Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly.

BACKGROUND: Electroencephalography (EEG) stands as a pivotal non-invasive tool, capturing brain signals with millisecond precision and enabling real-time monitoring of individuals' mental states. Using appropriate biomarkers extracted from these EEG signals and presenting them back in a neurofeedback loop offers a unique avenue for promoting neural compensation mechanisms. This approach empowers individuals to skillfully modulate their brain activity. Recent years have witnessed the identification of neural biomarkers associated with aging, underscoring the potential of neuromodulation to regulate brain activity in the elderly. METHODS AND OBJECTIVES: Within the framework of an EEG-based brain-computer interface, this study focused on three neural biomarkers that may be disturbed in the aging brain: Peak Alpha Frequency, Gamma-band synchronization, and Theta/Beta ratio. The primary objectives were twofold: (1) to investigate whether elderly individuals with subjective memory complaints can learn to modulate their brain activity, through EEG-neurofeedback training, in a rigorously designed double-blind, placebo-controlled study; and (2) to explore potential cognitive enhancements resulting from this neuromodulation. RESULTS: A significant self-modulation of the Gamma-band synchronization biomarker, critical for numerous higher cognitive functions and known to decline with age, and even more in Alzheimer's disease (AD), was exclusively observed in the group undergoing EEG-neurofeedback training. This effect starkly contrasted with subjects receiving sham feedback. While this neuromodulation did not directly impact cognitive abilities, as assessed by pre- versus post-training neuropsychological tests, the high baseline cognitive performance of all subjects at study entry likely contributed to this result. CONCLUSION: The findings of this double-blind study align with a key criterion for successful neuromodulation, highlighting the significant potential of Gamma-band synchronization in such a process. This important outcome encourages further exploration of EEG-neurofeedback on this specific neural biomarker as a promising intervention to counter the cognitive decline that often accompanies brain aging and, eventually, to modify the progression of AD.

Detecting Anosognosia from the Prodromal Stage of Alzheimer's Disease.

BACKGROUND: Though not originally developed for this purpose, the Healthy Aging Brain Care Monitor (HABC-M) seems a valuable instrument for assessing anosognosia in Alzheimer's disease (AD). OBJECTIVES: Our study aimed at 1) investigating the validity of the HABC-M (31 items), and its cognitive, psychological, and functional subscales, in discriminating AD patients from controls; 2) exploring whether the HABC-M discrepancy scores between the self-reports of patients/controls in these different domains and the respective ratings provided by their caregivers/informants correlate with an online measure of self-awareness; 3) determining whether the caregiver burden level, also derived from the HABC-M, could add additional support for detecting anosognosia. METHODS: The HABC-M was administered to 30 AD patients and 30 healthy controls, and to their caregivers/informants. A measure of online awareness was established from subjects' estimation of their performances in a computerized experiment. RESULTS: The HABC-M discrepancy scores distinguished AD patients from controls. The cognitive subscale discriminated the two groups from the prodromal AD stage, with an AUC of 0.88 [95% CI: 0.78;0.97]. Adding the caregiver burden level raised it to 0.94 [0.86;0.99]. Significant correlations between the HABC-M and online discrepancy scores were observed in the patients group, providing convergent validity of these methods. CONCLUSIONS: The cognitive HABC-M (six items) can detect anosognosia across the AD spectrum. The caregiver burden (four items) may corroborate the suspicion of anosognosia. The short-hybrid scale, built from these 10 items instead of the usual 31, showed the highest sensitivity for detecting anosognosia from the prodromal AD stage, which may further help with timely diagnosis.

Can a failure in the error-monitoring system explain unawareness of memory deficits in Alzheimer's disease?

Unawareness of memory deficits is an early manifestation in patients with Alzheimer's disease (AD), which often delays diagnosis. This intriguing behavior constitutes a form of anosognosia, whose neural mechanisms remain largely unknown. We hypothesized that anosognosia may depend on a critical synaptic failure in the error-monitoring system, which would prevent AD patients from being aware of their own memory impairment. To investigate, we measured event-related potentials (ERPs) evoked by erroneous responses during a word memory recognition task in two groups of amyloid positive individuals with only subjective memory complaints at study entry: those who progressed to AD within the five-year study period (PROG group), and those who remained cognitively normal (CTRL group). A significant reduction in the amplitude of the positivity error (Pe), an ERP related to error awareness, was observed in the PROG group at the time of AD diagnosis (vs study entry) in intra-group analysis, as well as when compared with the CTRL group in inter-group analysis, based on the last EEG acquisition for all subjects. Importantly, at the time of AD diagnosis, the PROG group exhibited clinical signs of anosognosia, overestimating their cognitive abilities, as evidenced by the discrepancy scores obtained from caregiver/informant vs participant reports on the cognitive subscale of the Healthy Aging Brain Care Monitor. To our knowledge, this is the first study to reveal the emergence of a failure in the error-monitoring system during a word memory recognition task at the early stages of AD. This finding, along with the decline of awareness for cognitive impairment observed in the PROG group, strongly suggests that a synaptic dysfunction in the error-monitoring system may be the critical neural mechanism at the origin of unawareness of deficits in AD.

