Avoiding Post-processing with Event-Based Detection in Biomedical Signals

OBJECTIVE

Finding events of interest is a common task in biomedical signal processing. The detection of epileptic seizures and signal artefacts are two key examples. Epoch-based classification is the typical machine learning framework to detect such signal events because of the straightforward application of classical machine learning techniques. Usually, post-processing is required to achieve good performance and enforce temporal dependencies. Designing the right post-processing scheme to convert these classification outputs into events is a tedious, and labor-intensive element of this framework.

METHODS

We propose an event-based modeling framework that directly works with events as learning targets, stepping away from ad-hoc post-processing schemes to turn model outputs into events. We illustrate the practical power of this framework on simulated data and real-world data, comparing it to epoch-based modeling approaches.

RESULTS

We show that event-based modeling (without tailored post-processing) performs on par with or better than epoch-based modeling with extensive post-processing.

CONCLUSION

These results show the power of treating events as direct learning targets, instead of using ad-hoc post-processing to obtain them, severely reducing design effort. Significance The event-based modeling framework can easily be applied to other event detection problems in signal processing, removing the need for intensive task-specific post-processing.

Identifying temporal correlations between natural single-shot videos and EEG signals

Objective.Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.Approach.We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.Main results.We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.Significance.This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.

What are wereallydecoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention

Objective.Spatial auditory attention decoding (Sp-AAD) refers to the task of identifying the direction of the speaker to which a person is attending in a multi-talker setting, based on the listener's neural recordings, e.g. electroencephalography (EEG). The goal of this study is to thoroughly investigate potential biases when training such Sp-AAD decoders on EEG data, particularly eye-gaze biases and latent trial-dependent confounds, which may result in Sp-AAD models that decode eye-gaze or trial-specific fingerprints rather than spatial auditory attention.Approach.We designed a two-speaker audiovisual Sp-AAD protocol in which the spatial auditory and visual attention were enforced to be either congruent or incongruent, and we recorded EEG data from sixteen participants undergoing several trials recorded at distinct timepoints. We trained a simple linear model for Sp-AAD based on common spatial patterns filters in combination with either linear discriminant analysis (LDA) or k-means clustering, and evaluated them both across- and within-trial.Main results.We found that even a simple linear Sp-AAD model is susceptible to overfitting to confounding signal patterns such as eye-gaze and trial fingerprints (e.g. due to feature shifts across trials), resulting in artificially high decoding accuracies. Furthermore, we found that changes in the EEG signal statistics across trials deteriorate the trial generalization of the classifier, even when the latter is retrained on the test trial with an unsupervised algorithm.Significance.Collectively, our findings confirm that there exist subtle biases and confounds that can strongly interfere with the decoding of spatial auditory attention from EEG. It is expected that more complicated non-linear models based on deep neural networks, which are often used for Sp-AAD, are even more vulnerable to such biases. Future work should perform experiments and model evaluations that avoid and/or control for such biases in Sp-AAD tasks.

Deciding individual treatment for primary retroperitoneal sarcoma

INTRODUCTION

Retroperitoneal sarcomas (RPS) are rare mesenchymal tumors that account for only 0.1-0.2% of all malignancies. Management of this disease is challenging, and resection remains the cornerstone of treatment. Ongoing international collaboration has expanded our knowledge of this disease, allowing for a more personalized approach to RPS patients resulting in improved survival over time. Due to the heterogeneity of RPS, with differing recurrence patterns and sensitivities to neoadjuvant therapies based on histology and grade, management of RPS should be tailored to the individual patient.

AREAS COVERED

Our review focuses on a histology-driven approach in the management of primary RPS. We searched relevant articles from 1993 to 2023 that investigated prognostic factors and treatment of patients with RPS and summarized recent advances and future directions in the field.

EXPERT OPINION

Deeper understanding of the role of neoadjuvant radiotherapy and ongoing trials investigating the role of neoadjuvant chemotherapy will potentially contribute to the development of individualized treatment pathways.

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

Objective.The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as 'spikes', in people with epilepsy.Approach.A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen's kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results.Main results.We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise<300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG.Significance.These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.

Rapid synthesis process and characterization for high purity sodium thioantimoniate nonahydrate

For the first time, Na₃SbS₄·9H₂O, also known as Schlippe's salt, has been synthesized through high-energy ball milling. This innovative synthesis way allows for obtaining high purity thioantimonate nonahydrate with around 90% yield in only approximately four hours. To validate the synthesis route described herein, the crystal structure has been refined, at room temperature, through high-resolution X-ray diffraction, pair distribution function analysis and energy dispersive spectrometry. Dehydration and rehydration of the compound have also been studied by thermogravimetric analysis and differential scanning calorimetry.

AB0410 EVOLUTION OF MONOCLONAL GAMMOPATHY OF UNDETERMINED SIGNIFICANCE IN PATIENTS TREATED WITH JAK INHIBITORS FOR RHEUMATIC DISEASES (JAKPIC STUDY)

Background

Monoclonal Gammopathy of Undetermined Significance (MGUS) is common in patients with inflammatory rheumatic diseases but there are scarce data regarding the effect of disease-modifying antirheumatic drugs (DMARDs) on this pre-malignant condition. Recently, preclinical data and phase I trial have shown efficacy of JAK inhibitors (JAKi) in multiple myeloma.

Objectives

We aimed to evaluate the impact of JAKi on MGUS when initiated for an active rheumatic disease.

Methods

Patients with monoclonal abnormality prior to JAKi initiation for an active rheumatic disease were identified through the MAJIK-SFR Registry, a french multicentre prospective study, and a call for observation via the “Club Rhumatismes et Inflammations”. Clinical and biological data were collected using a standardised case report form.

Results

Nineteen patients were identified, 10 women and 9 men, with a mean age of 65 years and a diagnosis of rheumatoid arthritis (n=14), psoriatic arthritis (n=3) or spondyloarthritis (n=2). The JAKi prescribed was baricitinib (n=8), tofacitinib (n=6) or upadacitinib (n=5), with a mean duration of 13 months.

