Showing papers in "Frontiers in Neuroscience in 2021"

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

Abstract: The present work aims to explore the performance of fuzzy system-based medical image processing for brain disease prediction. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. While ensuring the model safety performance, a brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation). Brain MRI images collected from the Department of Brain Oncology, XX Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrated that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 seconds on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under the DSC coefficient, reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. To sum up, the proposed algorithm can provide higher accuracy while ensuring energy consumption, a more apparent denoising effect, and the best segmentation and recognition effect than other models, which can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

Efficient Spike-Driven Learning With Dendritic Event-Based Processing.

Abstract: A critical challenge in neuromorphic computing is to present computationally efficient algorithms of learning. When implementing gradient-based learning, error information must be routed through the network, such that each neuron knows its contribution to output, and thus how to adjust its weight. This is known as the credit assignment problem. Exactly implementing a solution like backpropagation involves weight sharing, which requires additional bandwidth and computations in a neuromorphic system. Instead, models of learning from neuroscience can provide inspiration for how to communicate error information efficiently, without weight sharing. Here we present a novel dendritic event-based processing (DEP) algorithm, using a two-compartment leaky integrate-and-fire neuron with partially segregated dendrites that effectively solves the credit assignment problem. In order to optimize the proposed algorithm, a dynamic fixed-point representation method and piecewise linear approximation approach are presented, while the synaptic events are binarized during learning. The presented optimization makes the proposed DEP algorithm very suitable for implementation in digital or mixed-signal neuromorphic hardware. The experimental results show that spiking representations can rapidly learn, achieving high performance by using the proposed DEP algorithm. We find the learning capability is affected by the degree of dendritic segregation, and the form of synaptic feedback connections. This study provides a bridge between the biological learning and neuromorphic learning, and is meaningful for the real-time applications in the field of artificial intelligence.

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

Abstract: The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.

Microglia in Neuroinflammation and Neurodegeneration: From Understanding to Therapy

Abstract: Microglia are the resident macrophages of the central nervous system (CNS) acting as the first line of defense in the brain by phagocytosing harmful pathogens and cellular debris. Microglia emerge from early erythromyeloid progenitors of the yolk sac and enter the developing brain before the establishment of a fully mature blood-brain barrier. In physiological conditions, during brain development, microglia contribute to CNS homeostasis by supporting cell proliferation of neural precursors. In post-natal life, such cells contribute to preserving the integrity of neuronal circuits by sculpting synapses. After a CNS injury, microglia change their morphology and down-regulate those genes supporting homeostatic functions. However, it is still unclear whether such changes are accompanied by molecular and functional modifications that might contribute to the pathological process. While comprehensive transcriptome analyses at the single-cell level have identified specific gene perturbations occurring in the "pathological" microglia, still the precise protective/detrimental role of microglia in neurological disorders is far from being fully elucidated. In this review, the results so far obtained regarding the role of microglia in neurodegenerative disorders will be discussed. There is solid and sound evidence suggesting that regulating microglia functions during disease pathology might represent a strategy to develop future therapies aimed at counteracting brain degeneration in multiple sclerosis, Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.

APOE and Alzheimer's Disease: From Lipid Transport to Physiopathology and Therapeutics.

Abstract: Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterized by extracellular amyloid β (Aβ) and intraneuronal tau protein aggregations. One risk factor for developing AD is the APOE gene coding for the apolipoprotein E protein (apoE). Humans have three versions of APOE gene: e2, e3, and e4 allele. Carrying the e4 allele is an AD risk factor while carrying the e2 allele is protective. ApoE is a component of lipoprotein particles in the plasma at the periphery, as well as in the cerebrospinal fluid (CSF) and in the interstitial fluid (ISF) of brain parenchyma in the central nervous system (CNS). ApoE is a major lipid transporter that plays a pivotal role in the development, maintenance, and repair of the CNS, and that regulates multiple important signaling pathways. This review will focus on the critical role of apoE in AD pathogenesis and some of the currently apoE-based therapeutics developed in the treatment of AD.

Blood-Brain Barrier Breakdown: An Emerging Biomarker of Cognitive Impairment in Normal Aging and Dementia.

