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Metopic synostosis patients are at risk for neurodevelopmental disorders despite a negligible risk of intracranial hypertension. To gain insight into the underlying pathophysiology of metopic synostosis and associated neurodevelopmental disorders, we aimed to investigate brain volumes of non-syndromic metopic synostosis patients using preoperative MRI brain scans. MRI brain scans were processed with HyperDenseNet to calculate total intracranial volume (TIV), total brain volume (TBV), total grey matter volume (TGMV), total white matter volume (TWMV) and total cerebrospinal fluid volume (TCBFV). We compared global brain volumes of patients with controls corrected for age and sex using linear regression. Lobe-specific grey matter volumes were assessed in secondary analyses. We included 45 metopic synostosis patients and 14 controls (median age at MRI 0.56 years [IQR 0.36] and 1.1 years [IQR 0.47], respectively). We found no significant differences in TIV, TBV, TGMV or TCBFV in patients compared to controls. TWMV was significantly smaller in patients (-62,233 mm 3 [95% CI = -96,968; -27,498], Holm-corrected p = 0.004), and raw data show an accelerated growth pattern of white matter in metopic synostosis patients. Grey matter volume analyses per lobe indicated increased cingulate (1378 mm 3 [95% CI = 402; 2355]) and temporal grey matter (4747 [95% CI = 178; 9317]) volumes in patients compared to controls. To conclude, we found smaller TWMV with an accelerated white matter growth pattern in metopic synostosis patients, similar to white matter growth patterns seen in autism. TIV, TBV, TGMV and TCBFV were comparable in patients and controls. Secondary analyses suggest larger cingulate and temporal lobe volumes. These findings suggest a generalized intrinsic brain anomaly in the pathophysiology of neurodevelopmental disorders associated with metopic synostosis., (© 2024 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society.)

Structural brain markers are studied extensively in the field of neurodegeneration, but are thought to occur rather late in the process. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain structure and function are also affected by 'normal' brain ageing. More information is needed on how functional connectivity relates to aging, particularly in the absence of overt neurodegenerative disease. We investigated the association of age with resting-state functional connectivity in 2878 non-demented persons between 50 and 95 years of age (54.1% women) from the population-based Rotterdam Study. We obtained nine well-known resting state networks using data-driven methodology. Within the anterior default mode network, ventral attention network, and sensorimotor network, functional connectivity was significantly lower with older age. In contrast, functional connectivity was higher with older age within the visual network. Between resting state networks, we found patterns of both increases and decreases in connectivity in approximate equal proportions. Our results reinforce the notion that the aging brain undergoes a reorganization process, and serves as a solid basis for exploring functional connectivity as a preclinical marker of neurodegenerative disease., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Cortical brain infarcts are defined as infarcts involving cortical gray matter, but may differ considerably in size. It is unknown whether small cortical infarcts have a similar clinical phenotype as larger counterparts. We investigated prevalence, determinants, and cognitive correlates of small cortical infarcts in the general population and compared these with large cortical infarcts and lacunar infarcts., Methods: Four thousand nine hundred five nondemented individuals (age 63·95 ± 10·99) from a population-based study were included. Infarcts were rated on magnetic resonance imaging and participants were classified according to mean infarct diameter into small (≤15 mm in largest diameter) or large (>15 mm) cortical infarcts, lacunar infarcts, or a combination of subtypes. Spatial distribution maps were created for manually labeled small and large infarcts. Participants underwent cognitive testing. Analyses were performed using multinomial regression and analysis of covariance., Results: Three hundred eighty-one (7·8%) persons had any infarct on magnetic resonance imaging, among whom 54 with small (1·1%) and 77 (1·6%) with large cortical infarcts. Small cortical infarcts were mainly localized in external watershed areas, whereas large cortical infarcts were localized primarily in large arterial territories. Age (odds ratio = 1·06; 95% confidence interval = 1·02, 1·09), male gender (1·98; 1·01, 3·92), and smoking (2·55; 1·06, 6·14) were determinants of small cortical infarcts. Participants with these infarcts had worse scores in delayed memory, processing speed, and attention tests than persons without infarcts, even after adjustment for cardiovascular risk factors., Conclusions: In the elderly, small cortical infarcts appear as frequent as large infarcts but in different localization. Our results suggest that small cortical infarcts share cardiovascular risk factors and cognitive correlates with large cortical, but also with lacunar infarcts., (© 2015 World Stroke Organization.)

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.

We propose an infrastructure for the automated anonymization, extraction and processing of image data stored in clinical data repositories to make routinely acquired imaging data available for research purposes. The automated system, which was tested in the context of analyzing routinely acquired MR brain imaging data, consists of four modules: subject selection using PACS query, anonymization of privacy sensitive information and removal of facial features, quality assurance on DICOM header and image information, and quantitative imaging biomarker extraction. In total, 1,616 examinations were selected based on the following MRI scanning protocols: dementia protocol (246), multiple sclerosis protocol (446) and open question protocol (924). We evaluated the effectiveness of the infrastructure in accessing and successfully extracting biomarkers from routinely acquired clinical imaging data. To examine the validity, we compared brain volumes between patient groups with positive and negative diagnosis, according to the patient reports. Overall, success rates of image data retrieval and automatic processing were 82.5 %, 82.3 % and 66.2 % for the three protocol groups respectively, indicating that a large percentage of routinely acquired clinical imaging data can be used for brain volumetry research, despite image heterogeneity. In line with the literature, brain volumes were found to be significantly smaller (p-value <0.001) in patients with a positive diagnosis of dementia (915 ml) compared to patients with a negative diagnosis (939 ml). This study demonstrates that quantitative image biomarkers such as intracranial and brain volume can be extracted from routinely acquired clinical imaging data. This enables secondary use of clinical images for research into quantitative biomarkers at a hitherto unprecedented scale.

Background: Frontotemporal lobar degeneration is a neurodegenerative disease characterized by brain atrophy of the frontal and anterior temporal lobes. The associated frontotemporal dementia syndromes are clinically heterogeneous, and the pattern of affected cortical regions varies among subtypes. The TMEM106B rs1990622 polymorphism is associated with frontotemporal lobar degeneration, but little is known about how it affects the brain., Methods: We investigated the rs1990622 polymorphism in relation to regional brain volumes to identify potential structures through which TMEM106B confers risk for frontotemporal lobar degeneration. In 4413 nondemented and stroke-free participants from the population-based Rotterdam Study, 150 cortical brain structures and 6 commissural regions were segmented from magnetic resonance imaging., Results: A distinct pattern of association was found between rs1990622 and gray matter volume of left-sided temporal brain regions important for language processing, including the superior temporal gyrus (β=-88.8 μL per risk allele, p=7.64×10(-5)), which contains Wernicke's area. The risk allele was also associated with a smaller anterior commissure cross-sectional area (β=-.167 mm2 per risk allele, p=4.90×10(-5)) and posterior part of the corpus callosum (β=-15.3 μL per risk allele, p=1.23×10(-5)), both of which contain temporal lobe commissural tracts., Conclusions: The asymmetric, predominantly left-sided involvement suggests an effect of TMEM106B on functions lateralized to the dominant hemisphere, such as language. These results show that, in nondemented persons, TMEM106B influences the volume of temporal brain regions that are important for language processing., (Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2013 S. Karger AG, Basel.)

Background: Increasing evidence suggests a role of atherosclerosis in the pathogenesis of cognitive impairment and dementia. Calcification volume measured with computed tomography (CT) is a valid marker of atherosclerosis. This study investigates associations of atherosclerosis (measured using CT) at four locations with cognition and brain changes on magnetic resonance imaging (MRI)., Methods: To quantify calcification volume, 2414 nondemented people from the Rotterdam Study underwent CT of the coronary arteries, aortic arch, extracranial carotid arteries, and intracranial carotid arteries. To assess global cognition and performance on memory, executive function, information processing speed, and motor speed, they also underwent neuropsychological tests. In a random subgroup of 844 participants, brain MRI was performed. Automated segmentation and quantification of brain MRI scans yielded brain tissue volumes in milliliters. Diffusion tensor imaging was used to measure the microstructural integrity of the white matter. Relationships of atherosclerotic calcification with cognition, brain tissue volumes, and diffusion tensor imaging measures were assessed with linear regression models and adjusted for relevant confounders., Results: With larger calcification volumes, lower cognitive scores were observed. When calcification volumes were larger, total brain volumes were also smaller. Specifically, larger coronary artery calcification volumes related to smaller gray matter volumes, and extracranial and intracranial carotid calcification volumes related to smaller white matter volumes. Larger calcification volume in all vessel beds was accompanied by worse microstructural integrity of the white matter., Conclusions: Larger calcification volume is associated with worse cognitive performance. It also relates to smaller brain tissue volumes and worse white matter microstructural integrity, revealing possible mechanisms through which atherosclerosis may lead to poorer cognition., (Copyright © 2012 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

