Kaori (Lily) Ito

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain–computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain–computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12–30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle–computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.

Keywords: biofeedback; stroke; brain–computer interface; neurorehabilitation; corticomuscular coherence; electromyography; co-contraction; virtual reality

The effectiveness of transcranial direct current stimulation (tDCS) placed over the motor hotspot (thought to represent the primary motor cortex (M1)) to modulate motor network excitability is highly variable. The premotor cortex—particularly the dorsal premotor cortex (PMd)—may be a promising alternative target to reliably modulate motor excitability, as it influences motor control across multiple pathways, one independent of M1 and one with direct connections to M1. This double-blind, placebo-controlled preliminary study aimed to differentially excite motor and premotor regions using high-definition tDCS (HD-tDCS) with concurrent functional magnetic resonance imaging (fMRI). HD-tDCS applied over either the motor hotspot or the premotor cortex demonstrated high inter-individual variability in changes on cortical motor excitability. However, HD-tDCS over the premotor cortex led to a higher number of responders and greater changes in local fMRI-based complexity than HD-tDCS over the motor hotspot. Furthermore, an analysis of individual motor hotspot anatomical locations revealed that, in more than half of the participants, the motor hotspot is not located over anatomical M1 boundaries, despite using a canonical definition of the motor hotspot. This heterogeneity in stimulation site may contribute to the variability of tDCS results. Altogether, these preliminary findings provide new considerations to enhance tDCS reliability.

Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline for assessing the relationship between poststroke brain structure, function, and behavior. Many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, yet few algorithms are effective with only a single T1‐weighted (T1w) anatomical MRI. This is a critical gap because multimodal MRI is not commonly available due to time and cost constraints in the stroke rehabilitation setting. Although several attempts to automate the segmentation of chronic lesions on single‐channel T1w MRI have been made, these approaches have not been systematically evaluated on a large dataset. We performed an exhaustive review of the literature and identified one semiautomated and three fully automated approaches for segmentation of chronic stroke lesions using T1w MRI within the last 10 years: Clusterize, automated lesion identification (ALI), Gaussian naïve Bayes lesion detection (lesionGnb), and lesion identification with neighborhood data analysis (LINDA). We evaluated each method on a large T1w stroke dataset (N = 181). LINDA was the most computationally expensive approach, but performed best across the three main evaluation metrics (median values: dice coefficient = 0.50, Hausdorff's distance = 36.34 mm, and average symmetric surface distance = 4.97 mm). lesionGnb had the highest recall/least false negatives (median = 0.80). However, across the automated methods, many lesions were either misclassified (ALI: 28, lesionGnb: 39, LINDA: 45) or not identified (ALI: 24, LINDA: 23, lesionGnb: 0). Segmentation accuracy in all automated methods were influenced by size (small: worst) and stroke territory (brainstem, cerebellum: worst) of the lesion. To facilitate reproducible science, our analysis files have been made publicly available online.

Background. Increased activity in the lesioned hemisphere has been related to improved poststroke motor recovery. However, the role of the dominant hemisphere—and its relationship to activity in the lesioned hemisphere—has not been widely explored.

Objective. Here, we examined whether the dominant hemisphere drives the lateralization of brain activity after stroke and whether this changes based on if the lesioned hemisphere is the dominant hemisphere or not.

Methods. We used fMRI to compare cortical motor activity in the action observation network (AON), motor-related regions that are active both during the observation and execution of an action, in 36 left hemisphere dominant individuals. Twelve individuals had nondominant, right hemisphere stroke, twelve had dominant, left-hemisphere stroke, and twelve were healthy age-matched controls. We previously found that individuals with left dominant stroke show greater ipsilesional activity during action observation. Here, we examined if individuals with nondominant, right hemisphere stroke also showed greater lateralized activity in the ipsilesional, right hemisphere or in the dominant, left hemisphere and compared these results with those of individuals with dominant, left hemisphere stroke.

Results. We found that individuals with right hemisphere stroke showed greater activity in the dominant, left hemisphere, rather than the ipsilesional, right hemisphere. This left-lateralized pattern matched that of individuals with left, dominant hemisphere stroke, and both stroke groups differed from the age-matched control group.