Abstract: The blood-brain barrier (BBB) plays a vital role in maintaining the specialized microenvironment of the neural tissue. It separates the peripheral circulatory system from the brain parenchyma while facilitating communication. Alterations in the distinct physiological properties of the BBB lead to BBB breakdown associated with normal aging and various neurodegenerative diseases. In this review, we first briefly discuss the aging process, then review the phenotypes and mechanisms of BBB breakdown associated with normal aging that further cause neurodegeneration and cognitive impairments. We also summarize dementia such as Alzheimer's disease (AD) and vascular dementia (VaD) and subsequently discuss the phenotypes and mechanisms of BBB disruption in dementia correlated with cognition decline. Overlaps between AD and VaD are also discussed. Techniques that could identify biomarkers associated with BBB breakdown are briefly summarized. Finally, we concluded that BBB breakdown could be used as an emerging biomarker to assist to diagnose cognitive impairment associated with normal aging and dementia.

Defective Autophagy and Mitophagy in Aging and Alzheimer's Disease.

Abstract: Aging is the time-dependent process that all living organisms go through characterized by declining physiological function due to alterations in metabolic and molecular pathways. Many decades of research have been devoted to uncovering the cellular changes and progression of aging and have revealed that not all organisms with the same chronological age exhibit the same age-related declines in physiological function. In assessing biological age, factors such as epigenetic changes, telomere length, oxidative damage, and mitochondrial dysfunction in rescue mechanisms such as autophagy all play major roles. Recent studies have focused on autophagy dysfunction in aging, particularly on mitophagy due to its major role in energy generation and reactive oxidative species generation of mitochondria. Mitophagy has been implicated in playing a role in the pathogenesis of many age-related diseases, including Alzheimer's disease (AD), Parkinson's, Huntington's, and amyotrophic lateral sclerosis. The purpose of our article is to highlight the mechanisms of autophagy and mitophagy and how defects in these pathways contribute to the physiological markers of aging and AD. This article also discusses how mitochondrial dysfunction, abnormal mitochondrial dynamics, impaired biogenesis, and defective mitophagy are related to aging and AD progression. This article highlights recent studies of amyloid beta and phosphorylated tau in relation to autophagy and mitophagy in AD.

The Contribution of Gut Microbiota–Brain Axis in the Development of Brain Disorders

Abstract: Different bacterial families colonize most mucosal tissues in the human organism such as the skin, mouth, vagina, respiratory, and gastrointestinal districts. In particular, the mammalian intestine hosts a microbial community of between 1,000 and 1,500 bacterial species, collectively called "microbiota." Co-metabolism between the microbiota and the host system is generated and the symbiotic relationship is mutually beneficial. The balance that is achieved between the microbiota and the host organism is fundamental to the organization of the immune system. Scientific studies have highlighted a direct correlation between the intestinal microbiota and the brain, establishing the existence of the gut microbiota-brain axis. Based on this theory, the microbiota acts on the development, physiology, and cognitive functions of the brain, although the mechanisms involved have not yet been fully interpreted. Similarly, a close relationship between alteration of the intestinal microbiota and the onset of several neurological pathologies has been highlighted. This review aims to point out current knowledge as can be found in literature regarding the connection between intestinal dysbiosis and the onset of particular neurological pathologies such as anxiety and depression, autism spectrum disorder, and multiple sclerosis. These disorders have always been considered to be a consequence of neuronal alteration, but in this review, we hypothesize that these alterations may be non-neuronal in origin, and consider the idea that the composition of the microbiota could be directly involved. In this direction, the following two key points will be highlighted: (1) the direct cross-talk that comes about between neurons and gut microbiota, and (2) the degree of impact of the microbiota on the brain. Could we consider the microbiota a valuable target for reducing or modulating the incidence of certain neurological diseases?

CAR T Cell-Based Immunotherapy for the Treatment of Glioblastoma.

Abstract: Glioblastoma multiforme (GBM) is the most common and aggressive malignant primary brain tumor in adults. Current treatment options typically consist of surgery followed by chemotherapy or more frequently radiotherapy, however, median patient survival remains at just over 1 year. Therefore, the need for novel curative therapies for GBM is vital. Characterization of GBM cells has contributed to identify several molecules as targets for immunotherapy-based treatments such as EGFR/EGFRvIII, IL13Rα2, B7-H3, and CSPG4. Cytotoxic T lymphocytes collected from a patient can be genetically modified to express a chimeric antigen receptor (CAR) specific for an identified tumor antigen (TA). These CAR T cells can then be re-administered to the patient to identify and eliminate cancer cells. The impressive clinical responses to TA-specific CAR T cell-based therapies in patients with hematological malignancies have generated a lot of interest in the application of this strategy with solid tumors including GBM. Several clinical trials are evaluating TA-specific CAR T cells to treat GBM. Unfortunately, the efficacy of CAR T cells against solid tumors has been limited due to several factors. These include the immunosuppressive tumor microenvironment, inadequate trafficking and infiltration of CAR T cells and their lack of persistence and activity. In particular, GBM has specific limitations to overcome including acquired resistance to therapy, limited diffusion across the blood brain barrier and risks of central nervous system toxicity. Here we review current CAR T cell-based approaches for the treatment of GBM and summarize the mechanisms being explored in pre-clinical, as well as clinical studies to improve their anti-tumor activity.

