Wachinger et. al - Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala (paper summary)
paper summary of:
Wachinger, C., Salat, D. H., Weiner, M., Reuter, M., & Alzheimer’s DiseaseNeuroimaging Initiative. (2016). Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala. Brain, 139(12), 3253-3266.
Structural magnetic resonance imaging data are frequently analysed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this article, we introduce a novel technique to study shape asymmetries of neuroanatomical structures across subcortical and cortical brain regions. We demonstrate that neurodegeneration of subcortical structures in Alzheimer’s disease is not symmetric. The hippocampus shows a significant increase in asymmetry longitudinally and both hippocampus and amygdala show a significantly higher asymmetry cross-sectionally concurrent with disease severity above and beyond an ageing effect. Our results further suggest that the asymmetry in these structures is undirectional and that primarily the anterior hippocampus becomes asymmetric. Based on longitudinal asymmetry measures we subsequently study the progression from mild cognitive impairment to dementia, demonstrating that shape asymmetry in hippocampus, amygdala, caudate and cortex is predictive of disease onset. The same analyses on scalar volume measurements did not produce any significant results, indicating that shape asymmetries, potentially induced by morphometric changes in subnuclei, rather than size asymmetries are associated with disease progression and can yield a powerful imaging biomarker for the early presymptomatic classification and prediction of Alzheimer’s disease. Because literature has focused on contralateral volume differences, subcortical disease lateralization may have been overlooked thus far.
This paper is looking at subcortical structures of the brain using the spectral shape descriptors from Wachinger/Reuter's BrainPrint. This is essentially the Laplace Beltrami spectrum of the shapes. They are also looking at asymmetry of the structures in particular.
The author's propose using the BrainPrint method for four reasons:
1. it avoids lateral processing bias as it works on the two hemispheres independently
2. it does not require prior spatial alignment
3. it is a brain-wide analysis
4. it is a within-subject measure that identifies directional and undirectional asymmetry
directional asymmetry: hemispheric differences that show a stronger effect on one of the hemispheres.
undirectional asymmetry: no consistent hemispheric effect, magnitude of asymmetry independent of direction
Their results suggest that there is a strong increase in undirectional asymmetry with the progression of dementia.
They used an unstructured multi-cohort longitudinal data from ADNI.
Mixed effects models were used to differentiate across- and within-individual variations in asymmetry.
Materials and Methods
BrainPrint is based on the automated segmentation output from Freesurfer. The subjects in this study were processed with the longitudinal framework in Freesurfer. The marching cubes algorithm was used to create surface and volumetric meshes of the segmented structures. shapeDNA was used as the shape descriptor (aka the Laplace Beltrami Spectrum). They used 50 eigenvalues in this study, normalized by volume/surface area.
The eigenvalues of the Laplace Beltrami spectra are isometry invariant, meaning length-preserving deformations will not change the spectrum. This also includes, rigid body motion and reflections. No alignment is needed.
The figure below shows the first 6 non-constant eigenfunctions of the hippocampus. The eigenfunctions show natural vibrations of the shape when oscillating at a frequency specified by the square root of the eigenvalues. To localize the shape changes they use the level set analysis for the first eigenfunction as proposed in Reuter et al. 2009. this is show as green curves.
They computed the circumference of 100 level sets and avearage among 10.
Because shape DNA is invariant to reflections, they directly computed the Mahalanobis distance between the lateralized brain structures.
"The asymmetry measure presents a within-subject measure that can identify directional and undirectional asymmetry. The difference of eigenvalues can be used to differentiate directional and undirectional asymmetry. We compute the asymmetry for 11 lateralized structures: cerebral white matter, pial region, cerebellum white/grey matter, lateral ventricles, hippocampus, amygdala, thalamus, caudate, putamen, and accumbens. For white matter and pial region, the analysis is performed on volumetric meshes."
They used 697 individuals from ADNI.
They used a mixed effects model with a global intercept B0, age at baseline B1, years from baseline B2 and diagnosis (control, MCI stable, MCI progressor, Alzheimers AD).
To be finished...
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