Showing papers in "Frontiers in Neuroscience in 2018"

The Vagus Nerve at the Interface of the Microbiota-Gut-Brain Axis.

Abstract: The microbiota, the gut, and the brain communicate through the microbiota-gut-brain axis in a bidirectional way that involves the autonomic nervous system. The vagus nerve (VN), the principal component of the parasympathetic nervous system, is a mixed nerve composed of 80% afferent and 20% efferent fibers. The VN, because of its role in interoceptive awareness, is able to sense the microbiota metabolites through its afferents, to transfer this gut information to the central nervous system where it is integrated in the central autonomic network, and then to generate an adapted or inappropriate response. A cholinergic anti-inflammatory pathway has been described through VN's fibers, which is able to dampen peripheral inflammation and to decrease intestinal permeability, thus very probably modulating microbiota composition. Stress inhibits the VN and has deleterious effects on the gastrointestinal tract and on the microbiota, and is involved in the pathophysiology of gastrointestinal disorders such as irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) which are both characterized by a dysbiosis. A low vagal tone has been described in IBD and IBS patients thus favoring peripheral inflammation. Targeting the VN, for example through VN stimulation which has anti-inflammatory properties, would be of interest to restore homeostasis in the microbiota-gut-brain axis.

Reconsideration of Amyloid Hypothesis and Tau Hypothesis in Alzheimer's Disease

Abstract: The so-called amyloid hypothesis, that the accumulation and deposition of oligomeric or fibrillar amyloid β (Aβ) peptide is the primary cause of Alzheimer's disease (AD), has been the mainstream concept underlying AD research for over 20 years. However, all attempts to develop Aβ-targeting drugs to treat AD have ended in failure. Here, we review recent findings indicating that the main factor underlying the development and progression of AD is tau, not Aβ, and we describe the deficiencies of the amyloid hypothesis that have supported the emergence of this idea.

Deep Learning With Spiking Neurons: Opportunities and Challenges.

Abstract: Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing. This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. In this review, we address the opportunities that deep spiking networks offer and investigate in detail the challenges associated with training SNNs in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware. A wide range of training methods for SNNs is presented, ranging from the conversion of conventional deep networks into SNNs, constrained training before conversion, spiking variants of backpropagation, and biologically motivated variants of STDP. The goal of our review is to define a categorization of SNN training methods, and summarize their advantages and drawbacks. We further discuss relationships between SNNs and binary networks, which are becoming popular for efficient digital hardware implementation. Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications. We compare the suitability of various neuromorphic systems that have been developed over the past years, and investigate potential use cases. Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific advantages. Deep SNNs offer great opportunities to work with new types of event-based sensors, exploit temporal codes and local on-chip learning, and we have so far just scratched the surface of realizing these advantages in practical applications.

Peripheral Neuron Survival and Outgrowth on Graphene

Abstract: Graphene displays properties that make it appealing for neuroregenerative medicine, yet its interaction with peripheral neurons has been scarcely investigated. Here, we culture on graphene two established models for peripheral neurons: PC12 cells and DRG primary neurons. We perform a nano-resolved analysis of polymeric coatings on graphene and combine optical microscopy and viability assays to assess the material cytocompatibility and influence on differentiation. We find that differentiated PC12 cells display a remarkably increased neurite length on graphene (up to 27%) with respect to controls. Notably, DRG primary neurons survive both on bare and coated graphene. They present dense axonal networks on coated graphene, while they form cell islets characterized by dense axonal bundles on uncoated graphene. These findings indicate that graphene holds potential for nerve tissue regeneration and might pave the road to novel concepts of active nerve conduits.

Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks.

