Lijn, F. van der

Determinants of cerebellar and cerebral volume in the general elderly population (Article)

2012-12-01T00:00:00Z

In a population-based study of 3962 community-dwelling nondemented elderly we investigated the relation of age, sex, cardiovascular risk factors, and the presence of infarcts with cerebellar volume, and its interrelationship with cerebral volumes. Cerebellar and cerebral gray and white matter were segmented using Freesurfer version 4.5 (http://surfer.nmr.mgh.harvard.edu/). We used linear regression analyses to model the relationship between age, sex, cardiovascular risk factors, brain infarcts, white matter lesions (WMLs) and cerebellar and cerebral volume. Smaller cerebellar volumes with increasing age were mainly driven by loss of white matter. Diabetes, higher serum glucose and lower cholesterol levels were related to smaller cerebellar volume. No association was found between hypertension, smoking, apolipoprotein E (ApoE) genotype, and cerebellar volume. Supratentorial lacunar infarcts and WMLs were related to smaller cerebellar volume. Infratentorial infarcts were related to smaller cerebellar white matter volume and total cerebral volume. This study suggests that determinants of cerebellar volume do not entirely overlap with those established for cerebral volume. Furthermore, presence of infarcts or WMLs in the cerebrum can affect cerebellar volume.

Vascular risk factors, apolipoprotein E, and hippocampal decline on magnetic resonance imaging over a 10-year follow-up (Article)

2012-01-12T00:00:00Z

Background: 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. Methods: 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. Results: Persons carrying the apolipoprotein E (APOE) e{open}4 allele had lower hippocampal volumes than persons with the e{open}3/e{open}3 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. Conclusions: In a nondemented elderly population, persons with the APOE e{open}4 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.

Global and focal brain volume in long-term breast cancer survivors exposed to adjuvant chemotherapy (Article)

2011-12-28T00:00:00Z

A limited number of studies have associated adjuvant chemotherapy with structural brain changes. These studies had small sample sizes and were conducted shortly after cessation of chemotherapy. Results of these studies indicate local gray matter volume decrease and an increase in white matter lesions. Up till now, it is unclear if non-CNS chemotherapy is associated with long-term structural brain changes. We compared focal and total brain volume (TBV) of a large set of non-CNS directed chemotherapy-exposed breast cancer survivors, on average 21 years post-treatment, to that of a population-based sample of women without a history of cancer. Structural MRI (1.5T) was performed in 184 chemotherapy-exposed breast cancer patients, mean age 64.0 (SD = 6.5) years, who had been diagnosed with cancer on average 21.1 (SD = 4.4) years before, and 368 age-matched cancer-free reference subjects from a population-based cohort study. Outcome measures were: TBV and total gray and white matter volume, and hippocampal volume. In addition, voxel based morphometry was performed to analyze differences in focal gray matter. The chemotherapy-exposed breast cancer survivors had significantly smaller TBV (-3.5 ml, P = 0.019) and gray matter volume (-2.9 ml, P = 0.003) than the reference subjects. No significant differences were observed in white matter volume, hippocampal volume, or local gray matter volume. This study shows that adjuvant chemotherapy for breast cancer is associated with long-term reductions in TBV and overall gray matter volume in the absence of focal reductions. The observed smaller gray matter volume in chemotherapy-exposed survivors was comparable to the effect of almost 4 years of age on gray matter volume reduction. These volume differences might be associated with the slightly worse cognitive performance that we observed previously in this group of breast cancer survivors.

Genetic determination of human facial morphology: Links between cleft-lips and normal variation (Article)

2011-11-01T00:00:00Z

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.

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

2011-07-15T00:00:00Z

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 19951996 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 19992000 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 .0710.01 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 [.811.19], 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.

Statistical analysis of minimum cost path based structural brain connectivity (Article)

2011-03-15T00:00:00Z

Diffusion MRI can be used to study the structural connectivity within the brain. Brain connectivity is often represented by a binary network whose topology can be studied using graph theory. We present a framework for the construction of weighted structural brain networks, containing information about connectivity, which can be effectively analyzed using statistical methods. Network nodes are defined by segmentation of subcortical structures and by cortical parcellation. Connectivity is established using a minimum cost path (mcp) method with an anisotropic local cost function based directly on diffusion weighted images. We refer to this framework as Statistical Analysis of Minimum cost path based Structural Connectivity (SAMSCo) and the weighted structural connectivity networks as mcp-networks. In a proof of principle study we investigated the information contained in mcp-networks by predicting subject age based on the mcp-networks of a group of 974 middle-aged and elderly subjects. Using SAMSCo, age was predicted with an average error of 3.7. years. This was significantly better than predictions based on fractional anisotropy or mean diffusivity averaged over the whole white matter or over the corpus callosum, which showed average prediction errors of at least 4.8. years. Additionally, we classified subjects, based on the mcp-networks, into groups with low and high white matter lesion load, while correcting for age, sex and white matter atrophy. The SAMSCo classification outperformed the classification based on the diffusion measures with a classification accuracy of 76.0% versus 63.2%. We also performed a classification in groups with mild and severe atrophy, correcting for age, sex and white matter lesion load. In this case, mcp-networks and diffusion measures yielded similar classification accuracies of 68.3% and 67.8% respectively. The SAMSCo prediction and classification experiments indicate that the mcp-networks contain information regarding age, white matter lesion load and white matter atrophy, and that in case of age and white matter lesion load the mcp-network based models outperformed the predictions based on diffusion measures.

