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.

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.

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.

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.

EEG paroxysmal gamma waves during Bhramari Pranayama: a yoga breathing technique.

Here we report that a specific form of yoga can generate controlled high-frequency gamma waves. For the first time, paroxysmal gamma waves (PGW) were observed in eight subjects practicing a yoga technique of breathing control called Bhramari Pranayama (BhPr). To obtain new insights into the nature of the EEG during BhPr, we analyzed EEG signals using time-frequency representations (TFR), independent component analysis (ICA), and EEG tomography (LORETA). We found that the PGW consists of high-frequency biphasic ripples. This unusual activity is discussed in relation to previous reports on yoga and meditation. It is concluded this EEG activity is most probably non-epileptic, and that applying the same methodology to other meditation recordings might yield an improved understanding of the neurocorrelates of meditation.