Systematic Review: Quantitative Susceptibility Mapping (QSM) of Brain Iron Profile in Neurodegenerative Diseases.

Abstract: Iron has been increasingly implicated in the pathology of neurodegenerative diseases. In the past decade, development of the new magnetic resonance imaging technique, quantitative susceptibility mapping (QSM), has enabled for the more comprehensive investigation of iron distribution in the brain. The aim of this systematic review was to provide a synthesis of the findings from existing QSM studies in neurodegenerative diseases. We identified 80 records by searching MEDLINE, Embase, Scopus, and PsycInfo databases. The disorders investigated in these studies included Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Wilson's disease, Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, Fabry disease, myotonic dystrophy, pantothenate-kinase-associated neurodegeneration, and mitochondrial membrane protein-associated neurodegeneration. As a general pattern, QSM revealed increased magnetic susceptibility (suggestive of increased iron content) in the brain regions associated with the pathology of each disorder, such as the amygdala and caudate nucleus in Alzheimer's disease, the substantia nigra in Parkinson's disease, motor cortex in amyotrophic lateral sclerosis, basal ganglia in Huntington's disease, and cerebellar dentate nucleus in Friedreich's ataxia. Furthermore, the increased magnetic susceptibility correlated with disease duration and severity of clinical features in some disorders. Although the number of studies is still limited in most of the neurodegenerative diseases, the existing evidence suggests that QSM can be a promising tool in the investigation of neurodegeneration.

Neurofilament Proteins as Biomarkers to Monitor Neurological Diseases and the Efficacy of Therapies.

Abstract: Biomarkers of neurodegeneration and neuronal injury have the potential to improve diagnostic accuracy, disease monitoring, prognosis, and measure treatment efficacy. Neurofilament proteins (NfPs) are well suited as biomarkers in these contexts because they are major neuron-specific components that maintain structural integrity and are sensitive to neurodegeneration and neuronal injury across a wide range of neurologic diseases. Low levels of NfPs are constantly released from neurons into the extracellular space and ultimately reach the cerebrospinal fluid (CSF) and blood under physiological conditions throughout normal brain development, maturation, and aging. NfP levels in CSF and blood rise above normal in response to neuronal injury and neurodegeneration independently of cause. NfPs in CSF measured by lumbar puncture are about 40-fold more concentrated than in blood in healthy individuals. New ultra-sensitive methods now allow minimally invasive measurement of these low levels of NfPs in serum or plasma to track disease onset and progression in neurological disorders or nervous system injury and assess responses to therapeutic interventions. Any of the five Nf subunits - neurofilament light chain (NfL), neurofilament medium chain (NfM), neurofilament heavy chain (NfH), alpha-internexin (INA) and peripherin (PRPH) may be altered in a given neuropathological condition. In familial and sporadic Alzheimer's disease (AD), plasma NfL levels may rise as early as 22 years before clinical onset in familial AD and 10 years before sporadic AD. The major determinants of elevated levels of NfPs and degradation fragments in CSF and blood are the magnitude of damaged or degenerating axons of fiber tracks, the affected axon caliber sizes and the rate of release of NfP and fragments at different stages of a given neurological disease or condition directly or indirectly affecting central nervous system (CNS) and/or peripheral nervous system (PNS). NfPs are rapidly emerging as transformative blood biomarkers in neurology providing novel insights into a wide range of neurological diseases and advancing clinical trials. Here we summarize the current understanding of intracellular NfP physiology, pathophysiology and extracellular kinetics of NfPs in biofluids and review the value and limitations of NfPs and degradation fragments as biomarkers of neurodegeneration and neuronal injury.

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems.

Abstract: Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential role in enabling the brain-inspired spiking neural networks (SNNs) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer SNN trained with an unsupervised spike-timing-dependent plasticity (STDP) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations (SOPs), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that TTFS coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. TTFS coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer SOPs than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs' constraints and considerations in neuromorphic systems.

Adaptive Extreme Edge Computing for Wearable Devices.

