Showing papers in "Frontiers in Neuroscience in 2017"

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

Abstract: Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Membrane Active Antimicrobial Peptides: Translating Mechanistic Insights to Design.

Abstract: Antimicrobial peptides (AMPs) are promising next generation antibiotics that hold great potential for combating bacterial resistance. AMPs can be both bacteriostatic and bactericidal, induce rapid killing and display a lower propensity to develop resistance than do conventional antibiotics. Despite significant progress in the past 30 years, no peptide antibiotic has reached the clinic yet. Poor understanding of the action mechanisms and lack of rational design principles have been the two major obstacles that have slowed progress. Technological developments are now enabling multidisciplinary approaches including molecular dynamics simulations combined with biophysics and microbiology toward providing valuable insights into the interactions of AMPs with membranes at atomic level. This has led to increasingly robust models of the mechanisms of action of AMPs and has begun to contribute meaningfully toward the discovery of new AMPs. This review discusses the detailed action mechanisms that have been put forward, with detailed atomistic insights into how the AMPs interact with bacterial membranes. The review further discusses how this knowledge is exploited towards developing design principles for novel AMPs. Finally, the current status, associated challenges and future directions for the development of AMP therapeutics are discussed.

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses.

Abstract: Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or "shape") as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia.

Plasma Extracellular Vesicles Enriched for Neuronal Origin: A Potential Window into Brain Pathologic Processes.

Abstract: Our team has been a pioneer in harvesting extracellular vesicles (EVs) enriched for neuronal origin from peripheral blood and using them as a biomarker discovery platform for neurological disorders This methodology has demonstrated excellent diagnostic and predictive performance for Alzheimer’s and other neurodegenerative diseases in multiple studies, providing a strong proof of concept for this approach Here, we describe our methodology in detail and offer further evidence that isolated EVs are enriched for neuronal origin In addition, we present evidence that EVs enriched for neuronal origin represent a more sensitive and accurate base for biomarkers than plasma, serum or non-enriched total plasma EVs Finally, we proceed to investigate the protein content of EV enriched for neuronal origin and compare it with other relevant enriched and non-enriched populations of plasma EVs Neuronal-origin enriched plasma EVs contain higher levels of signaling molecules of great interest for cellular metabolism, survival, and repair, which may be useful as biomarkers and to follow response to therapeutic interventions in a mechanism-specific manner

Session-RPE Method for Training Load Monitoring: Validity, Ecological Usefulness, and Influencing Factors.

Abstract: Purpose: The aim of this review is to (1) retrieve all data validating the Session-rating of perceived exertion (RPE)-method using various criteria, (2) highlight the rationale of this method and its ecological usefulness, and (3) describe factors that can alter RPE and users of this method should take into consideration. Method: Search engines such as SPORTDiscus, PubMed, and Google Scholar databases in the English language between 2001 and 2016 were consulted for the validity and usefulness of the session-RPE method. Studies were considered for further analysis when they used the session-RPE method proposed by Foster et al. in 2001. Participants were athletes of any gender, age, or level of competition. Studies using languages other than English were excluded in the analysis of the validity and reliability of the session-RPE method. Other studies were examined to explain the rationale of the session-RPE method and the origin of RPE. Results: A total of 950 studies cited the Foster et al. study that proposed the session RPE-method. 36 studies have examined the validity and reliability of this proposed method using the modified CR-10. Conclusion: These studies confirmed the validity and good reliability and internal consistency of session-RPE method in several sports and physical activities with men and women of different age categories (children, adolescents, and adults) among various expertise levels. This method could be used as "standing alone" method for training load (TL) monitoring purposes though some recommend to combine it with other physiological parameters as heart rate.

Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.

