The neuron is high-energy utilizing tissue. The rate of neuronal cell respiration is higher than in other cells. Cellular respiration occurs with mitochondria. The healthy production and functions of mitochondria play a key role in the maintenance of healthy neurons. In pathological conditions such as neurodegenerative diseases, healthy mitochondria help to alleviate pathological events in neuronal cells. Conversely, mitochondrial dysfunction promotes the acceleration of the neurodegenerative process. Furthermore, glial-derived mitochondria contribute to multiple roles in the regulation of healthy neuron functions. It also supports releasing of the neurotransmitters; generation of the impulses, regulation of the membrane potential and molecular dynamics; controlling of the axonal transport; controlling of the mitochondrial fission and fusion functions in the peripheral as well as the central nervous system. Moreover, it plays a key role in the regeneration process of neuronal cells. Therefore, healthy mitochondria can provide a healthy environment for neuronal cell function and can treat neurodegenerative disorders. In this review, we explore the current view of healthy mitochondria and their role in healthy neuronal functions.
The neurovascular unit (NVU) is composed of vascular cells, glial cells, and neurons. As a fundamental functional module in the central nervous system, the NVU maintains homeostasis in the microenvironment and the integrity of the blood-brain barrier. Disruption of the NVU and interactions among its components are involved in the pathophysiology of synucleinopathies, which are characterized by the pathological accumulation of α-synuclein. Neuroinflammation contributes to the pathophysiology of synucleinopathies, including Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies. This review aims to summarize the neuroinflammatory response of glial cells and vascular cells in the NVU. We also review neuroinflammation in the context of the cross-talk between glial cells and vascular cells, between glial cells and pericytes, and between microglia and astroglia. Last, we discuss how α-synuclein affects neuroinflammation and how neuroinflammation influences the aggregation and spread of α-synuclein and analyze different properties of α-synuclein in synucleinopathies.
The fork cell and von Economo neuron, which are found in the insular cortex and/or the anterior cingulate cortex, are defined by their unique morphologies. Their shapes are not pyramidal; the fork cell has two primary apical dendrites and the von Economo neurons are spindle-shaped (bipolar). Presence of such neurons are reported only in the higher animals, especially in human and great ape, indicating that they are specific for most evolved species. Although it is likely that these neurons are involved in higher brain function, lack of results with experimental animals makes further investigation difficult. We here ask whether equivalent neurons exist in the mouse insular cortex. In human, Fezf2 has been reported to be highly expressed in these morphologically distinctive neurons and thus, we examined the detailed morphology of Fezf2-positive neurons in the mouse brain. Although von Economo-like neurons were not identified, Fezf2-positive fork cell-like neurons with two characteristic apical dendrites, were discovered. Examination with electron microscope indicated that these neurons did not embrace capillaries, rather they held another cell. We here term such neurons as holding neurons. We further observed several molecules, including neuromedin B (NMB) and gastrin releasing peptide (GRP) that are known to be localized in the fork cells and/or von Economo cells in human, were localized in the mouse insular cortex. Based on these observations, it is likely that an equivalent of the fork cell is present in the mouse.
GABAA receptors containing the α6 subunit are highly expressed in cerebellar granule cells and less abundantly in many other neuronal and peripheral tissues. Here, we for the first time summarize their importance for the functions of the cerebellum and the nervous system. The cerebellum is not only involved in motor control but also in cognitive, emotional, and social behaviors. α6βγ2 GABAA receptors located at cerebellar Golgi cell/granule cell synapses enhance the precision of inputs required for cerebellar timing of motor activity and are thus involved in cognitive processing and adequate responses to our environment. Extrasynaptic α6βδ GABAA receptors regulate the amount of information entering the cerebellum by their tonic inhibition of granule cells, and their optimal functioning enhances input filtering or contrast. The complex roles of the cerebellum in multiple brain functions can be compromised by genetic or neurodevelopmental causes that lead to a hypofunction of cerebellar α6-containing GABAA receptors. Animal models mimicking neuropsychiatric phenotypes suggest that compounds selectively activating or positively modulating cerebellar α6-containing GABAA receptors can alleviate essential tremor and motor disturbances in Angelman and Down syndrome as well as impaired prepulse inhibition in neuropsychiatric disorders and reduce migraine and trigeminal-related pain via α6-containing GABAA receptors in trigeminal ganglia. Genetic studies in humans suggest an association of the human GABAA receptor α6 subunit gene with stress-associated disorders. Animal studies support this conclusion. Neuroimaging and post-mortem studies in humans further support an involvement of α6-containing GABAA receptors in various neuropsychiatric disorders, pointing to a broad therapeutic potential of drugs modulating α6-containing GABAA receptors. SIGNIFICANCE STATEMENT: α6-Containing GABAA receptors are abundantly expressed in cerebellar granule cells, but their pathophysiological roles are widely unknown, and they are thus out of the mainstream of GABAA receptor research. Anatomical and electrophysiological evidence indicates that these receptors have a crucial function in neuronal circuits of the cerebellum and the nervous system, and experimental, genetic, post-mortem, and pharmacological studies indicate that selective modulation of these receptors offers therapeutic prospects for a variety of neuropsychiatric disorders and for stress and its consequences.
Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.
Matched MeSH terms: Motor Neurons/physiology*; Mirror Neurons/physiology*
Classification is one of the most hourly encountered problems in real world. Neural networks have
emerged as one of the tools that can handle the classification problem. Feed-Forward Neural Networks
(FFNN's) have been widely applied in many different fields as a classification tool. Designing an efficient
FFNN structure with the optimum number of hidden layers and minimum number of layer's neurons for
a given specific application or dataset, is an open research problem and more challenging depend on
the input data. The random selections of hidden layers and neurons may cause the problem of either
under fitting or over fitting. Over fitting arises because the network matches the data so closely as to
lose its generalization ability over the test data. In this research, the classification performance using
the Mean Square Error (MSE) of Feed-Forward Neural Network (FFNN) with back-propagation algorithm
with respect to the different number of hidden layers and hidden neurons is computed and analyzed to
find out the optimum number of hidden layers and minimum number of layer's neurons to help the
existing classification concepts by MATLAB version 13a. By this process, firstly the random data has
been generated using an suitable matlab function to prepare the training data as the input and target
vectors as the testing data for the classification purposes of FFNN. The generated input data is passed
on to the output layer through the hidden layers which process these data. From this analysis, it is find
out from the mean square error comparison graphs and regression plots that for getting the best
performance form this network, it is better to use the high number of hidden layers and more neurons in
the hidden layers in the network during designing its classifier but so more neurons in the hidden layers
and the high number of hidden layers in the network makes it complex and takes more time to execute.
So as the result it is suggested that three hidden layers and 26 hidden neurons in each hidden layers
are better for designing the classifier of this network for this type of input data features.
Alzheimer's disease is the most common neurological disease worldwide. Unfortunately, there are currently no effective treatment methods nor early detection methods. Furthermore, the disease underlying molecular mechanisms are poorly understood. Global bulk gene expression profiling suggested that the disease is governed by diverse transcriptional regulatory networks. Thus, to identify distinct transcriptional networks impacted into distinct neuronal populations in Alzheimer, we surveyed gene expression differences in over 25,000 single-nuclei collected from the brains of two Alzheimer's in disease patients in Braak stage I and II and age- and gender-matched controls hippocampal brain samples. APOE status was not measured for this study samples (as well as CERAD and THAL scores). Our bioinformatic analysis identified discrete glial, immune, neuronal and vascular cell populations spanning Alzheimer's disease and controls. Astrocytes and microglia displayed the greatest transcriptomic impacts, with the induction of both shared and distinct gene programs.
Gonadotropin-inhibitory hormone (GnIH) is an inhibitor of the hypothalamic-pituitary-gonadal (HPG) axis. GnIH is also called RFamide-related peptide (RFRP) as GnIH peptides have a characteristic C-terminal LPXRFiamide (X = L or Q) sequence. GnIH is thought to be the mediator of stress by negatively regulating the HPG axis as various stressors increase GnIH mRNA, GnIH peptide or GnIH neuronal activity. On the other hand, GnIH may also mediate behavioral stress responses as GnIH neuronal fibers and GnIH receptors are widely located in the limbic system of telencephalon, diencephalon and midbrain area. Previous studies have shown that intracerebroventricular (i.c.v.) administration of GnIH (RFRP) blocks morphine-induced analgesia in hot plate and formalin injection tests in rats suggesting that GnIH increases sensitivity to pain. GnIH (RFRP) also increases anxiety-like behavior in rats. RNA interference of GnIH gene (GnIH RNAi) increases locomotor activity of white-crowned sparrow and Japanese quail and i.c.v. administration of GnIH decreases GnIH RNAi induced locomotor activity. It was further shown that i.c.v. administration of GnIH (RFRP) decreases aggressive behavior in male quail and sexual behavior in male rats, female white-crowned sparrow and female hamsters. These results suggest that GnIH decreases threat to homeostasis of the organism by increasing pain sensitivity, anxiety and decreasing locomotor activity, aggressive behavior and sexual behavior. GnIH may also mediate the effect of stress on behavior.
