University of Southern California
University of Southern California
Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy
Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy
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Research
Research

Current PhD Students

Kaori (Lily) Ito

Faculty Mentor: Sook-Lei Liew PhD, OTR/L

Research Lab: Neural Plasticity and Neurorehabilitation Lab

Year of Entry: 2016

Kaori (Lily) Ito

Education

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

Research Interests

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.

Publications

Journal Articles

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 abstractHide 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.