Abstract: Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

Abstract: Background: Parkinson’s disease is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for Parkinson’s disease applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e. motor fluctuations and long-term remote monitoring). Data sources: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Study eligibility criteria: Since 1429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Results: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

Transcranial direct current stimulation (tDCS): A beginner’s guide for design and implementation

Abstract: Transcranial direct current stimulation (tDCS) is a popular brain stimulation method that is used to modulate cortical excitability, producing facilitatory or inhibitory effects upon a variety of behaviors. There is, however, a current lack of consensus between studies, with many results suggesting that polarity-specific effects are difficult to obtain. This article explores some of these differences and highlights the experimental parameters that may underlie their occurrence. We provide a general, practical snapshot of tDCS methodology, including what it is used for, how to use it, and considerations for designing an effective and safe experiment. Our aim is to equip researchers who are new to tDCS with the essential knowledge so that they can make informed and well-rounded decisions when designing and running successful experiments. By summarizing the varied approaches, stimulation parameters, and outcomes, this article should help inform future tDCS research in a variety of fields.

The Hsp70/Hsp90 Chaperone Machinery in Neurodegenerative Diseases.

Abstract: TThe accumulation of misfolded proteins in the human brain is one of the critical features of many neurodegenerative diseases, including Alzheimer’s disease (AD). Assembles of beta-amyloid (Aβ) peptide – either soluble (oligomers - Aβ) or insoluble (plaques) and of tau protein, which form neurofibrillary tangles, are the major hallmarks of AD. Chaperones and co-chaperones regulate protein folding and client maturation, but they also target misfolded or aggregated proteins for refolding or for degradation, mostly by the proteasome. They form an important line of defense against misfolded proteins and are part of the cellular quality control system. The heat shock protein (Hsp) family, particularly Hsp70 and Hsp90, plays a major part in this process and it is well known to regulate protein misfolding in a variety of diseases, including tau levels and toxicity in AD. However, the role of Hsp90 in regulating protein misfolding is not yet fully understood. For example, knockdown of Hsp90 and its co-chaperones in a C. elegans model of Aβ misfolding leads to increased toxicity. On the other hand, the use of Hsp90 inhibitors in AD mouse models reduces Aβ toxicity, and normalizes synaptic function. Stress-inducible phosphoprotein 1 (STI1), an intracellular co-chaperone, mediates the transfer of clients from Hsp70 to Hsp90. Importantly, STI1 has been shown to regulate aggregation of amyloid-like proteins in yeast. In addition to its intracellular function, STI1 can be secreted by diverse cell types, including astrocytes and microglia and function as a neurotrophic ligand by triggering signaling via the cellular prion protein (PrPC). Extracellular STI1 can prevent Aβ toxic signaling by (i) interfering with Aβ binding to PrPC and (ii) triggering pro-survival signaling cascades. Interestingly, decreased levels of STI1 in C. elegans can also increase toxicity in an amyloid model. In this review, we will discuss the role of intracellular and extracellular STI1 and the Hsp70/Hsp90 chaperone network in mechanisms underlying protein misfolding in neurodegenerative diseases, with particular focus on Alzheimer’s disease.

Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial

Abstract: Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p < 0.01) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p < 0.01). Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.

Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research

Abstract: In recent years there has been an increase in the number of portable low-cost electroencephalographic (EEG) systems available to researchers. However, to date the validation of the use of low-cost EEG systems has focused on continuous recording of EEG data and/or the replication of large system EEG setups reliant on event-markers to afford examination of event-related brain potentials (ERP). Here, we demonstrate that it is possible to conduct ERP research without being reliant on event markers using a portable MUSE EEG system and a single computer. Specifically, we report the results of two experiments using data collected with the MUSE EEG system – one using the well-known visual oddball paradigm and the other using a standard reward-learning task. Our results demonstrate that we could observe and quantify the N200 and P300 ERP components in the visual oddball task and the reward positivity (the mirror opposite component to the feedback-related negativity) in the reward-learning task. Specifically, single sample t-tests of component existence (all p’s < 0.05), computation of Bayesian credible intervals, and 95% confidence intervals all statistically verified the existence of the N200, P300, and reward positivity in all analyses. We provide with this research paper an open source website with all the instructions, methods, and software to replicate our findings and to provide researchers with an easy way to use the MUSE EEG system for ERP research. Importantly, our work highlights that with a single computer and a portable EEG system such as the MUSE one can conduct ERP research with ease thus greatly extending the possible use of the ERP methodology to a variety of novel contexts.

CIFAR10-DVS: An Event-Stream Dataset for Object Classification.