A novel design of nerve communications and networks using the coupling effects between bio-cells and optical dipoles is proposed. The electrical signals are coupled to the dipoles and cells which propagate within the optical networks for long distance without any electromagnetic interference. Results have shown that the use of optical spins in the spin networks, referred as Spinnet, can be formed. This technique can be used to improve the nerve communication performance. It is fabricated as a nano-biotic circuit system, and has great potential for future disability applications and diagnosis of the links of nerves across the dead cells.
Orexins are highly involved in regulating the circadian rhythm, the brain's reward mechanism, and the neuroendocrine response to stress. The disruption of orexin regulation is known to be associated with depression. Preclinical studies in rodents have identified the dorsomedial/perifornical and lateral areas of the hypothalamus as the population of orexinergic neurons that are primarily responsible for mediating depression-induced neuroanatomical changes in the brain. There is still no consensus regarding whether hyperactivity or hypoactivity of orexin signaling is responsible for producing depressive-like behaviour. Likewise, clinical studies indicated a general disruption in orexin signaling in depressive patients, but did not report definitive evidence of either hyperactivity or hypoactivity. Nevertheless, given the various reciprocal connections between orexin neurons and multiple brain regions, it is plausible that this involves a differential signaling network with orexin neurons as the coordination center. Here, an overview of preclinical and clinical evidence is provided as a basis for understanding the consequences of altered orexin signaling on neural circuitries modulating different aspects of the physiopathology of depression.
The hypothalamic neurohormone kisspeptin-10 (KP-10) was inherently implicated in cholinergic pathologies when aberrant fluctuations of expression patterns and receptor densities were discerned in neurodegenerative micromilieus. That said, despite variable degrees of functional redundancy, KP-10, which is biologically governed by its cognate G-protein-coupled receptor, GPR54, attenuated the progressive demise of α-synuclein (α-syn)-rich cholinergic-like neurons. Under explicitly modeled environments, in silico algorithms further rationalized the surface complementarities between KP-10 and α-syn when KP-10 was unambiguously accommodated in the C-terminal binding pockets of α-syn. Indeed, the neuroprotective relevance of KP-10's binding mechanisms can be insinuated in the amelioration of α-syn-mediated neurotoxicity; yet it is obscure whether these extenuative circumstances are contingent upon prior GPR54 activation. Herein, choline acetyltransferase (ChAT)-positive SH-SY5Y neurons were engineered ad hoc to transiently overexpress human wild-type or E46K mutant α-syn while the mitigation of α-syn-induced neuronal death was ascertained via flow cytometric and immunocytochemical quantification. Recapitulating the specificity observed on cell viability, exogenously administered KP-10 (0.1 µM) substantially suppressed wild-type and E46K mutant α-syn-mediated apoptosis and mitochondrial depolarization in cholinergic differentiated neurons. In particular, co-administrations with a GPR54 antagonist, kisspeptin-234 (KP-234), failed to abrogate the robust neuroprotection elicited by KP-10, thereby signifying a GPR54 dispensable mechanism of action. Consistent with these observations, KP-10 treatment further diminished α-syn and ChAT immunoreactivity in neurons overexpressing wild-type and E46K mutant α-syn. Overall, these findings lend additional credence to the previous notion that KP-10's binding zone may harness efficacious moieties of neuroprotective intent.
