![Figure 1: Maps of voxel-specific treatment-emergent activations for the cocaine-dependent subjects, adjusted for the corresponding baseline-to-follow-up changes in the control subjects. The maps portray voxel-specific posterior probabilities, exceeding 0.70, of increased activity from baseline to the post-treatment period for the cocaine addicts, relative to (minus) the corresponding difference in the control subjects. The axial slices are at various distances (in millimeters (mm)) from the anterior-posterior commissural plane. The activations include the medial orbital frontal cortex (BA 11) [at −14 mm], the left middle frontal gyrus (BA 46) and left thalamus [at +14 mm], bilaterally in the middle frontal gyrus (BA 9) [at +28 mm], and along the cingulate gyrus (BA 31, 32), with the posterior cingulate activation appearing most spatially extensive [at +36].](/figures/figure-1-maps-of-voxel-specific-treatment-emergent-ucppgjlc.webp)
Q2. What are the main purposes of functional neuroimaging?
Functional neuroimaging techniques enable in vivo investigations into the neural basis of human cognition, emotions, and behaviors.
Q3. What are the main advantages of Bowman's proposed spatial model?
The main advantages that their proposed spatial model yields are that it: 1) provides a novel approach to uncover prominent functional connections between remote voxels, 2) often provides higher accuracy and increased statistical power for inferences regarding task-related changes in brain activity by adjusting for spatial associations in the data, 3) extends the modeling assumptions underlying previously applied methods from the limited amount of research in this area, and 4) establishes a unified framework that yields results for voxel-specific inferences, regional or VOI inferences, and functional connectivity.
Q4. How do the authors avoid introducing prior information that does not seem physiologically plausible?
To avoid introducing prior information that does not seem physiologically plausible and that is not supported by the data, the authors consider small to moderate departures from the sample covariance matrix in their sensitivity analyses.
Q5. What is the probability of a decrease in activity in the medial orbital frontal cortex?
In the right hippocampus and in the left lateral orbital frontal cortex, the cocaine addicts demonstrate a decrease in activity following treatment, while the controls reveal increased activity.
Q6. What is the problem with applying MCMC methods in their context?
Applying MCMC methods in their context is complicated by the massive amount of data, the vast number of spatial locations, and the large number of parameters.
Q7. What is the significance of the voxel-level inferences?
It is important to note that the voxel-level inferences provided by their approach account for prominent spatial correlations or functional connections in the brain, as detected by their Bayesian model.
Q8. What is the main advantage of the Bayesian model?
Woolrich et al. (2004b) propose a Bayesian modeling framework for fMRI data allowing both separable and nonseparable spatio-temporal models.
Q9. How did Bassett et al. (2006) define at-risk subjects?
Subjects were defined as at-risk for Alzheimer’s disease by having an autopsy confirmed affected parent and at least one additional clinically diagnosed first degree relative.
Q10. What can be the useful way to estimate regional parameters?
The authors can easily estimate these regional parameters using samples from the joint posterior distribution for all of their model parameters, taking into account the potential correlations between voxel-specific parameters from the region.