was not only a process of visual perception and attention, but it was also related to a higher level of cognitive function, that of language.
Fulltext Theorizing how the brain encodes consciousness based on negentropic entanglement

Poznanski RR, Cacha LA, Latif AZA, Salleh SH, Ali J, Yupapin P, et al.

J Integr Neurosci, 2019 03 30;18(1):1-10.
PMID: 31091842 DOI: 10.31083/j.jin.2019.01.105

The physicality of subjectivity is explained through a theoretical conceptualization of guidance waves informing meaning in negentropically entangled non-electrolytic brain regions. Subjectivity manifests its influence at the microscopic scale of matter originating from de Broglie 'hidden' thermodynamics as action of guidance waves. The preconscious experienceability of subjectivity is associated with a nested hierarchy of microprocesses, which are actualized as a continuum of patterns of discrete atomic microfeels (or "qualia"). The mechanism is suggested to be through negentropic entanglement of hierarchical thermodynamic transfer of information as thermo-qubits originating from nonpolarized regions of actin-binding proteinaceous structures of nonsynaptic spines. The resultant continuous stream of intrinsic information entails a negentropic action (or experiential flow of thermo-quantum internal energy that results in a negentropic force) which is encoded through the non-zero real component of the mean approximation of the negentropic force as a 'consciousness code'. Consciousness consisting of two major subprocesses: (1) preconscious experienceability and (2) conscious experience. Both are encapsulated by nonreductive physicalism and panexperiential materialism. The subprocess (1) governing "subjectivity" carries many microprocesses leading to the actualization of discrete atomic microfeels by the 'consciousness code'. These atomic microfeels constitute internal energy that results in the transfer intrinsic information in terms of thermo-qubits. These thermo-qubits are realized as thermal entropy and sensed by subprocess (2) governing "self-awareness" in conscious experience.
Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study

Yuvaraj R, Murugappan M, Ibrahim NM, Omar MI, Sundaraj K, Mohamad K, et al.

J Integr Neurosci, 2014 Mar;13(1):89-120.
PMID: 24738541 DOI: 10.1142/S021963521450006X

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.
Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, et al.

J Integr Neurosci, 2017;16(3):275-289.
PMID: 28891512 DOI: 10.3233/JIN-170016

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).
Genomic instantiation of consciousness in neurons through a biophoton field theory

Cacha LA, Poznanski RR

J Integr Neurosci, 2014 Jun;13(2):253-92.
PMID: 25012712 DOI: 10.1142/S0219635214400081

A theoretical framework is developed based on the premise that brains evolved into sufficiently complex adaptive systems capable of instantiating genomic consciousness through self-awareness and complex interactions that recognize qualitatively the controlling factors of biological processes. Furthermore, our hypothesis assumes that the collective interactions in neurons yield macroergic effects, which can produce sufficiently strong electric energy fields for electronic excitations to take place on the surface of endogenous structures via alpha-helical integral proteins as electro-solitons. Specifically the process of radiative relaxation of the electro-solitons allows for the transfer of energy via interactions with deoxyribonucleic acid (DNA) molecules to induce conformational changes in DNA molecules producing an ultra weak non-thermal spontaneous emission of coherent biophotons through a quantum effect. The instantiation of coherent biophotons confined in spaces of DNA molecules guides the biophoton field to be instantaneously conducted along the axonal and neuronal arbors and in-between neurons and throughout the cerebral cortex (cortico-thalamic system) and subcortical areas (e.g., midbrain and hindbrain). Thus providing an informational character of the electric coherence of the brain - referred to as quantum coherence. The biophoton field is realized as a conscious field upon the re-absorption of biophotons by exciplex states of DNA molecules. Such quantum phenomenon brings about self-awareness and enables objectivity to have access to subjectivity in the unconscious. As such, subjective experiences can be recalled to consciousness as subjective conscious experiences or qualia through co-operative interactions between exciplex states of DNA molecules and biophotons leading to metabolic activity and energy transfer across proteins as a result of protein-ligand binding during protein-protein communication. The biophoton field as a conscious field is attributable to the resultant effect of specifying qualia from the metabolic energy field that is transported in macromolecular proteins throughout specific networks of neurons that are constantly transforming into more stable associable representations as molecular solitons. The metastability of subjective experiences based on resonant dynamics occurs when bottom-up patterns of neocortical excitatory activity are matched with top-down expectations as adaptive dynamic pressures. These dynamics of on-going activity patterns influenced by the environment and selected as the preferred subjective experience in terms of a functional field through functional interactions and biological laws are realized as subjectivity and actualized through functional integration as qualia. It is concluded that interactionism and not information processing is the key in understanding how consciousness bridges the explanatory gap between subjective experiences and their neural correlates in the transcendental brain.
Associable representations as field of influence for dynamic cognitive processes