Abstract: Neuromorphic vision research requires high-quality and appropriately challenging event-stream datasets to support continuous improvement of algorithms and methods However, creating event-stream datasets is a time-consuming task, which needs to be recorded using the neuromorphic cameras Currently, there are limited event-stream datasets available In this work, by utilizing the popular computer vision dataset CIFAR-10, we converted 10,000 frame-based images into 10,000 event streams using a dynamic vision sensor (DVS), providing a moderate-level event-stream dataset in 10 different classes, named as ‘CIFAR10-DVS’ The conversion of event-stream dataset was implemented by a repeated closed-loop smooth (RCLS) movement of frame-based images Unlike the conversion of frame-based images by moving the camera, the image movement is more realistic in respect of its practical applications The repeated closed-loop image movement generates rich local intensity changes in continuous time which are quantized by each pixel of the DVS camera to generate events Furthermore, a performance benchmark in event-driven object classification is provided based on state-of-the-art classification algorithms This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification

Protein Quality Control by Molecular Chaperones in Neurodegeneration

Abstract: Protein homeostasis (proteostasis) requires the timely degradation of misfolded proteins and their aggregates by protein quality control (PQC), of which molecular chaperones are an essential component. Compared with other cell types, PQC in neurons is particularly challenging because they have a unique cellular structure with long extensions. Making it worse, neurons are postmitotic, i.e., cannot dilute toxic substances by division, and, thus, are highly sensitive to misfolded proteins, especially as they age. Failure in PQC is often associated with neurodegenerative diseases, such as Huntington’s disease (HD), Alzheimer’s disease (AD), Parkinson’s disease (PD), and prion disease. In fact, many neurodegenerative diseases are considered to be protein misfolding disorders. To prevent the accumulation of disease-causing aggregates, neurons utilize a repertoire of chaperones that recognize misfolded proteins through exposed hydrophobic surfaces and assist their refolding. If such an effort fails, chaperones can facilitate the degradation of terminally misfolded proteins through either the ubiquitin (Ub)-proteasome system (UPS) or the autophagy-lysosome system (hereafter autophagy). If soluble, the substrates associated with chaperones, such as Hsp70, are ubiquitinated by Ub ligases and degraded through the proteasome complex. Some misfolded proteins carrying the KFERQ motif are recognized by the chaperone Hsc70 and delivered to the lysosomal lumen through a process called, chaperone-mediated autophagy (CMA). Aggregation-prone misfolded proteins that remain unprocessed are directed to macroautophagy in which cargoes are collected by adaptors, such as p62/SQSTM-1/Sequestosome-1, and delivered to the autophagosome for lysosomal degradation. The aggregates that have survived all these refolding/degradative processes can still be directly dissolved, i.e., disaggregated by chaperones. Studies have shown that molecular chaperones alleviate the pathogenic symptoms by neurodegeneration-causing protein aggregates. Chaperone-inducing drugs and anti-aggregation drugs are actively exploited for beneficial effects on symptoms of disease. Here, we discuss how chaperones protect misfolded proteins from aggregation and mediate the degradation of terminally misfolded proteins in collaboration with cellular degradative machinery. The topics also include therapeutic approaches to improve the expression and turnover of molecular chaperones and to develop anti-aggregation drugs.

Cross Talk: The Microbiota and Neurodevelopmental Disorders.

Abstract: Humans evolved within a microbial ecosystem resulting in an interlinked physiology. The gut microbiota can signal to the brain via the immune system, the vagus nerve or other host-microbe interactions facilitated by gut hormones, regulation of tryptophan metabolism and microbial metabolites such as short chain fatty acids (SCFA), to influence brain development, function and behaviour. Emerging evidence suggests that the gut microbiota may play a role in shaping cognitive networks encompassing emotional and social domains in neurodevelopmental disorders. Drawing upon preclinical and clinical evidence, we review the potential role of the gut microbiota in the origins and development of social and emotional domains related to Autism spectrum disorders (ASD) and Schizophrenia. Small preliminary clinical studies have demonstrated gut microbiota alterations in both ASD and Schizophrenia compared to healthy controls. However, we await the further development of mechanistic insights, together with large scale longitudinal clinical trials, that encompass a systems level dimensional approach, to investigate whether promising pre-clinical and initial clinical findings lead to clinical relevance.