Sixteen patients had individualized serum monoclonal protein (IgG Kappa n=9; IgG Lambda n=6; IgM Kappa n=3; IgA Lambda n=1) ranging from 0,16g/dL to 2,3g/dL. With a follow-up of 2 to 47 months, 8 of 16 patients experienced a decrease in serum monoclonal protein level and 8 had a stable serum monoclonal protein level. The maximal decrease observed was an initial IgG Kappa of 2.3g/dL decreasing to 0.2g/dL at month 14. During follow-up, two patients did not have any detectable serum monoclonal protein on serum electrophoresis (initial value of 5.2g/l and 1.6g/l), but still a positive immunofixation. One patient had bone marrow aspirate with 8% of plasma cells before JAKi introduction and 3% after 4 months of treatment.

Three patients did not have initial measurable spike but a positive immunofixation that became negative at month 8 and 11 (IgG Lambda, n=2) or stable (IgG Kappa, n=1).

Conclusion

This study brings reassuring and promising data on the MGUS evolution in patients treated with JAKi for rheumatic diseases, which may guide the choice of treatment in patients with both conditions.

References

[1]Berenson JR, To J, Spektor TM, et al. A Phase I Study of Ruxolitinib, Lenalidomide, and Steroids for Patients with Relapsed/Refractory Multiple MyelomaClin Cancer Res. 2020 May 15;26(10):2346-2353.

Acknowledgements

MAJIK-SFR Registry and Club Rhumatismes et Inflammations

Disclosure of Interests

None declared

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Quantitative ultrasound methods aim to estimate the acoustic properties of the underlying medium, such as the attenuation and backscatter coefficients, and have applications in various areas including tissue characterization. In practice, tissue heterogeneity makes the coefficient estimation challenging. In this work, we propose a computationally efficient algorithm to map spatial variations of the attenuation coefficient. Our proposed approach adopts a fast, linear least-squares strategy to fit the signal model to data from pulse-echo measurements. As opposed to existing approaches, we directly estimate the attenuation map, that is, the local attenuation coefficient at each axial location by solving a joint estimation problem. In particular, we impose a physical model that couples all these local estimates and combine it with a smooth regularization to obtain a smooth map. Compared to the conventional spectral log difference method and the more recent ALGEBRA approach, we demonstrate that the attenuation estimates obtained by our method are more accurate and better correlate with the ground-truth attenuation profiles over a wide range of spatial and contrast resolutions.

Time-adaptive Unsupervised Auditory Attention Decoding Using EEG-based Stimulus Reconstruction

The goal of auditory attention decoding (AAD) is to determine to which speaker out of multiple competing speakers a listener is attending based on the brain signals recorded via, e.g., electroencephalography (EEG). AAD algorithms are a fundamental building block of so-called neuro-steered hearing devices that would allow identifying the speaker that should be amplified based on the brain activity. A common approach is to train a subject-specific stimulus decoder that reconstructs the amplitude envelope of the attended speech signal. However, training this decoder requires a dedicated 'ground-truth' EEG recording of the subject under test, during which the attended speaker is known. Furthermore, this decoder remains fixed during operation and can thus not adapt to changing conditions and situations. Therefore, we propose an online time-adaptive unsupervised stimulus reconstruction method that continuously and automatically adapts over time when new EEG and audio data are streaming in. The adaptive decoder does not require ground-truth attention labels obtained from a training session with the end-user and instead can be initialized with a generic subject-independent decoder or even completely random values. We propose two different implementations: a sliding window and recursive implementation, which we extensively validate on three independent datasets based on multiple performance metrics. We show that the proposed time-adaptive unsupervised decoder outperforms a time-invariant supervised decoder, representing an important step toward practically applicable AAD algorithms for neuro-steered hearing devices.

Objective evaluation of stimulation artefact removal techniques in the context of neural spike sorting

Objective - We present a framework to objectively test and compare stimulation artefact removal techniques in the context of neural spike sorting. Approach - To this end, we used realistic hybrid ground-truth spiking data, with superimposed artefacts from in vivo recordings. We used the framework to evaluate and compare several techniques: blanking, template subtraction by averaging, linear regression, and a multi-channel Wiener filter (MWF). Main results - Our study demonstrates that blanking and template subtraction result in a poorer spike sorting performance than linear regression and MWF, while the latter two perform similarly. Finally, to validate the conclusions found from the hybrid evaluation framework, we also performed a qualitative analysis on in vivo recordings without artificial manipulations. Significance - Our framework allows direct quantification of the impact of the residual artefact on the spike sorting accuracy, thereby allowing for a more objective and more relevant comparison compared to indirect signal quality metrics that are estimated from the signal statistics. Furthermore, the availability of a ground truth in the form of single-unit spiking activity also facilitates a better estimation of such signal quality metrics.

EEG miniaturization limits for stimulus decoding with EEG sensor networks

Objective. Unobtrusive electroencephalography (EEG) monitoring in everyday life requires the availability of highly miniaturized EEG devices (mini-EEGs), which ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. By attaching a multitude of mini-EEGs at relevant positions on the scalp, a wireless 'EEG sensor network' (WESN) can be formed. However, each mini-EEG in the network only has access to its own local electrodes, thereby recording local scalp potentials with short inter-electrode distances. This is unlike using traditional cap-EEG, which by the virtue of re-referencing can measure EEG across arbitrarily large distances on the scalp. We evaluate the implications and limitations of such far-driven miniaturization on neural decoding performance.Approach. We collected 255-channel EEG data in an auditory attention decoding (AAD) task. As opposed to previous studies with a lower channel density, this new high-density dataset allows emulation of mini-EEGs with inter-electrode distances down to 1 cm in order to identify and quantify the lower bound on miniaturization for EEG-based stimulus decoding.Main results. We demonstrate that the performance remains reasonably stable for inter-electrode distances down to 3 cm, but decreases quickly for shorter distances if the mini-EEG nodes can be placed at optimal scalp locations and orientations selected by a data-driven algorithm.Significance. The results indicate the potential for the use of mini-EEGs in a WESN context for AAD applications and provide guidance on inter-electrode distances while designing such devices for neuro-steered hearing devices.

