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.

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.

Environment and body-brain interplay affect inhibition and decision-making.

The fine-tuned interplay of brain and body underlies human ability to cope with changes in the internal and external milieus. Previous research showed that cardiac interoceptive changes (e.g., cardiac phase) affect cognitive functions, notably inhibition that is a key element for adaptive behaviour. Here we investigated the influence on cognition of vestibular signal, which provides the brain with sensory information about body position and movement. We used a centrifuge-based design to disrupt vestibular signal in healthy human volunteers while their inhibition and decision-making functions were assessed with the stop-signal paradigm. Participants performed the standard and a novel, sensorial version of the stop-signal task to determine whether disrupted vestibular signal influences cognition as a function of its relevance to the context. First, we showed that disrupted vestibular signal was associated with a larger variability of longest inhibition latencies, meaning that participants were even slower to inhibit in the trials where they had the most difficulty inhibiting. Second, we revealed that processing of bodily information, as required in the sensorial stop-signal task, also led to a larger variability of longest inhibition latencies, which was all the more important when vestibular signal was disrupted. Lastly, we found that such a degraded response inhibition performance was due in part to the acceleration of decision-making process, meaning that participants made a decision more quickly even when strength of sensory evidence was reduced. Taken together, these novel findings provide direct evidence that vestibular signal affects the cognitive functions of inhibition and decision-making.

The heartbeat evoked potential does not support strong interoceptive sensibility in trait mindfulness.

The enhancement of body awareness is proposed as one of the cognitive mechanisms that characterize mindfulness. To date, this hypothesis is supported by self-report and behavioral measures but still lacks physiological evidence. The current study investigated relation between trait mindfulness (i.e., individual differences in the ability to be mindful in daily life) and body awareness in combining a self-report measure (Multidimensional Assessment of Interoceptive Awareness [MAIA] questionnaire) with analysis of the heartbeat evoked potential (HEP), which is an event-related potential reflecting the cortical processing of the heartbeat. The HEP data were collected from 17 healthy participants under five minutes of resting-state condition. In addition, each participant completed the Freiburg Mindfulness Inventory and the MAIA questionnaire. Taking account of the important variability of HEP effects, analyses were replicated with the same participants three times (in three distinct sessions). First, group-level analyses showed that HEP amplitude and trait mindfulness do not correlate. Secondly, we observed that HEP amplitude could positively correlate with self-reported body awareness; however, this association was unreliable over time. Interestingly, we found that HEP measure shows very poor reliability over time at the individual level, potentially explaining the lack of reliable association between HEP and psychological traits. Lastly, a reliable positive correlation was found between self-reported trait mindfulness and body awareness. Taken together, these findings provide preliminary evidence that the HEP might not support the increased subjective body awareness in trait mindfulness, thus suggesting that perhaps objective and subjective measures of body awareness could be independent.

Toward a Refined Mindfulness Model Related to Consciousness and Based on Event-Related Potentials.

Neuroimaging, behavioral, and self-report evidence suggests that there are four main cognitive mechanisms that support mindfulness: (a) self-regulation of attention, (b) improved body awareness, (c) improved emotion regulation, and (d) change in perspective on the self. In this article, we discuss these mechanisms on the basis of the event-related potential (ERP). We reviewed the ERP literature related to mindfulness and examined a data set of 29 articles. Our findings show that the neural features of mindfulness are consistently associated with the self-regulation of attention and, in most cases, reduced reactivity to emotional stimuli and improved cognitive control. On the other hand, there appear to be no studies of body awareness. We link these electrophysiological findings to models of consciousness and introduce a unified, mechanistic mindfulness model. The main idea in this refined model is that mindfulness decreases the threshold of conscious access. We end with several working hypotheses that could direct future mindfulness research and clarify our results.

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.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Relieving peripheral neuropathic pain by increasing the power-ratio of low-ß over high-ß activities in the central cortical region with EEG-based neurofeedback: Study protocol for a controlled pilot trial (SMRPain study).

BACKGROUND: Chronic neuropathic pain associated with peripheral neuropathies cannot be attributed solely to lesions of peripheral sensory axons and likely involves alteration in the processing of nociceptive information in the central nervous system in most patients. Few data are available regarding EEG correlates of chronic neuropathic pain. The fact is that effective cortical neuromodulation strategies to treat neuropathic pain target the precentral cortical region, i.e. a cortical area corresponding to the motor cortex. It is not known how these strategies might modulate brain rhythms in the central cortical region, but it can be speculated that sensorimotor rhythms (SMRs) are modified. Another potent way of modulating cortical rhythms is to use EEG-based neurofeedback (NFB). Rare studies previously aimed at relieving neuropathic pain using EEG-NFB training. METHODS/DESIGN: The objective of this single-centre, single-blinded, randomized controlled pilot study is to assess the value of an EEG-NFB procedure to relieve chronic neuropathic pain in patients with painful peripheral neuropathy. A series of 32 patients will be randomly assigned to one of the two following EEG-NFB protocols, aimed at increasing either the low-ß(SMR)/high-ß ratio (n=16) or the α(µ)/θ ratio (n=16) at central (rolandic) cortical level. Various clinical outcome measures will be collected before and one week after 12 EEG-NFB sessions performed over 4weeks. Resting-state EEG will also be recorded immediately before and after each NFB session. The primary endpoint will be the change in the impact of pain on patient's daily functioning, as assessed on the Interference Scale of the short form of the Brief Pain Inventory. DISCUSSION: The value of EEG-NFB procedures to relieve neuropathic pain has been rarely studied. This pilot study will attempt to show the value of endogenous modulation of brain rhythms in the central (rolandic) region in the frequency band corresponding to the frequency of stimulation currently used by therapeutic motor cortex stimulation. In the case of significant clinical benefit produced by the low-ß(SMR)/high-ß ratio increasing strategy, this work could pave the way for using EEG-NFB training within the armamentarium of neuropathic pain therapy.

