Application of fMRI and Simultaneous fMRI-EEG Neurofeedback in Post-Stroke Motor Rehabilitation

Безматерных, Д. Д.; Калгин, К. В.; Maximova, P. E.; Мельников, М. Е.; Петровский, Е. Д.; Predtechenskaya, E. V.; Савелов, А. А.; Semenikhina, A. A.; Tsaplina, T. N.; Штарк, М. Б.; Shurunova, A. V.

doi:10.1007/s10517-021-05232-1

Cited by 6 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our data, CG and/or a sham condition are only rarely represented (i.e., 41% of the studies do not employ a CG) (Zotev et al, 2014;Zich et al, 2015;Perronnet et al, 2017;Savelov et al, 2019;Lioi et al, 2020a,b;Bezmaternykh et al, 2021), without following consensus on the reporting and experimental design of clinical and cognitive-behavioral neurofeedback studies (CRED-NF checklist; Ros et al, 2020). In a few studies, single-blinded CG showed clinical and NF change improvements that were not statistically different from EG (Zotev et al, 2016.…”

Section: Discussionmentioning

confidence: 69%

“…Concurrently, a significant reduction of high beta current density was observed in the right prefrontal region, indicating a reduction in avoidance-motivation related to anxiety . Three studies enrolled stroke patients (Savelov et al, 2019;Lioi et al, 2020a;Bezmaternykh et al, 2021). Bezmaternykh et al used a hybrid EEG-fMRI system and the OpenNFT suite for real-time estimation of the feedback.…”

Section: Multimodal Eeg-fmri-nf On Clinical Populationmentioning

confidence: 99%

“…Bezmaternykh et al used a hybrid EEG-fMRI system and the OpenNFT suite for real-time estimation of the feedback. The ROIs chosen for fMRI were in the premotor and supplementary motor regions; EEG target was alpha and beta band in C3 and C4 electrodes (Bezmaternykh et al, 2021). Lioi et al used the integrated EEG-fMRI-NF platform introduced by Mano et al (2017), computing NF signal with Matlab.…”

Section: Multimodal Eeg-fmri-nf On Clinical Populationmentioning

confidence: 99%

“…Perronnet et al, 2017;Keynan et al, 2019;Cury et al, 2020;Lioi et al, 2020b;Simões et al, 2020), while seven studies enrolled patients(Zotev et al, 2016(Zotev et al, , 2018bSavelov et al, 2019;Lioi et al, 2020a;Bezmaternykh et al, 2021).Studies involving patients included small sample sizes and quite a compact clinical field of application, principally psychiatric. Therefore, such studies may appear not very well suited for detecting mild or moderate effects.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Simultaneous real-time EEG-fMRI neurofeedback: A systematic review

Ciccarelli¹,

Federico²,

Mele³

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Neurofeedback (NF) is a biofeedback technique that teaches individuals self-control of brain functions by measuring brain activations and providing an online feedback signal to modify emotional, cognitive, and behavioral functions. NF approaches typically rely on a single modality, such as electroencephalography (EEG-NF) or a brain imaging technique, such as functional magnetic resonance imaging (fMRI-NF). The introduction of simultaneous EEG-fMRI tools has opened up the possibility of combining the high temporal resolution of EEG with the high spatial resolution of fMRI, thereby increasing the accuracy of NF. However, only a few studies have actively combined both techniques. In this study, we conducted a systematic review of EEG-fMRI-NF studies (N = 17) to identify the potential and effectiveness of this non-invasive treatment for neurological conditions. The systematic review revealed a lack of homogeneity among the studies, including sample sizes, acquisition methods in terms of simultaneity of the two procedures (unimodal EEG-NF and fMRI-NF), therapeutic targets field, and the number of sessions. Indeed, because most studies are based on a single session of NF, it is difficult to draw any conclusions regarding the therapeutic efficacy of NF. Therefore, further research is needed to fully understand non-clinical and clinical potential of EEG-fMRI-NF.

show abstract

Section: Discussionmentioning

confidence: 69%

Section: Multimodal Eeg-fmri-nf On Clinical Populationmentioning

confidence: 99%

Section: Multimodal Eeg-fmri-nf On Clinical Populationmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous real-time EEG-fMRI neurofeedback: A systematic review