Abstract: Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Glymphatic System as a Gateway to Connect Neurodegeneration From Periphery to CNS

Abstract: The classic concept of the absence of lymphatic vessels in the central nervous system (CNS), suggesting the immune privilege of the brain in spite of its high metabolic rate, was predominant until recent times. On the other hand, this idea left questioned how cerebral interstitial fluid is cleared of waste products. It was generally thought that clearance depends on cerebrospinal fluid (CSF). Not long ago, an anatomically and functionally discrete paravascular space was revised to provide a pathway for the clearance of molecules drained within the interstitial space. According to this model, CSF enters the brain parenchyma along arterial paravascular spaces. Once mixed with interstitial fluid and solutes in a process mediated by aquaporin-4, CSF exits through the extracellular space along venous paravascular spaces, thus being removed from the brain. This process includes the participation of perivascular glial cells due to a sieving effect of their end-feet. Such draining space resembles the peripheral lymphatic system, therefore, the term "glymphatic" (glial-lymphatic) pathway has been coined. Specific studies focused on the potential role of the glymphatic pathway in healthy and pathological conditions, including neurodegenerative diseases. This mainly concerns Alzheimer's disease (AD), as well as hemorrhagic and ischemic neurovascular disorders; other acute degenerative processes, such as normal pressure hydrocephalus or traumatic brain injury are involved as well. Novel morphological and functional investigations also suggested alternative models to drain molecules through perivascular pathways, which enriched our insight of homeostatic processes within neural microenvironment. Under the light of these considerations, the present article aims to discuss recent findings and concepts on nervous lymphatic drainage and blood-brain barrier (BBB) in an attempt to understand how peripheral pathological conditions may be detrimental to the CNS, paving the way to neurodegeneration.

Therapeutic Potential of Vagus Nerve Stimulation for Inflammatory Bowel Diseases.

Abstract: The vagus nerve is a mixed nerve, comprising 80% afferent fibers and 20% efferent fibers. It allows a bidirectional communication between the central nervous system and the digestive tract. It has a dual anti-inflammatory properties via activation of the hypothalamic pituitary adrenal axis, by its afferents, but also through a vago-vagal inflammatory reflex involving an afferent (vagal) and an efferent (vagal) arm, called the cholinergic anti-inflammatory pathway. Indeed, the release of acetylcholine at the end of its efferent fibers is able to inhibit the release of tumor necrosis factor (TNF) alpha by macrophages via an interneuron of the enteric nervous system synapsing between the efferent vagal endings and the macrophages and releasing acetylcholine. The vagus nerve also synapses with the splenic sympathetic nerve to inhibit the release of TNF-alpha by splenic macrophages. It can also activate the spinal sympathetic system after central integration of its afferents. This anti-TNF-alpha effect of the vagus nerve can be used in the treatment of chronic inflammatory bowel diseases, represented by Crohn's disease and ulcerative colitis where this cytokine plays a key role. Bioelectronic medicine, via vagus nerve stimulation, may have an interest in this non-drug therapeutic approach as an alternative to conventional anti-TNF-alpha drugs, which are not devoid of side effects feared by patients.

A Systematic Review of Glucose Transport Alterations in Alzheimer's Disease.

Abstract: Introduction: Alzheimer's disease (AD) is characterized by cerebral glucose hypometabolism. Hypometabolism may be partly due to reduced glucose transport at the blood-brain barrier (BBB) and across astrocytic and neuronal cell membranes. Glucose transporters (GLUTs) are integral membrane proteins responsible for moving glucose from the bloodstream to parenchymal cells where it is metabolized, and evidence indicates vascular and non-vascular GLUTs are altered in AD brains, a process which could starve the brain of glucose and accelerate cognitive decline. Here we review the literature on glucose transport alterations in AD from human and rodent studies. Methods: Literature published between 1st January 1946 and 1st November 2020 within EMBASE and MEDLINE databases was searched for the terms "glucose transporters" AND "Alzheimer's disease". Human and rodent studies were included while reviews, letters, and in-vitro studies were excluded. Results: Forty-three studies fitting the inclusion criteria were identified, covering human (23 studies) and rodent (20 studies). Post-mortem studies showed consistent reductions in GLUT1 and GLUT3 in the hippocampus and cortex of AD brains, areas of the brain closely associated with AD pathology. Tracer studies in rodent models of AD and human AD also exhibit reduced uptake of glucose and glucose-analogs into the brain, supporting these findings. Longitudinal rodent studies clearly indicate that changes in GLUT1 and GLUT3 only occur after amyloid-β pathology is present, and several studies indicate amyloid-β itself may be responsible for GLUT changes. Furthermore, evidence from human and rodent studies suggest GLUT depletion has severe effects on brain function. A small number of studies show GLUT2 and GLUT12 are increased in AD. Anti-diabetic medications improved glucose transport capacity in AD subjects. Conclusions: GLUT1 and GLUT3 are reduced in hippocampal and cortical regions in patients and rodent models of AD, and may be caused by high levels of amyloid-β in these regions. GLUT3 reductions appear to precede the onset of clinical symptoms. GLUT2 and GLUT12 appear to increase and may have a compensatory role. Repurposing anti-diabetic drugs to modify glucose transport shows promising results in human studies of AD.

