Editorial: Multimodal approaches to investigating neural dynamics in cognition and related clinical conditions: integrating EEG, MEG, and fMRI data

Frontiers in Systems Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Feb. 11, 2025

neuroanatomical information, behavioral data including performance evaluations on a working memory task, were analyzed as key indicator of the effects neurofeedback and associated cerebral hemodynamic changes over time. The findings indicated that training was effective in enhancing prefrontal activity maintaining cognitive function acute stroke patients. multidimensional approach enabled thorough examination relationship between brain structure, function, behavior.Jones, Keith G., et al. conducted detailed EEG patterns during propofol-induced burst suppression patients with treatment-resistant depression. Their study identifies distinct types activity, highlighting variability neural responses to anesthesia. Participants randomized receive either high-or low-dose propofol infusions. identified three 1-broadband bursts, 2-spindles, 3-low-frequency each varying significantly across subjects terms occurrence, spectral power, spatial scalp. Combining clinical (e.g., drug dosages patient characteristics) provided valuable insights into individualization anesthetic dosing diverse propofol, potentially leading more tailored treatment strategies practice.In another study, Mertiens, Sean, investigated how Parkinson's disease (PD) affects resting state networks (RSNs) using MEG measure phase-amplitude coupling (PAC). This combined T1-MRI scans. captured dynamic oscillations PAC within RSNs, while accurate source reconstruction signals. integration allows for precise mapping RSNs revealed significant alterations these PD compared healthy controls, particularly sensorimotor network (SMN). Levodopa medication normalized SMN toward similar though no observed optimal frequencies, suggesting levodopa primarily motor symptoms without influencing all same way.The results above-mentioned studies demonstrate combining different modalities can enhance process provide deeper diseases disorders. However, previously discussed, integrating multiple present challenges, some are focused improving addressing technical issues. Karittevlis, Christodoulos, introduce novel method analyzing evoked somatosensory stimulation, aimed at identifying early thalamus cortex. EEG-MEG often face challenges accurately determining due reliance complex models many assumptions. These be inaccurate because skull distorts electrical Additionally, stimulus trials complicate achievement consistent results. proposes straightforward bypasses modeling uses called virtual sensors (VS), which combines directly capture activity. improves accuracy elicited trial-to-trial variability, offering clearer reliable communication regions. By simplifying computational processes, authors potential applications real-time brain-computer interfaces, practical benefits multimodal approaches applied neuroscience.The by Dayarian, N, Khadem, A leverages recording MRI-based head new algorithm localization. Accurate localization is crucial diagnosing planning treatments neurological disorders such epilepsy, precisely location abnormal It also plays role surgical planning, helping minimize damage critical regions, aids monitoring progression tailoring individual patients, thus personalized medicine (Yang al., 2023).However, faces several geometry brain, complicates modeling. Traditional methods struggle heterogeneous properties tissues low resolution EEG, making it difficult differentiate closely situated sources. signals susceptible noise artifacts, muscle or eye movements, obscure true hinder (Michel Brunet, 2019;Hirata 2024). Many techniques rely assumptions about models, may not reflect anatomical variations, further impacting (Hirata co-registration model critical, any misalignment affect results.Dayarian, propose hybrid strengths boundary element (BEM) finite (FEM), address challenges. BEM effectively isotropic regions dipolar sources, FEM handles complex, anisotropic greater accuracy. validating this realistic achieves improvements forward problem solutions. demonstrates enhanced error metrics alone, value incorporating information from MRI improve precision neuroimaging analyses.Each imaging has its own advantages limitations, outlined Table 1. To fast dynamics, high temporal essential rapid Among non-invasive