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.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback.

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

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.

Oculomotricity and Neurological Soft Signs: Can we refine the endophenotype? A study in subjects belonging to the spectrum of schizophrenia.

Alterations in eye tracking and motor impairments as well as Neurological Soft Signs (NSS) are frequently reported in patients with schizophrenia as well as in their relatives, and are proposed as endophenotype of the disease. This study investigated smooth pursuit eye movement and fixation task with distractors with a gap condition, two markers of inhibitory control mechanism, in 49 patients with schizophrenia, 24 ultra-high risk subjects, 41 full biological clinical siblings of patients and 48 controls. NSS were assessed as a marker of abnormal neurodevelopment. The results revealed more intrusive saccades respectively in smooth pursuit eye movement and in fixation task with distractors with a gap condition in patients, respect to controls and full siblings. Ultra high-risk participants with high NSS committed intrusive saccades compared to controls. Patients with schizophrenia with high NSS also displayed more of these abnormalities, compared to patients with schizophrenia with low NSS and controls. These findings highlight a global inhibitory control defect, and suggested that ultra-high risk subjects and patients with schizophrenia could share oculomotor abnormalities, especially when they express a high neurodevelopmental deviance. These oculomotor alterations might suggest that cerebral structures such as prefrontal and cerebellum could be involved in the expression of this vulnerability.

Cognitive Decline and Reorganization of Functional Connectivity in Healthy Aging: The Pivotal Role of the Salience Network in the Prediction of Age and Cognitive Performances.

Normal aging is related to a decline in specific cognitive processes, in particular in executive functions and memory. In recent years a growing number of studies have focused on changes in brain functional connectivity related to cognitive aging. A common finding is the decreased connectivity within multiple resting state networks, including the default mode network (DMN) and the salience network. In this study, we measured resting state activity using fMRI and explored whether cognitive decline is related to altered functional connectivity. To this end we used a machine learning approach to classify young and old participants from functional connectivity data. The originality of the approach consists in the prediction of the performance and age of the subjects based on functional connectivity by using a machine learning approach. Our findings showed that the connectivity profile between specific networks predicts both the age of the subjects and their cognitive abilities. In particular, we report that the connectivity profiles between the salience and visual networks, and the salience and the anterior part of the DMN, were the features that best predicted the age. Moreover, independently of the age of the subject, connectivity between the salience network and various specific networks (i.e., visual, frontal) predicted episodic memory skills either based on a standard assessment or on an autobiographical memory task, and short-term memory binding. Finally, the connectivity between the salience and the frontal networks predicted inhibition and updating performance, but this link was no longer significant after removing the effect of age. Our findings confirm the crucial role of episodic memory and executive functions in cognitive aging and suggest a pivotal role of the salience network in neural reorganization in aging.

Towards Semi-Automatic Artifact Rejection for the Improvement of Alzheimer's Disease Screening from EEG Signals.

A large number of studies have analyzed measurable changes that Alzheimer's disease causes on electroencephalography (EEG). Despite being easily reproducible, those markers have limited sensitivity, which reduces the interest of EEG as a screening tool for this pathology. This is for a large part due to the poor signal-to-noise ratio of EEG signals: EEG recordings are indeed usually corrupted by spurious extra-cerebral artifacts. These artifacts are responsible for a consequent degradation of the signal quality. We investigate the possibility to automatically clean a database of EEG recordings taken from patients suffering from Alzheimer's disease and healthy age-matched controls. We present here an investigation of commonly used markers of EEG artifacts: kurtosis, sample entropy, zero-crossing rate and fractal dimension. We investigate the reliability of the markers, by comparison with human labeling of sources. Our results show significant differences with the sample entropy marker. We present a strategy for semi-automatic cleaning based on blind source separation, which may improve the specificity of Alzheimer screening using EEG signals.

A novel method of early diagnosis of Alzheimer's disease based on EEG signals.

Studies have reported that electroencephalogram signals in Alzheimer's disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer's disease patients and healthy subjects. We have compared the right and left temporal lobes of the brain with the rest of the brain areas (frontal, central, and occipital) as temporal regions are relatively the first ones to be affected by Alzheimer's disease. Moreover, electroencephalogram signals are further classified into five different frequency bands (delta, theta, alpha beta, and gamma) because each frequency band has its own physiological significance in terms of signal evaluation. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with Average technique. The simulation results indicated that applying principal component analysis before synchrony measurement techniques shows significantly better results as compared to the lateral one. At the end, all the aforementioned techniques are assessed by a statistical test (Mann-Whitney U test) to compare the results.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease.

OBJECTIVE: Recently, significant advances have been made in the early diagnosis of Alzheimer's disease (AD) from electroencephalography (EEG). However, choosing suitable measures is a challenging task. Among other measures, frequency relative power (RP) and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency RP on EEG signals, examining the changes found in different frequency ranges. APPROACH: We first explore the use of a single feature for computing the classification rate (CR), looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing mild cognitive impairment (MCI) and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4 ± 11.5). MAIN RESULTS: Using a single feature to compute CRs we achieve a performance of 78.33% for the MCI data set and of 97.56% for Mild AD. Results are clearly improved using the multiple feature classification, where a CR of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using four features. SIGNIFICANCE: The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

A theta-band EEG based index for early diagnosis of Alzheimer's disease.

Despite recent advances, early diagnosis of Alzheimer's disease (AD) from electroencephalography (EEG) remains a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony in the α band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects. The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients and healthy subjects is increased using the proposed ratio.