Signal and Noise Flashcards
What is the primary concern regarding noise in fMRI studies?
The noise may be synchronous with the signal, causing a multicollinearity problem if not eliminated.
In fMRI, what is the significance of understanding the synchronization of stimulus and signal?
It is crucial to eliminate noise; if noise is synchronous with the signal, it leads to a multicollinearity problem.
What does the Raw Signal-to-Noise (S/N) Ratio measure in fMRI?
It measures MRI scanner performance by dividing the intensity of the image in the brain by the intensity of the image outside the brain.
What information does the Contrast to Noise Ratio (CNR) provide in fMRI?
It describes how easy it is to see differences between two tissues, considering the intensity difference divided by noise.
How does Functional SNR (fSNR) differ from CNR in fMRI?
While CNR depends on intensity differences between voxels across space, fSNR depends on intensity differences within a voxel or cluster over time.
What is the typical range for Signal-to-Noise Ratio (SNR) in fMRI data?
The total range is 0.1-4.0, with a typical range of 0.2-0.5, considering percent signal change amplitudes.
How can experimental power be increased in fMRI studies?
By increasing the number of participants, stimuli per condition, and conditions.
What does fSNR represent in fMRI, and where are the largest changes observed?
fSNR represents the ratio between task-related and non-task-related variability. The largest changes occur in primary motor and sensory areas.
Why is the selection of stimuli critical for fSNR?
Selecting the right stimuli is crucial to enhance sensitivity in experimental manipulations.
What is the primary factor affecting fMRI data when there is lower SNR?
Lower SNR leads to more confounds in simulated data.
How is noise distributed across the brain in fMRI studies?
Noise is not equally distributed, with some regions, such as edges and areas close to the eyes, being more affected.
What is a major source of noise in fMRI caused by fluctuations in MR signal intensity over space or time?
Thermal noise, caused by the thermal motion of electrons within the sample or scanner hardware.
How can head motion artifacts be minimized in fMRI?
By using restraints, such as padding, vacuum packs, head masks, or thermoplastic masks, and providing specific instructions to participants.
What is the purpose of Prospective Motion Correction (e.g. Siemens PACE) in fMRI?
It shifts slices on-the-fly to follow motion, improving data quality but preventing the acquisition of raw data.
What is the primary solution for mass motion artifacts in fMRI?
Co-registration/realignment steps to correct for bulk head motion and inclusion of movement parameters as regressors of no interest.
How can individual subject’s hemodynamic responses be used to address inter-subject variability?
It corrects for differences in latency and shape, requiring appropriate statistical measures like random effects analyses.
What is the role of filtering approaches in improving SNR in fMRI?
They reduce power around unwanted frequency variations, such as drift or physiological noise.
How does increasing field strength impact raw SNR in fMRI?
Raw signal increases as the square of field strength, leading to a higher BOLD signal change. However, higher field strength also increases thermal and physiological noise.
How can physiological artifacts be addressed in fMRI studies?
Co-registration/realignment steps to correct for bulk head motion and including movement parameters as regressors of no interest.
What is the premise behind improving (f)SNR with trial averaging in fMRI?
MR data on each trial are composed of constant signal and random noise. Averaging decreases noise and increases SNR.
What are the effects of increasing the number of trials in trial averaging?
It increases signal detection, determined by the capacity to locate the signal and the spatial extent of activated voxels.
What does signal averaging assume about noise in fMRI?
It assumes noise is uncorrelated over time, and data is the sum of signal and temporally random noise.
Why might signal averaging ignore critical information in fMRI?
It may ignore potentially critical information when noise is temporally correlated with the task, such as physiological noise time-locked with the stimulus.
How does non-task-related neural variability impact averaging in fMRI?
If unsynchronized, averaging could reduce its impact; what is considered noise in one study may become the focus of another.
What factors contribute to behavioral and cognitive variability in fMRI?
Reaction times (RTs), practice/fatigue effects, and the speed-accuracy trade-off, leading to changes in hemodynamic response function (HRF) based on prioritized tasks.
What is the issue with reproducibility of fMRI activity across sessions?
High variability in activity, especially in cognitive tasks, poses challenges for subject averaging and drawing inferences at the population level.
How can individual subject’s hemodynamic responses be used to address inter-subject variability?
It corrects for differences in latency and shape, requiring appropriate statistical measures like random effects analyses.
What are the potential solutions for improving SNR in fMRI?
Filtering approaches, denoising of fMRI time series using techniques like independent component analysis (ICA) and principal component analysis (PCA), and increasing field strength.
What is the primary aim of co-registration/realignment steps in fMRI?
To correct for bulk head motion and include movement parameters as regressors of no interest, controlling for their residual effects in statistical results.
How does trial averaging impact SNR in fMRI?
Averaging decreases noise and increases SNR by assuming that MR data on each trial consists of a constant signal and random noise.
