SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data.

ABSTRACT:

Objective

Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To overcome the necessity of presuming a specific model for the hemodynamic response, we introduce a semi-supervised automatic detection (SAD) method.

Materials and methods

The proposed SAD method employs a Bi-LSTM neural network to classify high temporal resolution fMRI data. Network training utilized an fMRI dataset with 75-ms temporal resolution in an iterative scheme. Classification performance was evaluated on a second fMRI dataset from the same participant, collected on a different day. Comparative analysis with the standard G

SUBMITTER: Schmidt T 

PROVIDER: S-EPMC11582144 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature