Fedde van der Lijn

White matter lesion extension to automatic brain tissue segmentation on MRI.

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.

A Genome-Wide Association Study Identifies Five Loci Influencing Facial Morphology in Europeans

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.

Common variants at 12q14 and 12q24 are associated with hippocampal volume

Aging is associated with reductions in hippocampal volume that are accelerated by Alzheimers disease and vascular risk factors. Our genome-wide association study (GWAS) of dementia-free persons (n = 9,232) identified 46 SNPs at four loci with P values of <4.0 × 10−7. In two additional samples (n = 2,318), associations were replicated at 12q14 within MSRB3-WIF1 (discovery and replication; rs17178006; P = 5.3 × 10−11) and at 12q24 near HRK-FBXW8 (rs7294919; P = 2.9 × 10−11). Remaining associations included one SNP at 2q24 within DPP4 (rs6741949; P = 2.9 × 10−7) and nine SNPs at 9p33 within ASTN2 (rs7852872; P = 1.0 × 10−7); along with the chromosome 12 associations, these loci were also associated with hippocampal volume (P < 0.05) in a third younger, more heterogeneous sample (n = 7,794). The SNP in ASTN2 also showed suggestive association with decline in cognition in a largely independent sample (n = 1,563). These associations implicate genes related to apoptosis (HRK), development (WIF1), oxidative stress (MSR3B), ubiquitination (FBXW8) and neuronal migration (ASTN2), as well as enzymes targeted by new diabetes medications (DPP4), indicating new genetic influences on hippocampal size and possibly the risk of cognitive decline and dementia.

Brain tissue volumes in the general elderly population. The Rotterdam Scan Study.

We investigated how volumes of cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) varied with age, sex, small vessel disease and cardiovascular risk factors in the Rotterdam Scan Study. Participants (n=490; 60-90 years) were non-demented and 51.0% had hypertension, 4.9% had diabetes mellitus, 17.8% were current smoker and 54.0% were former smoker. We segmented brain MR-images into GM, normal WM, white matter lesion (WML) and CSF. Brain infarcts were rated visually. Volumes were expressed as percentage of intra-cranial volume. With increasing age, volumes of total brain, normal WM and total WM decreased; that of GM remained unchanged; and that of WML increased, in both men and women. Excluding persons with infarcts did not alter these results. Persons with larger load of small vessel disease had smaller brain volume, especially normal WM volume. Diastolic blood pressure, diabetes mellitus and current smoking were also related to smaller brain volume. In the elderly, higher age, small vessel disease and cardiovascular risk factors are associated with smaller brain volume, especially WM volume.

A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline

Hippocampal atrophy is frequently observed on magnetic resonance images from patients with Alzheimers disease and persons with mild cognitive impairment. Even in asymptomatic elderly, a small hippocampal volume on magnetic resonance imaging is a risk factor for developing Alzheimers disease. However, not everyone with a small hippocampus develops dementia. With the increased interest in the use of sequential magnetic resonance images as potential surrogate biomarkers of the disease process, it has also been shown that the rate of hippocampal atrophy is higher in persons with Alzheimers disease compared to those with mild cognitive impairment and the healthy elderly. Whether a higher rate of hippocampal atrophy also predicts Alzheimers disease or subtle cognitive decline in non-demented elderly is unknown. We examine these associations in a group of 518 elderly (age 60-90 years, 50% female), taken from the population-based Rotterdam Scan Study. A magnetic resonance imaging examination was performed at baseline in 1995-96 that was repeated in 1999-2000 (in 244 persons) and in 2006 (in 185 persons). Using automated segmentation procedures, we assessed hippocampal volumes on all magnetic resonance imaging scans. All persons were free of dementia at baseline and followed over time for cognitive decline and incident dementia. Persons had four repeated neuropsychological tests at the research centre over a 10-year period. We also continuously monitored the medical records of all 518 participants for incident dementia. During a total follow-up of 4360 person-years, (mean 8.4, range 0.1-11.3), 50 people developed incident dementia (36 had Alzheimers disease). We found an increased risk to develop incident dementia per standard deviation faster rate of decline in hippocampal volume [left hippocampus 1.6 (95% confidence interval 1.2-2.3, right hippocampus 1.6 (95% confidence interval 1.2-2.1)]. Furthermore, decline in hippocampal volume predicted onset of clinical dementia when corrected for baseline hippocampal volume. In people who remained free of dementia during the whole follow-up period, we found that decline in hippocampal volume paralleled, and preceded, specific decline in delayed word recall. No associations were found in this sample between rate of hippocampal atrophy, Mini Mental State Examination and tests of executive function. Our results suggest that rate of hippocampal atrophy is an early marker of incipient memory decline and dementia, and could be of additional value when compared with a single hippocampal volume measurement as a surrogate biomarker of dementia.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Genetic determination of human facial morphology: links between cleft-lips and normal variation