Abstract: Spiking neural networks (SNNs) are promising in ascertaining brain-like behaviors since spikes are capable of encoding spatio-temporal information. Recent schemes, e.g. pre-training from artificial neural networks (ANNs) or direct training based on backpropagation (BP), make the high-performance supervised training of SNNs possible. However, these methods primarily fasten more attention on its spatial domain information, and the dynamics in temporal domain are attached less significance. Consequently, this might lead to the performance bottleneck, and scores of training techniques shall be additionally required. Another underlying problem is that the spike activity is naturally non-differentiable, raising more difficulties in supervised training of SNNs. In this paper, we propose a spatio-temporal backpropagation (STBP) algorithm for training high-performance spiking neural networks. In order to solve the non-differentiable problem of SNNs, an approximated derivative for spike activity is proposed, being appropriate for gradient descent training. The STBP algorithm combines the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD), and does not require any additional complicated skill. We evaluate this method through adopting both the fully connected and convolutional architecture on the static MNIST dataset, a custom object detection dataset, and the dynamic N-MNIST dataset. Results bespeak that our approach achieves the best accuracy compared with existing state-of-the-art algorithms on spiking networks. This work provides a new perspective to investigate the high-performance SNNs for future brain-like computing paradigm with rich spatio-temporal dynamics.

Parkinson's Disease: Biomarkers, Treatment, and Risk Factors.

Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder caused mainly by lack of dopamine in the brain. Dopamine is a neurotransmitter involved in movement, motivation, memory, and other functions; its level is decreased in PD brain as a result of dopaminergic cell death. Dopamine loss in PD brain is a cause of motor deficiency and, possibly, a reason of the cognitive deficit observed in some PD patients. PD is mostly not recognized in its early stage because of a long latency between the first damage to dopaminergic cells and the onset of clinical symptoms. Therefore, it is very important to find reliable molecular biomarkers that can distinguish PD from other conditions, monitor its progression, or give an indication of a positive response to a therapeutic intervention. PD biomarkers can be subdivided into four main types: clinical, imaging, biochemical, and genetic. For a long time protein biomarkers, dopamine metabolites, amino acids, etc. in blood, serum, cerebrospinal liquid (CSF) were considered the most promising. Among the candidate biomarkers that have been tested, various forms of α-synuclein (α-syn), i.e., soluble, aggregated, post-translationally modified, etc. were considered potentially the most efficient. However, the encouraging recent results suggest that microRNA-based analysis may bring considerable progress, especially if it is combined with α-syn data. Another promising analysis is the advanced metabolite profiling of body fluids, called "metabolomics" which may uncover metabolic fingerprints specific for various stages of PD. Conventional pharmacological treatment of PD is based on the replacement of dopamine using dopamine precursors (levodopa, L-DOPA, L-3,4 dihydroxyphenylalanine), dopamine agonists (amantadine, apomorphine) and MAO-B inhibitors (selegiline, rasagiline), which can be used alone or in combination with each other. Potential risk factors include environmental toxins, drugs, pesticides, brain microtrauma, focal cerebrovascular damage, and genomic defects. This review covers molecules that might act as the biomarkers of PD. Then, PD risk factors (including genetics and non-genetic factors) and PD treatment options are discussed.

Exercise-Mediated Neurogenesis in the Hippocampus via BDNF.

Abstract: Exercise is known to have numerous neuroprotective and cognitive benefits, especially pertaining to memory and learning related processes. One potential link connecting them is exercise-mediated hippocampal neurogenesis, in which new neurons are generated and incorporated into hippocampal circuits. The present review synthesizes the extant literature detailing the relationship between exercise and hippocampal neurogenesis, and identifies a key molecule mediating this process, brain-derived neurotrophic factor (BDNF). As a member of the neurotrophin family, BDNF regulates many of the processes within neurogenesis, such as differentiation and survival. Although much more is known about the direct role that exercise and BDNF have on hippocampal neurogenesis in rodents, their corresponding cognitive benefits in humans will also be discussed. Specifically, what is known about exercise-mediated hippocampal neurogenesis will be presented as it relates to BDNF to highlight the critical role that it plays. Due to the inaccessibility of the human brain, much less is known about the role BDNF plays in human hippocampal neurogenesis. Limitations and future areas of research with regards to human neurogenesis will thus be discussed, including indirect measures of neurogenesis and single nucleotide polymorphisms within the BDNF gene.