Conclusions. These findings suggest that action observation is lateralized to the dominant, rather than ipsilesional, hemisphere, which may reflect an interaction between the lesioned hemisphere and the dominant hemisphere in driving lateralization of brain activity after stroke. Hemispheric dominance and laterality should be carefully considered when characterizing poststroke neural activity.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (z=3.43, p=0.0018). PALS can be found online at https://github.com/npnl/PALS. Future work will expand the PALS capabilities to include multimodal stroke imaging. We hope PALS will be a useful tool for the stroke neuroimaging community and foster new clinical insights.

The laterality index (LI) is one way to assess hemispheric dominance in a variety of tasks, such as language, cognitive functions, and changes in laterality in clinical populations, such as after stroke. In stroke neuroimaging, however, an optimal method of calculating the LI remains controversial, largely due to lesion variability in post-stroke brains.

Two main methods of calculating LI have evolved in neuroimaging literature. The first, more traditional approach counts the number of active voxels in a given region of interest (ROI) for each hemisphere. This method has been criticized for its inability to account for differences in signal intensity. Hence, a second approach calculates laterality based on the percent signal change within a given region; however, this method also has problems, such as difficulty handling negative values.

A laterality toolbox that addresses some of these issues has been implemented in the statistical neuroimaging analysis package SPM, which provides users with options of using either method, along with more advanced statistical tests for robust LI calculations [2] No such toolbox is yet available for FSL. Therefore, we developed a series of scripts to calculate LI in FSL using both voxel count and percent signal change methods. However, in the interest of space, here we present only results from the more robust method of the two (voxel count method).

Introduction. Transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) can be an efficient way to modulate cortical excitability and promote motor recovery following stroke(1). However, the effects of M1 tDCS on behavior have been highly variable across individuals. Here we explored whether another motor region that is less often affected by stroke (dorsal premotor cortex, PMd) could also modulate cortical excitability. We also examined motor network neural complexity changes following stimulation of either M1 or PMd using simultaneous fMRI.

Methods. Thirty healthy participants were randomized into 3 groups (over left M1, left PMd or sham (electrodes randomized over M1 or PMd)) in this double-blind study. tDCS (1mA) was applied concurrently during an fMRI session, using a Soterix MRI-compatible high-definition tDCS (HD-tDCS) system (4x1 configuration). Participants underwent 3 resting state runs (7 minutes each): pre, during and post the 7min HD-tDCS. Changes in neurophysiology were measured using motor evoked potentials (MEP) while motor network complexity was explored using a multiscale entropy (MSE) measure, which examines the variability of biological signals across a range of temporal scales(2). fMRI data were motion-realigned and noise-corrected using white-matter, CSF and motion-parameters as regressors. For MSE computations, we used a pattern matching threshold (r) of 0.5 and a pattern length (m) of 2. In total 20 coarse-sampled scales were investigated.

Results. Changes in cortical physiology were measured with a one-way ANOVA with 'Group' (M1, PMd, sham) as a factor. Group differences in cortical excitability (measured as an MEP ratio of post-tDCS/pre-tDCS) were marginally significantly different following HD-tDCS (F(1,27)=2.78, p=0.06; M1=1.56+-1.80, PMd=2.03+-1.03, Sham=0.74+-0.35). Post-hoc Tukey tests showed that cortical excitability in the PMd group was increased compared to sham (z=2.3, p=0.05). M1 versus sham was not statistically different (z=1.4, p=0.29).Changes in MSE were measured with repeated-measures ANOVAs with 'Time' (Pre, During, Post) and 'Group' as factors, performed for each of 4 regions of interest (left/right M1, left/right PMd). There was an MSE increase in the M1 group in the right PMd (F(2,54)=4.55, p=0.01; Post-hoc tests: M1-During>M1-Pre: t(54)=2.8; p=0.01; M1-Post>M1-Pre: t(54)=2.6, p=0.03) and an MSE increase in the PMd group in the left PMd (F(2,54)=6.032, p=0.004; Post-hoc tests: PMd-Post>PMd-Pre: t(54)=2.4, p=0.049).

Conclusion. This preliminary work suggests that both M1 and PMd HD-tDCS may modulate motor network neurophysiology and complexity, and that multiscale entropy may be a sensitive measurement of changes following noninvasive brain stimulation.