MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study

Abstract

Objective Neurofeedback training may improve cognitive function in patients with neurological disorders. However, the underlying cerebral mechanisms of such improvements are poorly understood. Therefore, we aimed to investigate MRI correlates of cognitive improvement after EEG-based neurofeedback training in patients with MS (pwMS). Methods Fourteen pwMS underwent ten neurofeedback training sessions within 3–4 weeks at home using a tele-rehabilitation system. Half of the pwMS (N = 7, responders) learned to self-regulate sensorimotor rhythm (SMR, 12–15 Hz) by visual feedback and improved cognitively after training, whereas the remainder (non-responders, n = 7) did not. Diffusion-tensor imaging and resting-state fMRI of the brain was performed before and after training. We analyzed fractional anisotropy (FA) and functional connectivity (FC) of the default-mode, sensorimotor (SMN) and salience network (SAL). Results At baseline, responders and non-responders were comparable regarding sex, age, education, disease duration, physical and cognitive impairment, and MRI parameters. After training, compared to non-responders, responders showed increased FA and FC within the SAL and SMN. Cognitive improvement correlated with increased FC in SAL and a correlation trend with increased FA was observed. Conclusions This exploratory study suggests that successful neurofeedback training may not only lead to cognitive improvement, but also to increases in brain microstructure and functional connectivity.

Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study

“…BCIs allow for device control without motor involvement as they translate brain activation into output signals for communication or environmental control [ 7 ], and the sensorimotor rhythm (SMR) based BCIs detect characteristic changes in SMR in response to the motor task. The SMR based on BCIs training was adopted in several studies [ 8 – 10 ]. SMRs can be measured over the sensorimotor cortex (SMC) and modulated by motor imagery (MI), motor attempt (MA), or motor execution (ME) tasks [ 11 , 12 ].…”

Clinical applications of neurofeedback based on sensorimotor rhythm: a systematic review and meta-analysis

“…It was possible to verify the presence of the use of neuroimaging techniques such as fMRI, as an auxiliary way of evaluating the results (Shindo et al, 2011 ; Pinter et al, 2021 ). With regard to the presence of Follow-up, it is noted that after the interventions of the 44 studies analyzed, only 12 studies carried out the follow-up ( Figure 3 ) (Hammer et al, 2011 ; Shindo et al, 2011 ; Schabus et al, 2017 ; Marlats et al, 2020 ; Lau et al, 2021 ).…”

“…In studies with patients diagnosed with Multiple Sclerosis, the presence of improvement in cognitive functions was seen, such as in short- and long-term verbal memory, short-term visuospatial memory, working memory, and in the functional connectivity of patients with Multiple Sclerosis (Kober et al, 2019 ; Pinter et al, 2021 ). Hammer et al ( 2011 ), showed improvements in the quality of sleep of patients with insomnia after neurofeedback training, but, such improvements were not visualized by Schabus et al ( 2017 ), not being more efficacious than Cognitive Behavioral therapy.…”

The Effective Cognitive Assessment and Training Methods for COVID-19 Patients With Cognitive Impairment

“…In BCI technology, EEG signals can be used to objectively and accurately detect the brain specificity of patients with cognitive impairment, which could be performed in the community or even at home (PoŽar et al, 2020 ; San-Juan et al, 2020 ; Pinter et al, 2021 ). VR technology provides an immersive environment for patients with cognitive impairment, improves patient participation and training effect (Snider et al, 2013 ; Appel et al, 2020 ; Bauer and Andringa, 2020 ; Mancuso et al, 2020 ; Thielbar et al, 2020 ).…”

“…The rapid development of a brain-computer interface (BCI), virtual reality (VR), and artificial intelligence has promoted the diagnosis and treatment of cognitive impairment in the direction of intellectual development. Studies have shown that BCI-VR technology can compensate for the limitations of BCI alone and provide new rehabilitation and assessment methods for patients with cognitive impairment, which has attracted increasing attention (Wen et al, 2018 , 2020 ; Bauer and Andringa, 2020 ; Mancuso et al, 2020 ; Pinter et al, 2021 ).…”