Last modified by mhashemi on 2024/12/03 18:26

From version 6.1
edited by mhashemi
on 2024/11/27 17:58
Change comment: There is no comment for this version
To version 7.1
edited by mhashemi
on 2024/11/27 18:02
Change comment: There is no comment for this version

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1 +{{{The integration of an individual’s brain
2 +imaging data in VBMs has improved patient-specific predictivity, although Bayesian
3 +estimation of spatially distributed parameters remains challenging even with state-
4 +of-the-art Monte Carlo sampling. VBMs imply latent nonlinear state space models
5 +driven by noise and network input, necessitating advanced probabilistic machine
6 +learning techniques for widely applicable Bayesian estimation. Here we present
7 +Simulation-based Inference on Virtual Brain Models (SBI-VBMs), and demonstrate
8 +that training deep neural networks on both spatio-temporal and functional fea-
9 +tures allows for accurate estimation of generative parameters in brain disorders.
10 +The systematic use of brain stimulation provides an effective remedy for the non-
11 +identifiability issue in estimating the degradation limited to smaller subset of con-
12 +nections. By prioritizing model structure over data, we show that the hierarchical
13 +structure in SBI-VBMs renders the inference more effective, precise and biologically
14 +plausible. This approach could broadly advance precision medicine by enabling fast
15 +and reliable prediction of patient-specific brain disorders.
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21 +Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]