Probing the posture with machine learning provides physiological evidence supporting the enhanced body awareness hypothesis in trait mindfulness.

Enhanced body awareness has been suggested as one of the cognitive mechanisms that characterize mindfulness. Yet neuroscience literature still lacks strong empirical evidence to support this claim. Body awareness contributes to postural control during quiet standing; in particular, it may be argued that body awareness is more strongly engaged when standing quietly with eyes closed, because only body cues are available, than with eyes open. Under these theoretical assumptions, we recorded the postural signals of 156 healthy participants during quiet standing in Eyes closed (EC) and Eyes open (EO) conditions. In addition, each participant completed the Freiburg Mindfulness Inventory, and his/her mindfulness score was computed. Following a well-established machine learning methodology, we designed two numerical models per condition: one regression model intended to estimate the mindfulness score of each participant from his/her postural signals, and one classifier intended to assign each participant to one of the classes "Mindful" or "Non-mindful." We show that the two models designed from EC data are much more successful in their regression and classification tasks than the two models designed from EO data. We argue that these findings provide the first physiological evidence that contributes to support the enhanced body awareness hypothesis in mindfulness.

A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments.

We developed a brain-computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.

A New Machine-Learning Tool for Fast Estimation of Liquid Viscosity. Application to Cosmetic Oils.

The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whereby models are designed and trained from a database of viscosities of 300 molecules at 25 °C. Group contributions and graph machines make use of the 2D-structures only (the SMILES codes of the molecules), while neural networks estimations are based on a set of five descriptors: COSMO-RS σ-moments. For the first time, leave-one-out is used for graph machine selection, and it is shown that it can be replaced with the much faster virtual leave-one-out algorithm. The database covers a wide diversity of chemical structures, namely, alkanes, ethers, esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes, as well as different chemical backbone, i.e., straight, branched, or cyclic chains. A comparison of the viscosities of liquids of an independent set of 22 cosmetic oils shows that the graph machine approach provides the most accurate results given the available data. The results obtained by the neural network based on sigma-moments and by the graph machines can be duplicated easily by using a demonstration tool based on the Docker technology, available for download as explained in the Supporting Information. This demonstration also allows the reader to predict, at 25 °C, the viscosity of any liquid of moderate molecular size (M < 600 Da) that contains C, H, O, or Si atoms, starting either from its SMILES code or from its σ-moments computed with the COSMOtherm software.

Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach.

We developed a framework to study brain dynamics under cognition. In particular, we investigated the spatiotemporal properties of brain state switches under cognition. The lack of electroencephalography stationarity is exploited as one of the signatures of the metastability of brain states. We correlated power law exponents in the variables that we proposed to describe brain states, and dynamical properties of non-stationarities with cognitive conditions. This framework was successfully tested with three different datasets: a working memory dataset, an Alzheimer disease dataset, and an emotions dataset. We discuss the temporal organization of switches between states, providing evidence suggesting the need to reconsider the piecewise model, in which switches appear at discrete times. Instead, we propose a more dynamically rich view, in which besides the seemingly discrete switches, switches between neighbouring states occur all the time. These micro switches are not (physical) noise, as their properties are also affected by cognition.

A cognitive brain-computer interface monitoring sustained attentional variations during a continuous task.

We introduce a cognitive brain-computer interface based on a continuous performance task for the monitoring of variations of visual sustained attention, i.e. the self-directed maintenance of cognitive focus in non-arousing conditions while possibly ignoring distractors and avoiding mind wandering. We introduce a visual sustained attention continuous performance task with three levels of task difficulty. Pairwise discrimination of these task difficulties from electroencephalographic features was performed using a leave-one-subject-out cross validation approach. Features were selected using the orthogonal forward regression supervised feature selection method. Cognitive load was best predicted using a combination of prefrontal theta power, broad spatial range gamma power, fronto-central beta power, and fronto-central alpha power. Generalization performance estimates for pairwise classification of task difficulty using these features reached 75% for 5 s epochs, and 85% for 30 s epochs.

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework.

This study addresses the problem of Alzheimer's disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer's disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.

