The relation of uric acid to brain atrophy and cognition: the Rotterdam Scan Study.

BACKGROUND: Uric acid has been associated with focal vascular brain disease. However, it is unknown whether uric acid also relates to global brain changes such as brain atrophy. We therefore studied the relation of uric acid to brain atrophy and whether this is accompanied by worse cognitive function. METHODS: In 814 persons of the population-based Rotterdam Study (mean age 62.0 years), we studied the relation of uric acid levels to brain tissue atrophy and cognition using linear regression models adjusted for age, sex and putative confounders. Brain atrophy was assessed using automated processing of magnetic resonance imaging. Cognition was assessed using a validated neuropsychological test battery and we computed compound scores of cognitive domains. RESULTS: Higher uric acid levels were associated with white matter atrophy [difference in Z-score of white matter volume per standard deviation increase in uric acid: -0.07 (95% CI: -0.12; -0.01)], but not with gray matter atrophy. This was particularly marked when comparing hyperuricemic to normouricemic persons [Z-score difference: -0.27 (-0.43; -0.11)]. Worse cognition was primarily found in persons with hyperuricemia [-0.28 (-0.48; -0.08)]. CONCLUSIONS: Hyperuricemia is related to white matter atrophy and worse cognition.

High blood pressure and cerebral white matter lesion progression in the general population.

High blood pressure is considered an important risk factor for cerebral white matter lesions (WMLs) in the aging population. In a longitudinal population-based study of 665 nondemented persons, we investigated the longitudinal relationship of systolic blood pressure, diastolic blood pressure, and pulse pressure with annual progression of WMLs. Means of blood pressure were calculated over a 5-year period before longitudinal MRI scanning. WML progression was subsequently measured on 2 scans 3.5 years apart. We performed analyses with linear regression models and evaluated adjustments for age, sex, cardiovascular risk factors, and baseline WML volume. In addition, we evaluated whether treatment of hypertension is related to less WML progression. Both systolic and diastolic blood pressures were significantly associated with annual WML progression (regression coefficient [95% confidence interval], 0.08 [0.03; 0.14] mL/y and 0.09 [0.03; 0.15] mL/y per SD increase in systolic and diastolic blood pressure, respectively). Pulse pressure was also significantly associated with WML progression, but not independent from hypertension. After adjustment for baseline WML volume, only systolic blood pressure remained significantly associated: 0.05 (0.00; 0.09) mL/y per SD increase. People with uncontrolled untreated hypertension had significantly more WML progression than people with uncontrolled treated hypertension (difference [95% confidence interval], 0.12 [0.00; 0.23] mL/y). The present study further establishes high blood pressure to precede WMLs and implies that hypertension treatment could reduce WML progression in the general population.

Changes in normal-appearing white matter precede development of white matter lesions.

BACKGROUND AND PURPOSE: It is unknown whether white matter lesions (WML) develop abruptly in previously normal brain areas, or whether tissue changes are already present before WML become apparent on MRI. We therefore investigated whether development of WML is preceded by quantifiable changes in normal-appearing white matter (NAWM). METHODS: In 689 participants from the general population (mean age 67 years), we performed 2 MRI scans (including diffusion tensor imaging and Fluid Attenuation Inversion Recovery [FLAIR] sequences) 3.5 years apart using the same 1.5-T scanner. Using automated tissue segmentation, we identified NAWM at baseline. We assessed which NAWM regions converted into WML during follow-up and differentiated new WML into regions of WML growth and de novo WML. Fractional anisotropy, mean diffusivity, and FLAIR intensity of regions converting to WML and regions of persistent NAWM were compared using 3 approaches: a whole-brain analysis, a regionally matched approach, and a voxel-wise approach. RESULTS: All 3 approaches showed that low fractional anisotropy, high mean diffusivity, and relatively high FLAIR intensity at baseline were associated with WML development during follow-up. Compared with persistent NAWM regions, NAWM regions converting to WML had significantly lower fractional anisotropy (0.337 vs 0.387; P<0.001), higher mean diffusivity (0.910 × 10(-3) mm(2)/s vs 0.729 × 10(-3) mm(2)/s; P<0.001), and relatively higher normalized FLAIR intensity (1.233 vs -0.340; P<0.001). This applied to both NAWM developing into growing and de novo WML. CONCLUSIONS: White matter changes in NAWM are present and can be quantified on diffusion tensor imaging and FLAIR before WML develop. This suggests that WML develop gradually, and that visually appreciable WML are only the tip of the iceberg of white matter pathology.

Replication study of chr17q25 with cerebral white matter lesion volume.