Down syndrome (DS) is the most frequently diagnosed chromosomal disorder of chromosome 21 (HSA21) aneuploidy, characterized by intellectual disability and reduced lifespan. The transcription repressor, Repressor Element-1 Silencing Transcription factor (REST), which acts as an epigenetic regulator, is a crucial regulator of neuronal and glial gene expression. In this study, we identified and investigated the role of REST-target genes in human brain tissues, cerebral organoids, and neural cells in Down syndrome. Gene expression datasets generated from healthy controls and DS samples of human brain tissues, cerebral organoids, NPC, neurons, and astrocytes were retrieved from the Gene Ontology (GEO) and Sequence Read Archive (SRA) databases. Differential expression analysis was performed on all datasets to produce differential expression genes (DEGs) between DS and control groups. REST-targeted DEGs were subjected to functional ontologies, pathways, and network analyses. We found that REST-targeted DEGs in DS were enriched for the JAK-STAT and HIF-1 signaling pathways across multiple distinct brain regions, ages, and neural cell types. We also identified REST-targeted DEGs involved in nervous system development, cell differentiation, fatty acid metabolism and inflammation in the DS brain. Based on the findings, we propose REST as the critical regulator and a promising therapeutic target to modulate homeostatic gene expression in the DS brain.
Excitotoxicity arises from unusually excessive activation of excitatory amino acid receptors such as glutamate receptors. Following an energy crisis, excitotoxicity is a major cause for neuronal death in neurological disorders. Many glutamate antagonists have been examined for their efficacy in mitigating excitotoxicity, but failed to generate beneficial outcome due to their side effects on healthy neurons where glutamate receptors are also blocked. In this study, we found that during chronic hypoxia there is upregulation and activation of a nonselective cation channel TRPM4 that contributes to the depolarized neuronal membrane potential and enhanced glutamate-induced calcium entry. TRPM4 is involved in modulating neuronal membrane excitability and calcium signaling, with a complex and multifaceted role in the brain. Here, we inhibited TRPM4 using a newly developed blocking antibody M4P, which could repolarize the resting membrane potential and ameliorate calcium influx upon glutamate stimulation. Importantly, M4P did not affect the functions of healthy neurons as the activity of TRPM4 channel is not upregulated under normoxia. Using a rat model of chronic hypoxia with both common carotid arteries occluded, we found that M4P treatment could reduce apoptosis in the neurons within the hippocampus, attenuate long-term potentiation impairment and improve the functions of learning and memory in this rat model. With specificity to hypoxic neurons, TRPM4 blocking antibody can be a novel way of controlling excitotoxicity with minimal side effects that are common among direct blockers of glutamate receptors.
The lack of curative therapies for neurodegenerative diseases has high economic impact and places huge burden on the society. The contribution of stem cells to cure neurodegenerative diseases has been unraveled and explored extensively over the past few years. Beyond substitution of the lost neurons, stem cells act as immunomodulators and neuroprotectors. A large number of preclinical and a small number of clinical studies have shown beneficial outcomes in this context. In this review, we have summarized the current concepts of stem cell therapy in neurodegenerative diseases and the recent advances in this field, particularly between 2010 and 2012. Further studies should be encouraged to resolve the clinical issues and vague translational findings for maximum optimization of the efficacy of stem cell therapy in neurodegenerative diseases.
Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.
Kajian garis pangkal pengimejan resonans magnet kefungsian (fMRI) telah dijalankan di Jabatan Radiologi, Hospital Universiti Kebangsaan Malaysia ke atas seorang subjek lelaki sihat berumur 25 tahun menggunakan sistem pengimejan resonans magnet (MRI) 1.5 T. Kajian ini menggunakan gerakan jari tangan kanan dan kiri untuk merangsang aktiviti neuron di dalam korteks serebrum. Subjek diarahkan supaya menekan jari-jari pada ibu jari secara bergilir-gilir semasa imbasan kefungsian dilakukan. Paradigma 5 kitar aktifrehat digunakan dengan setiap kitar masing-masing mengandungi 20 siri pengukuran. Keputusan menunjukkan bahawa rantau otak yang aktif akibat gerakan jari adalah girus presentral merangkumi kawasan motor primer. Pengaktifan otak adalah secara kontralateral terhadap gerakan jari tangan kanan dan kiri. Keamatan isyarat keadaan aktif didapati lebih tinggi daripada keamatan isyarat keadaan rehat. Analisis yang dilakukan ke atas beberapa rantau pengaktifan yang diminati (ROI) pada beberapa hirisan menunjukkan perbezaan yang bererti (p < 0.05) antara keamatan keadaan aktif dan rehat untuk nilai ambang statistik (Z) = 1.0 dan 1.5. Perbezaan purata antara kedua-dua purata keamatan isyarat keadaan aktif dan rehat pada manamana hirisan untuk kedua-dua nilai Z menunjukkan magnitud pengaktifan yang lebih tinggi pada hemisfera kanan otak iaitu apabila subjek menggerakkan tangan kirinya. Bilangan voksel yang aktif juga didapati lebih tinggi pada hemisfera kanan berbanding pada hemisfera kiri otak. Keputusan ini menyokong fakta bahawa bagi subjek yang tidak kidal, kawasan pengaktifan motor pada hemisfera kanan otak semasa gerakan jari tangan kiri mengalami rangsangan hemodinamik yang lebih tinggi berbanding dengan hemisfera kiri otak semasa gerakan jari tangan kanan. Fenomena rangsangan hemodinamik yang diperhatikan dalam kajian ini dibincangkan berdasarkan kepada kebergantungan kontras isyarat kepada aras oksigen darah (BOLD).