Targeting Nrf2 to Suppress Ferroptosis and Mitochondrial Dysfunction in Neurodegeneration.

Abstract: Ferroptosis is a newly described form of regulated cell death, distinct from apoptosis, necroptosis and other forms of cell death. Ferroptosis is induced by disruption of glutathione synthesis or inhibition of glutathione peroxidase 4, exacerbated by iron, and prevented by radical scavengers such as ferrostatin-1, liproxstatin-1, and endogenous vitamin E. Ferroptosis terminates with mitochondrial dysfunction and toxic lipid peroxidation. Although conclusive identification of ferroptosis in vivo is challenging, several salient and very well established features of neurodegenerative diseases are consistent with ferroptosis, including lipid peroxidation, mitochondrial disruption and iron dysregulation. Accordingly, interest in the role of ferroptosis in neurodegeneration is escalating and specific evidence is rapidly emerging. One aspect that has thus far received little attention is the antioxidant transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2). This transcription factor regulates hundreds of genes, of which many are either directly or indirectly involved in modulating ferroptosis, including metabolism of glutathione, iron and lipids, and mitochondrial function. This potentially positions Nrf2 as a key deterministic component modulating the onset and outcomes of ferroptotic stress. The minimal direct evidence currently available is consistent with this and indicates that Nrf2 may be critical for protection against ferroptosis. In contrast, abundant evidence demonstrates that enhancing Nrf2 signaling is potently neuroprotective in models of neurodegeneration, although the exact mechanism by which this is achieved is unclear. Further studies are required to determine to extent to which the neuroprotective effects of Nrf2 activation involve the prevention of ferroptosis.

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Abstract: Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data

Abstract: Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

Mechanisms of α-Synuclein Induced Synaptopathy in Parkinson's Disease.

Abstract: Parkinson's disease (PD) is characterized by intracellular inclusions of aggregated and misfolded α-Synuclein (α-Syn), and the loss of dopaminergic (DA) neurons in the brain. The resulting motor abnormalities mark the progression of PD, while non-motor symptoms can already be identified during early, prodromal stages of disease. Recent studies provide evidence that during this early prodromal phase, synaptic and axonal abnormalities occur before the degenerative loss of neuronal cell bodies. These early phenotypes can be attributed to synaptic accumulation of toxic α-Syn. Under physiological conditions, α-Syn functions in its native conformation as a soluble monomer. However, PD patient brains are characterized by intracellular inclusions of insoluble fibrils. Yet, oligomers and protofibrils of α-Syn have been identified to be the most toxic species, with their accumulation at presynaptic terminals affecting several steps of neurotransmitter release. First, high levels of α-Syn alter the size of synaptic vesicle pools and impair their trafficking. Second, α-Syn overexpression can either misregulate or redistribute proteins of the presynaptic SNARE complex. This leads to deficient tethering, docking, priming and fusion of synaptic vesicles at the active zone (AZ). Third, α-Syn inclusions are found within the presynaptic AZ, accompanied by a decrease in AZ protein levels. Furthermore, α-Syn overexpression reduces the endocytic retrieval of synaptic vesicle membranes during vesicle recycling. These presynaptic alterations mediated by accumulation of α-Syn, together impair neurotransmitter exocytosis and neuronal communication. Although α-Syn is expressed throughout the brain and enriched at presynaptic terminals, DA neurons are the most vulnerable in PD, likely because α-Syn directly regulates dopamine levels. Indeed, evidence suggests that α-Syn is a negative modulator of dopamine by inhibiting enzymes responsible for its synthesis. In addition, α-Syn is able to interact with and reduce the activity of VMAT2 and DAT. The resulting dysregulation of dopamine levels directly contributes to the formation of toxic α-Syn oligomers. Together these data suggest a vicious cycle of accumulating α-Syn and deregulated dopamine that triggers synaptic dysfunction and impaired neuronal communication, ultimately causing synaptopathy and progressive neurodegeneration in Parkinson's disease.