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases.

Abstract: Background Brain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease's underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques. Objective This study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation. Method A systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer's disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson's disease, stroke, schizophrenia, and traumatic brain injury. Results Sixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16-20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature. Conclusion An appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

Oxidative Stress, Inflammation, and Autophagy: Potential Targets of Mesenchymal Stem Cells-Based Therapies in Ischemic Stroke

Abstract: Ischemic stroke is a leading cause of death worldwide; currently available treatment approaches for ischemic stroke are to restore blood flow, which reduce disability but are time limited. The interruption of blood flow in ischemic stroke contributes to intricate pathophysiological processes. Oxidative stress and inflammatory activity are two early events in the cascade of cerebral ischemic injury. These two factors are reciprocal causation and directly trigger the development of autophagy. Appropriate autophagy activity contributes to brain recovery by reducing oxidative stress and inflammatory activity, while autophagy dysfunction aggravates cerebral injury. Abundant evidence demonstrates the beneficial impact of mesenchymal stem cells (MSCs) and secretome on cerebral ischemic injury. MSCs reduce oxidative stress through suppressing reactive oxygen species (ROS) and reactive nitrogen species (RNS) generation and transferring healthy mitochondria to damaged cells. Meanwhile, MSCs exert anti-inflammation properties by the production of cytokines and extracellular vesicles, inhibiting proinflammatory cytokines and inflammatory cells activation, suppressing pyroptosis, and alleviating blood-brain barrier leakage. Additionally, MSCs regulation of autophagy imbalances gives rise to neuroprotection against cerebral ischemic injury. Altogether, MSCs have been a promising candidate for the treatment of ischemic stroke due to their pleiotropic effect.

Comparison of Artificial and Spiking Neural Networks on Digital Hardware.

Abstract: Despite the success of Deep Neural Networks-a type of Artificial Neural Network (ANN)-in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an unsustainable rate in the push for greater accuracy. There is a temptation to look for radical new approaches to these applications, and one such approach is the notion that replacing the abstract neuron used in most deep networks with a more biologically-plausible spiking neuron might lead to savings in both energy and resource cost. The most common spiking networks use rate-coded neurons for which a simple translation from a pre-trained ANN to an equivalent spike-based network (SNN) is readily achievable. But does the spike-based network offer an improvement of energy efficiency over the original deep network? In this work, we consider the digital implementations of the core steps in an ANN and the equivalent steps in a rate-coded spiking neural network. We establish a simple method of assessing the relative advantages of rate-based spike encoding over a conventional ANN model. Assuming identical underlying silicon technology we show that most rate-coded spiking network implementations will not be more energy or resource efficient than the original ANN, concluding that more imaginative uses of spikes are required to displace conventional ANNs as the dominant computing framework for neural computation.

μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks.

Abstract: The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and cost reduction constraints in the Internet of Things (IoT) application areas. Toward this goal, we present μBrain: the first digital yet fully event-driven without clock architecture, with co-located memory and processing capability that exploits event-based processing to reduce an always-on system's overall energy consumption (μW dynamic operation). The chip area in a 40 nm Complementary Metal Oxide Semiconductor (CMOS) digital technology is 2.82 mm2 including pads (without pads 1.42 mm2). This small area footprint enables μBrain integration in re-trainable sensor ICs to perform various signal processing tasks, such as data preprocessing, dimensionality reduction, feature selection, and application-specific inference. We present an instantiation of the μBrain architecture in a 40 nm CMOS digital chip and demonstrate its efficiency in a radar-based gesture classification with a power consumption of 70 μW and energy consumption of 340 nJ per classification. As a digital architecture, μBrain is fully synthesizable and lends to a fast development-to-deployment cycle in Application-Specific Integrated Circuits (ASIC). To the best of our knowledge, μBrain is the first tiny-scale digital, spike-based, fully parallel, non-Von-Neumann architecture (without schedules, clocks, nor state machines). For these reasons, μBrain is ultra-low-power and offers software-to-hardware fidelity. μBrain enables always-on neuromorphic computing in IoT sensor nodes that require running on battery power for years.