MEG Signatures of a Perceived Match or Mismatch between Individual and Group Opinions

Abstract: Humans often adjust their opinions to the perceived opinions of others. Neural responses to a perceived match or mismatch between individual and group opinions have been investigated previously, but some findings are inconsistent. In this study, we used magnetoencephalographic source imaging to investigate further neural responses to the perceived opinions of others. We found that group opinions mismatching with individual opinions evoked responses in the anterior and posterior medial prefrontal cortices, as well as in the temporoparietal junction and ventromedial prefrontal cortex in the 220–320 and 380–530 ms time windows. Evoked responses were accompanied by an increase in the power of theta oscillations (4–8 Hz) over a number of frontal cortical sites. Group opinions matching with individual opinions evoked an increase in amplitude of beta oscillations (13–30 Hz) in the anterior cingulate and ventral medial prefrontal cortices. Based on these results, we argue that distinct valuation and performance-monitoring neural circuits in the medial cortices of the brain may monitor compliance of individual behavior to the perceived group norms.

Bile Acid Signaling Pathways from the Enterohepatic Circulation to the Central Nervous System

Abstract: Bile acids are best known as detergents involved in the digestion of lipids. In addition, new data in the last decade have shown that bile acids also function as gut hormones capable of influencing metabolic processes via receptors such as FXR (farnesoid X receptor) and TGR5 (Takeda G protein-coupled receptor 5). These effects of bile acids are not restricted to the gastrointestinal tract, but can affect different tissues throughout the organism. It is still unclear whether these effects also involve signaling of bile acids to the central nervous system (CNS). Bile acid signaling to the CNS encompasses both direct and indirect pathways. Bile acids can act directly in the brain via central FXR and TGR5 signaling. In addition, there are two indirect pathways that involve intermediate agents released upon interaction with bile acids receptors in the gut. Activation of intestinal FXR and TGR5 receptors can result in the release of fibroblast growth factor 19 (FGF19) and glucagon-like peptide 1 (GLP-1), both capable of signaling to the CNS. We conclude that when plasma bile acids levels are high all three pathways may contribute in signal transmission to the CNS. However, under normal physiological circumstances, the indirect pathway involving GLP-1 may evoke the most substantial effect in the brain.

GRP78 at the Centre of the Stage in Cancer and Neuroprotection.

Abstract: The 78-kDa glucose-regulated protein GRP78, also known as BiP and HSP5a, is a multifunctional protein with activities far beyond its well-known role in the unfolded protein response (UPR) which is activated after endoplasmic reticulum (ER) stress in the cells. Most of these newly discovered activities depend on its position within the cell. GRP78 is located mainly in the ER, but it has also been observed in the cytoplasm, the mitochondria, the nucleus, the plasma membrane and secreted, although it is dedicated mostly to engage endogenous cytoprotective processes. Hence, GRP78 may control either UPR and macroautophagy or may activated phosphatidylinositol 3-kinase (PI3K)/AKT pro-survival pathways. GRP78 influences how tumour cells survive, proliferate, and develop chemoresistance. In neurodegeneration, endogenous mechanisms of neuroprotection are frequently insufficient or dysregulated. Lessons from tumour biology may give us clues about how boosting endogenous neuroprotective mechanisms in age-related neurodegeneration. Herein, the functions of GRP78 are revealed at the centre of the stage of apparently opposite sites of the same coin regarding cytoprotection: neurodegeneration and cancer. The goal is to give a comprehensive and critical review that may serve to guide future experiments to identify interventions that will enhance neuroprotection.

Changes in functional brain networks during epileptogenesis in a rodent model of temporal lobe epilepsy

Abstract: scanner. Usual preprocessing of fMRI and diffusion images and also parcellation to 90 regions (AAL template) were performed using FSL and ExploreDTI. FC and SC matrices were calculated based on time series correlation and number of deterministic tractography streamlines, respectively. After vectorization and concatenation of FC and SC matrices, ICA was applied. To find reproducible components and reliable connectivity, RAICAR (Ranking and averaging independent component analysis by reproducibility) [2] and bootstrap resampling were performed. Reliable connectivity was selected by applying threshold at value corresponding to the 96% confidence interval. Results: We selected the 3 components which best overlapped with well-known functional subnetworks. Fig. 1 shows the functional and structural parts of components in red and blue, respectively. According to Fig. 1.a (red part), brain functional connectivity of the visual system is captured in the first component. The second functional network includes regions in medial prefrontal cortex, anterior cingulate cortex, precuneus etc., which can be interpreted as Default Mode Network (Fig. 1b). Also, functional connectivity in right parietal-frontal part of the brain may encode the right attentional/ executive network (Fig. 1c). The corresponding structural networks of functional parts are illustrated as well.