Kajian garis pangkal pengimejan resonans magnet kefungsian (fMRI) telah dijalankan ke atas 2 orang subjek lelaki sihat (kidal dan tidak kidal) masing-masing berumur 22 dan 25 tahun. Imbasan fMRI dijalankan menggunakan sistem pengimejan resonans magnet (MRI) 1.5 T di Jabatan Radiologi, Hospital Universiti Kebangsaan Malaysia. Kajian ini menggunakan gerakanjari tangan kanan dan kiri untuk merangsang aktiviti neuron di dalam korteks serebrum. Paradigma 5 kitar aktifIrehat digunakan dengan setiap kitar mengandungi satu blok aktif dan satu blok rehat yang masing-masing mengandungi 10 siri pengukuran. Imej fMRI dianalisis menggunakan pekej perisian MatLab dan pemetaan statistik berparameter 2 (sPM2). Proses pendaftaran jasad tegar menggunakan penjelmaan afin 6 parameter dilakukan ke atas kesemua imej kefungsian berwajaran T2*. Keputusan menunjukkan bahawa pergerakan subjek adalah minimum sama ada dalam arah translasi (< 1 mm) atau putaran (< 1 ). Kesemua imej dinormalkan melalui proses peledingan tak linear menggunakan penjelmaan afin 12 parameter dan didapati sepadan dengan pencontoh yang telahpun mematuhi ruang anatomi piawai. Walau bagaimanapun, bentuk, resolusi dan kontras imej kefungsian telah berubah sedikit berbanding dengan imej asal. Pelicinan imej menggunakan kernel Gaussian isotropik 6 mm menyebabkan data imej lebih bersifat parametrik dengan kehilangan yang ketara dalam resolusi dan kontras. Pengasingan struktur yang dilakukan ke atas imej berwajaran T1 mengklaskan tisu otak kepadajirim kelabu, jirim putih dan bendalir serebrospina. Pasca pemprosesan ruang bagi imej kefungsian dan struktur menjadikan data imej bersifat parametrik dengan taburan jenis Gaussian dan sedia untuk dianalisis menggunakan model linear am dan teori medan rawak Gaussian.
Proper integrated management of a dam reservoir requires that all components of the water resource system be known. One of these components is the daily reservoir inflow which is the subject matter of this study, i.e. to establish predictions of what is coming in the next rainfall-runoff process over a catchment. The transformation of rainfall into runoff is an extremely complex, dynamic, and more of a non-linear process. The available six-year average daily rainfall data across the Sembrong dam catchment were computed using the well-known Theissen’s polygon method. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Modelling of relationship between rainfall and reservoir inflow data was done using feed-forward back-propagation neural networks. The final selected model has one hidden layer with 11 neurons in the hidden layer. The selected model was applied for an independent data series testing. Results in relation to specific climatic and hydrologic properties of a small tropical catchment suggested that the model is suitable to be used in forecasting the next day’s reservoir inflow. The efficiencies of the model Abtained indicated the validity of using the neural network for modelling reservoir inflow series.
The conversion of plastic waste into fuel by pyrolysis has been recognized as a potential strategy for commercialization. The amount of plastic waste is basically different for each country which normally refers to non-recycled plastics data; consequently, the production target will also be different. This study attempted to build a model to predict fuel production from different non-recycled plastics data. The predictive model was developed via Levenberg-Marquardt approach in feed-forward neural networks model. The optimal number of hidden neurons was selected based on the lowest total of the mean square error. The proposed model was evaluated using the statistical analysis and graphical presentation for its accuracy and reliability. The results showed that the model was capable to predict product yields from pyrolysis of non-recycled plastics with high accuracy and the output values were strongly correlated with the values in literature.