Implications of Glycosylation in Alzheimer’s Disease

Abstract: Alzheimer’s disease (AD) is the most common cause of dementia, affecting millions of people worldwide, and no cure is currently available. The major pathological hallmarks of AD are considered to be amyloid beta plaques and neurofibrillary tangles, generated by respectively APP processing and Tau phosphorylation. Recent evidence imply that glycosylation of these proteins, and a number of other AD-related molecules is altered in AD, suggesting a potential implication of this process in disease pathology. In this review we summarize the understanding of glycans in AD pathogenesis, and discuss how glycobiology can contribute to early diagnosis and treatment of AD, serving as potential biomarkers and therapeutic targets. Furthermore, we look into the potential link between the emerging topic neuroinflammation and glycosylation, combining two interesting, and until recent years, understudied topics in the scope of AD. Lastly, we discuss how new model platforms such as induced pluripotent stem cells can be exploited and contribute to a better understanding of a rather unexplored area in AD.

On Local Activity and Edge of Chaos in a NaMLab Memristor.

Abstract: Local activity is the capability of a system to amplify infinitesimal fluctuations in energy. Complex phenomena, including the generation of action potentials in neuronal axon membranes, may never emerge in an open system unless some of its constitutive elements operate in a locally active regime. As a result, the recent discovery of solid-state volatile memory devices, which, biased through appropriate DC sources, may enter a local activity domain, and, most importantly, the associated stable yet excitable sub-domain, referred to as edge of chaos, which is where the seed of complexity is actually planted, is of great appeal to the neuromorphic engineering community. This paper applies fundamentals from the theory of local activity to an accurate model of a niobium oxide volatile resistance switching memory to derive the conditions necessary to bias the device in the local activity regime. This allows to partition the entire design parameter space into three domains, where the threshold switch is locally passive (LP), locally active but unstable, and both locally active and stable, respectively. The final part of the article is devoted to point out the extent by which the response of the volatile memristor to quasi-static excitations may differ from its dynamics under DC stress. Reporting experimental measurements, which validate the theoretical predictions, this work clearly demonstrates how invaluable is non-linear system theory for the acquirement of a comprehensive picture of the dynamics of highly non-linear devices, which is an essential prerequisite for a conscious and systematic approach to the design of robust neuromorphic electronics. Given that, as recently proved, the potassium and sodium ion channels in biological axon membranes are locally active memristors, the physical realization of novel artificial neural networks, capable to reproduce the functionalities of the human brain more closely than state-of-the-art purely CMOS hardware architectures, should not leave aside the adoption of resistance switching memories, which, under the appropriate provision of energy, are capable to amplify the small signal, such as the niobium dioxide micro-scale device from NaMLab, chosen as object of theoretical and experimental study in this work.

The Evolution of Neurofilament Light Chain in Multiple Sclerosis.

Abstract: Multiple sclerosis (MS) is an autoimmune, inflammatory neurodegenerative disease of the central nervous system characterized by demyelination and axonal damage. Diagnosis and prognosis are mainly assessed through clinical examination and neuroimaging. However, more sensitive biomarkers are needed to measure disease activity and guide treatment decisions in MS. Prompt and individualized management can reduce inflammatory activity and delay disease progression. Neurofilament Light chain (NfL), a neuron-specific cytoskeletal protein that is released into the extracellular fluid following axonal injury, has been identified as a biomarker of disease activity in MS. Measurement of NfL levels can capture the extent of neuroaxonal damage, especially in early stages of the disease. A growing body of evidence has shown that NfL in cerebrospinal fluid (CSF) and serum can be used as reliable indicators of prognosis and treatment response. More recently, NfL has been shown to facilitate individualized treatment decisions for individuals with MS. In this review, we discuss the characteristics that make NfL a highly informative biomarker and depict the available technologies used for its measurement. We further discuss the growing role of serum and CSF NfL in MS research and clinical settings. Finally, we address some of the current topics of debate regarding the use of NfL in clinical practice and examine the possible directions that this biomarker may take in the future.

Mitochondrial Dynamics: A Key Role in Neurodegeneration and a Potential Target for Neurodegenerative Disease.

Abstract: In neurodegenerative diseases, neurodegeneration has been related to several mitochondrial dynamics imbalances such as excessive fragmentation of mitochondria, impaired mitophagy, and blocked mitochondria mitochondrial transport in axons. Mitochondria are dynamic organelles, and essential for energy conversion, neuron survival, and cell death. As mitochondrial dynamics have a significant influence on homeostasis, in this review, we mainly discuss the role of mitochondrial dynamics in several neurodegenerative diseases. There is evidence that several mitochondrial dynamics-associated proteins, as well as related pathways, have roles in the pathological process of neurodegenerative diseases with an impact on mitochondrial functions and metabolism. However, specific pathological mechanisms need to be better understood in order to propose new therapeutic strategies targeting mitochondrial dynamics that have shown promise in recent studies.