An Overview of the Role of Lipofuscin in Age-Related Neurodegeneration.

Abstract: Despite aging being by far the greatest risk factor for highly prevalent neurodegenerative disorders, the molecular underpinnings of age-related brain changes are still not well understood, particularly the transition from normal healthy brain aging to neuropathological aging. Aging is an extremely complex, multifactorial process involving the simultaneous interplay of several processes operating at many levels of the functional organization. The buildup of potentially toxic protein aggregates and their spreading through various brain regions has been identified as a major contributor to these pathologies. One of the most striking morphologic changes in neurons during normal aging is the accumulation of lipofuscin (LF) aggregates, as well as, neuromelanin pigments. LF is an autofluorescent lipopigment formed by lipids, metals and misfolded proteins, which is especially abundant in nerve cells, cardiac muscle cells and skin. Within the Central Nervous System (CNS), LF accumulates as aggregates, delineating a specific senescence pattern in both physiological and pathological states, altering neuronal cytoskeleton and cellular trafficking and metabolism, and being associated with neuronal loss, and glial proliferation and activation. Traditionally, the accumulation of LF in the CNS has been considered a secondary consequence of the aging process, being a mere bystander of the pathological buildup associated with different neurodegenerative disorders. Here, we discuss recent evidence suggesting the possibility that LF aggregates may have an active role in neurodegeneration. We argue that LF is a relevant effector of aging that represents a risk factor or driver for neurodegenerative disorders.

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.

Abstract: Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

Human Postural Control.

Abstract: From ancient Greece to nowadays, research on posture control was guided and shaped by many concepts. Equilibrium control is often considered part of postural control. However, two different levels have become increasingly apparent in the postural control system, one level sets a distribution of tonic muscle activity ("posture") and the other is assigned to compensate for internal or external perturbations ("equilibrium"). While the two levels are inherently interrelated, both neurophysiological and functional considerations point toward distinct neuromuscular underpinnings. Disturbances of muscle tone may in turn affect movement performance. The unique structure, specialization and properties of skeletal muscles should also be taken into account for understanding important peripheral contributors to postural regulation. Here, we will consider the neuromechanical basis of habitual posture and various concepts that were rather influential in many experimental studies and mathematical models of human posture control.

Mitochondria and Mood: Mitochondrial Dysfunction as a Key Player in the Manifestation of Depression.

Abstract: Human and animal studies suggest an intriguing link between mitochondrial diseases and depression. Although depression has historically been linked to alterations in monoaminergic pharmacology and adult hippocampal neurogenesis, new data increasingly implicate broader forms of dampened plasticity, including plasticity within the cell. Mitochondria are the cellular powerhouse of eukaryotic cells, and they also regulate brain function through oxidative stress and apoptosis. In this paper, we make the case that mitochondrial dysfunction could play an important role in the pathophysiology of depression. Alterations in mitochondrial functions such as oxidative phosphorylation (OXPHOS) and membrane polarity, which increase oxidative stress and apoptosis, may precede the development of depressive symptoms. However, the data in relation to antidepressant drug effects are contradictory: some studies reveal they have no effect on mitochondrial function or even potentiate dysfunction, whereas other studies show more beneficial effects. Overall, the data suggest an intriguing link between mitochondrial function and depression that warrants further investigation. Mitochondria could be targeted in the development of novel antidepressant drugs, and specific forms of mitochondrial dysfunction could be identified as biomarkers to personalize treatment and aid in early diagnosis by differentiating between disorders with overlapping symptoms.

