A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients

Abstract

Traditional rehabilitation techniques present limitations and the majority of patients show poor 1-year post-stroke recovery. Thus, Neurofeedback (NF) or Brain-Computer-Interface applications for stroke rehabilitation purposes are gaining increased attention. Indeed, NF has the potential to enhance volitional control of targeted cortical areas and thus impact on motor function recovery. However, current implementations are limited by temporal, spatial or practical constraints of the specific imaging modality used. In this pilot work and for the first time in literature, we applied bimodal EEG-fMRI NF for upper limb stroke recovery on four stroke-patients with different stroke characteristics and motor impairment severity. We also propose a novel, multi-target training approach that guides the training towards the activation of the ipsilesional primary motor cortex. In addition to fMRI and EEG outcomes, we assess the integrity of the corticospinal tract (CST) with tractography. Preliminary results suggest the feasibility of our approach and show its potential to induce an augmented activation of ipsilesional motor areas, depending on the severity of the stroke deficit. Only the two patients with a preserved CST and subcortical lesions succeeded in upregulating the ipsilesional primary motor cortex and exhibited a functional improvement of upper limb motricity. These findings highlight the importance of taking into account the variability of the stroke patients' population and enabled to identify inclusion criteria for the design of future clinical studies.

Keywords: EEG; fMRI; multimodal neuroimaging; neurofeedback; rehabilitation; stroke.

Copyright © 2020 Lioi, Butet, Fleury, Bannier, Lécuyer, Bonan and Barillot.

Figures

Figure 1

Experimental protocol of multisession Neurofeedback (NF) training procedure. Motor assessment (MA) and NF training sessions timeline is shown in the first row (bimodal EEG-FMRI b-s1 and b-s5 and unimodal EEG s2, s3 and s4). The second row is a schematic of the training protocol that was repeated at each session; finally, the third row shows the time-course of an NF run (block design alternating 20 s rest and 20 s NF training). Abbreviations: b-s, bimodal NF session; s, EEG only NF session; NFT, Neurofeedback training.

Figure 2

NF calculation schematic. The visual NF at time †* is equal to the average of EEG and fMRI NF scores, updated respectively every 250 ms and 1 s. The fMRI NF score, in turn, is equal to the weighted sum of blood-oxygen-level-dependent (BOLD) activations (contrast NF TASK > REST) in the supplementary motor area (SMA) and primary motor cortex (M1) regions of interest (ROIs) (in blue and red on a normalized anatomical scan, with calibration a priori masks in black). The weights assigned to the two contributions M1 and SMA vary from the first training session (a = 0.5, b = 0.5) to the second (a = 0.25, b = 0.75). The EEG score was obtained computing the Event Related Desynchronization (ERD) on a combination of electrodes given by Common Spatial Pattern (CSP) or laplacian filter weights.

Figure 3

Group results. (A) fMRI NF scores values (mean ± standard error across subjects and NF runs) with relative statistics; *indicates statistically significant difference (p < 0.01) between rest and NF task as assessed with a Wilcoxon test across subjects. (B) Scatter plot relating change in the clinical outcome (FMA-UE score) and ipsilesional M1 BOLD regulation for the four patients.

Figure 4

Patient P01 outcome measures. (A) M1 regulation during NF training: Normalized NF scores as showed to the patient (mean + standard error across NF sessions—NF1, NF2, NF3- for sessions b-s1 (orange) and b-s5 (blue). Resting blocks are indicated in white, NF training blocks in gray. (B) Bar plots of mean normalized NF scores in SMA (left bar plot) and M1 (right) with relative standard error and statistics for b-s1 and b-s5. *Indicates statistically significant difference (p < 0.01) between b-s1 and b-s5 as assessed with a Wilcoxon test across blocks of the same training session. (C) Corticospinal tract (CST) reconstruction from diffusion MRI imaging. Ipsilesional CST is represented in red and contralesional CST in green. (D) Manual Lesion Segmentation (in red) on an anatomical scan. (E) Individual contrast activation maps (NF TASK > REST, voxel-wise Family-Wise error (FWE) corrected, p < 0.05) during NF training in session b-s1 (orange) and b-s5 (blue). (F) Scalp plots of mean EEG ERD (across NF runs) in b-s1 (left) and b-s5 (right; bimodal EEG-fMRI sessions). (G) Unimodal EEG-NF outcomes: mean and standard error ERD estimated from the ipsilesional motor electrode (C4) for the three unimodal EEG-NF training sessions (left) with topoplot of the mean ERD values over motor electrodes (right). Results shown in panels (F,G) were obtained offline. For each motor channel (18 for the bimodal sessions, five for the unimodal EEG-NF runs) ERD was computed as the normalized difference in the 8–30 Hz band power (BP) between the rest block and the following training block. The mean ERD value for each channel is displayed in scalp plots representing “ERD activation maps.” For panel (G), in order to have a synthetic view of the ERD across the three unimodal sessions, only the ERD from channel C4, the electrode corresponding to the ipsilesional M1, was shown.

Figure 5

Patient 02 outcome measures. Legend as for Figure 4.

Figure 6

Patient 03 outcome measures. Legend as for Figure 4.

Figure 7

Patient 04 outcome measures. Legend as for Figure 4.