Omasal flow of nonstructural carbohydrates and nitrogenous compounds in lactating dairy cows fed diets containing timothy cut in the afternoon or morning

Shifting the cutting of grass from morning to afternoon has been shown to increase the concentration of nonstructural carbohydrates (NSC) in forages. We compared the effects of diets (66:34, forage:concentrate ratio) containing a mix (% of the diet dry matter) of baleages (46.5%) and silages (19.3%) harvested from timothy cut in the afternoon (p.m.-cut TIM diet) or morning (a.m.-cut TIM diet) on omasal flows of NSC and nitrogenous fractions, ruminal and total-tract digestibilities of nutrients, plasma concentration of AA, and milk yield and composition. Eight ruminally cannulated Holstein cows averaging (mean ± standard deviation) 31.4 ± 6.13 kg/d of milk, 136 ± 17.0 d in milk, and 611 ± 66.4 kg of body weight in the beginning of the experiment were used in a crossover design with 21-d periods (14 d for diet adaptation and 7 d for data and sample collection). Intake of total ethanol-soluble carbohydrates (TESC; +150 g/d), starch (+129 g/d), and total NSC (TESC plus starch = +278 g/d) was greater with feeding the p.m.- than the a.m.-cut TIM diet. Likewise, the apparent ruminal digestibilities of TESC (+149 g/d), starch (+167 g/d), and total NSC (+316 g/d) increased in the p.m.-cut TIM diet. Diets, however, had no effect on the omasal flows and apparent ruminal and total-tract digestibilities of dry matter and organic matter. Intake of N increased in cows fed the p.m.- versus the a.m.-cut TIM diet (562 and 528 g/d, respectively) despite no effect of diets on dry matter intake. Diets did not affect the omasal flows of total nonammonia N, total bacterial nonammonia N, nonammonia and nonbacterial N, and individual AA, and the efficiency of microbial protein synthesis in the rumen. Contrarily, supply of rumen-degradable protein increased (+9.2%) in cows fed the p.m.-cut TIM diet, with this response driven by the 6.4% increase in N intake. Plasma concentrations of essential and nonessential AA followed the omasal flow of AA and were not changed by diets. Feeding the p.m.- versus the a.m.-cut diet significantly increased yields of 4% fat-corrected milk and milk fat, and tended to increase energy-corrected milk, milk true protein, and milk lactose yields. Overall, feeding the p.m.-cut TIM diet to mid-lactation dairy cows did not improve microbial protein synthesis and omasal flow of AA, and these responses were in line with the lack of a treatment effect on dry matter intake.

Fast linear least-squares method for ultrasound attenuation and backscatter estimation

The ultrasonic attenuation and backscatter coefficients of tissues are relevant acoustic parameters due to their wide range of clinical applications. In this paper, a linear least-squares method for the estimation of these coefficients in a homogeneous region of interest based on pulse-echo measurements is proposed. The method efficiently fits an ultrasound backscattered signal model to the measurements in both the frequency and depth dimension simultaneously at a low computational cost. It is demonstrated that the inclusion of depth information has a positive effect particularly on the accuracy of the estimated attenuation. The sensitivity of the attenuation and backscatter coefficients' estimates to several predefined parameters such as the window length, window overlap and usable bandwidth of the spectrum is also studied. Comparison of the proposed method with a benchmark approach based on dynamic programming highlights better performance of our method in estimating these coefficients, both in terms of accuracy and computation time. Further analysis of the computation time as a function of the predefined parameters indicates our method's potential to be used in real-time clinical settings.

Electrocardiogram Quality Assessment using Unsupervised Deep Learning

OBJECTIVE

Noise and disturbances hinder effective interpretation of recorded ECG. To identify the clean parts of a recording, free from such disturbances, various quality indicators have been developed. Previous instances of these indicators focus on human-defined desirable properties of a clean signal. The reliance on human-specified properties places an inherent limitation on the potential power of signal quality indicators. To move away from this limitation, we propose a data-driven quality indicator.

METHODS

We use an unsupervised deep learning model, the auto-encoder, to derive the quality indicator. For different quality assessment settings we compare the performance of our quality indicator with traditional indicators.

RESULTS

The data-driven method performs consistently strong across tasks while performance of the traditional indicators varies strongly from task to task.

CONCLUSION

This strong performance indicates the potential of data-driven quality indicators for use in ECG processing, removing the reliance on expert-specified desirable properties.

SIGNIFICANCE

The proposed methodology can easily be extended towards learning quality indicators in other data modalities.

End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax

Objective.To develop an efficient, embedded electroencephalogram (EEG) channel selection approach for deep neural networks, allowing us to match the channel selection to the target model, while avoiding the large computational burdens of wrapper approaches in conjunction with neural networks.Approach.We employ a concrete selector layer to jointly optimize the EEG channel selection and network parameters. This layer uses a Gumbel-softmax trick to build continuous relaxations of the discrete parameters involved in the selection process, allowing them be learned in an end-to-end manner with traditional backpropagation. As the selection layer was often observed to include the same channel twice in a certain selection, we propose a regularization function to mitigate this behavior. We validate this method on two different EEG tasks: motor execution and auditory attention decoding. For each task, we compare the performance of the Gumbel-softmax method with a baseline EEG channel selection approach tailored towards this specific task: mutual information and greedy forward selection with the utility metric respectively.Main results.Our experiments show that the proposed framework is generally applicable, while performing at least as well as (and often better than) these state-of-the-art, task-specific approaches.Significance.The proposed method offers an efficient, task- and model-independent approach to jointly learn the optimal EEG channels along with the neural network weights.

