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Li, Wentao

0000-0002-7114-762X

Mayo Clinic

The University of Texas MD Anderson Cancer Center

Citations 4

1236 Machine learning-based clinico-genomic prediction of benefits to add chemotherapy to immunotherapy in metastatic non-small cell lung cancer
Saad, Maliazurina B. , Hong, Lingzhi , Boiarsky, Daniel , Li, Shenduo , Petranović, Milena , Cascone, Tina , Le, Xiuning, Chang, Joe H. , Vaporciyan, Ara A. , Lee, J Jack, Mirjalili, Sayedali, Gainor, Justin F. , Lou, Yanyan , DiFederico, Alessandro, Pecci, Federica , Wu, Jia, Al-Tashi, Qasem , Li, Wentao , Wu, Carol C. , Carter, Brett, Gibbons, Don L , Lee, J Jack, Mirjalili, Sayedali, Jaffray, David A. , Ricciuti, Biagio , Awad, Mark M. , Heymach, John V. , Vokes, Natalie I. , Zhang, Jianjun

Regular and Young Investigator Award Abstracts | November 5, 2024 | Volume: No volume available | Issue: No issue available | Pages: A1379-A1380

10.1136/jitc-2024-sitc2024.1236 Get It

Alzheimer’s Disease Cerebrospinal Fluid Biomarkers Differentiate Creutzfeldt-Jakob Disease from its Mimicker Autoimmune Encephalitis (P1-1.Virtual)
Chang, Bryce Kalei , Graff‐Radford, Jonathan , Algeciras-Schimnich, Alicia , Petersen, Ronald C. , Toledano, Michel , Day, Gregory S. , McKeon, Andrew , Flanagan, Eoin P. , Jones, David T. , Li, Wentao

Neurology | December 6, 2023 | Volume: 98 | Issue: 18_supplement | Pages: No page available

10.1212/wnl.98.18_supplement.757 Get It

Clinical Features and Neuroimaging of Anomic Progressive Aphasia (P3-6.005)
Brown, Christine Cliatt, Graff‐Radford, Jonathan , Whitwell, Jennifer L. , Josephs, Keith A. , Duffy, Joseph R. , Utianski, Rene L. , Clark, Heather M. , Machulda, Mary M. , Li, Wentao , Whitwell, Jennifer L. , Botha, Hugo

Neurology | December 6, 2023 | Volume: 98 | Issue: 18_supplement | Pages: No page available

10.1212/wnl.98.18_supplement.2375 Get It

Quantitative electroencephalography features of dysexecutive Alzheimer's disease
Varatharajah, Yogatheesan , Barnard, Leland R , Allen, Kay S., Li, Wentao , Brinkmann, Benjamin H. , Worrell, Gregory A. , Jones, David T.

Alzheimer's & Dementia | December 31, 2021 | Volume: 17 | Issue: S5 | Pages: No page available

10.1002/alz.051939 Get It

AbstractBackgroundProgressive dysexecutive syndrome (dAD) is an atypical presentation of early onset Alzheimer’s disease (AD) with increased frontal and parietal region hypometabolism on FDG‐PET imaging compared to classic amnestic AD. We compared electroencephalogram (EEG) and FDG‐PET in dAD patients to determine if there was a correlation between EEG spectral activity and standard uptake value ratios (SUVR) in those regions.MethodFDG‐PET and resting state scalp EEG were obtained on AD‐biomarker confirmed, behavioral neurologist diagnosed cases of dAD (n = 11). We isolated awake‐eyes‐closed segments (mean duration: 113s, median:118s, range: 58‐138s) of each EEG and calculated the relative power of delta, theta, alpha, beta, and gamma bands within a wideband of 0.5‐40 Hz. In bilateral frontal and parietal regions, we averaged select channels across 6 to 14 10‐second windows per subject (fig.1). We evaluated the correlation between relative band powers and SUVRs of the regions by linear regression analysis.ResultThere is a strong negative correlation (R2 = 0.54, p < 0.01) between alpha band relative power and SUVR in bilateral frontal regions (fig 2). Delta band relative power showed a strong positive correlation (R2 = 0.54, p < 0.05) with SUVR in the left frontal region. A positive trend was noted in the delta band in the right frontal region but did not reach statistical significance. Beta band relative power was negatively correlated with frontal region SUVR bilaterally, but only the left side reached statistical significance. These findings remained significant after multiple comparisons correction. No statistically significant correlations were noted in the parietal regions. Exploratory analysis revealed negative correlations between alpha bands and temporal region SUVRs bilaterally.ConclusionIn this small sample of selected individuals with dysexecutive AD, we report a strong correlation between EEG relative spectral power and PET regional SUVR in multiple EEG frequency bands and brain regions. In the bilateral frontal regions, slower delta frequencies and faster alpha and beta frequencies were associated with lower FDG‐PET signal. Future studies are needed to determine if this spectral power pattern and other EEG properties of dAD differ from other clinical dementia phenotypes and its clinical significance.