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
London taxi drivers
Trainees who successfully acquired an internal spatial representation of London showed selective increase in gray matter density in posterior hippocampi with concomitant changes to their memory profile
partial volume effect
- higher likelihood of i.e. gray matter in one voxel, but you don’t know why
- one voxel is only one number, even though there are different substances in there, you don’t know why there is more gray matter there
- factors: folding, thickness, misclassification, misregistration
human cerebral cortex
- outer layer of gray matter
- 1-5mm thick
- highly folded
- 2-dimensional, embedded in 3D
cortical thickness - cortical surface model (FreeSurfer)
- triangle mesh (“finite element”)
- ~ 300 000 triangles
- point of triangle intersections: vertex
- surface of the triangles: face
- XYZ coordinates at each vertex
- area, distance, curvature, thickness, …
- cortical thickness estimation: shortest distance between white-matter surface and pial (gray-matter) surface
cortical thickness - pros and cons
pros:
- automated, continuous, whole cortex
- processing and measurement respect cortical topology
- direct, biologically meaningful measure in millimeters
- surface-registration may increase sensitivity
cons:
- heavy post-processing (6-24 hours/scan)
- dependent on classification
- manual corrections often necessary
- limited to (neo)cortex
shape-related measures of brain morphology - gyrification index (GI)
- estimates the amount of cortex buried within sulcal folds
- can be estimated by Free Surfer
shape-related measures of brain morphology - regional fractal dimensionality (FD)
- measure of ‘structural complexity’
- quantifies how irregular the shape of an object is
- yields a ‘broken’ topological dimension, typically between 2 and 3