techniques, stand out their millisecondlevel resolution, them ideal studying processes. In contrast, fMRI fNIRS offer order seconds, limits applicability investigating dynamics.In most sensitive fMRI, followed fNIRS, MEG, EEG. High mapping. offers highest (in millimeters), allowing visualization active layers. lowest resolution. Although have been developed mitigate limitation, they volume conduction require density electrodes determine (Liu 2023).Apart factors usage complexity, portability, cost considerations when se Apart selecting method. leverage various limitations technique, recommended. simultaneous use two presents following sections.The primary advantage EEG-fMRI co-recording ability both (through data) data), resulting comprehensive understanding One issue need MR-compatible hardware, increase costs. systems designed non-magnetic induced voltages currents could arise radiofrequency gradient fields scans (Mele 2019;Scrivener, 2021;Warbrick, 2022). If adequately controlled, cause heating ECG leads, posing risks participants (Kugel 2003).To risks, careful design wires materials resistant built-in resistors necessary. addition, magnetic environment quality signals, reduce signal-to-noise ratio, post-processing influence MRI's fields, effect generally minimal disregarded. typically lying down, suitable requiring positions, sitting, standing, walking. Moreover, environment's cold temperatures intense uncomfortable especially children elderly, confounding Lastly, well-suited sleep disorder discomfort impracticality extended scanning sessions (Duyn, 2012;Mele 2019;Ebrahimzadeh 2022).Analyzing recordings unique requires specific methods. Common analysis include (Huster 2012;Abreu 2018):1. Symmetrical Approaches  Model-Based Techniques: involve creating mathematical understand data. complexities ensuring Despite mentioned frequently used than other approaches. preference likely maturity technologies relatively lower systems.While notable lack inability record sitting standing restricted duration, an noisy. Functional near-infrared spectroscopy (fNIRS) addresses measuring metabolic similarly but flexibility. influenced differences skin color hair type, positions (sitting, walking) periods. Consequently, EEG-fNIRS provides solution concerns portability systems. smaller maintenance requirements 2021;Uchitel 2021).One challenge configuring related interference. Ongoing technological development needed (Ahn Jun, 2017;Chen 2020;Li 2022).Pre-processing processing modality steps, introduces additional negative impact one modalities. solutions (Hossain 2022;Li 2022;Mughal 2022) facilitates data, thereby overall function.Simultaneous achieve necessity systems, adverse recordings. researchers employ (Im 2005;Plis 2010;Hall 2014;Cichy 2016):1. Minimize Temporal Overlap: Reduce concurrent time lessen 2. Use Precise Timing Triggers: Ensure alignment timing triggers. 3. Shield sensors: Protect external electromagnetic 4. Apply ICA: ICA separate signals.These help difficulties facilitating data.To MEG-fMRI co-recording, replacing advantageous. substitution reducing MEG's higher superior ratio common MEG-fNIRS MEG-fNIRS. difficulty achieving arises differing physical modality. For instance, configuration optodes necessitates placement align optimally measurement points (Ru 2022).To analyze integrate preprocessing fusion recommended:1. Alignment: Synchronize reference. MEG: filtering remove drifts high-frequency noise, artifacts. fNIRS: Perform baseline correction detrending manage slow wavelet adaptive handle motion physiological noise.3. Spatial Align coordinates reference.4. Data Standardization: comparability standardizing data.Feature Extraction Fusion: Extract relevant features datasets combine advanced canonical correlation (CCA), joint independent component (jICA), algorithms.These enable interpretation datasets.Along methods, functions Behavioral includes person's psychological tasks, response time, correlate behaviors, providing processed brain. electrophysiological reveal brain-behavioral interactions view functions.Thus, choice depends research type objectives. 2 summary, continue advance, pivotal developing diagnostic therapeutic psychiatric Future should focus analysis, optimizing sensor configurations, robust Such advancements deepen our pathology, opportunities neuroscience applications. Notably, review articles cover combination separately, collection original empirical value.