Impact of Metabolic Syndrome on Neuroinflammation and the Blood–Brain Barrier

Abstract: Metabolic syndrome, which includes diabetes and obesity, is one of the most widespread medical conditions. It induces systemic inflammation, causing far reaching effects on the body that are still being uncovered. Neuropathologies triggered by metabolic syndrome often result from increased permeability of the blood-brain-barrier (BBB). The BBB, a system designed to restrict entry of toxins, immune cells, and pathogens to the brain, is vital for proper neuronal function. Local and systemic inflammation induced by obesity or type 2 diabetes mellitus can cause BBB breakdown, decreased removal of waste, and increased infiltration of immune cells. This leads to disruption of glial and neuronal cells, causing hormonal dysregulation, increased immune sensitivity, or cognitive impairment depending on the affected brain region. Inflammatory effects of metabolic syndrome have been linked to neurodegenerative diseases. In this review, we discuss the effects of obesity and diabetes-induced inflammation on the BBB, the roles played by leptin and insulin resistance, as well as BBB changes occurring at the molecular level. We explore signaling pathways including VEGF, HIFs, PKC, Rho/ROCK, eNOS, and miRNAs. Finally, we discuss the broader implications of neural inflammation, including its connection to Alzheimer's disease, multiple sclerosis, and the gut microbiome.

Exploring EEG Features in Cross-Subject Emotion Recognition

Abstract: Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the "leave-one-subject-out" verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition.

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning.

Abstract: Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas. Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split. Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model. Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.

HMGB1: A Common Biomarker and Potential Target for TBI, Neuroinflammation, Epilepsy, and Cognitive Dysfunction.

Abstract: High mobility group box protein 1 (HMGB1) is a ubiquitous nuclear protein released by glia and neurons upon inflammasome activation and activates receptor for advanced glycation end products (RAGE) and toll-like receptor (TLR) 4 on the target cells HMGB1/TLR4 axis is a key initiator of neuroinflammation In recent days, more attention has been paid to HMGB1 due to its contribution in traumatic brain injury (TBI), neuroinflammatory conditions, epileptogenesis, and cognitive impairments and has emerged as a novel target for those conditions Nevertheless, HMGB1 has not been portrayed as a common prognostic biomarker for these HMGB1 mediated pathologies The current review discusses the contribution of HMGB1/TLR4/RAGE signaling in several brain injury, neuroinflammation mediated disorders, epileptogenesis and cognitive dysfunctions and in the light of available evidence, argued the possibilities of HMGB1 as a common viable biomarker of the above mentioned neurological dysfunctions Furthermore, the review also addresses the result of preclinical studies focused on HMGB1 targeted therapy by the HMGB1 antagonist in several ranges of HMGB1 mediated conditions and noted an encouraging result These findings suggest HMGB1 as a potential candidate to be a common biomarker of TBI, neuroinflammation, epileptogenesis, and cognitive dysfunctions which can be used for early prediction and progression of those neurological diseases Future study should explore toward the translational implication of HMGB1 which can open the windows of opportunities for the development of innovative therapeutics that could prevent several associated HMGB1 mediated pathologies discussed herein

Synaptic Tau Seeding Precedes Tau Pathology in Human Alzheimer's Disease Brain

Abstract: Alzheimer's disease (AD) is defined by the presence of intraneuronal neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau aggregates as well as extracellular amyloid-beta plaques. The presence and spread of tau pathology through the brain is classified by Braak stages and thought to correlate with the progression of AD. Several in vitro and in vivo studies have examined the ability of tau pathology to move from one neuron to the next, suggesting a "prion-like" spread of tau aggregates may be an underlying cause of Braak tau staging in AD. Using the HEK293 TauRD-P301S-CFP/YFP expressing biosensor cells as a highly sensitive and specific tool to identify the presence of seed competent aggregated tau in brain lysate-i.e., tau aggregates that are capable of recruiting and misfolding monomeric tau-, we detected substantial tau seeding levels in the entorhinal cortex from human cases with only very rare NFTs, suggesting that soluble tau aggregates can exist prior to the development of overt tau pathology. We next looked at tau seeding levels in human brains of varying Braak stages along six regions of the Braak Tau Pathway. Tau seeding levels were detected not only in the brain regions impacted by pathology, but also in the subsequent non-pathology containing region along the Braak pathway. These data imply that pathogenic tau aggregates precede overt tau pathology in a manner that is consistent with transneuronal spread of tau aggregates. We then detected tau seeding in frontal white matter tracts and the optic nerve, two brain regions comprised of axons that contain little to no neuronal cell bodies, implying that tau aggregates can indeed traverse along axons. Finally, we isolated cytosolic and synaptosome fractions along the Braak Tau Pathway from brains of varying Braak stages. Phosphorylated and seed competent tau was significantly enriched in the synaptic fraction of brain regions that did not have extensive cellular tau pathology, further suggesting that aggregated tau seeds move through the human brain along synaptically connected neurons. Together, these data provide further evidence that the spread of tau aggregates through the human brain along synaptically connected networks results in the pathogenesis of human Alzheimer's disease.

