RESUMO
OBJECTIVE: To determine the efficacy of neural interface-based neurorehabilitation, including brain-computer interface, through conventional and individual patient data (IPD) meta-analysis and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation. DATA SOURCES: PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed. STUDY SELECTION: Studies using neural interface-controlled physical effectors (functional electrical stimulation and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper-extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed. DATA EXTRACTION: Changes in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD. DATA SYNTHESIS: Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE scores increased by a mean of 5.23 (95% confidence interval [CI]: 3.85-6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 vs ≤4 weeks. On IPD analysis, the mean time-to-improvement above minimal clinically important difference (MCID) was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41%-70%). Patients with severe impairment (P=.042) and age >50 years (P=.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (hazard ratio [HR] 0.15, P=.08 and HR 0.47, P=.06, respectively). CONCLUSION: Neural interface-based motor rehabilitation resulted in significant, although modest, reductions in poststroke impairment and should be considered for wider applications in stroke neurorehabilitation.
RESUMO
Diffusion tensor imaging (DTI) is a relatively novel magnetic resonance-based imaging methodology that can provide valuable insight into the microstructure of white matter tracts of the brain. In this paper, we evaluated the reliability and reproducibility of deriving a semi-automated pseudo-atlas DTI tractography method vs. standard atlas-based analysis alternatives, for use in clinical cohorts with neurodegeneration and ventriculomegaly. We showed that the semi-automated pseudo-atlas DTI tractography method was reliable and reproducible across different cohorts, generating 97.7% of all tracts. However, DTI metrics obtained from both methods were significantly different across the majority of cohorts and white matter tracts (p < 0.001). Despite this, we showed that both methods produced patterns of white matter injury that are consistent with findings reported in the literature and with DTI profiles generated from these methodologies. Scatter plots comparing DTI metrics obtained from each methodology showed that the pseudo-atlas method produced metrics that implied a more preserved neural structure compared to its counterpart. When comparing DTI metrics against a measure of ventriculomegaly (i.e., Evans' Index), we showed that the standard atlas-based method was able to detect decreasing white matter integrity with increasing ventriculomegaly, while in contrast, metrics obtained using the pseudo-atlas method were sensitive for stretch or compression in the posterior limb of the internal capsule. Additionally, both methods were able to show an increase in white matter disruption with increasing ventriculomegaly, with the pseudo-atlas method showing less variability and more specificity to changes in white matter tracts near to the ventricles. In this study, we found that there was no true gold-standard for DTI methodologies or atlases. Whilst there was no congruence between absolute values from DTI metrics, differing DTI methodologies were still valid but must be appreciated to be variably sensitive to different changes within white matter injury occurring concurrently. By combining both atlas and pseudo-atlas based methodologies with DTI profiles, it was possible to navigate past such challenges to describe white matter injury changes in the context of confounders, such as neurodegenerative disease and ventricular enlargement, with transparency and consistency.