Language: Английский

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Multisource Associate Domain Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2023, Volume and Issue: 72, P. 1 - 12

Published: Jan. 1, 2023

Emotion recognition is important in the application of brain-computer interface (BCI). Building a robust emotion model across subjects and sessions critical based BCI systems. Electroencephalogram (EEG) widely used tool to recognize different states. However, EEG has disadvantages such as small amplitude, low signal-to-noise ratio, non-stationary properties, resulting large differences subjects. To solve these problems, this paper proposes new method on multi-source associate domain adaptation network, considering both invariant domain-specific features. First, separate branches were constructed for multiple source domains, assuming that data shared same low-level Secondly, specific features extracted by using one-to-one adaptation. Then, weighted scores sources obtained according distribution distance, classifiers deduced with corresponding scores. Finally, experiments conducted datasets, including SEED, DEAP, SEED-IV dataset. Results indicated that, cross-subject experiment, average accuracy SEED dataset was 86.16%, DEAP 65.59%, 59.29%. In cross-session accuracies datasets 91.10% 66.68%, respectively. Our proposed achieved better classification results compared state-of-the-art methods.

Language: Английский

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Implementation of artificial intelligence and machine learning-based methods in brain–computer interaction

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 163, P. 107135 - 107135

Published: June 8, 2023

Brain–computer interfaces are used for direct two-way communication between the human brain and computer. Brain signals contain valuable information about mental state activity of examined subject. However, due to their non-stationarity susceptibility various types interference, processing, analysis interpretation challenging. For these reasons, research in field brain–computer is focused on implementation artificial intelligence, especially five main areas: calibration, noise suppression, communication, condition estimation, motor imagery. The use algorithms based intelligence machine learning has proven be very promising application domains, ability predict learn from previous experience. Therefore, within medical technologies can contribute more accurate subjects, alleviate consequences serious diseases or improve quality life disabled patients.

Language: Английский

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Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

Frontiers in Integrative Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Feb. 27, 2023

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements hardware, software, research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience community. We spotlight through lens of different end-application users, including unique perspective a manufacturer, report challenges using this technology across several disciplines populations. Through review domains utilized, we identify address presence bias, specifically due restraints current technology, limited diversity among sample populations, societal prejudice that infiltrates today's research. Finally, provide resources minimizing application agenda future use equitable, diverse, inclusive.

Language: Английский

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

Journal of NeuroEngineering and Rehabilitation, Journal Year: 2024, Volume and Issue: 21(1)

Published: Jan. 3, 2024

Abstract Background Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate diagnosis rehabilitation motor deficits. These allow for synchronous acquisition analysis complex signal streams neurophysiological data (e.g., EEG, fNIRS) behavioral capture). The fusion those has potential provide new insights into cortical mechanisms during movement, guide practices, become a tool assessment therapy neurorehabilitation. Research objective This paper aims review existing literature on combined use rehabilitation. is understand diversity maturity technological solutions employed explore clinical advantages this multimodal approach. Methods reviews related following PRISMA guidelines. Besides study participant characteristics, aspects used systems, processing methods, nature feature synchronization were extracted. Results Out 908 publications, 19 included final review. Basic or translation studies mainly represented based predominantly healthy participants stroke patients. EEG mechanical technologies most biomechanical acquisition, their subsequent traditional methods. system techniques at large underreported. features supported identification movement-related activity, statistical occasionally examine cortico-kinematic relationships. Conclusion might offer future. facilitating cognitive processes real-world settings, it could also improve rehabilitative devices’ usability environments. Further, by better understanding cortico-peripheral coupling, neuro-rehabilitation can be developed, such as personalized proprioceptive training. However, further research needed advance our knowledge cortical-peripheral evaluate validity reliability parameters, enhance user-friendly adaptation.