Ciccarelli¹,

Federico²,

Mele³

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Another study attempted to compare the clinical and neurophysiological effects of a mono-and bimodal IBS platform (i.e., fMRI IBS and fMRI EEG IBS) in motor stroke rehabilitation (Bezmaternykh et al, 2021). Assessing the sample as nonrepresentative (one patient for each method), the authors concluded that both patients learned to increase C3/C4 coherence with other central leads in the EEG μ-band on the basis of feedback, and both of them improved their functional performance.…”

Section: Bs At Right Brodmann Area 4 Ibs At Left Brodmann Areamentioning

confidence: 99%

Interactive Brain Stimulation Neurotherapy Based on BOLD Signal in Stroke Rehabilitation

Khruscheva

Мельников

Безматерных

et al. 2022

View full text Add to dashboard Cite

Interactive brain stimulation is a new generation of neurofeedback characterized by a radical change in the targets of cognitive (volitional, adaptive) influence. These targets are represented by specific cerebral structures and neural networks, the reconstruction of which leads to the brain functions' restoration and behavioral metamorphoses. Functional magnetic resonance imaging (fMRI) in the neurofeedback contour uses a natural intravascular tracer, a blood-oxygenation-level-dependent (BOLD) signal as feedback. The subject included into the "interactive brain contour" learns to modulate and modify his or her own cerebral networks, creating new ones or "awakening" pre-existing ones, in order to improve (or restore) mental, sensory, or motor functions. In this review we focus on interactive brain stimulation based on BOLD signal and its role in the motor rehabilitation of stroke, briefly introducing the basic concepts of the so-called "network vocabulary" and general biophysical basis of the BOLD signal. We also discuss a bimodal fMRI-EEG neurofeedback platform and the prospects of fMRI technology in controlling functional connectivity, a numerical assessment of neuroplasticity.

show abstract

QCBO‐WSVM: Quantum chaos butterfly optimization‐based weighted support vector machine for neuropathic pain detection from EEG signal

Bobby¹,

Kapali²,

Kumar

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Chronic pain is a common problem among stroke patients, resulting from neurological damage to the central nervous system. This discomfort is primarily caused by the improper use of unaffected limbs or musculoskeletal issues. It can be challenging to differentiate neuropathic pain resulting from central nervous system damage. To address these challenges, researchers have developed a cutting‐edge technology called a Brain‐Computer Interface (BCI) based on electroencephalogram (EEG) data. In this paper, a novel BCI classifier has been developed using the Weighted Incremental‐Decremental Support Vector Machine (WIDSVM) classification method. The classifier has been trained using EEG‐based motor images from patients with central nervous system damage. The Quantum Chaos Butterfly Optimization Algorithm (QCBOA) has been used to enhance the performance of the WIDSVM classifier by creating a new dataset. The efficiency of the proposed model has been evaluated by comparing the results obtained from normal participants and those who developed chronic pain. The classification accuracy has been calculated for different regions, including the left hand, right hand, and feet, among the different participant groups. A total of 28 participants have been separated into three groups with pain in different regions, such as the lower abdomen and legs. The classifier has been tested using both 3‐channel bipolar montages and Common Spatial Patterns (CSPs). The results have shown that the proposed model offers higher classification accuracy and statistical significance in identifying the patient's risk of developing central neuropathic pain. However, it is important to note that further studies with larger sample sizes and different types of chronic pain are needed to validate the efficacy of the proposed model.

show abstract

Application of fMRI and Simultaneous fMRI-EEG Neurofeedback in Post-Stroke Motor Rehabilitation

Cited by 6 publications

References 7 publications

Simultaneous real-time EEG-fMRI neurofeedback: A systematic review

Simultaneous real-time EEG-fMRI neurofeedback: A systematic review

Interactive Brain Stimulation Neurotherapy Based on BOLD Signal in Stroke Rehabilitation

QCBO‐WSVM: Quantum chaos butterfly optimization‐based weighted support vector machine for neuropathic pain detection from EEG signal

Contact Info

Product

Resources

About