A data-driven spike sorting feature map for resolving spike overlap in the feature space

Objective.Spike sorting is the process of extracting neuronal action potentials, or spikes, from an extracellular brain recording, and assigning each spike to its putative source neuron. Spike sorting is usually treated as a clustering problem. However, this clustering process is known to be affected by overlapping spikes. Existing methods for resolving spike overlap typically require an expensive post-processing of the clustering results. In this paper, we propose the design of a domain-specific feature map, which enables the resolution of spike overlap directly in the feature space.Approach.The proposed domain-specific feature map is based on a neural network architecture that is trained to simultaneously perform spike sorting and spike overlap resolution. Overlapping spikes clusters can be identified in the feature space through a linear relation with the single-neuron clusters for which the neurons contribute to the overlapping spikes. To aid the feature map training, a data augmentation procedure is presented that is based on biophysical simulations.Main results.We demonstrate the potential of our method on independent and realistic test data. We show that our novel approach for resolving spike overlap generalizes to unseen and realistic test data. Furthermore, the sorting performance of our method is shown to be similar to the state-of-the-art, but our method does not assume the availability of spike templates for resolving spike overlap.Significance.Resolving spike overlap directly in the feature space, results in an overall simplified spike sorting pipeline compared to the state-of-the-art. For the state-of-the-art, the overlapping spike snippets exhibit a large spread in the feature space and do not appear as concentrated clusters. This can lead to biased spike template estimates which affect the sorting performance of the state-of-the-art. In our proposed approach, overlapping spikes form concentrated clusters and spike overlap resolution does not depend on the availability of spike templates.

Factors contributing to ice nucleation and sequential freezing of leaves in wheat

Anatomical, metabolic and microbial factors were identified that contribute to sequential freezing in wheat leaves and likely contribute to supercooling in the youngest leaves and potentially meristematic regions. Infrared thermography (IR) has been used to observe wheat leaves freezing independently and in an age-related sequence with older leaves freezing first. To determine mechanisms that might explain this sequence of freezing several analytical approaches were used: (1) The size of xylem vessels, in proximity to where freezing initiated, was measured to see if capillary freezing point depression explained sequential freezing. The sequence of freezing in the four youngest leaves was correlated, with the largest vessels freezing first. (2) Carbohydrate and amino acids were analyzed to determine if solute concentrations as well as interactions with membranes explained the freezing sequence. Sucrose was highly correlated to the freezing sequence for all leaves suggesting a prominent role for this sugar as compared to other simple sugars and fructans. Among individual free amino acids proline and serine were correlated to the freezing sequence, with younger leaves having the highest concentrations. (3) Microflora within and on leaf surfaces were determined to measure potential freezing initiation. Levels of bacteria and fungi were correlated to the freezing sequence for all leaves, and species or genera associated with high ice nucleation activity were absent in younger leaves. Moisture content and transcript expression of ice binding proteins were also measured. As expected, our results show that no single mechanism explains the freezing sequence observed via infrared analyses. While these multiple mechanisms are operative at different levels according to the leaf age, they seem to converge when it comes to the protection of vital meristematic tissues. This provides potential phenotypic characters that could be used by breeders to develop more winter-hardy genotypes.

EEG-based detection of the locus of auditory attention with convolutional neural networks

In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1-2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.

EEG-based detection of the locus of auditory attention with convolutional neural networks

In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1-2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.

Unsupervised Self-Adaptive Auditory Attention Decoding

When multiple speakers talk simultaneously, a hearing device cannot identify which of these speakers the listener intends to attend to. Auditory attention decoding (AAD) algorithms can provide this information by, for example, reconstructing the attended speech envelope from electroencephalography (EEG) signals. However, these stimulus reconstruction decoders are traditionally trained in a supervised manner, requiring a dedicated training stage during which the attended speaker is known. Pre-trained subject-independent decoders alleviate the need of having such a per-user training stage but perform substantially worse than supervised subject-specific decoders that are tailored to the user. This motivates the development of a new unsupervised self-adapting training/updating procedure for a subject-specific decoder, which iteratively improves itself on unlabeled EEG data using its own predicted labels. This iterative updating procedure enables a self-leveraging effect, of which we provide a mathematical analysis that reveals the underlying mechanics. The proposed unsupervised algorithm, starting from a random decoder, results in a decoder that outperforms a supervised subject-independent decoder. Starting from a subject-independent decoder, the unsupervised algorithm even closely approximates the performance of a supervised subject-specific decoder. The developed unsupervised AAD algorithm thus combines the two advantages of a supervised subject-specific and subject-independent decoder: it approximates the performance of the former while retaining the `plug-and-play character of the latter. As the proposed algorithm can be used to automatically adapt to new users, as well as over time when new EEG data is being recorded, it contributes to more practical neuro-steered hearing devices.

Fast EEG-Based Decoding Of The Directional Focus Of Auditory Attention Using Common Spatial Patterns

OBJECTIVE

Noise reduction algorithms in current hearing devices lack informationabout the sound source a user attends to when multiple sources are present. To resolve this issue, they can be complemented with auditory attention decoding (AAD) algorithms, which decode the attention using electroencephalography (EEG) sensors. State-of-the-art AAD algorithms employ a stimulus reconstruction approach, in which the envelope of the attended source is reconstructed from the EEG and correlated with the envelopes of the individual sources. This approach, however, performs poorly on short signal segments, whilelonger segments yield impractically long detection delays when the user switches attention.

METHODS

We propose decoding the directional focus of attention using filterbank common spatial pattern filters (FB-CSP) as an alternative AAD paradigm, whichdoes not require access to the clean source envelopes.

RESULTS

The proposed FB-CSP approach outperforms both the stimulus reconstruction approach on short signal segments, as well as a convolutional neural network approach on the same task. We achieve a high accuracy (80% for [Formula: see text] windows and 70% for quasi-instantaneous decisions), which is sufficient to reach minimal expected switch durations below [Formula: see text]. We also demonstrate that the decoder can adapt to unlabeled data from anunseen subject and works with only a subset of EEG channels located around the ear to emulate a wearable EEG setup.