Cacha LA, Poznanski RR

J Integr Neurosci, 2011 Dec;10(4):423-37.
PMID: 22262534

In earlier models, synaptic plasticity forms the basis for cellular signaling underlying learning and memory. However, synaptic computation of learning and memory in the brain remains controversial. In this paper, we discuss ways in which synaptic plasticity remodels subcellular networks by deflecting trajectories in neuronal state-space as regulating patterns for the synthesis of dynamic continuity that form cognitive networks of associable representations through endogenous dendritic coding to consolidate memory.
Cellular inhibitory behavior underlying the formation of retinal direction selectivity in the starburst network

Poznanski RR

J Integr Neurosci, 2010 Sep;9(3):299-335.
PMID: 21064220

Optical imaging of dendritic calcium signals provided evidence of starburst amacrine cells exhibiting calcium bias to somatofugal motion. In contrast, it has been impractical to use a dual-patch clamp technique to record membrane potentials from both proximal dendrites and distal varicosities of starburst amacrine cells in order to unequivocally prove that they are directionally sensitive to voltage, as was first suggested almost two decades ago. This paper aims to extend the passive cable model to an active cable model of a starburst amacrine cell that is intrinsically dependent on the electrical properties of starburst amacrine cells, whose various macroscopic currents are described quantitatively. The coupling between voltage and calcium just below the membrane results in a voltage-calcium system of coupled nonlinear Volterra integral equations whose solutions must be integrated into a prescribed model for example, for a synaptic couplet of starburst amacrine cells. Networks of starburst amacrine cells play a fundamental role in the retinal circuitry underlying directional selectivity. It is suggested that the dendritic plexus of starburst amacrine cells provides the substrate for the property of directional selectivity, while directional selectivity is a property of the exclusive layerings and confinement of their interconnections within the sublaminae of the inner plexiform layer involving cone bipolar cells and directionally selective ganglion cells.
Analytical solution of reaction-diffusion equations for calcium wave propagation in a starburst amacrine cell

Poznanski RR

J Integr Neurosci, 2010 Sep;9(3):283-97.
PMID: 21064219

A reaction-diffusion model is presented to encapsulate calcium-induced calcium release (CICR) as a potential mechanism for somatofugal bias of dendritic calcium movement in starburst amacrine cells. Calcium dynamics involves a simple calcium extrusion (pump) and a buffering mechanism of calcium binding proteins homogeneously distributed over the plasma membrane of the endoplasmic reticulum within starburst amacrine cells. The system of reaction-diffusion equations in the excess buffer (or low calcium concentration) approximation are reformulated as a nonlinear Volterra integral equation which is solved analytically via a regular perturbation series expansion in response to calcium feedback from a continuously and uniformly distributed calcium sources. Calculation of luminal calcium diffusion in the absence of buffering enables a wave to travel at distances of 120 μm from the soma to distal tips of a starburst amacrine cell dendrite in 100 msec, yet in the presence of discretely distributed calcium-binding proteins it is unknown whether the propagating calcium wave-front in the somatofugal direction is further impeded by endogenous buffers. If so, this would indicate CICR to be an unlikely mechanism of retinal direction selectivity in starburst amacrine cells.
Model-based neuroimaging for cognitive computing

Poznanski RR

J Integr Neurosci, 2009 Sep;8(3):345-69.
PMID: 19938210

The continuity of the mind is suggested to mean the continuous spatiotemporal dynamics arising from the electrochemical signature of the neocortex: (i) globally through volume transmission in the gray matter as fields of neural activity, and (ii) locally through extrasynaptic signaling between fine distal dendrites of cortical neurons. If the continuity of dynamical systems across spatiotemporal scales defines a stream of consciousness then intentional metarepresentations as templates of dynamic continuity allow qualia to be semantically mapped during neuroimaging of specific cognitive tasks. When interfaced with a computer, such model-based neuroimaging requiring new mathematics of the brain will begin to decipher higher cognitive operations not possible with existing brain-machine interfaces.
A hybrid method for classifying cognitive states from fMRI data

Parida S, Dehuri S, Cho SB, Cacha LA, Poznanski RR

J Integr Neurosci, 2015 Sep;14(3):355-68.
PMID: 26455882 DOI: 10.1142/S0219635215500223

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.
A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects

Cacha LA, Parida S, Dehuri S, Cho SB, Poznanski RR

J Integr Neurosci, 2016 Dec;15(4):593-606.
PMID: 28093025 DOI: 10.1142/S0219635216500345