Independent Vector Analysis for SSVEP Signal Enhancement, Detection, and Topographical Mapping.

Steady state visual evoked potentials (SSVEPs) have been identified as an effective solution for brain computer interface (BCI) systems as well as for neurocognitive investigations. SSVEPs can be observed in the scalp-based recordings of electroencephalogram signals, and are one component buried amongst the normal brain signals and complex noise. We present a novel method for enhancing and improving detection of SSVEPs by leveraging the rich joint blind source separation framework using independent vector analysis (IVA). IVA exploits the diversity within each dataset while preserving dependence across all the datasets. This approach is shown to enhance the detection of SSVEP signals across a range of frequencies and subjects for BCI systems. Furthermore, we show that IVA enables improved topographic mapping of the SSVEP propagation providing a promising new tool for neuroscience and neurocognitive research.

Predicting the Surface Tension of Liquids: Comparison of Four Modeling Approaches and Application to Cosmetic Oils.

The efficiency of four modeling approaches, namely, group contributions, corresponding-states principle, σ-moment-based neural networks, and graph machines, are compared for the estimation of the surface tension (ST) of 269 pure liquid compounds at 25 °C from their molecular structure. This study focuses on liquids containing only carbon, oxygen, hydrogen, or silicon atoms since our purpose is to predict the surface tension of cosmetic oils. Neural network estimations are performed from σ-moment descriptors as defined in the COSMO-RS model, while methods based on group contributions, corresponding-states principle, and graph machines use 2D molecular information (SMILES codes). The graph machine approach provides the best results, estimating the surface tensions of 23 cosmetic oils, such as hemisqualane, isopropyl myristate, or decamethylcyclopentasiloxane (D5), with accuracy better than 1 mN·m-1. A demonstration of the graph machine model using the recent Docker technology is available for download in the Supporting Information.

Robust contour tracking in ultrasound tongue image sequences.

A new contour-tracking algorithm is presented for ultrasound tongue image sequences, which can follow the motion of tongue contours over long durations with good robustness. To cope with missing segments caused by noise, or by the tongue midsagittal surface being parallel to the direction of ultrasound wave propagation, active contours with a contour-similarity constraint are introduced, which can be used to provide 'prior' shape information. Also, in order to address accumulation of tracking errors over long sequences, we present an automatic re-initialization technique, based on the complex wavelet image similarity index. Experiments on synthetic data and on real 60 frame per second (fps) data from different subjects demonstrate that the proposed method gives good contour tracking for ultrasound image sequences even over durations of minutes, which can be useful in applications such as speech recognition where very long sequences must be analyzed in their entirety.

QSPR prediction of the stability constants of gadolinium(III) complexes for magnetic resonance imaging.

Gadolinium(III) complexes constitute the largest class of compounds used as contrast agents for Magnetic Resonance Imaging (MRI). A quantitative structure-property relationship (QSPR) machine-learning based method is applied to predict the thermodynamic stability constants of these complexes (log KGdL), a property commonly associated with the toxicity of such organometallic pharmaceuticals. In this approach, the log KGdL value of each complex is predicted by a graph machine, a combination of parametrized functions that encodes the 2D structure of the ligand. The efficiency of the predictive model is estimated on an independent test set; in addition, the method is shown to be effective (i) for estimating the stability constants of uncharacterized, newly synthesized polyamino-polycarboxylic compounds and (ii) for providing independent log KGdL estimations for complexants for which conflicting or questionable experimental data were reported. The exhaustive database of log KGdL values for 158 complexants, reported for potential application as contrast agents for MRI and used in the present study, is available in the Supporting Information (122 primary literature sources).

Bimodal BCI using simultaneously NIRS and EEG.

Although noninvasive brain-computer interfaces (BCI) based on electroencephalographic (EEG) signals have been studied increasingly over the recent decades, their performance is still limited in two important aspects. First, the difficulty of performing a reliable detection of BCI commands increases when EEG epoch length decreases, which makes high information transfer rates difficult to achieve. Second, the BCI system often misclassifies the EEG signals as commands, although the subject is not performing any task. In order to circumvent these limitations, the hemodynamic fluctuations in the brain during stimulation with steady-state visual evoked potentials (SSVEP) were measured using near-infrared spectroscopy (NIRS) simultaneously with EEG. BCI commands were estimated based on responses to a flickering checkerboard (ON-period). Furthermore, an "idle" command was generated from the signal recorded by the NIRS system when the checkerboard was not flickering (OFF-period). The joint use of EEG and NIRS was shown to improve the SSVEP classification. For 13 subjects, the relative improvement in error rates obtained by using the NIRS signal, for nine classes including the "idle" mode, ranged from 85% to 53 %, when the epoch length increase from 3 to 12 s. These results were obtained from only one EEG and one NIRS channel. The proposed bimodal NIRS-EEG approach, including detection of the idle mode, may make current BCI systems faster and more reliable.