CONCLUSION

The proposed FB-CSP method provides fast and accurate decoding of the directional focus of auditory attention.

SIGNIFICANCE

The high accuracy on very short data segments is a major step forward towards practical neuro-steered hearing devices.

Utility metric for unsupervised feature selection

Feature selection techniques are very useful approaches for dimensionality reduction in data analysis. They provide interpretable results by reducing the dimensions of the data to a subset of the original set of features. When the data lack annotations, unsupervised feature selectors are required for their analysis. Several algorithms for this aim exist in the literature, but despite their large applicability, they can be very inaccessible or cumbersome to use, mainly due to the need for tuning non-intuitive parameters and the high computational demands. In this work, a publicly available ready-to-use unsupervised feature selector is proposed, with comparable results to the state-of-the-art at a much lower computational cost. The suggested approach belongs to the methods known as spectral feature selectors. These methods generally consist of two stages: manifold learning and subset selection. In the first stage, the underlying structures in the high-dimensional data are extracted, while in the second stage a subset of the features is selected to replicate these structures. This paper suggests two contributions to this field, related to each of the stages involved. In the manifold learning stage, the effect of non-linearities in the data is explored, making use of a radial basis function (RBF) kernel, for which an alternative solution for the estimation of the kernel parameter is presented for cases with high-dimensional data. Additionally, the use of a backwards greedy approach based on the least-squares utility metric for the subset selection stage is proposed. The combination of these new ingredients results in the utility metric for unsupervised feature selection U2FS algorithm. The proposed U2FS algorithm succeeds in selecting the correct features in a simulation environment. In addition, the performance of the method on benchmark datasets is comparable to the state-of-the-art, while requiring less computational time. Moreover, unlike the state-of-the-art, U2FS does not require any tuning of parameters.

SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance

Spike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.

Modulating ultrasound contrast generation from injectable nanodroplets for proton range verification by varying the degree of superheat

PURPOSE

Despite the physical benefits of protons over conventional photon radiation in cancer treatment, range uncertainties impede the ability to harness the full potential of proton therapy. While monitoring the proton range in vivo could reduce the currently adopted safety margins, a routinely applicable range verification technique is still lacking. Recently, phase-change nanodroplets were proposed for proton range verification, demonstrating a reproducible relationship between the proton range and generated ultrasound contrast after radiation-induced vaporization at 25°C. In this study, previous findings are extended with proton irradiations at different temperatures, including the physiological temperature of 37°C, for a novel nanodroplet formulation. Moreover, the potential to modulate the linear energy transfer (LET) threshold for vaporization by varying the degree of superheat is investigated, where the aim is to demonstrate vaporization of nanodroplets directly by primary protons.

METHODS

Perfluorobutane nanodroplets with a shell made of polyvinyl alcohol (PVA-PFB) or 10,12-pentacosadyinoic acid (PCDA-PFB) were dispersed in polyacrylamide hydrogels and irradiated with 62 MeV passively scattered protons at temperatures of 37°C and 50°C. Nanodroplet transition into echogenic microbubbles was assessed using ultrasound imaging (gray value and attenuation analysis) and optical images. The proton range was measured independently and compared to the generated contrast.

RESULTS

Nanodroplet design proved crucial to ensure thermal stability, as PVA-shelled nanodroplets dramatically outperformed their PCDA-shelled counterpart. At body temperature, a uniform radiation response proximal to the Bragg peak is attributed to nuclear reaction products interacting with PVA-PFB nanodroplets, with the 50% drop in ultrasound contrast being 0.17 mm ± 0.20 mm (mean ± standard deviation) in front of the proton range. Also at 50°C, highly reproducible ultrasound contrast profiles were obtained with shifts of -0.74 mm ± 0.09 mm (gray value analysis), -0.86 mm ± 0.04 mm (attenuation analysis) and -0.64 mm ± 0.29 mm (optical analysis). Moreover, a strong contrast enhancement was observed near the Bragg peak, suggesting that nanodroplets were sensitive to primary protons.

CONCLUSIONS

By varying the degree of superheat of the nanodroplets' core, one can modulate the intensity of the generated ultrasound contrast. Moreover, a submillimeter reproducible relationship between the ultrasound contrast and the proton range was obtained, either indirectly via the visualization of secondary reaction products or directly through the detection of primary protons, depending on the degree of superheat. The potential of PVA-PFB nanodroplets for in vivo proton range verification was confirmed by observing a reproducible radiation response at physiological temperature, and further studies aim to assess the nanodroplets' performance in a physiological environment. Ultimately, cost-effective online or offline ultrasound imaging of radiation-induced nanodroplet vaporization could facilitate the reduction of safety margins in treatment planning and enable adaptive proton therapy.

Optimal Versus Approximate Channel Selection Methods for EEG Decoding With Application to Topology-Constrained Neuro-Sensor Networks

Channel selection or electrode placement for neural decoding is a commonly encountered problem in electroencephalography (EEG). Since evaluating all possible channel combinations is usually infeasible, one usually has to settle for heuristic methods or convex approximations without optimality guarantees. To date, it remains unclear how large the gap is between the selection made by these approximate methods and the truly optimal selection. The goal of this paper is to quantify this optimality gap for several state-of-the-art channel selection methods in the context of least-squares based neural decoding. To this end, we reformulate the channel selection problem as a mixed-integer quadratic program (MIQP), which allows the use of efficient MIQP solvers to find the optimal channel combination in a feasible computation time for up to 100 candidate channels. As this reveals the exact solution to the combinatorial problem, it allows to quantify the performance losses when using state-of-the-art sub-optimal (yet faster) channel selection methods. In a context of auditory attention decoding, we find that a greedy channel selection based on the utility metric does not show a significant optimality gap compared to optimal channel selection, whereas other state-of-the-art greedy or l₁ -norm penalized methods do show a significant loss in performance. Furthermore, we demonstrate that the MIQP formulation also provides a natural way to incorporate topology constraints in the selection, e.g., for electrode placement in neuro-sensor networks with galvanic separation constraints. Furthermore, a combination of this utility-based greedy selection with an MIQP solver allows to perform a topology constrained electrode placement, even in large scale problems with more than 100 candidate positions.

