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.
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.