BACKGROUND AND PURPOSE: Recently, the first genomewide association study on cerebral white matter lesion burden identified chr17q25 to be significantly associated with white matter lesions. We report on the first independent replication study of this genetic association. METHODS: In a population-based cohort study, we investigated the association between the 6 genomewide significant single nucleotide polymorphisms at that locus and cerebral white matter lesion volume on MRI, measured quantitatively, adjusted for age, sex, and intracranial volume. Adjustments for ApoE4 carriership and cardiovascular risk factors were evaluated separately. Finally, we performed a meta-analysis of all published data for the single most significant single nucleotide polymorphism, rs3744028. RESULTS: The risk alleles of all the 6 single nucleotide polymorphisms were significantly associated with white matter lesion volume with P=1.1*10(-3) for rs3744028, adjusted for age, sex, and intracranial volume. Additional adjustments only had minor influence on these associations. A meta-analysis with all published data for rs3744028 resulted in a probability value of 5.3*10(-17). CONCLUSIONS: This study further establishes chr17q25 as a novel genetic locus for WML volume.

Genome-wide association studies of cerebral white matter lesion burden: the CHARGE consortium.

OBJECTIVE: White matter hyperintensities (WMHs) detectable by magnetic resonance imaging are part of the spectrum of vascular injury associated with aging of the brain and are thought to reflect ischemic damage to the small deep cerebral vessels. WMHs are associated with an increased risk of cognitive and motor dysfunction, dementia, depression, and stroke. Despite a significant heritability, few genetic loci influencing WMH burden have been identified. METHODS: We performed a meta-analysis of genome-wide association studies (GWASs) for WMH burden in 9,361 stroke-free individuals of European descent from 7 community-based cohorts. Significant findings were tested for replication in 3,024 individuals from 2 additional cohorts. RESULTS: We identified 6 novel risk-associated single nucleotide polymorphisms (SNPs) in 1 locus on chromosome 17q25 encompassing 6 known genes including WBP2, TRIM65, TRIM47, MRPL38, FBF1, and ACOX1. The most significant association was for rs3744028 (p(discovery) = 4.0 × 10(-9) ; p(replication) = 1.3 × 10(-7) ; p(combined) = 4.0 × 10(-15) ). Other SNPs in this region also reaching genome-wide significance were rs9894383 (p = 5.3 × 10(-9) ), rs11869977 (p = 5.7 × 10(-9) ), rs936393 (p = 6.8 × 10(-9) ), rs3744017 (p = 7.3 × 10(-9) ), and rs1055129 (p = 4.1 × 10(-8) ). Variant alleles at these loci conferred a small increase in WMH burden (4-8% of the overall mean WMH burden in the sample). INTERPRETATION: This large GWAS of WMH burden in community-based cohorts of individuals of European descent identifies a novel locus on chromosome 17. Further characterization of this locus may provide novel insights into the pathogenesis of cerebral WMH.

Statistical analysis of minimum cost path based structural brain connectivity.

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.

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.

Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods.

The ability to study changes in brain morphometry in longitudinal studies majorly depends on the accuracy and reproducibility of the brain tissue quantification. We evaluate the accuracy and reproducibility of four previously proposed automatic brain tissue segmentation methods: FAST, SPM5, an automatically trained k-nearest neighbor (kNN) classifier, and a conventional kNN classifier based on a prior training set. The intensity nonuniformity correction and skull-stripping mask were the same for all methods. Evaluations were performed on MRI scans of elderly subjects derived from the general population. Accuracy was evaluated by comparison to two manual segmentations of MRI scans of six subjects (mean age 65.9+/-4.4 years). Reproducibility was assessed by comparing the automatic segmentations of 30 subjects (mean age 57.0+/-3.7 years) who were scanned twice within a short time interval. All methods showed good accuracy and reproducibility, with only small differences between methods. The conventional kNN classifier was the most accurate method with similarity indices of 0.82/0.90/0.94 for cerebrospinal fluid/gray matter/white matter, but it showed the lowest reproducibility. FAST yielded the most reproducible segmentation volumes with volume difference standard deviations of 0.55/0.49/0.38 (percentage of intracranial volume) respectively. The results of the reproducibility experiment can be used to calculate the required number of subjects in the design of a longitudinal study with sufficient power to detect changes over time in brain (tissue) volume. Example sample size calculations demonstrate a rather large effect of the choice of segmentation method on the required number of subjects.

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.

Iterative co-linearity filtering and parameterization of fiber tracts in the entire cingulum.

We present a method for the fully automated extraction of the cingulum using diffusion tensor imaging (DTI) data. We perform whole-brain tractography and initialize tract selection in the cingulum with a registered DTI atlas. Tracts are parameterized from which tract co-linearity is derived. The tract set, filtered on the basis of co-linearity with the cingulum shape, yields an improved segmentation of the cingulum and is subsequently optimized in an iterative fashion to further improve the tract selection. We evaluate the method using a large DTI database of 500 subjects from the general population and show robust extraction of tracts in the entire cingulate bundle in both hemispheres. We demonstrate the use of the extracted fiber-tracts to compare left and right cingulate bundles. Our asymmetry analysis shows a higher fractional anisotropy in the left anterior part of the cingulum compared to the right side, and the opposite effect in the posterior part.