2011年3月17日 · The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to …

The time-series represents the signal that is measured at each voxel, but where does that signal come from? In the next chapter we will briefly review the history of fMRI and how we generate the signal you see in the viewer.

from the FMRI time series? •Event-related developments •Linearity (Neuronal and/or Hemodynamic?) •Hemodynamic Latency •Sensitivity and “Noise” •Design and analysis innovations •Neuronal current imaging

Remember that fMRI datasets contain several volumes strung together like beads on a string; we call this concatenated string of volumes a run of data. The signal that is measured at each voxel across the entire run is called a time-series .

For each time point in the fMRI time series, images are acquired at two or more different echo times (TEs). Figure 2a shows images from acquisition at 3 TEs. Figure 2b shows the three time series corresponding to each TE.

for this correlation is the fast acquisition time (TR) for fMRI (typically 2-4s, cf. 8-12 minutes for PET) relative to the duration of the BOLD response (at least 30s). Treating fMRI data as timeseries also allows us to view statistical analyses in signal-processing terms.

Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. Spatial models and Bayesian approaches for inference in such models have advantages over more traditional mass univariate approaches; however, a ...

We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous.

2024年12月1日 · When a classifier as simple as logistic regression is applied to feature-engineered fMRI data, it can match or even outperform more sophisticated recent models. Classification of the raw time series fMRI data generally …

Current ML models working with the brain fMRI data have been used to analyze time series data as well as func-tional connectivity. Time series fMRI captures the dynamics of blood-oxygenation-level-dependent (BOLD) signals in the brain (Kundu et al.,2017), which correlate with the brain activ-ity. While fMRI images are captured at the voxel ...