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Liya Wang

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2 papers

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YNICL Journal 2019 Journal Article

Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI

Bing Ji
Silun Wang
Zhou Liu
Brent D. Weinberg
Xiaofeng Yang
Tianming Liu
Liya Wang
Hui Mao

Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions.

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YNICL Journal 2016 Journal Article

Increased frontal functional networks in adult survivors of childhood brain tumors

Hongbo Chen
Liya Wang
Tricia Z. King
Hui Mao

Childhood brain tumors and associated treatment have been shown to affect brain development and cognitive outcomes. Understanding the functional connectivity of brain many years after diagnosis and treatment may inform the development of interventions to improve the long-term outcomes of adult survivors of childhood brain tumors. This work investigated the frontal region functional connectivity of 16 adult survivors of childhood cerebellar tumors after an average of 14.9 years from diagnosis and 16 demographically-matched controls using resting state functional MRI (rs-fMRI). Independent component analysis (ICA) was applied to identify the resting state activity from rs-fMRI data and to select the specific regions associated with executive functions, followed by the secondary analysis of the functional networks connecting these regions. It was found that survivors exhibited differences in the functional connectivity in executive control network (ECN), default mode network (DMN) and salience network (SN) compared to demographically-matched controls. More specifically, the number of functional connectivity observed in the survivors is higher than that in the controls, and with increased strength, or stronger correlation coefficient between paired seeds, in survivors compared to the controls. Observed hyperconnectivity in the selected frontal functional network thus is consistent with findings in patients with other neurological injuries and diseases.

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