A Mechanistic Understanding of Axon Degeneration in Chemotherapy-Induced Peripheral Neuropathy

Abstract: Chemotherapeutic agents cause many short and long term toxic side effects to peripheral nervous system that drastically alter quality of life. Chemotherapy-induced peripheral neuropathy (CIPN) is a common and enduring disorder caused by several anti-neoplastic agents. CIPN typically presents with neuropathic pain, numbness of distal extremities and/or oversensitivity to thermal or mechanical stimuli. This adverse side effect often requires a reduction in chemotherapy dosage or even discontinuation of treatment. Currently there are no effective treatment options for CIPN. While the underlying mechanisms for CIPN are not understood, current data identify a “dying back” axon degeneration of distal nerve endings as the major pathology in this disorder. Therefore, mechanistic understanding of axon degeneration will provide insights into the pathway and molecular players responsible for CIPN. Here, we review recent findings that expand our understanding of the pathogenesis of CIPN and discuss pathways that may be shared with the axonal degeneration that occurs during developmental axon pruning and during injury-induced Wallerian degeneration. These mechanistic insights provide new avenues for development of therapies to prevent or treat CIPN.

Cannabinoid Receptor 2 Signaling in Neurodegenerative Disorders: From Pathogenesis to a Promising Therapeutic Target

Abstract: As a consequence of an increasingly aging population, the number of people affected by neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease and Huntington's disease, is rapidly increasing. Although the etiology of these diseases has not been completely defined, common molecular mechanisms including neuroinflammation, excitotoxicity and mitochondrial dysfunction have been confirmed and can be targeted therapeutically. Moreover, recent studies have shown that endogenous cannabinoid signaling plays a number of modulatory roles throughout the central nervous system (CNS), including the neuroinflammation and neurogenesis. In particular, the up-regulation of type-2 cannabinoid (CB2) receptors has been found in a number of neurodegenerative disorders. Thus, the modulation of CB2 receptor signaling may represent a promising therapeutic target with minimal psychotropic effects that can be used to modulate endocannabinoid-based therapeutic approaches and to reduce neuronal degeneration. For these reasons this review will focus on the CB2 receptor as a promising pharmacological target in a number of neurodegenerative diseases.

A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks

Abstract: Previous research has provided qualitative evidence for overlap in a number of brain regions across the subjective value network (SVN) and the default mode network (DMN). In order to quantitatively assess this overlap, we conducted a series of coordinate-based meta-analyses (CBMA) of results from 466 functional magnetic resonance imaging experiments on task-negative or subjective value-related activations in the human brain. In these analyses, we first identified significant overlaps and dissociations across activation foci related to SVN and DMN. Second, we investigated whether these overlapping subregions also showed similar patterns of functional connectivity, suggesting a shared functional subnetwork. We find considerable overlap between SVN and DMN in subregions of central ventromedial prefrontal cortex (cVMPFC) and dorsal posterior cingulate cortex (dPCC). Further, our findings show that similar patterns of bidirectional functional connectivity between cVMPFC and dPCC are present in both networks. We discuss ways in which our understanding of how subjective value is computed and represented in the brain can be synthesized with what we know about the DMN, mind-wandering, and self-referential processing in light of our findings.