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.
Nonsynaptic plasticity model of long-term memory engrams

Cacha LA, Ali J, Rizvi ZH, Yupapin PP, Poznanski RR

J Integr Neurosci, 2017;16(4):493-509.
PMID: 28891529 DOI: 10.3233/JIN-170038

Using steady-state electrical properties of non-ohmic dendrite based on cable theory, we derive electrotonic potentials that do not change over time and are localized in space. We hypothesize that clusters of such stationary, local and permanent pulses are the electrical signatures of enduring memories which are imprinted through nonsynaptic plasticity, encoded through epigenetic mechanisms, and decoded through electrotonic processing. We further hypothesize how retrieval of an engram is made possible by integration of these permanently imprinted standing pulses in a neural circuit through neurotransmission in the extracellular space as part of conscious recall that acts as a guiding template in the reconsolidation of long-term memories through novelty characterized by uncertainty that arises when new fragments of memories reinstate an engram by way of nonsynaptic plasticity that permits its destabilization. Collectively, these findings seem to reinforce this hypothesis that electrotonic processing in non-ohmic dendrites yield insights into permanent electrical signatures that could reflect upon enduring memories as fragments of long-term memory engrams.
Does heterogeneity of intracellular Ca[Formula: see text] dynamics underlie speed tuning of direction-selective responses in starburst amacrine cells?

Koizumi A, Poznanski RR

J Integr Neurosci, 2016 Jan 14.
PMID: 26762484

The starburst amacrine cell (SAC) plays a fundamental role in retinal motion perception. In the vertebrate retina, SAC dendrites have been shown to be directionally selective in terms of their Ca[Formula: see text] responses for stimuli that move centrifugally from the soma. The mechanism by which SACs show Ca[Formula: see text] bias for centrifugal motion is yet to be determined with precision. Recent morphological studies support a presynaptic delay in glutamate receptor activation induced Ca[Formula: see text] release from bipolar cells preferentially contacting SACs. However, bipolar cells are known to be electrotonically coupled so time delays between the bipolar cells that provide input to SACs seem unlikely. Using fluorescent microscopy and imunnostaining, we found that the endoplasmic reticulum (ER) is omnipresent in the soma extending to the distal processes of SACs. Consequently, a working hypothesis on heterogeneity of intracellular Ca[Formula: see text] dynamics from ER is proposed as a possible explanation for the cause of speed tuning of direction-selective Ca[Formula: see text] responses in dendrites of SACs.
Fulltext Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

Sahu R, Dash SR, Cacha LA, Poznanski RR, Parida S

J Integr Neurosci, 2020 Mar 30;19(1):1-9.
PMID: 32259881 DOI: 10.31083/j.jin.2020.01.24

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold cross-validation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.
A118G mu opioid receptor polymorphism among drug addicts in Malaysia

Nagaya D, Ramanathan S, Ravichandran M, Navaratnam V

J Integr Neurosci, 2012 Mar;11(1):117-22.
PMID: 22744787

Drug addiction is an important social problem in many countries. Genetic and environmental factors contribute to the predisposition of drug addiction. Genetic variations at the μ opioid receptor (OPRM1) gene locus have been associated with opiate addiction. The present study aims to delineate the frequency of A118G allele of OPRM1 among Malaysian subjects. The frequency of A allele and G allele were 51% and 49%, respectively for addicts and about 73% and 27% respectively for healthy volunteers. The frequency of G allele was 1.77-fold higher in addicts by odds ratio calculation at 95% Cl, which indicate the G allele to be strongly associated with addiction X(2) = 15.31,P < 0.0001; odds ratio 2.51; 95% Cl (1.575-3.994), compared to healthy volunteers. A significant association was observed between A118G polymorphism in μ opioid receptor gene and drug addiction.
Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders

Zafar R, Qayyum A, Mumtaz W

J Integr Neurosci, 2019 Sep 30;18(3):217-229.
PMID: 31601069 DOI: 10.31083/j.jin.2019.03.164

In the electroencephalogram recorded data are often confounded with artifacts, especially in the case of eye blinks. Different methods for artifact detection and removal are discussed in the literature, including automatic detection and removal. Here, an automatic method of eye blink detection and correction is proposed where sparse coding is used for an electroencephalogram dataset. In this method, a hybrid dictionary based on a ridgelet transformation is used to capture prominent features by analyzing independent components extracted from a different number of electroencephalogram channels. In this study, the proposed method has been tested and validated with five different datasets for artifact detection and correction. Results show that the proposed technique is promising as it successfully extracted the exact locations of eye blinking artifacts. The accuracy of the method (automatic detection) is 89.6% which represents a better estimate than that obtained by an extreme machine learning classifier.
High-Mobility Group Box 1 (HMGB1) Protein in Parkinson's Disease Research: A 10-Year Bibliometric Analysis