Neurofilament Light Chain as Biomarker for Amyotrophic Lateral Sclerosis and Frontotemporal Dementia.

Abstract: Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two related currently incurable neurodegenerative diseases. ALS is characterized by degeneration of upper and lower motor neurons causing relentless paralysis of voluntary muscles, whereas in FTD, progressive atrophy of the frontal and temporal lobes of the brain results in deterioration of cognitive functions, language, personality, and behavior. In contrast to Alzheimer’s disease (AD), ALS and FTD still lack a specific neurochemical biomarker reflecting neuropathology ex vivo. However, in the past 10 years, considerable progress has been made in the characterization of neurofilament light chain (NFL) as cerebrospinal fluid (CSF) and blood biomarker for both diseases. NFL is a structural component of the axonal cytoskeleton and is released into the CSF as a consequence of axonal damage or degeneration, thus behaving in general as a relatively non-specific marker of neuroaxonal pathology. However, in ALS, the elevation of its CSF levels exceeds that observed in most other neurological diseases, making it useful for the discrimination from mimic conditions and potentially worthy of consideration for introduction into diagnostic criteria. Moreover, NFL correlates with disease progression rate and is negatively associated with survival, thus providing prognostic information. In FTD patients, CSF NFL is elevated compared with healthy individuals and, to a lesser extent, patients with other forms of dementia, but the latter difference is not sufficient to enable a satisfying diagnostic performance at individual patient level. However, also in FTD, CSF NFL correlates with several measures of disease severity. Due to technological progress, NFL can now be quantified also in peripheral blood, where it is present at much lower concentrations compared with CSF, thus allowing less invasive sampling, scalability, and longitudinal measurements. The latter has promoted innovative studies demonstrating longitudinal kinetics of NFL in presymptomatic individuals harboring gene mutations causing ALS and FTD. Especially in ALS, NFL levels are generally stable over time, which, together with their correlation with progression rate, makes NFL an ideal pharmacodynamic biomarker for therapeutic trials. In this review, we illustrate the significance of NFL as biomarker for ALS and FTD and discuss unsolved issues and potential for future developments.

Glycolytic Metabolism, Brain Resilience, and Alzheimer's Disease.

Abstract: Alzheimer's disease (AD) is the most common form of age-related dementia. Despite decades of research, the etiology and pathogenesis of AD are not well understood. Brain glucose hypometabolism has long been recognized as a prominent anomaly that occurs in the preclinical stage of AD. Recent studies suggest that glycolytic metabolism, the cytoplasmic pathway of the breakdown of glucose, may play a critical role in the development of AD. Glycolysis is essential for a variety of neural activities in the brain, including energy production, synaptic transmission, and redox homeostasis. Decreased glycolytic flux has been shown to correlate with the severity of amyloid and tau pathology in both preclinical and clinical AD patients. Moreover, increased glucose accumulation found in the brains of AD patients supports the hypothesis that glycolytic deficit may be a contributor to the development of this phenotype. Brain hyperglycemia also provides a plausible explanation for the well-documented link between AD and diabetes. Humans possess three primary variants of the apolipoprotein E (ApoE) gene - ApoE∗ϵ2, ApoE∗ϵ3, and ApoE∗ϵ4 - that confer differential susceptibility to AD. Recent findings indicate that neuronal glycolysis is significantly affected by human ApoE isoforms and glycolytic robustness may serve as a major mechanism that renders an ApoE2-bearing brain more resistant against the neurodegenerative risks for AD. In addition to AD, glycolytic dysfunction has been observed in other neurodegenerative diseases, including Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis, strengthening the concept of glycolytic dysfunction as a common pathway leading to neurodegeneration. Taken together, these advances highlight a promising translational opportunity that involves targeting glycolysis to bolster brain metabolic resilience and by such to alter the course of brain aging or disease development to prevent or reduce the risks for not only AD but also other neurodegenerative diseases.

Roles of Selenoproteins in Brain Function and the Potential Mechanism of Selenium in Alzheimer's Disease.

