Measuring neuronal avalanches to inform brain-computer interfaces.

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

Brain fingerprint is based on the aperiodic, scale-free, neuronal activity.

Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.

High-Iron Bauxite Residue (Red Mud) Valorization Using Hydrochemical Conversion of Goethite to Magnetite.

Bauxite residue (BR), also known as red mud, is a byproduct of the alumina production using the Bayer process. This material is not used to make iron or other iron-containing products worldwide, owing to its high content of sodium oxide and other impurities. In this study, we investigated the hydrochemical conversion of goethite (FeOOH) to magnetite (Fe3O4) in high-iron BR from the Friguia alumina refinery (Guinea) by Fe2+ ions in highly concentrated alkaline media. The simultaneous extraction of Al and Na made it possible to obtain a product containing more than 96% Fe3O4. The results show that the magnetization of Al-goethite and Al-hematite accelerates the dissolution of the Al from the iron mineral solid matrix and from the desilication product (DSP). After ferrous sulfate (FeSO4·7H2O) was added directly at an FeO:Fe2O3 molar ratio of 1:1 at 120 °C for 150 min in solution with the 360 g L-1 Na2O concentration, the alumina extraction ratio reached 96.27% for the coarse bauxite residue size fraction (Sands) and 87.06% for fine BR obtained from red mud. The grade of iron (total iron in the form of iron elements) in the residue can be increased to 69.55% for sands and 58.31% for BR. The solid residues obtained after leaching were studied by XRD, XRF, TG-DTA, VSM, Mössbauer spectroscopy, and SEM to evaluate the conversion and leaching mechanisms, as well as the recovery ratio of Al from various minerals. The iron-rich residues can be used in the steel industry or as a pigment.

Mechanisms of chronic alcohol exposure-induced aggressiveness in cellular model of HCC and recovery after alcohol withdrawal.

Alcohol-related liver disease is the most prevalent chronic liver disease worldwide, accounting for 30% of hepatocellular carcinoma (HCC) cases and HCC-specific deaths. However, the knowledge on mechanisms by which alcohol consumption leads to cancer progression and its aggressiveness is limited. Better understanding of the clinical features and the mechanisms of alcohol-induced HCC are of critical importance for prevention and the development of novel treatments. Early stage Huh-7 and advanced SNU449 liver cancer cell lines were subjected to chronic alcohol exposure (CAE), at different doses for 6 months followed by 1-month alcohol withdrawal period. ADH activity and ALDH expression were much lower in SNU449 compared with Huh-7 cells and at the 270 mM dose, CAE decreased cell viability by about 50% and 80%, respectively, in Huh-7 and SNU449 cells but induced mortality only in Huh-7 cells. Thus, Huh-7 may be more vulnerable to ethanol toxicity because of the higher levels of acetaldehyde. CAE induced a dose-dependent increase in cell migration and invasion and also in the expression of cancer stem cells markers (CD133, CD44, CD90). CAE in Huh-7 cells selectively activated ERK1/2 and inhibited GSK3ß signaling pathways. Most of the changes induced by CAE were reversed after alcohol withdrawal. Interestingly, we confirmed the increase in CD133 mRNA levels in the tumoral tissue of patients with ethanol-related HCC compared to other HCC etiologies. Our results may explain the benefits observed in epidemiological studies showing a significant increase of overall survival in abstinent compared with non-abstinent patients.

Anti-inflammatory drugs prevent memory and hippocampal plasticity deficits following initial binge-like alcohol exposure in adolescent male rats.

RATIONALE: Binge drinking during adolescence impairs learning and memory on the long term, and many studies suggest a role of neuroinflammation. However, whether neuroinflammation occurs after the very first exposures to alcohol remains unclear, while initial alcohol exposure impairs learning for several days in male rats. OBJECTIVES: To investigate the role of neuroinflammation in the effects of only two binge-like episodes on learning and on neuronal plasticity in adolescent male rat hippocampus. METHODS: Animals received two ethanol i.p. injections (3 g/kg) 9 h apart. Forty-eight hours later, we recorded long-term depression (LTD) and potentiation (LTP) in CA1 area of hippocampus slices. In isolated CA1, we measured immunolabelings for microglial activation and Toll-like receptor 4 (TLR4) and mRNA levels for several cytokines. RESULTS: Forty-eight hours after the two binges, rats performed worse than control rats in novel object recognition, LTD was reduced, LTP was increased, and excitatory neurotransmission was more sensitive to an antagonist of the GluN2B subunit of the NMDA receptor. Exposure to ethanol with minocycline or indomethacin, two anti-inflammatory drugs, or with a TLR4 antagonist, prevented all effects of ethanol. Immunolabelings at 48 h showed a reduction of neuronal TLR4 that was prevented by minocycline pretreatment, while microglial reactivity was undetected and inflammatory cytokines mRNA levels were unchanged. CONCLUSION: Two binge-like ethanol exposures during adolescence in rat involved neuroinflammation leading to changes in TLR4 expression and in GluN2B functioning inducing disturbances in synaptic plasticity and cognitive deficits. Anti-inflammatory drugs are good candidates to prevent brain function and memory deficits induced by few binge-drinking episodes.

