Wachinger et al. 2017 - Latent Processes Governing Neuroanatomical Change in Aging and Dementia (Paper Summary)
Paper summary for:
Wachinger, C., Rieckmann, A., & Reuter, M. (2017, September). Latent Processes Governing Neuroanatomical Change in Aging and Dementia. In International Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 30-37). Springer, Cham.
In this article, they look at using the shape of brain structures in combination with behavioral/cognitive data to identify disease-related changes in brain morphology from normal age related changes. This paper uses BrainPrint and does a PLS analysis.
Aging is characterized by a multifaceted set of neurobiological changes that occur at different rates in different people with complex and interdependent effects on cognitive decline. Some of these changes are normal, but some are due to specific diseases. Some may be similar to a disease but arise from a different cause. This study focuses on differentiating which morphological brain changes are normal and which are associated with the development of Alzheimer's.
Structural changes in the brain do not occur uniformly across the cortical surface. Some regions are likely affected by disease and age related changes, while others may be affect only by aging. Paper gives the example of the striatum - it is affected by aging, but not Alzheimer's disease.
In order to take advantage of the heterogeneity of aging and disease-related effects, this paper looks at joint modeling changes across many structures rather than focusing on single structures alone.
It is understood that there are many underlying processes that can cause morphological changes - modeled here as latent factors. It is also known that aging and disease can cause different changes in different subregions of specific structures. Volume or surface area measurements of these structures may not show any difference, but there may be noticeable shape changes.
'To obtain a discriminative characterization of neuroanatomy' the authors uses "BrianPrint' (see link above). BrainPrint calculates the Laplace Beltrami Spectrum for 3D surfaces of cortical areas of the brain segmented using Freesurfer.
They did a cross-sectional and longitudinal study. They 'identify neuroanatomical processes that are best associated to aging and disease by maximizing the covariance between morphology and response variables, yielding the projection of the data to latent structures. '
See paper for short paragraph on related work.
They used BrainPrint to represent the brain morphology based on the automated segmentation of anatomical brain structures with Freesurfer. BrainPrint uses ShapeDNA (the Laplace Beltrami Spectrum). They normalized the spectra by surface area and eigenvalue index. Normalizing by index balances the impact of higher eigenvalues that typically show higher variance.
They applied this method to both cross-sectional data and for longitudinal data.
Latent Factor Model
The looked at the morphology and response variables - age and performance on the mini-mental state examination (MMSE) (clinical screening for loss of memory and intellectual abilities). Objective is to extract the latent variables that account for much of the factor variation in the data. To take the response variables into account they used PLS or projections to latent structures or partial least squares. PLS combines information about both the predictors and the resonses and the correlations among them. See the paper for details of applying the PLS.
They used ADNI data. subjects with baseline scans and with follow up scans after 6, 12, and 14 months. 393 subjects total. The diagnostic groups were Healthy Control (HC), Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD).
Meshes from 23 brain structures were used and 5 eigenvalues.
Of the components reported from the PLS ( Iguess it is similar to PCA) for the longitudinal analysis, two were significantly correlated only with Age and two were significantly correlated only with the MMSE
Figures 1 and 2 show the 4 processes for the longitudinal data. The first a third are related to progression of dementia. The color of the brain areas are related to importance.
The first process shows opposing effects on the hippocampus and amygdala vs. the lateral and third ventricle. The authors suggest this reflects the typical brain changes associated with dementia, shrinkage of the hippocampus and amygdala and expansion of the ventricles.
the third process is showing opposing effects on the amygdala vs. hippocampus. This suggests that there are two separable dementia related processes.
For the 2nd and 4th processes the weights of the amygdala and hippocmapus are lower than for the dementia processes, and higher for the ventricles.
Lastly, they evaluated the predictive performance for the latent factor model compared to traditional multiple linear regression and with volume instead of shape in the PLS model.
The mean absolute prediction error was significantly lower in the PLS with shape model than the other 2.
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