Abstract: Selenium (Se) and its compounds have been reported to have great potential in the prevention and treatment of Alzheimer's disease (AD). However, little is known about the functional mechanism of Se in these processes, limiting its further clinical application. Se exerts its biological functions mainly through selenoproteins, which play vital roles in maintaining optimal brain function. Therefore, selenoproteins, especially brain function-associated selenoproteins, may be involved in the pathogenesis of AD. Here, we analyze the expression and distribution of 25 selenoproteins in the brain and summarize the relationships between selenoproteins and brain function by reviewing recent literature and information contained in relevant databases to identify selenoproteins (GPX4, SELENOP, SELENOK, SELENOT, GPX1, SELENOM, SELENOS, and SELENOW) that are highly expressed specifically in AD-related brain regions and closely associated with brain function. Finally, the potential functions of these selenoproteins in AD are discussed, for example, the function of GPX4 in ferroptosis and the effects of the endoplasmic reticulum (ER)-resident protein SELENOK on Ca2+ homeostasis and receptor-mediated synaptic functions. This review discusses selenoproteins that are closely associated with brain function and the relevant pathways of their involvement in AD pathology to provide new directions for research on the mechanism of Se in AD.

Hippocampal Function Is Impaired by a Short-Term High-Fat Diet in Mice: Increased Blood-Brain Barrier Permeability and Neuroinflammation as Triggering Events.

Abstract: Worldwide, and especially in Western civilizations, most of the staple diets contain high amounts of fat and refined carbohydrates, leading to an increasing number of obese individuals. In addition to inducing metabolic disorders, energy dense food intake has been suggested to impair brain functions such as cognition and mood control. Here we demonstrate an impaired memory function already 3 days after the start of a high-fat diet (HFD) exposure, and depressive-like behavior, in the tail suspension test, after 5 days. These changes were followed by reduced synaptic density, changes in mitochondrial function and astrocyte activation in the hippocampus. Preceding or coinciding with the behavioral changes, we found an induction of the proinflammatory cytokines TNF-α and IL-6 and an increased permeability of the blood-brain barrier (BBB), in the hippocampus. Finally, in mice treated with a TNF-α inhibitor, the behavioral and BBB alterations caused by HFD-feeding were mitigated suggesting that inflammatory signaling was critical for the changes. In summary, our findings suggest that HFD rapidly triggers hippocampal dysfunction associated with BBB disruption and neuroinflammation, promoting a progressive breakdown of synaptic and metabolic function. In addition to elucidating the link between diet and cognitive function, our results might be relevant for the comprehension of the neurodegenerative process.

Selective Neuromodulation of the Vagus Nerve

Abstract: Vagus nerve stimulation (VNS) is an effective technique for the treatment of refractory epilepsy and shows potential for the treatment of a range of other serious conditions. However, until now stimulation has generally been supramaximal and non-selective, resulting in a range of side effects. Selective VNS (sVNS) aims to mitigate this by targeting specific fiber types within the nerve to produce functionally specific effects. In recent years, several key paradigms of sVNS have been developed-spatially selective, fiber-selective, anodal block, neural titration, and kilohertz electrical stimulation block-as well as various stimulation pulse parameters and electrode array geometries. sVNS can significantly reduce the severity of side effects, and in some cases increase efficacy of the treatment. While most studies have focused on fiber-selective sVNS, spatially selective sVNS has demonstrated comparable mitigation of side-effects. It has the potential to achieve greater specificity and provide crucial information about vagal nerve physiology. Anodal block achieves strong side-effect mitigation too, but is much less specific than fiber- and spatially selective paradigms. The major hurdle to achieving better selectivity of VNS is a limited knowledge of functional anatomical organization of vagus nerve. It is also crucial to optimize electrode array geometry and pulse shape, as well as expand the applications of sVNS beyond the current focus on cardiovascular disease.

Electrophysiological Analysis of Brain Organoids: Current Approaches and Advancements

Abstract: Brain organoids, or cerebral organoids, have become widely used to study the human brain in vitro. As pluripotent stem cell-derived structures capable of self-organization and recapitulation of physiological cell types and architecture, brain organoids bridge the gap between relatively simple two-dimensional human cell cultures and non-human animal models. This allows for high complexity and physiological relevance in a controlled in vitro setting, opening the door for a variety of applications including development and disease modeling and high-throughput screening. While technologies such as single cell sequencing have led to significant advances in brain organoid characterization and understanding, improved functional analysis (especially electrophysiology) is needed to realize the full potential of brain organoids. In this review, we highlight key technologies for brain organoid development and characterization, then discuss current electrophysiological methods for brain organoid analysis. While electrophysiological approaches have improved rapidly for two-dimensional cultures, only in the past several years have advances been made to overcome limitations posed by the three-dimensionality of brain organoids. Here, we review major advances in electrophysiological technologies and analytical methods with a focus on advances with applicability for brain organoid analysis.