Hippocampal atrophy on MRI is frequently observed in patients with Alzheimer's disease and persons with mild cognitive impairment. Even in asymptomatic elderly, a small hippocampal volume on MRI is a risk factor to develop Alzheimer's disease. However, a large proportion of persons with a small hippocampal volume do not develop dementia. With follow-up imaging, hippocampal atrophy rates are higher in persons with Alzheimer's disease and mild cognitive impairment, but it is uncertain whether this could be observed before clinical symptoms of dementia. In the large population-based Rotterdam Scan Study, we studied a cohort of 518 initially non-demented elderly from 1995 onwards. At baseline, we made a high resolution 3-dimensional MRI of the brain, and the MRI scan was repeated twice over a period of 10 years (1999-2000 and 2006). Also, rigorous follow-up for incident dementia and subtle cognitive decline with extensive neuropsychological testing, was done in this period. Hippocampal volumes on all MRI scans were assessed with an automated segmentation procedure. The results of this segmentation procedure were checked and -if needed- manually corrected. Rates of decline in hippocampal volume were then calculated with a mixed random effect model. On average, there was a small decline in hippocampal volume over the ten-year follow-up of 1.2 % per year. From the cohort of 518 persons, 50 persons developed dementia within the follow-up period. Increased rate of hippocampal volume decline was evident before clinical onset of dementia (relative risk of dementia per SD increase in rate of 1.5, 95% confidence interval 1.1-2.1, similar for left and right-sided volumes), even when adjusting for baseline hippocampal volumes. Moreover, in the population that remained free of dementia over a ten year period, rate of hippocampal decline was associated with the risk to decline in memory function (delayed recall) (per SD relative risk of 1.4, 95 % confidence interval 1.1-1.9). Increased rates of hippocampal decline are found in the presymptomatic phase of Alzheimer's disease.
We present a method for automated brain-tissue segmentation through voxelwise classification. Our algorithm uses manually labeled training images to train a support vector machine (SVM) classifier, which is then used for the segmentation of target images. The classification incorporates voxel intensities from a T1-weighted scan, an IR scan, and a FLAIR scan; features to encode the voxel position in the image; and Gaussian-scale-space features and Gaussian-derivative features at multiple scales to facilitate a smooth segmentation. An experiment on data from the MRBrainS13 brain-tissue-segmentation challenge showed that our algorithm produces reasonable segmentations in a reasonable amount of time.
It is still unclear whether periventricular and subcortical white matter lesions (WMLs) differ in etiology or clinical consequences. Studies addressing this issue would benefit from automated segmentation and localization of WMLs. Several papers have been published on WML segmentation in MR images. Automated localization however, has not been investigated as much. This work presents and evaluates a novel method to label segmented WMLs as periventricular and subcortical. The proposed technique combines tissue classification and registration-based segmentation to outline the ventricles in MRI brain data. The segmented lesions can then be labeled into periventricular WMLs and subcortical WMLs by applying region growing and morphological operations. The technique was tested on scans of 20 elderly subjects in which neuro-anatomy experts manually segmented WMLs. Localization accuracy was evaluated by comparing the results of the automated method with a manual localization. Similarity indices and volumetric intraclass correlations between the automated and the manual localization were 0.89 and 0.95 for periventricular WMLs and 0.64 and 0.89 for subcortical WMLs, respectively. We conclude that this automated method for WML localization performs well to excellent in comparison to the gold standard.
Decline of hippocampal volume on magnetic resonance imaging (MRI) may be considered as a surrogate biomarker of accumulating Alzheimer disease (AD) pathology. Previously, we showed in the prospective population-based Rotterdam Scan Study that a higher rate of decline of hippocampal volume on MRI precedes clinical AD or memory decline. We studied potential risk factors for decline of hippocampal volume.At baseline (1995-1996), 518 nondemented elderly subjects were included, and the cohort was re-examined in 1999 and in 2006. At each examination, hippocampal volume was determined using an automated segmentation procedure. In all, 301 persons had at least two three-dimensional MRI scans to assess decline in hippocampal volume.Persons carrying the apolipoprotein E (APOE) ɛ4 allele had lower hippocampal volumes than persons with the ɛ3/ɛ3 genotype, but the rate of decline was not influenced by APOE genotype. In persons who did not use antihypertensive treatment, both a high (>90 mm Hg) and a low (<70 mm Hg) diastolic blood pressure were associated with a faster decline in hippocampal volume. Also, white matter lesions on baseline MRI were associated with a higher rate of decline in hippocampal volume.In a nondemented elderly population, persons with the APOE ɛ4 allele have a smaller hippocampal volume but not a higher rate of decline. Rate of decline of hippocampal volume was influenced by white matter lesions and diastolic blood pressure, supporting their hypothesized role in the pathogenesis of AD.
A method to automatically segment cerebrospinal fluid, gray matter, white matter and white matter lesions is presented. The method uses magnetic resonance brain images from proton density, T1-weighted and fluid-attenuated inversion recovery sequences. The method is based on an automatically trained k-nearest neighbour classifier extended with an additional step for the segmentation of white matter lesions. On six datasets, segmentations are quantitatively compared with manual segmentations, which have been carried out by two expert observers. For the tissues, similarity indices between method and observers approximate those between manual segmentations. Reasonably good lesion segmentation results are obtained compared to interobserver variability.
Inter-individual variation in facial shape is one of the most noticeable phenotypes in humans, and it is clearly under genetic regulation; however, almost nothing is known about the genetic basis of normal human facial morphology. We therefore conducted a genome-wide association study for facial shape phenotypes in multiple discovery and replication cohorts, considering almost ten thousand individuals of European descent from several countries. Phenotyping of facial shape features was based on landmark data obtained from three-dimensional head magnetic resonance images (MRIs) and two-dimensional portrait images. We identified five independent genetic loci associated with different facial phenotypes, suggesting the involvement of five candidate genes—PRDM16, PAX3, TP63, C5orf50, and COL17A1—in the determination of the human face. Three of them have been implicated previously in vertebrate craniofacial development and disease, and the remaining two genes potentially represent novel players in the molecular networks governing facial development. Our finding at PAX3 influencing the position of the nasion replicates a recent GWAS of facial features. In addition to the reported GWA findings, we established links between common DNA variants previously associated with NSCL/P at 2p21, 8q24, 13q31, and 17q22 and normal facial-shape variations based on a candidate gene approach. Overall our study implies that DNA variants in genes essential for craniofacial development contribute with relatively small effect size to the spectrum of normal variation in human facial morphology. This observation has important consequences for future studies aiming to identify more genes involved in the human facial morphology, as well as for potential applications of DNA prediction of facial shape such as in future forensic applications.
