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- W2354785768 abstract "Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r=0.7033, MCCB social cognition r=0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r=0.7785, PANSS negative r=0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making." @default.
- W2354785768 created "2016-06-24" @default.
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- W2354785768 date "2017-01-01" @default.
- W2354785768 modified "2023-10-16" @default.
- W2354785768 title "Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data" @default.
- W2354785768 cites W1500895378 @default.
- W2354785768 cites W1516226610 @default.
- W2354785768 cites W1540475776 @default.
- W2354785768 cites W1587698431 @default.
- W2354785768 cites W1591612963 @default.
- W2354785768 cites W1599154571 @default.
- W2354785768 cites W1804828504 @default.
- W2354785768 cites W1921135725 @default.
- W2354785768 cites W1965445933 @default.
- W2354785768 cites W1965974192 @default.
- W2354785768 cites W1967737804 @default.
- W2354785768 cites W197326298 @default.
- W2354785768 cites W1973741448 @default.
- W2354785768 cites W1985267538 @default.
- W2354785768 cites W1987293923 @default.
- W2354785768 cites W1991491984 @default.
- W2354785768 cites W1997111256 @default.
- W2354785768 cites W1998891398 @default.
- W2354785768 cites W2000292092 @default.
- W2354785768 cites W2005211539 @default.
- W2354785768 cites W2012059337 @default.
- W2354785768 cites W2012693272 @default.
- W2354785768 cites W2015620054 @default.
- W2354785768 cites W2017337590 @default.
- W2354785768 cites W2018255469 @default.
- W2354785768 cites W2022842031 @default.
- W2354785768 cites W2026483346 @default.
- W2354785768 cites W2033128599 @default.
- W2354785768 cites W2034457280 @default.
- W2354785768 cites W2039018899 @default.
- W2354785768 cites W2042385018 @default.
- W2354785768 cites W2043200458 @default.
- W2354785768 cites W2044893824 @default.
- W2354785768 cites W2046602920 @default.
- W2354785768 cites W2047225915 @default.
- W2354785768 cites W2051231702 @default.
- W2354785768 cites W2059369452 @default.
- W2354785768 cites W2063237661 @default.
- W2354785768 cites W2065131965 @default.
- W2354785768 cites W2067152776 @default.
- W2354785768 cites W2071029082 @default.
- W2354785768 cites W2074117397 @default.
- W2354785768 cites W2076795533 @default.
- W2354785768 cites W2077567218 @default.
- W2354785768 cites W2079549965 @default.
- W2354785768 cites W2082372626 @default.
- W2354785768 cites W2082723614 @default.
- W2354785768 cites W2084358449 @default.
- W2354785768 cites W2085020654 @default.
- W2354785768 cites W2085245717 @default.
- W2354785768 cites W2086240345 @default.
- W2354785768 cites W2089572795 @default.
- W2354785768 cites W2090347203 @default.
- W2354785768 cites W2091087712 @default.
- W2354785768 cites W2091373356 @default.
- W2354785768 cites W2094758440 @default.
- W2354785768 cites W2097304444 @default.
- W2354785768 cites W2099920129 @default.
- W2354785768 cites W2102108192 @default.
- W2354785768 cites W2106292462 @default.
- W2354785768 cites W2107014760 @default.
- W2354785768 cites W2108646822 @default.
- W2354785768 cites W2109132758 @default.
- W2354785768 cites W2112891119 @default.
- W2354785768 cites W2117038895 @default.
- W2354785768 cites W2119347922 @default.
- W2354785768 cites W2122066039 @default.
- W2354785768 cites W2126159475 @default.
- W2354785768 cites W2127689638 @default.
- W2354785768 cites W2128042129 @default.
- W2354785768 cites W2130678362 @default.
- W2354785768 cites W2131564374 @default.
- W2354785768 cites W2131696871 @default.
- W2354785768 cites W2132824928 @default.
- W2354785768 cites W2134248637 @default.
- W2354785768 cites W2134873723 @default.
- W2354785768 cites W2135893370 @default.
- W2354785768 cites W2136763679 @default.
- W2354785768 cites W2139949772 @default.
- W2354785768 cites W2139977289 @default.
- W2354785768 cites W2140606211 @default.