EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry

Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of the entire brain. Functional magnetic resonance imaging (fMRI) can depict spatiotemporal functional neural networks and dynamic characteristics due to its excellent spatial resolution. However, its temporal resolution is limited. Neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG), which have higher temporal resolutions, are utilized for multi-temporal scale and multi-frequency-band analyzes. With this advantage, numerous EEG/MEG-bases studies have revealed the frequency-band specific functional networks involving dynamic functional connectivity and multiple temporal-scale time-series patterns of neural activity. In addition to analyzing neural data, the examination of behavioral data can unveil additional aspects of brain activity through unimodal and multimodal data analyzes performed using appropriate integration techniques. Among the behavioral data assessments, pupillometry can provide comprehensive spatial-temporal-specific features of neural activity. In this perspective, we summarize the recent progress in the development of metrics for analyzing neural data obtained from neuroimaging modalities such as fMRI, EEG, and MEG, as well as behavioral data, with a special focus on pupillometry data. First, we review the typical metrics of neural activity, emphasizing functional connectivity, complexity, dynamic functional connectivity, and dynamic state transitions of whole-brain activity. Second, we examine the metrics related to the time-series data of pupillary diameters and discuss the possibility of multimodal metrics that combine neural and pupillometry data. Finally, we discuss future perspectives on these multiple and multimodal metrics.

Association of EEG and cognitive impairment in overweight and non-overweight patients with schizophrenia

OBJECTIVE

Schizophrenia (SCZ) is a globally prevalent, severe chronic mental disorder, with cognitive dysfunction being one of its core symptoms. Notably, overweight is exceedingly common among individuals with SCZ, and overweight can also impact cognitive function. Therefore, the relationship between overweight and cognition in SCZ is a clinical issue that is in need of research attention.

METHODS

This study enrolled 77 patients with SCZ, including 36 overweight and 41 non-overweight patients. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity, while cognitive functions were evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Electroencephalography (EEG) testing was performed, with power spectral analysis conducted across various frequency bands (δ, θ, α, β, low γ, and high γ).

RESULTS

Compared to non-overweight SCZ patients, those overweight exhibited significantly lower RBANS total and index scores in immediate memory, visuospatial/constructional abilities, and delayed memory. EEG spectral analysis revealed that overweight SCZ patients demonstrated significantly lower oscillation power ratios in the β, low γ, and high γ frequency bands compared to their non-overweight counterparts. Correlation analyses indicated a significant positive relationship between β wave activity and RBANS total scores among overweight SCZ patients, suggesting that reduced β power correlates with more severe cognitive dysfunction.

CONCLUSION

Our findings indicate that overweight SCZ patients experience more severe cognitive impairments in a resting state than those who are not overweight, with significant differences in EEG spectrum observed in the β and γ frequency bands. Additionally, our study establishes a correlation between various EEG spectrum dimensions and cognition. This research highlights the effects of overweight on cognition in individuals with SCZ. Additionally, employing EEG technology to study cognitive function in overweight SCZ patients can offer valuable electrophysiological insights.

Neural oscillatory markers of respiratory sensory gating in human cortices

BACKGROUND

Human respiratory sensory gating is a neural process associated with inhibiting the cortical processing of repetitive respiratory mechanical stimuli. While this gating is typically examined in the time domain, the neural oscillatory dynamics, which could offer supplementary insights into respiratory sensory gating, remain unknown. The purpose of the present study was to investigate central neural gating of respiratory sensation using both time- and frequency-domain analyses.

METHODS

A total of 37 healthy adults participated in this study. Two transient inspiratory occlusions were presented within one inspiration, while responses in the electroencephalogram (EEG) were recorded. N1 amplitudes and oscillatory activities to the first stimulus (S1) and the second stimulus (S2) were measured. The perceived level of breathlessness and level of unpleasantness elicited by the occlusions were measured after the experiment.

RESULTS

As expected, the N1 peak amplitude to the S1 was significantly larger than to the S2. The averaged respiratory sensory gating S2/S1 ratio for the N1 peak amplitude was 0.71. For both the evoked- and induced-oscillations, time-frequency analysis showed higher theta activations in response to S1 relative to S2. A positive correlation was observed between the perceived unpleasantness and induced theta power.

CONCLUSIONS

Our results suggest that theta oscillations, evoked as well as induced, reflect the "gating" of respiratory sensation. Theta oscillation, particularly theta-induced power, may be indicative of the emotional processing of respiratory mechanosensation. The findings of this study serve as a foundation for future investigations into the underlying mechanisms of respiratory sensory gating, particularly in patient populations.

The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro

Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

BACKGROUND

Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation.

RESEARCH OBJECTIVE

This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach.

METHODS

This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted.

RESULTS

Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships.