[Association of hypertension and antihypertensive agents and the severity of COVID-19 pneumonia. A monocentric French prospective study]

BACKGROUND AND AIM

Angiotensin converting enzyme (ACE) type 2 is the receptor of SARSCoV-2 for cell entry into lung cells. Because ACE-2 may be modulated by ACE inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs), there are concern that patients treated with ACEIs and ARBs are at higher risk for COVID-19 infection or severity. This study sought to analyse the association of severe forms of COVID-19 and mortality with hypertension and a previous treatment with ACEI and ARB.

METHODS

Prospective follow-up of 433 consecutive patients hospitalised for COVID-19 pneumonia confirmed by PCR or highly probable on clinical, biological, and radiological findings, and included in the COVHYP study. Mortality and severe COVID-19 (criteria: death, intensive care unit, or hospitalisation >30 days) were compared in patients receiving or not ACEIs and ARBs. Follow-up was 100% at hospital discharge, and 96.5% at >1month.

RESULTS

Age was 63.6±18.7 years, and 40%) were female. At follow-up (mean 78±50 days), 136 (31%) patients had severity criteria (death, 64 ; intensive care unit, 73; hospital stay >30 days, 49). Hypertension (55.1% vs 36.7%, P<0.001) and antihypertensive treatment were associated with severe COVID-19 and mortality. The association between ACEI/ARB treatment and COVID-19 severity criteria found in univariate analysis (Odds Ratio 1.74, 95%CI [1.14-2.64], P=0.01) was not confirmed when adjusted on age, gender, and hypertension (adjusted OR1.13 [0.59-2.15], P=0.72). Diabetes and hypothyroidism were associated with severe COVID-19, whereas history of asthma was not.

CONCLUSION

This study suggests that previous treatment with ACEI and ARB is not associated with hospital mortality, 1- and 2-month mortality, and severity criteria in patients hospitalised for COVID-19. No protective effect of ACEIs and ARBs on severe pneumonia related to COVID-19 was demonstrated.

Computationally-Efficient Algorithm for Real-Time Absence Seizure Detection in Wearable Electroencephalography

Advances in electroencephalography (EEG) equipment now allow monitoring of people with epilepsy in their daily-life environment. The large volumes of data that can be collected from long-term out-of-clinic monitoring require novel algorithms to process the recordings on board of the device to identify and log or transmit only relevant data epochs. Existing seizure-detection algorithms are generally designed for post-processing purposes, so that memory and computing power are rarely considered as constraints. We propose a novel multi-channel EEG signal processing method for automated absence seizure detection which is specifically designed to run on a microcontroller with minimal memory and processing power. It is based on a linear multi-channel filter that is precomputed offline in a data-driven fashion based on the spatial-temporal signature of the seizure and peak interference statistics. At run-time, the algorithm requires only standard linear filtering operations, which are cheap and efficient to compute, in particular on microcontrollers with a multiply-accumulate unit (MAC). For validation, a dataset of eight patients with juvenile absence epilepsy was collected. Patients were equipped with a 20-channel mobile EEG unit and discharged for a day-long recording. The algorithm achieves a median of 0.5 false detections per day at 95% sensitivity. We compare our algorithm with state-of-the-art absence seizure detection algorithms and conclude it performs on par with these at a much lower computational cost.

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive techniques like magneto- or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials. Therefore, we present a data-driven method that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method first estimates the stimulus-driven neural response using the given stimulus, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus using EEG, our method resulted in more accurate short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.

An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a Context of Neuro-Steered Gain Control

In a multi-speaker scenario, a hearing aid lacks information on which speaker the user intends to attend, and therefore it often mistakenly treats the latter as noise while enhancing an interfering speaker. Recently, it has been shown that it is possible to decode the attended speaker from the brain activity, e.g., recorded by electroencephalography sensors. While numerous of these auditory attention decoding (AAD) algorithms appeared in the literature, their performance is generally evaluated in a non-uniform manner. Furthermore, AAD algorithms typically introduce a trade-off between the AAD accuracy and the time needed to make an AAD decision, which hampers an objective benchmarking as it remains unclear which point in each algorithm's trade-off space is the optimal one in a context of neuro-steered gain control. To this end, we present an interpretable performance metric to evaluate AAD algorithms, based on an adaptive gain control system, steered by AAD decisions. Such a system can be modeled as a Markov chain, from which the minimal expected switch duration (MESD) can be calculated and interpreted as the expected time required to switch the operation of the hearing aid after an attention switch of the user, thereby resolving the trade-off between AAD accuracy and decision time. Furthermore, we show that the MESD calculation provides an automatic and theoretically founded procedure to optimize the number of gain levels and decision time in an AAD-based adaptive gain control system.

A data-driven regularization approach for template matching in spike sorting with high-density neural probes

Spike sorting is the process of assigning neural spikes in an extracellular brain recording to their putative neurons. Optimal pre-whitened template matching filters that are used in spike sorting typically suffer from ill-conditioning. In this paper, we investigate the origin of this ill-conditioning and the way in which it influences the resulting filters. Two data-driven subspace regularization approaches are proposed, and those are shown to outperform a regularization approach used in recent literature. The comparison of the methods is based on ground truth data that are recorded in-vivo.

Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection

OBJECTIVE

Concealable, miniaturized electroencephalography (mini-EEG) recording devices are crucial enablers toward long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. We investigate whether optimizing the WESN topology can compensate for miniaturization effects in an auditory attention detection (AAD) paradigm.

METHODS

Starting from standard full-cap high-density EEG data, we emulate several candidate mini-EEG sensor nodes that locally collect EEG data with embedded electrodes separated by short distances. We propose a greedy group-utility based channel selection strategy to select a subset of these candidate nodes to form a WESN. We compare the AAD performance of this WESN with the performance obtained using long-distance EEG recordings.