Oxytosis/Ferroptosis-(Re-) Emerging Roles for Oxidative Stress-Dependent Non-apoptotic Cell Death in Diseases of the Central Nervous System.

Abstract: Although nerve cell death is the hallmark of many neurological diseases, the processes underlying this death are still poorly defined. However, there is a general consensus that neuronal cell death predominantly proceeds by regulated processes. Almost 30 years ago, a cell death pathway eventually named oxytosis was described in neuronal cells that involved glutathione depletion, reactive oxygen species production, lipoxygenase activation, and calcium influx. More recently, a cell death pathway that involved many of the same steps was described in tumor cells and termed ferroptosis due to a dependence on iron. Since then there has been a great deal of discussion in the literature about whether these are two distinct pathways or cell type- and insult-dependent variations on the same pathway. In this review, we compare and contrast in detail the commonalities and distinctions between the two pathways concluding that the molecular pathways involved in the regulation of ferroptosis and oxytosis are highly similar if not identical. Thus, we suggest that oxytosis and ferroptosis should be regarded as two names for the same cell death pathway. In addition, we describe the potential physiological relevance of oxytosis/ferroptosis in multiple neurological diseases.

Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Abstract: Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principal advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications.

Interplay Between the Gut-Brain Axis, Obesity and Cognitive Function.

Abstract: Obesity continues to be one of the major public health problems due to its high prevalence and co-morbidities. Common co-morbidities not only include cardiometabolic disorders but also mood and cognitive disorders. Obese subjects often show deficits in memory, learning and executive functions compared to normal weight subjects. Epidemiological studies also indicate that obesity is associated with a higher risk of developing depression and anxiety, and vice versa. These associations between pathologies that presumably have different etiologies suggest shared pathological mechanisms. Gut microbiota is a mediating factor between the environmental pressures (e.g., diet, lifestyle) and host physiology, and its alteration could partly explain the cross-link between those pathologies. Westernized dietary patterns are known to be a major cause of the obesity epidemic, which also promotes a dysbiotic drift in the gut microbiota; this, in turn, seems to contribute to obesity-related complications. Experimental studies in animal models and, to a lesser extent, in humans suggest that the obesity-associated microbiota may contribute to the endocrine, neurochemical and inflammatory alterations underlying obesity and its comorbidities. These include dysregulation of the HPA-axis with overproduction of glucocorticoids, alterations in levels of neuroactive metabolites (e.g., neurotransmitters, short-chain fatty acids) and activation of a pro-inflammatory milieu that can cause neuro-inflammation. This review updates current knowledge about the role and mode of action of the gut microbiota in the cross-link between energy metabolism, mood and cognitive function.

Alzheimer's Disease and Type 2 Diabetes: A Critical Assessment of the Shared Pathological Traits.

