We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is ...
Aug 15, 2016 · We propose a transmodal learning framework that estimates a predictive model classifying AD from rs-fMRI –noninvasive but weakly accurate– ...
It reveals functional interactions between brain networks that predict brain states [10], via the intrinsic functional connectivity (FC) es- timated from the ...
Aug 23, 2016 · In section 2, we detail the transmodal learning framework for the connectivity- based prediction. Then we present in section 3 data used in our ...
We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is ...
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Rahim et al. Transmodal learning of functional networks for Alzheimer's disease prediction. IEEE J. Selected Top. Signal Proc. (2016). B. Lei et al ...
Jun 12, 2024 · One promising approach is to incorporate brain networks, also known as the connectome, in individual models of tau accumulation.
The result indicates that feature selection and multimodal imaging data can improve brain age prediction with linear support vector and partial least ...
Oct 9, 2024 · We aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling.
May 25, 2024 · Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction. Article. Oct 2016. Mehdi Rahim ...