RESULTS

The AAD performance using short-distance EEG measurements is comparable to using an equal number of long-distance EEG measurements if, in both cases, the optimal electrode positions are selected. A significant increase in performance was found when using nodes with three electrodes over nodes with two electrodes.

CONCLUSION

When the nodes are optimally placed, WESNs do not significantly suffer from EEG miniaturization effects in the case of AAD.

SIGNIFICANCE

WESN-like platforms allow us to achieve similar AAD performance as with long-distance EEG recordings while adhering to the stringent miniaturization constraints for ambulatory EEG. Their applicability in an AAD task is important for the design of neuro-steered auditory prostheses.

EEG-based auditory attention detection: boundary conditions for background noise and speaker positions

OBJECTIVE

A listener's neural responses can be decoded to identify the speaker the person is attending to in a cocktail party environment. Such auditory attention detection methods have the potential to provide noise suppression algorithms in hearing devices with information about the listener's attention. A challenge is the effect of noise and other acoustic conditions that can reduce the attention detection accuracy. Specifically, noise can impact the ability of the person to segregate the sound sources and perform selective attention, as well as the external signal processing necessary to decode the attention effectively. The aim of this work is to systematically analyze the effect of noise level and speaker position on attention decoding accuracy.

APPROACH

28 subjects participated in the experiment. Auditory stimuli consisted of stories narrated by different speakers from two different locations, along with surrounding multi-talker background babble. EEG signals of the subjects were recorded while they focused on one story and ignored the other. The strength of the babble noise as well as the spatial separation between the two speakers were varied between presentations. Spatio-temporal decoders were trained for each subject, and applied to decode attention of the subjects from every 30 s segment of data. Behavioral speech recognition thresholds were obtained for the different speaker separations.

MAIN RESULTS

Both the background noise level and the angular separation between speakers affected attention decoding accuracy. Remarkably, attention decoding performance was seen to increase with the inclusion of moderate background noise (versus no noise), while across the different noise conditions performance dropped significantly with increasing noise level. We also observed that decoding accuracy improved with increasing speaker separation, exhibiting the advantage of spatial release from masking. Furthermore, the effect of speaker separation on the decoding accuracy became stronger when the background noise level increased. A significant correlation between speech intelligibility and attention decoding accuracy was found across conditions.

SIGNIFICANCE

This work shows how the background noise level and relative positions of competing talkers impact attention decoding accuracy. It indicates in which circumstances a neuro-steered noise suppression system may need to operate, in function of acoustic conditions. It also indicates the boundary conditions for the operation of EEG-based attention detection systems in neuro-steered hearing prostheses.

A foldable electrode array for 3D recording of deep-seated abnormal brain cavities

OBJECTIVE

This study describes the design and microfabrication of a foldable thin-film neural implant and investigates its suitability for electrical recording of deep-lying brain cavity walls.

APPROACH

A new type of foldable neural electrode array is presented, which can be inserted through a cannula. The microfabricated electrode is specifically designed for electrical recording of the cavity wall of thalamic lesions resulting from stroke. The proof-of-concept is demonstrated by measurements in rat brain cavities. On implantation, the electrode array unfolds in the brain cavity, contacting the cavity walls and allowing recording at multiple anatomical locations. A three-layer microfabrication process based on UV-lithography and Reactive Ion Etching is described. Electrochemical characterization of the electrode is performed in addition to an in vivo experiment in which the implantation procedure and the unfolding of the electrode are tested and visualized.

MAIN RESULTS

Electrochemical characterization validated the suitability of the electrode for in vivo use. CT imaging confirmed the unfolding of the electrode in the brain cavity and analysis of recorded local field potentials showed the ability to record neural signals of biological origin.

SIGNIFICANCE

The conducted research confirms that it is possible to record neural activity from the inside wall of brain cavities at various anatomical locations after a single implantation procedure. This opens up possibilities towards research of abnormal brain cavities and the clinical conditions associated with them, such as central post-stroke pain.

French validation and adaptation of the Grobman nomogram for prediction of vaginal birth after cesarean delivery

OBJECTIVE

To validate Grobman nomogram for predicting vaginal birth after cesarean delivery (VBAC) in a French population and adapt it.

STUDY DESIGN

Multicenter retrospective study of maternal and obstetric factors associated with VBAC between May 2012 and May 2013 in 6 maternity units. External validation and adaptation of the prenatal and intrapartum Grobman nomograms for vaginal birth prediction after cesarean delivery in a French cohort.

RESULTS

The study included 523 women with previous cesarean deliveries; 70% underwent a trial of labor for a subsequent delivery (n=367) with a success rate of 65% (n=240). In the univariate analysis, 5 factors were associated with successful VBAC: previous vaginal delivery before the cesarean (P<0.001), the number of previous vaginal deliveries (P<0.001), and a favorable cervix at delivery room admission, cervical effacement (P=0.035), or cervical dilatation at least 3cm (P<0.001), or a Bishop score >6 (P=0.03). A potentially recurrent indication (defined as arrest of dilation or descent as the indication for the previous cesarean) (P=0.039), a hypertensive disorder during pregnancy (P=0.05), and labor induction (P=0.017) were each associated with failed VBAC. External validation of the prenatal and intrapartum Grobman nomograms showed an area under the ROC curve of 69% (95% CI: 0.638, 0.736) and 65% (95% CI: 0.599, 0.700) respectively. Adaptation of the nomogram to the French cohort resulted in the inclusion of the following factors: maternal age, body mass index at last prenatal visit, hypertensive disorder, gestational age at delivery, recurring indication, cervical dilatation, and induction of labor. Its area under the curve to predict successful VBAC was 78% (95% CI: 0.738, 0.825).