Rescuing SLAMF3 Expression Restores Sorafenib Response in Hepatocellular Carcinoma Cells through the Induction of Mesenchymal-to-Epithelial Transition.

BACKGROUND: Acquired resistance to sorafenib in hepatocellular carcinoma (HCC) patients results in poor prognosis. Epithelial-to-mesenchymal transition (EMT) is the major mechanism implicated in the resistance to sorafenib. We have reported the tumor suppressor role of SLAMF3 (signaling lymphocytic activation molecules family 3) in HCC progression and highlighted its implication in controlling the MRP-1 transporter activity. These data suggest the implication of SLAMF3 in sorafenib resistance mechanisms. METHODS: We evaluated the resistance to sorafenib in Huh-7 cells treated with progressive doses (Res cells). We investigated the link between acquired resistance to sorafenib and SLAMF3 expression by flow cytometry and Western blot methods. Furthermore, we analyzed the EMT and the stem cell potential of cells resistant to sorafenib. RESULTS: Sorafenib resistance was confirmed in Res cells by analyzing the cell viability in the presence of sorafenib. The mesenchymal transition, in Res cells, was confirmed by high migratory index and the expression of EMT antigens. Interestingly, we found that loss of SLAMF3 expression corresponded to sorafenib-resistant phenotypes. The overexpression of SLAMF3 reversed EMT, decreased metastatic potential and inhibited mTOR/ERK1/2 in Res cells. CONCLUSIONS: We propose that rescuing SLAMF3 expression in resistant cells could represent a potential therapeutic strategy to enhance sorafenib efficacy in HCC patients.

Functional Connectivity Ensemble Method to Enhance BCI Performance (FUCONE).

OBJECTIVE: Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery. METHODS: A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets. RESULTS: Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. CONCLUSION: The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability. SIGNIFICANCE: Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.

Towards multimodal BCIs: the impact of peripheral control on motor cortex activity and sense of agency.

In the recent years, brain computer interfaces (BCI) using motor imagery have shown some limitations regarding the quality of control. In an effort to improve this promising technology, some studies intended to develop hybrid BCI with other technologies such as eye tracking which shows more reliability. However, the use of an eye tracker in the control of a robot might affect by itself the sense of agency (SoA) and the brain activity in the regions used for motor imagery (MI). Here, we explore the link between the sense of agency and the activity of the motor cortex. For this purpose, we used of a virtual arm projected on a surface which is either controlled by motion capture or controlled by gaze using an eye tracker. We found out that there is an activity in the motor cortex during the task of control by gaze and that having control over a projected robotic arm presents significant differences with the situation of observing the robot moving.

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.

Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks.

In the last decade, functional connectivity (FC) has been increasingly adopted based on its ability to capture statistical dependencies between multivariate brain signals. However, the role of FC in the context of brain-computer interface applications is still poorly understood. To address this gap in knowledge, we considered a group of 20 healthy subjects during an EEG-based hand motor imagery (MI) task. We studied two well-established FC estimators, i.e. spectral- and imaginary-coherence, and we investigated how they were modulated by the MI task. We characterized the resulting FC networks by extracting the strength of connectivity of each EEG sensor and we compared the discriminant power with respect to standard power spectrum features. At the group level, results showed that while spectral-coherence based network features were increasing in the sensorimotor areas, those based on imaginary-coherence were significantly decreasing. We demonstrated that this opposite, but complementary, behavior was respectively determined by the increase in amplitude and phase synchronization between the brain signals. At the individual level, we eventually assessed the potential of these network connectivity features in a simple off-line classification scenario. Taken together, our results provide fresh insights into the oscillatory mechanisms subserving brain network changes during MI and offer new perspectives to improve BCI performance.

