Current PhD Students
Kaori (Lily) Ito
Kaori (Lily) Ito
Faculty Mentor: Sook-Lei Liew PhD, OTR/L
Research Lab: Neural Plasticity and Neurorehabilitation Lab
Year of Entry: 2016
Master of Arts (MA)
in Occupational Therapy
2016 | University of Southern California
Bachelor of Arts (BS)
in Cognitive Science
2013 | University of California, Los Angeles
Stroke is a neurological disorder that can leave an individual with lasting motor impairments. Among a wide range of factors that can influence stroke recovery, hemispheric dominance relative to the stroke — that is, whether a stroke is in a person’s dominant or non-dominant hemisphere — is likely to have a significant impact on function. For example, an individual whose stroke is in the dominant hemisphere may have difficulty performing daily activities that depend on the dominant hand, such as hand-writing and teeth-brushing.
At the Neural Plasticity and Neurorehabilitation Lab, I’m interested in understanding the effects of stroke laterality on neural plasticity and its implication on stroke recovery and rehabilitation. Through the use of functional MRI, I’m studying changes in brain activity and connectivity in motor-related networks following a stroke using various statistical modeling techniques, such as GLM-weighted correlation analyses and dynamic causal modeling.
Ito, K. L., Kumar, A., Zavaliangos-Petropulu, A., Cramer, S. C., & Liew, S.-L. (2018). Pipeline for Analyzing Lesions After Stroke (PALS). Frontiers in Neuroinformatics, 12, 63. https://doi.org/10.3389/fninf.2018.00063 Show abstract
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.
Liew, S.-L., Garrison, K. A., Ito, K. L., Heydari, P., Sobhani, M., Werner, J., Damasio, H., Winstein, C. J., & Aziz-Zadeh, L. (2018). Laterality of poststroke cortical motor activity during action observation is related to hemispheric dominance. Neural Plasticity, 2018, 3524960. https://doi.org/10.1155/2018/3524960 Show abstract
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.
Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., Lefebvre, S., Nakamura, W., Saldana, D., Schmiesing, A., Tran, C., Vo, D., Ard, T., Heydari, P., Kim, B., Aziz-Zadeh, L., Cramer, S. C., Liu, J., Soekadar, S., Nordvik, J.-E., Westlye, L. T., Wang, J., Winstein, C., Yu, C., Ai, L., Koo, B., Craddock, R. C., Milham, M., Lakich, M., Pienta, A., & Stroud, A. (2018). A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data, 5, 180011. https://doi.org/10.1038/sdata.2018.11 Show abstract
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.
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  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).