CONCLUSION

The nomogram to predict VBAC developed by Grobman et al. is validated in the French population. Adaptation to the French population, by excluding ethnicity, appeared to improve its performance. Impact of the nomogram use on the caesarean section rate has to be validated in a randomized control trial.

Metal accumulation and detoxification mechanisms in mycorrhizal Betula pubescens

Metal detoxification in plants is a complex process that involves different mechanisms, such as the retention of metals to the cell wall and their chelation and subsequent compartmentalization in plant vacuoles. In order to identify the mechanisms involved in metal accumulation and tolerance in Betula pubescens, as well as the role of mycorrhization in these processes, mycorrhizal and non-mycorrhizal plants were grown in two industrial soils with contrasting concentrations of heavy metals. Mycorrhization increased metal uptake at low metal concentrations in the soil and reduced it at high metal concentrations, which led to an enhanced growth and biomass production of the host when growing in the most polluted soil. Our results suggest that the sequestration on the cell wall is the main detoxification mechanism in white birch exposed to acute chronic metal-stress, while phytochelatins play a role mitigating metal toxicity inside the cells. Given its high Mn and Zn root-to-shoot translocation rate, Betula pubescens is a very promising species for the phytoremediation of soils polluted with these metals.

Mercury contamination level and speciation inventory in Lakes Titicaca & Uru-Uru (Bolivia): Current status and future trends

Aquatic ecosystems of the Bolivian Altiplano (∼3800 m a.s.l.) are characterized by extreme hydro-climatic constrains (e.g., high UV-radiations and low oxygen) and are under the pressure of increasing anthropogenic activities, unregulated mining, agricultural and urban development. We report here a complete inventory of mercury (Hg) levels and speciation in the water column, atmosphere, sediment and key sentinel organisms (i.e., plankton, fish and birds) of two endorheic Lakes of the same watershed differing with respect to their size, eutrophication and contamination levels. Total Hg (THg) and monomethylmercury (MMHg) concentrations in filtered water and sediment of Lake Titicaca are in the lowest range of reported levels in other large lakes worldwide. Downstream, Hg levels are 3-10 times higher in the shallow eutrophic Lake Uru-Uru than in Lake Titicaca due to high Hg inputs from the surrounding mining region. High percentages of MMHg were found in the filtered and unfiltered water rising up from <1 to ∼50% THg from the oligo/hetero-trophic Lake Titicaca to the eutrophic Lake Uru-Uru. Such high %MMHg is explained by a high in situ MMHg production in relation to the sulfate rich substrate, the low oxygen levels of the water column, and the stabilization of MMHg due to abundant ligands present in these alkaline waters. Differences in MMHg concentrations in water and sediments compartments between Lake Titicaca and Uru-Uru were found to mirror the offset in MMHg levels that also exist in their respective food webs. This suggests that in situ MMHg baseline production is likely the main factor controlling MMHg levels in fish species consumed by the local population. Finally, the increase of anthropogenic pressure in Lake Titicaca may probably enhance eutrophication processes which favor MMHg production and thus accumulation in water and biota.

Mycorrhization protects Betula pubescens Ehr. from metal-induced oxidative stress increasing its tolerance to grow in an industrial polluted soil

In recent years, the use of woody plants in phytoremediation has gained popularity due to their high biomass production and their association with mycorrhizal fungi, which can improve their survival and development rates under stress conditions. In this study, mycorrhized and non-mycorrhized white birch plants (Betula pubescens Ehr.) were grown in control and a metal-polluted industrial soil. After 60days of culture, plant growth and metal accumulation, the content of photosynthetic pigments and oxidative-stress markers, as well as the enzymatic activities and gene expressions of antioxidant enzymes were measured. According to our results, mycorrhized birch plants grown in control soil showed an increased activity and gene expression of catalase and ascorbate peroxidase, along with hydrogen peroxide overproduction, which could support the importance of the reactive oxygen species as signaling molecules in the regulation of plant-fungus interactions. Additionally, in polluted soil mycorrhized plants had higher biomass but lower metal accumulation, probably because the symbiotic fungus acted as a barrier to the entrance of metals into the host plants. This behavior led to mitigation in the oxidative challenge, reduced hydrogen peroxide content and diminished activities of the antioxidant enzymes in comparison to non-mycorrhized plants.

A clinical and neurophysiological motor signature of Unverricht-Lundborg disease

OBJECTIVES

Unverricht-Lundborg disease (ULD) is the most common form of progressive myoclonus epilepsy. Cerebellar dysfunction may appear over time, contributing along with myoclonus to motor disability. The purpose of the present work was to clarify the motor and neurophysiological characteristics of ULD patients.

METHODS

Nine patients with genetically proven ULD were evaluated clinically (medical history collected from patient charts, the Scale for the Assessment and Rating of Ataxia and Unified Myoclonus Rating Scale). Neurophysiological investigations included EEG, surface polymyography, long-loop C-reflexes, somatosensory evoked potentials, EEG jerk-locked back-averaging (JLBA) and oculomotor recordings. All patients underwent brain MRI. Non-parametric Mann-Whitney tests were used to compare ULD patients' oculomotor parameters with those of a matched group of healthy volunteers (HV).

RESULTS

Myoclonus was activated by action but was virtually absent at rest and poorly induced by stimuli. Positive myoclonus was multifocal, often rhythmic and of brief duration, with top-down pyramidal temporospatial propagation. Cortical neurophysiology revealed a transient wave preceding myoclonus on EEG JLBA (n=8), enlarged somatosensory evoked potentials (n=7) and positive long-loop C-reflexes at rest (n=5). Compared with HV, ULD patients demonstrated decreased saccadic gain, increased gain dispersion and a higher frequency of hypermetric saccades associated with decreased peak velocity.

CONCLUSION

A homogeneous motor pattern was delineated that may represent a ULD clinical and neurophysiological signature. Clinical and neurophysiological findings confirmed the pure cortical origin of the permanent myoclonus. Also, oculomotor findings shed new light on ULD pathophysiology by evidencing combined midbrain and cerebellar dysfunction.