Network-based brain-computer interfaces: principles and applications.

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback rehabilitation. In general, BCI usability depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modeling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from brain networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.

Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.

OBJECTIVE: Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. APPROACH: Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. MAIN RESULTS: We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention. SIGNIFICANCE: The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

Functional disconnection of associative cortical areas predicts performance during BCI training.

Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.

EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease.

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer's disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer's disease. However, the impact of amyloid-ß deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer's disease in 318 cognitively normal individuals aged 70-85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer's disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer's disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer's disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to 'suspected non-Alzheimer's pathophysiology'. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer's disease.

Mandibular reconstruction with the scapula tip free flap.

INTRODUCTION: The scapula tip free flap has been described for reconstruction of short mandible defects with extensive soft tissue needs. The versatility of this flap has not been extensively described. METHODS: Retrospective case series of patients who underwent mandibulectomy and reconstruction with the scapula tip free flap from 2005 to 2016. Outcomes include bony union, complications, dental rehabilitation, and donor site morbidity. RESULTS: A total of 120 patients were identified. Average harvested bone was 7.7 cm, with 54% undergoing one osteotomy, and 76.7% harvested as a chimeric flap. Radiographically, average inset bone was 6.6 cm. Complete or partial bony union was observed at 74.4% of proximal and 82.6% of distal osteotomies. A 95% of reconstructions met criteria for dental implants. Mean disabilities of the arm, shoulder and hand score was 21.2. CONCLUSIONS: The scapular tip is an excellent option for reconstruction of segmental mandible defects with the option of osteotomy, excellent bony union rates, low donor site morbidity, and potential for dental rehabilitation.

Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface.

We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

Magnetoencephalography With Optically Pumped 4He Magnetometers at Ambient Temperature.

In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with optically pumped magnetometers (OPMs) based on the parametric resonance of 4He atoms. The main advantage of this kind of OPM is the possibility to provide a tri-axis vector measurement of the magnetic field at room-temperature (the 4He vapor is neither cooled nor heated). The sensor achieves a sensitivity of 210 fT/ √ Hz in the bandwidth [2-300 Hz]. MEG simulation studies with a brain phantom were cross-validated with real MEG measurements on a healthy subject. For both studies, MEG signal was recorded consecutively with OPMs and superconducting quantum interference devices (SQUIDs) used as reference sensors. For healthy subject MEG recordings, three MEG proofs of concept were carried out: auditory evoked fields, visual evoked fields, and spontaneous activity. M100 peaks have been detected on evoked responses recorded by both OPMs and SQUIDs with no significant difference in latency. Concerning spontaneous activity, an attenuation of the signal power between 8-12 Hz (alpha band) related to eyes opening has been observed with OPM similarly to SQUID. All these results confirm that the room temperature vector 4He OPMs can record MEG signals and provide reliable information on brain activity.

Radiation Cataractogenesis: The Progression of Our Understanding and Its Clinical Consequences.

In the high-volume and increasingly complex world of image-guided therapy and medical imaging, awareness of the potential risks secondary to occupational radiation exposure in medical professionals needs greater focus. One of these risks is radiation-induced cataracts, a recently recognized entity, which may impact the physician's professional proficiency, quality of life, and career span. This review article aims to explain the pathogenesis of radiation-induced cataracts, exploring emerging evidence regarding their development. It also explores the existing monitoring and protection measures available to protect against such radiation-induced pathologic conditions.

Should future interventional neuroradiologists be screened for mutations that impair radiation-induced DNA repair?

In our current medical practice, an increasing number of specialists now have access to radiology technical platforms in order to perform imaging-guided procedures. Although knowledge about the current guidelines and radiation protection devices is a pre-requisite for the use of radiation, the preventive measures are often more or less strictly followed, leading to chronic daily exposure to significant doses of radiation and large accumulated lifetime exposures. Aortic intervention, electrophysiology, and neuro intervention in particular can result in large doses to the operators. Interventionalists might try to rationalize their dismissal of the exposure risks with various excuses: they don't know where they left their badges (even though, guiltily, they would readily admit it is good practice to always wear them), the estimated short duration of the procedure, significant muscular strain and spasm caused by the heaviness of lead aprons, decreased dexterity with lead gloves, or discomfort in wearing lead protective glasses. But their dismissive attitude is most likely due to the inherent inability to feel threatened by something they cannot see or feel, a commitment to the patient at all cost, and a culture of bravado that reinforces their behavior.