CONCLUSION

The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

Effects of Virtual Reality Cognitive Training on Neuroplasticity: A Quasi-Randomized Clinical Trial in Patients with Stroke

Cognitive Rehabilitation (CR) is a therapeutic approach designed to improve cognitive functioning after a brain injury, including stroke. Two major categories of techniques, namely traditional and advanced (including virtual reality-VR), are widely used in CR for patients with various neurological disorders. More objective outcome measures are needed to better investigate cognitive recovery after a stroke. In the last ten years, the application of electroencephalography (EEG) as a non-invasive and portable neuroimaging method has been explored to extract the hallmarks of neuroplasticity induced by VR rehabilitation approaches, particularly within the chronic stroke population. The aim of this study is to investigate the neurophysiological effects of CR conducted in a virtual environment using the VRRS device. Thirty patients with moderate-to-severe ischemic stroke in the chronic phase (at least 6 months after the event), with a mean age of 58.13 (±8.33) for the experimental group and 57.33 (±11.06) for the control group, were enrolled. They were divided into two groups: an experimental group and a control group, receiving neurocognitive stimulation using VR and the same amount of conventional neurorehabilitation, respectively. To study neuroplasticity changes after the training, we focused on the power band spectra of theta, alpha, and beta EEG rhythms in both groups. We observed that when VR technology was employed to amplify the effects of treatments on cognitive recovery, significant EEG-related neural improvements were detected in the primary motor circuit in terms of power spectral density and time-frequency domains. Indeed, EEG analysis suggested that VR resulted in a significant increase in both the alpha band power in the occipital areas and the beta band power in the frontal areas, while no significant variations were observed in the theta band power. Our data suggest the potential effectiveness of a VR-based rehabilitation approach in promoting neuroplastic changes even in the chronic phase of ischemic stroke.

Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer's disease

Failed proteostasis is a well-documented feature of Alzheimer's disease, particularly, reduced protein degradation and clearance. However, the contribution of failed proteostasis to neuronal circuit dysfunction is an emerging concept in neurodegenerative research and will prove critical in understanding cognitive decline. Our objective is to convey Alzheimer's disease progression with the growing evidence for a bidirectional relationship of sleep disruption and proteostasis failure. Proteostasis dysfunction and tauopathy in Alzheimer's disease disrupts neurons that regulate the sleep-wake cycle, which presents behavior as impaired slow wave and rapid eye movement sleep patterns. Subsequent sleep loss further impairs protein clearance. Sleep loss is a defined feature seen early in many neurodegenerative disorders and contributes to memory impairments in Alzheimer's disease. Canonical pathological hallmarks, β-amyloid, and tau, directly disrupt sleep, and neurodegeneration of locus coeruleus, hippocampal and hypothalamic neurons from tau proteinopathy causes disruption of the neuronal circuitry of sleep. Acting in a positive-feedback-loop, sleep loss and circadian rhythm disruption then increase spread of β-amyloid and tau, through impairments of proteasome, autophagy, unfolded protein response and glymphatic clearance. This phenomenon extends beyond β-amyloid and tau, with interactions of sleep impairment with the homeostasis of TDP-43, α-synuclein, FUS, and huntingtin proteins, implicating sleep loss as an important consideration in an array of neurodegenerative diseases and in cases of mixed neuropathology. Critically, the dynamics of this interaction in the neurodegenerative environment are not fully elucidated and are deserving of further discussion and research. Finally, we propose sleep-enhancing therapeutics as potential interventions for promoting healthy proteostasis, including β-amyloid and tau clearance, mechanistically linking these processes. With further clinical and preclinical research, we propose this dynamic interaction as a diagnostic and therapeutic framework, informing precise single- and combinatorial-treatments for Alzheimer's disease and other brain disorders.

Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

Background

Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification.

Objective

With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues.

Methods

In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy.

Results

Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics.

Significance

This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.

Concurrent behavioral and electrophysiological longitudinal recordings for in vivo assessment of aging

Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.

Determining the Individual Theta Frequency for Associative Memory Targeted Personalized Transcranial Brain Stimulation

Non-invasive brain stimulation (NIBS) methods have gained increased interest in research and therapy of associative memory (AM) and its impairments. However, the one-size-fits-all approach yields inconsistent findings, thus putting forward the need for electroencephalography (EEG)-guided personalized frequency-modulated NIBS protocols to increase the focality and the effectiveness of the interventions. Still, extraction of individual frequency, especially in the theta band, turned out to be a challenging task. Here we present an approach to extracting the individual theta-band frequency (ITF) from EEG signals recorded during the AM task. The method showed a 93% success rate, good reliability, and the full range of variability of the extracted ITFs. This paper provides a rationale behind the adopted approach and critically evaluates it in comparison to the alternative methods that have been reported in the literature. Finally, we discuss how it could be used as an input parameter for personalized frequency-modulated NIBS approaches—transcranial alternating current stimulation (tACS) and transcranial oscillatory current stimulation (otDCS) directed at AM neuromodulation.

Effects of online parietal transcranial electric stimulation on associative memory: a direct comparison between tDCS, theta tACS, and theta-oscillatory tDCS

Associative memory (AM) is the ability to remember and retrieve multiple items bound together. Previous studies aiming to modulate AM by various transcranial electric stimulation (tES) techniques were inconclusive, although overall suggestive that tES could be a tool for AM enhancement. However, evidence from a direct comparison between different tES techniques is lacking. Here, in a sham-controlled cross-over experiment, we comparatively assessed the effects of three types of tES-anodal tDCS, theta-band transcranial alternating current stimulation (tACS), and theta-oscillatory tDCS (otDCS), delivered over the left posterior parietal cortex, during a short-term digit-color AM task with cued-recall. The effects were tested in 40 healthy young participants while both oscillatory tES were delivered at a previously determined individual theta frequency (4-8 Hz). All three active stimulations facilitated the overall AM performance, and no differences could be detected between them on direct comparison. However, unlike tDCS, the effects of which appeared to stem mainly from the facilitation of low-memory demand trials, both theta-modulated tACS and otDCS primarily promoted AM in high memory demand trials. Comparable yet differential effects of tDCS, theta tACS, and otDCS could be attributed to differences in their presumed modes of action.