Razali K, Mohamed WMY

J Integr Neurosci, 2023 Jul 04;22(4):87.
PMID: 37519176 DOI: 10.31083/j.jin2204087

BACKGROUND: Parkinson's disease (PD), the most prevalent motoric neurodegenerative disease, has been intensively studied to better comprehend its complicated pathogenesis. Chronic neuroinflammation is a major factor contributing to the development of PD. Reportedly, high-mobility group box 1 (HMGB1) protein is capable of mediating neuroinflammatory response. In this regard, knowledge mapping of the research linking HMGB1 to PD is necessary.
OBJECTIVE: Herein, we perform a dynamic and longitudinal bibliometric analysis to explore the hotspots and current trends of HMGB1-related PD publications during the past decade.
METHODS: All PD publications focusing on HMGB1 protein were retrieved from the PubMed database using the search terms "Parkinson's disease" and "hmgb1". Using filters, only English articles published between 2011 and 2022 were selected. The Bibliometrix and Biblioshiny packages from R software were used to conduct the bibliometric analysis.
RESULTS: The filtered search identified 47 articles (34 original articles and 13 review articles), published between 2011 and 2022. There was an increase trend in the number of articles published, with an annual growth rate of 19.35 percent. In terms of research and scientific collaboration in this field, the United States is in the lead, followed by China, Malaysia, and Australia. Compared to other countries, the United States and China had the highest level of collaboration in this research area. Neuroinflammation, microglia, and receptor for advanced glycation end-products (RAGE) represent the top three frontiers and hotspots for HMGB1-related PD research. According to the thematic evolution analysis, over the last decade, PD, HMGB1 and microglia were addressed individually, however, since 2017, these topics were frequently discussed within the same cluster: neuroinflammation. Furthermore, PD, HMGB1, and neuroinflammation domains co-occurred in majority of the research discussion.
CONCLUSIONS: The link between HMGB1 and PD was realized a decade ago and becomes increasingly important over time. Our findings can aid scholars in comprehending the global context of HMGB1/PD relationship and provide significant insights for future PD research.
Fulltext Clinical interpretations of the effectiveness of changes in body position during aerobic fitness after neurologic injury

Izan NF, Salleh SH, Ting CM, Noman F, Sh-Hussain H, Poznanski RR, et al.

J Integr Neurosci, 2020 Sep 30;19(3):479-487.
PMID: 33070527 DOI: 10.31083/j.jin.2020.03.222

The purpose is to estimate the effectiveness of electrocardiograms during resting and active participation by the differentiation between the electrical activity of the heart while standing and sitting in a resting state. The concern is to identify the electrocardiogram parameters that did not show significant changes within these positions. The electrocardiogram parameters can be considered to be a standard marker for medically compromised patients. The electrocardiogram is recorded in the standing and sitting positions focusing on healthy participants using standard electrode placement of lead-I. Combined lead-I patterns (camel-hump or ST-segment prolongation) are usually seen in neurologic injury or hypothermia patients. The pairwise comparisons of a year data are about 454,400 cycles of sitting and 493,470 cycles of standing data. Thus, it is essential to quantify the nature and magnitude of changes seen in the electrocardiogram with a change of posture from sitting to standing in a healthy individual. This makes the findings of electrocardiogram analysis in this paper interesting in which some parameters (i.e., camel-hump patterns in lead-I) are helpful for clinical interpretations and could be suggestive of neurologic injury.
Fulltext Biological databases and tools for neurological disorders

Usman MB, Ojha S, Jha SK, Chellappan DK, Gupta G, Singh SK, et al.

J Integr Neurosci, 2022 Jan 28;21(1):41.
PMID: 35164477 DOI: 10.31083/j.jin2101041

Computational approach to study of neuronal impairment is rapidly evolving, as experiments and intuition alone could not explain the complexity of brain system. The increase in an overwhelming amount of new data from both theory and computational modeling necessitate the development of databases and tools for analysis, visualization, and interpretation of neuroscience data. To ensure the sustainability of this development, consistent update and training of young professionals are imperative. For this purpose, relevant articles, chapters, and modules are essential to keep abreast of developments. Therefore, this article seeks to outline the biological databases and analytical tools along with their applications. It's envisaged that knowledge along this line would be a "training recipe" for young talents and guide for professionals and researchers in neuroscience.

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