Interim Results of a Multicenter Trial with the New Electronic Subretinal Implant Alpha AMS in 15 Patients Blind from Inherited Retinal Degenerations

Abstract: Purpose: We assessed the safety and efficacy of a technically advanced subretinal electronic implant, RETINA IMPLANT Alpha AMS, in end stage retinal degeneration in an interim analysis of two ongoing prospective clinical trials. Methods: The subretinal visual prosthesis RETINA IMPLANT Alpha AMS (Retina Implant AG, Reutlingen, Germany) was implanted in 15 blind patients with hereditary retinal degenerations at four study sites with a follow-up period of 12 months (www.clinicaltrials.gov NCT01024803 and NCT02720640). Functional outcome measures included 1) screen-based standardized 2- or 4-alternative forced-choice tests of light perception, light localization, grating detection (basic grating acuity (BaGA) test) and Landolt C-rings; 2) grey level discrimination; 3) performance during activities of daily living (ADL-table tasks). Results: Implant-mediated light perception was observed in 13/15 patients. During the observation period implant mediated localization of visual targets was possible in 13/15 patients. Correct grating detection was achieved for spatial frequencies of 0.1 cpd (cycles per degree) in 4/15; 0.33 cpd in 3/15; 0.66 cpd in 2/15; 1.0 cpd in 2/15 and 3.3 cpd in 1/15 patients. In two patients visual acuity assessed with Landolt C- rings was 20/546 and 20/1111. Of 6 possible grey levels on average 4.6 +/-0.8 (mean +/- SD, n=10) were discerned. Improvements (power ON vs. OFF) of ADL table tasks were measured in 13/15 patients. Overall, results were stable during the observation period. Serious adverse events were reported in 4 patients: 2 movements of the implant, readjusted in a second surgery; 4 conjunctival erosion/dehiscence, successfully treated; 1 pain event around the coil, successfully treated; 1 partial reduction of silicone oil tamponade leading to distorted vision (silicon oil successfully refilled). The majority of adverse events were transient and mostly of mild to moderate intensity. Conclusions: Psychophysical and subjective data show that RETINA IMPLANT Alpha AMS is reliable, well tolerated and can restore limited visual functions in blind patients with degenerations of the outer retina. Compared with the previous implant Alpha IMS, longevity of the new implant Alpha AMS has been considerably improved. Alpha AMS has meanwhile been certified as a commercially available medical device, reimbursed in Germany by the public health system.

A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology.

Abstract: As Moore's law reaches its end, traditional computing technology based on the Von Neumann architecture is facing fundamental limits. Among them is poor energy efficiency. This situation motivates the investigation of different processing information paradigms, such as the use of spiking neural networks (SNNs), which also introduce cognitive characteristics. As applications at very high scale are addressed, the energy dissipation needs to be minimized. This effort starts from the neuron cell. In this context, this paper presents the design of an original artificial neuron, in standard 65 nm CMOS technology with optimized energy efficiency. The neuron circuit response is designed as an approximation of the Morris-Lecar theoretical model. In order to implement the non-linear gating variables, which control the ionic channel currents, transistors operating in deep subthreshold are employed. Two different circuit variants describing the neuron model equations have been developed. The first one features spike characteristics, which correlate well with a biological neuron model. The second one is a simplification of the first, designed to exhibit higher spiking frequencies, targeting large scale bio-inspired information processing applications. The most important feature of the fabricated circuits is the energy efficiency of a few femtojoules per spike, which improves prior state-of-the-art by two to three orders of magnitude. This performance is achieved by minimizing two key parameters: the supply voltage and the related membrane capacitance. Meanwhile, the obtained standby power at a resting output does not exceed tens of picowatts. The two variants were sized to 200 and 35 μm2 with the latter reaching a spiking output frequency of 26 kHz. This performance level could address various contexts, such as highly integrated neuro-processors for robotics, neuroscience or medical applications.

Lactate Shuttles in Neuroenergetics-Homeostasis, Allostasis and Beyond.