Statistical analysis of structural brain connectivity (Article)

2010-11-22T00:00:00Z

We present a framework for statistical analysis in large cohorts of structural brain connectivity, derived from diffusion weighted MRI. A brain network is defined between subcortical gray matter structures and a cortical parcellation obtained with FreeSurfer. Connectivity is established through minimum cost paths with an anisotropic local cost function and is quantified per connection. The connectivity network potentially encodes important information about brain structure, and can be analyzed using multivariate regression methods. The proposed framework can be used to study the relation between connectivity and e.g. brain function or neurodegenerative disease. As a proof of principle, we perform principal component regression in order to predict age and gender, based on the connectivity networks of 979 middle-aged and elderly subjects, in a 10-fold cross-validation. The results are compared to predictions based on fractional anisotropy and mean diffusivity averaged over the white matter and over the corpus callosum. Additionally, the predictions are performed based on the best predicting connection in the network. Principal component regression outperformed all other prediction models, demonstrating the age and gender information encoded in the connectivity network.

Prediction of dementia by hippocampal shape analysis (Article)

2010-10-25T00:00:00Z

This work investigates the possibility of predicting future onset of dementia in subjects who are cognitively normal, using hippocampal shape and volume information extracted from MRI scans. A group of 47 subjects who were non-demented normal at the time of the MRI acquisition, but were diagnosed with dementia during a 9 year follow-up period, was selected from a large population based cohort study. 47 Age and gender matched subjects who stayed cognitively intact were selected from the same cohort study as a control group. The hippocampi were automatically segmented and all segmentations were inspected and, if necessary, manually corrected by a trained observer. From this data a statistical model of hippocampal shape was constructed, using an entropy-based particle system. This shape model provided the input for a Support Vector Machine classifier to predict dementia. Cross validation experiments showed that shape information can predict future onset of dementia in this dataset with an accuracy of 70%. By incorporating both shape and volume information into the classifier, the accuracy increased to 74%.

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

2010-04-01T00:00:00Z

Hippocampal atrophy is frequently observed on magnetic resonance images from patients with Alzheimer's 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 Alzheimer's 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 Alzheimer's disease compared to those with mild cognitive impairment and the healthy elderly. Whether a higher rate of hippocampal atrophy also predicts Alzheimer's 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 Alzheimer's 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.

White matter lesion extension to automatic brain tissue segmentation on MRI (Article)

2009-05-01T00:00:00Z

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.

Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts (Article)

2008-12-01T00:00:00Z

Since hippocampal volume has been found to be an early biomarker for Alzheimer's disease, there is large interest in automated methods to accurately, robustly, and reproducibly extract the hippocampus from MRI data. In this work we present a segmentation method based on the minimization of an energy functional with intensity and prior terms, which are derived from manually labelled training images. The intensity energy is based on a statistical intensity model that is learned from the training images. The prior energy consists of a spatial and regularity term. The spatial prior is obtained from a probabilistic atlas created by registering the training images to the unlabelled target image, and deforming and averaging the training labels. The regularity prior energy encourages smooth segmentations. The resulting energy functional is globally minimized using graph cuts. The method was evaluated using image data from a population-based study on diseases among the elderly. Two set of images were used: a small set of 20 manually labelled MR images and a larger set of 498 images, for which manual volume measurements were available, but no segmentations. This data was previously used in a volumetry study that found significant associations between hippocampal volume and cognitive decline and incidence of dementia. Cross-validation experiments with the labelled set showed similarity indices of 0.852 and 0.864 and mean surface distances of 0.40 and 0.36 mm for the left and right hippocampus. 83% of the automated segmentations of the large set were rated as 'good' by a trained observer. Also, the proposed method was used to repeat the manual hippocampal volumetry study. The automatically obtained hippocampal volumes showed significant associations with cognitive decline and dementia, similar to the manually measured volumes. Finally, direct quantitative and qualitative comparisons showed that the proposed method outperforms a multi-atlas based segmentation method.

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

2008-06-01T00:00:00Z

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.

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

2007-08-01T00:00:00Z

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.