Abstract: Alzheimer's disease (AD) and Type 2 Diabetes Mellitus (T2DM) are two of the most prevalent diseases in the elderly population worldwide. A growing body of epidemiological studies suggest that people with T2DM are at a higher risk of developing AD. Likewise, AD brains are less capable of glucose uptake from the surroundings resembling a condition of brain insulin resistance. Pathologically AD is characterized by extracellular plaques of Aβ and intracellular neurofibrillary tangles of hyperphosphorylated tau. T2DM, on the other hand is a metabolic disorder characterized by hyperglycemia and insulin resistance. In this review we have discussed how Insulin resistance in T2DM directly exacerbates Aβ and tau pathologies and elucidated the pathophysiological traits of synaptic dysfunction, inflammation, and autophagic impairments that are common to both diseases and indirectly impact Aβ and tau functions in the neurons. Elucidation of the underlying pathways that connect these two diseases will be immensely valuable for designing novel drug targets for Alzheimer's disease.

The Axon-Myelin Unit in Development and Degenerative Disease.

Abstract: Axons are electrically excitable, cable-like neuronal processes that relay information between neurons within the nervous system and between neurons and peripheral target tissues. In the central and peripheral nervous systems, most axons over a critical diameter are enwrapped by myelin, which reduces internodal membrane capacitance and facilitates rapid conduction of electrical impulses. The spirally wrapped myelin sheath, which is an evolutionary specialisation of vertebrates, is produced by oligodendrocytes and Schwann cells; in most mammals myelination occurs during postnatal development and after axons have established connection with their targets. Myelin covers the vast majority of the axonal surface, influencing the axon's physical shape, the localisation of molecules on its membrane and the composition of the extracellular fluid (in the periaxonal space) that immerses it. Moreover, myelinating cells play a fundamental role in axonal support, at least in part by providing metabolic substrates to the underlying axon to fuel its energy requirements. The unique architecture of the myelinated axon, which is crucial to its function as a conduit over long distances, renders it particularly susceptible to injury and confers specific survival and maintenance requirements. In this review we will describe the normal morphology, ultrastructure and function of myelinated axons, and discuss how these change following disease, injury or experimental perturbation, with a particular focus on the role the myelinating cell plays in shaping and supporting the axon.

Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning

Abstract: Spiking Neural Networks (SNNs) are fast becoming a promising candidate for brain-inspired neuromorphic computing because of their inherent power efficiency and impressive inference accuracy across several cognitive tasks such as image classification and speech recognition. The recent efforts in SNNs have been focused on implementing deeper networks with multiple hidden layers to incorporate exponentially more difficult functional representations. In this paper, we propose a pre-training scheme using biologically plausible unsupervised learning, namely Spike-Timing-Dependent-Plasticity (STDP), in order to better initialize the parameters in multi-layer systems prior to supervised optimization. The multi-layer SNN is comprised of alternating convolutional and pooling layers followed by fully-connected layers, which are populated with leaky integrate-and-fire spiking neurons. We train the deep SNNs in two phases wherein, first, convolutional kernels are pre-trained in a layer-wise manner with unsupervised learning followed by fine-tuning the synaptic weights with spike-based supervised gradient descent backpropagation. Our experiments on digit recognition demonstrate that the STDP-based pre-training with gradient-based optimization provides improved robustness, faster (~2.5 ×) training time and better generalization compared with purely gradient-based training without pre-training.

Mitochondrial Quality Control in Neurodegenerative Diseases: Focus on Parkinson's Disease and Huntington's Disease.

Abstract: In recent years, several important advances have been made in our understanding of the pathways that lead to cell dysfunction and death in Parkinson's disease (PD) and Huntington's disease (HD). Despite distinct clinical and pathological features, these two neurodegenerative diseases share critical processes, such as the presence of misfolded and/or aggregated proteins, oxidative stress, and mitochondrial anomalies. Even though the mitochondria are commonly regarded as the "powerhouses" of the cell, they are involved in a multitude of cellular events such as heme metabolism, calcium homeostasis, and apoptosis. Disruption of mitochondrial homeostasis and subsequent mitochondrial dysfunction play a key role in the pathophysiology of neurodegenerative diseases, further highlighting the importance of these organelles, especially in neurons. The maintenance of mitochondrial integrity through different surveillance mechanisms is thus critical for neuron survival. Mitochondria display a wide range of quality control mechanisms, from the molecular to the organellar level. Interestingly, many of these lines of defense have been found to be altered in neurodegenerative diseases such as PD and HD. Current knowledge and further elucidation of the novel pathways that protect the cell through mitochondrial quality control may offer unique opportunities for disease therapy in situations where ongoing mitochondrial damage occurs. In this review, we discuss the involvement of mitochondrial dysfunction in neurodegeneration with a special focus on the recent findings regarding mitochondrial quality control pathways, beyond the classical effects of increased production of reactive oxygen species (ROS) and bioenergetic alterations. We also discuss how disturbances in these processes underlie the pathophysiology of neurodegenerative disorders such as PD and HD.