Language: Английский

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Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review

Biomimetics, Journal Year: 2024, Volume and Issue: 9(4), P. 237 - 237

Published: April 16, 2024

Digital health tracking is a source of valuable insights for public research and consumer technology. The brain the most complex organ, containing information about psychophysical physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), photoplethysmography (PPG) technologies have allowed development devices can remotely monitor changes activity. inclusion criteria papers this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total 23 were reviewed, comprising 17 using EEGs remote monitoring 6 neurofeedback interventions, while no found related to fNIRS PPG. This reveals previous leveraged mobile EEG across mental health, neurological, sleep domains, as well delivering interventions. With headsets ear-EEG being common, feasible implementation study protocols providing reliable signal quality. Moderate substantial agreement overall between clinical-grade was statistical tests. results highlight promise portable brain-imaging regard continuously evaluating patients natural settings, though further validation usability enhancements are needed technology develops.

Language: Английский

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Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry

Neurophotonics, Journal Year: 2023, Volume and Issue: 10(01)

Published: Feb. 7, 2023

Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, good tolerance motion, past few decades have witnessed a rapid increase in research clinical use fNIRS variety psychiatric disorders. In this perspective article, we first briefly summarize state-of-the-art concerning psychiatry. particular, highlight diverse applications research, advanced development instruments, novel study designs exploring We then discuss some open challenges share our perspectives on future practice. conclude that holds promise becoming useful tool settings respect developing closed-loop systems improving individualized treatments diagnostics.

Language: Английский

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fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Bioengineering, Journal Year: 2023, Volume and Issue: 10(12), P. 1393 - 1393

Published: Dec. 6, 2023

Motor impairment has a profound impact on significant number of individuals, leading to substantial demand for rehabilitation services. Through brain–computer interfaces (BCIs), people with severe motor disabilities could have improved communication others and control appropriately designed robotic prosthetics, so as (at least partially) restore their abilities. BCI plays pivotal role in promoting smoother interactions between individuals impairments others. Moreover, they enable the direct assistive devices through brain signals. In particular, most potential lies realm rehabilitation, where BCIs can offer real-time feedback assist users training continuously monitor brain’s state throughout entire process. Hybridization different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) electroencephalography (EEG), shown great creation rehabilitating motor-impaired populations. EEG, well-established methodology, be combined fNIRS compensate inherent disadvantages achieve higher temporal spatial resolution. This paper reviews recent works hybrid fNIRS-EEG emphasizing methodologies that utilized imagery. An overview system its key components was introduced, followed by an introduction various devices, strengths weaknesses signal processing techniques, applications neuroscience clinical contexts. The review concludes discussing possible challenges opportunities future development.

Language: Английский

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The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Frontiers in Network Physiology, Journal Year: 2024, Volume and Issue: 3

Published: Jan. 16, 2024

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus—a discrete cortical area which seizures originate—to widespread network—spanning lobes hemispheres—considerably advanced our understanding epilepsy continues to influence both research clinical treatment this multi-faceted high-impact neurological disorder. network, however, not static but evolves in time requires novel approaches for in-depth characterization. In review, we discuss conceptual basics theory critically examine state-of-the-art recording techniques analysis tools used assess characterize time-evolving human network. We give account on current shortcomings highlight potential developments towards improved management epilepsy.

Language: Английский

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A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study

International Journal of Intelligent Systems, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 14

Published: March 27, 2023

In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) employed to select the appropriate correlated fNIRS channels. Furthermore, in extraction phase, performance of two filter-based algorithms, (i) minimum redundancy maximum relevance (mRMR) (ii) ReliefF, investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, kurtosis) are used, whereas conventional support vector machine (SVM) utilized as classifier training testing. proposed validated an available online dataset motor imagery (left- right-hand), mental arithmetic, baseline tasks. First, efficacy shown two-class BCI left- vs. right-hand arithmetic baseline). Second, framework applied four-class results show that number channels was significantly reduced, resulting significant increase classification accuracy both applications, respectively. mRMR 87.8% imagery, 87.1% 78.7% four-class) ReliefF 90.7% 93.7% 81.6% yielded high average $\left(p<0.05\right)$ . However, algorithm more stable significant.

Language: Английский

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