Abstract: Understanding brain energy metabolism — neuroenergetics — is becoming increasingly important as it can be identified repeatedly as the source of neurological perturbations. Within the scientific community we are seeing a shift in paradigms from the traditional neurocentric view to that of a more dynamic, integrated one where astrocytes are no longer considered as being just supportive, and activated microglia have a profound influence. Lactate is emerging as the ‘good guy’, contrasting its classical ‘bad guy’ position in the now superseded medical literature. This review begins with the evolution of the concept of ‘lactate shuttles’; goes on to the recent shift in ideas regarding normal neuroenergetics (homeostasis) — specifically, the astrocyte–neuron lactate shuttle; and progresses to covering the metabolic implications whereby homeostasis is lost — a state of allostasis, and the function of microglia. The role of lactate, as a substrate and shuttle, is reviewed in light of allostatic stress, and beyond — to a state of allostatic overload (neuropathology), in terms of physical brain trauma, and reflected upon with respect to neurodegenerative diseases. Finally, the recently proposed astrocyte–microglia lactate shuttle is discussed in terms of chronic neuroinflammatory infectious diseases, using tuberculous meningitis as an example. The novelty extended by this review is that the directionality of lactate, as shuttles in the brain, in neuropathophysiological states is emerging as crucial in neuroenergetics.

Development and Function of the Blood-Brain Barrier in the Context of Metabolic Control.

Abstract: Under physiological conditions, the brain consumes over 20% of the whole body energy supply. The blood-brain barrier (BBB) allows dynamic interactions between blood capillaries and the neuronal network in order to provide an adequate control of molecules that are transported in and out of the brain. Alterations in the BBB structure and function affecting brain accessibility to nutrients and exit of toxins are found in a number of diseases, which in turn may disturb brain function and nutrient signaling. In this review, we explore the major advances obtained in the understanding of the BBB development and how its structure impacts on function. Furthermore, we focus on the particularities of the barrier permeability in the hypothalamus, its role in the metabolic control and the potential impact of hypothalamic BBB abnormities in metabolic-related diseases.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

Abstract: The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients versus typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher’s score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t test p-value < 0.05) while nineteen of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician’s expert knowledge. Meanwhile, the potential reason of obtaining nineteen FCs which are not statistically

Axonal Degeneration during Aging and Its Functional Role in Neurodegenerative Disorders.

Abstract: Aging constitutes the main risk factor for the development of neurodegenerative diseases. This represents a major health issue worldwide that is only expected to escalate due to the ever-increasing life expectancy of the population. Interestingly, axonal degeneration, which occurs at early stages of neurodegenerative disorders (ND) such as Alzheimer’s disease, Amyotrophic lateral sclerosis and Parkinson’s disease, also takes place as a consequence of normal aging. Moreover, the alteration of several cellular processes such as proteostasis, response to cellular stress and mitochondrial homeostasis, which have been described to occur in the aging brain, can also contribute to axonal pathology. Compelling evidence indicate that the degeneration of axons precedes clinical symptoms in NDs and occurs before cell body loss, constituting an early event in the pathological process and providing a potential therapeutic target to treat neurodegeneration before neuronal cell death. Although normal aging and the development of neurodegeneration are two processes that are closely linked, the molecular basis of the switch that triggers the transition from healthy aging to neurodegeneration remains unrevealed. In this review we discuss the potential role of axonal degeneration in this transition and provide a detailed overview of the literature and current advances in the molecular understanding of the cellular changes that occur during aging that promote axonal degeneration and then discuss this in the context of ND.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Abstract: In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Weight Bearing Over-ground Stepping in an Exoskeleton with Non-invasive Spinal Cord Neuromodulation after Motor Complete Paraplegia.

Abstract: We asked whether coordinated voluntary movement of the lower limbs could be regained in an individual having been completely paralyzed (>4 yr) and completely absent of vision (>15 yr) using two novel strategies – transcutaneous electrical spinal cord stimulation at selected sites over the spine as well as pharmacological neuromodulation by buspirone. We also asked whether these neuromodulatory strategies could facilitate stepping assisted by an exoskeleton (EKSO, EKSO Bionics, CA) that is designed so that the subject can voluntarily complement the work being performed by the exoskeleton. We found that spinal cord stimulation and drug enhanced the level of effort that the subject could generate while stepping in the exoskeleton. In addition, stimulation improved the coordination patterns of the lower limb muscles resulting in a more continuous, smooth stepping motion in the exoskeleton along with changes in autonomic functions including cardiovascular and thermoregulation. Based on these data from this case study it appears that there is considerable potential for positive synergistic effects after complete paralysis by combining the over-ground step training in an exoskeleton, combined with transcutaneous electrical spinal cord stimulation either without or with pharmacological modulation.

Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics.

Abstract: Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes-microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.