Structural MRI Flashcards
What is a voxel with equal length, width and height called?
isotropic
3 planes of brain image
- axial: dividing in superior and inferior
- coronal: dividing in anterior and posterior
- sagittal: dividing in left and right
Motion artifacts
- interindividual difference: people who move more in one image, also do so in another one
- no relationship with age
- women move less
- higher motion -> lower gray matter volume estimates
classical lesion analysis - lesion-defined approach
- behavioral performance of a group of patients with a common area of injury (e.g., DLPFC) compared to that of a control group / another patient group
- good for assessing the functional roles of particular regions of interest
- BUT: loss of information if ROI contains multiple subregions that each contribute to behavior
- regions outside the ROI that are part of a distributed functional network may be overlooked
classical lesion analysis - behavior-defined approach
- patients are grouped according to whether or not they show a specific behavioral deficit
- lesion reconstructions are overlaid to find common areas and compared to lesion overlays from patients without the deficit
- effective in identifying brain regions that may contribute to a cognitive skill
- BUT: when behavioral data are continuous cut-off needed, information reflecting varying degrees of performance can be lost
voxel-wise lesion-symptom mapping
- mass-univariate statistical analysis (akin to standard analysis techniques for fMRI)
- does not require patients to be grouped by either lesion site or behavioral cutoff a priori
- makes use of continuous behavioral and lesion information
- voxel-by-voxel analysis
- for each voxel, patients are categorized according to whether they did or did not have a lesion affecting that voxel
- behavioral scores are then compared for these two groups, yielding a t statistic for each voxel
Volumetry - manual segmentation (T1-weighted images)
pros:
- remains the gold standard
- ideal for delineating structures with intricate anatomy/multiple subregions
- well-suited for smaller studies with focused hypothesis
- biologically and anatomically meaningful
cons:
- labor-intense: impossible for large studies (> 1.000 scans)
- requires expert anatomical knowledge
- requires at least two blinded tracers to avoid bias
- intra-rater variability
- inter-rater variability
- inter-protocol variability
Volumetry - automated segmentation
pros:
- replaces manual segmentation for most applications
- substantially faster (large datasets)
- higher reliability
- standardized
cons
- Freesurfer can overestimate total hippocampal volumes
- problems with accurately detecting boundaries between hippocampus and neighboring structures
- differences in segmentation outcomes with regard to age effects and hemispheric asymmetry
However, agreement between manual and automated approaches is continuously improving
automated segmentation in FSL FIRST
model-based segmentation of 15 subcortical structures
FSL FIRST vs. FreeSurfer
- relative difference between areas is the same
- absolute volumes differ between software
Whole-brain volumetry with FSL SIENA(X)
- Software package for both single time point (cross-sectional) and two time point (longitudinal) analysis of brain change
- particularly useful for the estimation of atrophy (volumetric loss of brain tissue)
- brain volume normalized to skull
Voxel-based morphometry 1
- voxel-wise analysis of the local concentration of gray matter
- characterizes local differences in gray matter topography, while discounting large scale differences in anatomy (relative concentration of gray matter structures in the spatially normalized images)
- align images globally and compare GM likelihood at each voxel
voxel-based morphometry - segmentation into tissue classes
- often combined with intensity normalization
- classification commonly combination of intensity-based clustering informed by spatial priors
- cleaning step (remove non-brain classes)
voxel-based morphometry - normalization
- linear vs. non-linear
- linear: manipulation (rotation, translation) is applied equally to all parts of the image
- non-linear: manipulation is applied locally (i.e., different changes to different parts)
- choice of template matters (MNI305, MNI152, …)
voxel-based morphometry - (spatial) smoothing
- smoothing makes each voxel more similar to its neighbors (weighted means)
- typical smoothing kernels are 4-16 mm full width at half maximum (FWHM)
- parametric tests assume Gaussianity of residuals, smoothing helps satisfy this assumption
- spatial normalization is not perfect, and smoothing helps accommodate inter-individual differences in local anatomy
- smoothing makes the analysis sensitive to the kernel size, such that very small differences are disregarded