Transient brain activity explains the spectral content of steady-state visual evoked potentials.

Steady-state visual evoked potentials (SSVEPs) are widely used in the design of brain-computer interfaces (BCIs). A lot of effort has therefore been devoted to find a fast and reliable way to detect SSVEPs. We study the link between transient and steady-state VEPs and show that it is possible to predict the spectral content of a subject's SSVEPs by simulating trains of transient VEPs. This could lead to a better understanding of evoked potentials as well as to better performances of SSVEP-based BCIs, by providing a tool to improve SSVEP detection algorithms.

Arrhythmia discrimination in implantable cardioverter defibrillators using support vector machines applied to a new representation of electrograms.

Arrhythmia classification remains a major challenge for appropriate therapy delivery in implantable cardioverter defibrillators (ICDs). The purpose of this paper is to present a new algorithm for arrhythmia discrimination based on a statistical classification by support vector machines of a novel 2-D representation of electrograms (EGMs) named spatial projection of tachycardia (SPOT) EGMs. SPOT-based discrimination algorithm provided sensitivity and specificity of 98.8% and 91.3%, respectively, on a test database. A simplified version of the algorithm is also presented, which can be directly implemented in the ICD.

The time course of new T-wave ECG descriptors following single- and double-dose administration of sotalol in healthy subjects.

INTRODUCTION: The aim of the study was to assess the time course effect of IKr blockade on ECG biomarkers of ventricular repolarization and to evaluate the accuracy of a fully automatic approach for QT duration evaluation. METHODS: Twelve-lead digital ECG Holter was recorded in 38 healthy subjects (27 males, mean age = 27.4 + or - 8.0 years) on baseline conditions (day 0) and after administration of 160 mg (day 1) and 320 mg (day 2) of d-l sotalol. For each 24-hour period and each subject, ECGs were extracted every 10 minutes during the 4-hour period following drug dosage. Ventricular repolarization was characterized using three biomarker categories: conventional ECG time intervals, principal component analysis (PCA) analysis on the T wave, and fully automatic biomarkers computed from a mathematical model of the T wave. RESULTS: QT interval was significantly prolonged starting 1 hour 20 minutes after drug dosing with 160 mg and 1 hour 10 minutes after drug dosing with 320 mg. PCA ventricular repolarization parameters sotalol-induced changes were delayed (>3 hours). After sotalol dosing, the early phase of the T wave changed earlier than the late phase prolongation. Globally, the modeled surrogate QT paralleled manual QT changes. The duration of manual QT and automatic surrogate QT were strongly correlated (R(2) = 0.92, P < 0.001). The Bland and Altman plot revealed a nonstationary systematic bias (bias = 26.5 ms + or - 1.96*SD = 16 ms). CONCLUSIONS: Changes in different ECG biomarkers of ventricular repolarization display different kinetics after administration of a potent potassium channel blocker. These differences need to be taken into account when designing ventricular repolarization ECG studies.

Towards the optimal design of numerical experiments.

This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagreement from experiment resampling (LDR), which borrows ideas from active learning and from resampling methods: the analysis of the divergence of the predictions provided by a population of models, constructed by resampling, allows an iterative determination of the point of input space, where a numerical experiment should be performed in order to improve the accuracy of the predictor. The LDR method is illustrated on neural network models with bootstrap resampling, and on orthogonal polynomials with leave-one-out resampling. Other methods of experimental design such as random selection and D-optimal selection are investigated on the same benchmark problems.

Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators.

This paper describes the automatic extraction of the P, Q, R, S and T waves of electrocardiographic recordings (ECGs), through the combined use of a new machine-learning algorithm termed generalized orthogonal forward regression (GOFR) and of a specific parameterized function termed Gaussian mesa function (GMF). GOFR breaks up the heartbeat signal into Gaussian mesa functions, in such a way that each wave is modeled by a single GMF; the model thus generated is easily interpretable by the physician. GOFR is an essential ingredient in a global procedure that locates the R wave after some simple pre-processing, extracts the characteristic shape of each heart beat, assigns P, Q, R, S and T labels through automatic classification, discriminates normal beats (NB) from abnormal beats (AB), and extracts features for diagnosis. The efficiency of the detection of the QRS complex, and of the discrimination of NB from AB, is assessed on the MIT and AHA databases; the labeling of the P and T wave is validated on the QTDB database.