Recent genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with non-syndromic cleft lip with or without cleft palate (NSCL/P), and other previous studies showed distinctly differing facial distance measurements when comparing unaffected relatives of NSCL/P patients with normal controls. Here, we test the hypothesis that genetic loci involved in NSCL/P also influence normal variation in facial morphology. We tested 11 SNPs from 10 genomic regions previously showing replicated evidence of association with NSCL/P for association with normal variation of nose width and bizygomatic distance in two cohorts from Germany (N=529) and the Netherlands (N=2497). The two most significant associations found were between nose width and SNP rs1258763 near the GREM1 gene in the German cohort (P=6 × 10−4), and between bizygomatic distance and SNP rs987525 at 8q24.21 near the CCDC26 gene (P=0.017) in the Dutch sample. A genetic prediction model explained 2% of phenotype variation in nose width in the German and 0.5% of bizygomatic distance variation in the Dutch cohort. Although preliminary, our data provide a first link between genetic loci involved in a pathological facial trait such as NSCL/P and variation of normal facial morphology. Moreover, we present a first approach for understanding the genetic basis of human facial appearance, a highly intriguing trait with implications on clinical practice, clinical genetics, forensic intelligence, social interactions and personal identity.

Automated Brain Structure Segmentation Based on Atlas Registration and Appearance Models

Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structures location and appearance. The spatial model is implemented by registering multiple atlas images to the target image and creating a spatial probability map. The structures appearance is modeled by a classifier based on Gaussian scale-space features. These components are combined with a regularization term in a Bayesian framework that is globally optimized using graph cuts. The incorporation of the appearance model enables the method to segment structures with complex intensity distributions and increases its robustness against errors in the spatial model. The method is tested in cross-validation experiments on two datasets acquired with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results with mean Dice similarity indices of 0.95 for the cerebellum, and 0.87 for the hippocampus. This was comparable to or better than the other methods, whereas the proposed technique is more widely applicable and robust.

A study of the bidirectional association between hippocampal volume on magnetic resonance imaging and depression in the elderly.

BACKGROUND Hippocampal volume loss on magnetic resonance imaging (MRI) has been reported in patients with depression. It is uncertain whether a small hippocampus renders a person vulnerable to develop depression or whether it is a consequence of depression. In this study, we addressed whether smaller baseline MRI hippocampal volumes increase the risk of incident depression. We also examined whether depressive symptoms at baseline were associated with decline in hippocampal volume during follow-up. METHODS Data were obtained in a prospective population-based study over a 10-year period. A sample of 514 nondemented persons aged 60 to 90 years underwent baseline measurements in 1995-1996 including three-dimensional MRI scans for assessment of hippocampal volumes and depressive symptoms (measured with Center for Epidemiologic Studies Depression Scale). Follow-up MRIs were made in 1999-2000 and in 2006. Incident depression was identified through standardized psychiatric examinations and continuous monitoring of medical and pharmaceutical records. RESULTS During a mean follow-up of 6.8 years per person (range .07-10.01 years), 135 of the 514 persons developed a clinically relevant episode of incident depressive symptoms. There was no association between baseline hippocampal volumes and incident depression (hazard ratio per SD decrease of average hippocampal volume .98 [.81-1.19], p = .84). A baseline Center for Epidemiologic Studies Depression Scale score of 16 or higher predicted a faster rate of decline in hippocampal volume. Also, incident depression was accompanied by a faster decline in left hippocampal volume. CONCLUSIONS This study provides no evidence that a small hippocampal volume precedes the development of late-life depression. Depression, however, may lead to a faster rate of hippocampal volume decline.