Iron and Alzheimer's Disease: From Pathogenesis to Therapeutic Implications.

Abstract: As people age, iron deposits in different areas of the brain may impair normal cognitive function and behavior. Abnormal iron metabolism generates hydroxyl radicals through the Fenton reaction, triggers oxidative stress reactions, damages cell lipids, protein and DNA structure and function, and ultimately leads to cell death. There is an imbalance in iron homeostasis in Alzheimer's disease (AD). Excessive iron contributes to the deposition of β-amyloid and the formation of neurofibrillary tangles, which in turn, promotes the development of AD. Therefore, iron-targeted therapeutic strategies have become a new direction. Iron chelators, such as desferoxamine, deferiprone, deferasirox, and clioquinol, have received a great deal of attention and have obtained good results in scientific experiments and some clinical trials. Given the limitations and side effects of the long-term application of traditional iron chelators, alpha-lipoic acid and lactoferrin, as self-synthesized naturally small molecules, have shown very intriguing biological activities in blocking Aβ-aggregation, tauopathy and neuronal damage. Despite a lack of evidence for any clinical benefits, the conjecture that therapeutic chelation, with a special focus on iron ions, is a valuable approach for treating AD remains widespread.

Indole, a Signaling Molecule Produced by the Gut Microbiota, Negatively Impacts Emotional Behaviors in Rats.

Abstract: Gut microbiota produces a wide and diverse array of metabolites that are an integral part of the host metabolome. The emergence of the gut microbiome-brain axis concept has prompted investigations on the role of gut microbiota dysbioses in the pathophysiology of brain diseases. Specifically, the search for microbe-related metabolomic signatures in human patients and animal models of psychiatric disorders has pointed out the importance of the microbial metabolism of aromatic amino acids. Here, we investigated the effect of indole on brain and behavior in rats. Indole is produced by gut microbiota from tryptophan, through the tryptophanase enzyme encoded by the tnaA gene. First, we mimicked an acute and high overproduction of indole by injecting this compound in the cecum of conventional rats. This treatment led to a dramatic decrease of motor activity. The neurodepressant oxidized derivatives of indole, oxindole and isatin, accumulated in the brain. In addition, increase in eye blinking frequency and in c-Fos protein expression in the dorsal vagal complex denoted a vagus nerve activation. Second, we mimicked a chronic and moderate overproduction of indole by colonizing germ-free rats with the indole-producing bacterial species Escherichia coli. We compared emotional behaviors of these rats with those of germ-free rats colonized with a genetically-engineered counterpart strain unable to produce indole. Rats overproducing indole displayed higher helplessness in the tail suspension test, and enhanced anxiety-like behavior in the novelty, elevated plus maze and open-field tests. Vagus nerve activation was suggested by an increase in eye blinking frequency. However, unlike the conventional rats dosed with a high amount of indole, the motor activity was not altered and neither oxindole nor isatin could be detected in the brain. Further studies are required for a comprehensive understanding of the mechanisms supporting indole effects on emotional behaviors. As our findings suggest that people whose gut microbiota is highly prone to produce indole could be more likely to develop anxiety and mood disorders, we addressed the issue of the inter-individual variability of indole producing potential in humans. An in silico investigation of metagenomic data focused on the tnaA gene products definitively proved this inter-individual variability.

From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)

Abstract: In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.