During aging, intracranial volume remains unchanged and represents maximally attained brain size, while various interacting biological phenomena lead to brain volume loss. Consequently, intracranial volume and brain volume in late life reflect different genetic influences. Our genome-wide association study (GWAS) in 8,175 community-dwelling elderly persons did not reveal any associations at genome-wide significance (P < 5 × 10(-8)) for brain volume. In contrast, intracranial volume was significantly associated with two loci: rs4273712 (P = 3.4 × 10(-11)), a known height-associated locus on chromosome 6q22, and rs9915547 (P = 1.5 × 10(-12)), localized to the inversion on chromosome 17q21. We replicated the associations of these loci with intracranial volume in a separate sample of 1,752 elderly persons (P = 1.1 × 10(-3) for 6q22 and 1.2 × 10(-3) for 17q21). Furthermore, we also found suggestive associations of the 17q21 locus with head circumference in 10,768 children (mean age of 14.5 months). Our data identify two loci associated with head size, with the inversion at 17q21 also likely to be involved in attaining maximal brain size.

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., (Copyright © 2010 Elsevier Inc. All rights reserved.)

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., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

We investigated in a population-based cohort study the association of global and lobar brain tissue volumes with specific cognitive domains and risk of dementia. Participants (n=490; 60-90 years) were non-demented at baseline (1995-1996). From baseline brain MRI-scans we obtained global and lobar volumes of CSF, GM, normal WM, white matter lesions and hippocampus. We performed neuropsychological testing at baseline to assess information processing speed, executive function, memory function and global cognitive function. Participants were followed for incident dementia until January 1, 2005. Larger volumes of CSF and WML were associated with worse performance on all neuropsychological tests, and an increased risk of dementia. Smaller WM volume was related to poorer information processing speed and executive function. In contrast, smaller GM volume was associated with worse memory function and increased risk of dementia. When investigating lobar GM volumes, we found that hippocampal volume and temporal GM volume were most strongly associated with risk of dementia, even in persons without objective and subjective cognitive deficits at baseline, followed by frontal and parietal GM volumes., (Copyright 2008 Elsevier Inc. All rights reserved.)

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.

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.

Context: The role of macrostructural white matter changes, such as atrophy and white matter lesions, in cognitive decline is increasingly being recognized. However, in the elderly population, these macrostructural changes do not account for all variability in cognition. Measures reflecting white matter microstructural integrity may provide additional information to investigate the relation between white matter changes and cognition., Objective: To study the relation between white matter integrity and cognition in the general elderly population, using diffusion tensor imaging and taking into account macrostructural white matter changes., Design: Cross-sectional population-based study., Setting: A general community in the Netherlands., Participants: A population-based sample of 860 persons, older than 60 years, free of dementia. We performed multisequence magnetic resonance imaging, which included diffusion tensor imaging, and extensive neuropsychological testing. Fractional anisotropy, mean diffusivity, and directional diffusivities were measured globally in white matter lesions and normal-appearing white matter., Main Outcome Measures: Performance on neuropsychological tests in the following cognitive domains: memory, executive function, information processing speed, global cognition, and motor speed., Results: Regardless of macrostructural white matter changes, a higher mean diffusivity or higher axial and radial diffusivities within white matter lesions or normal-appearing white matter were related to worse performance on tasks assessing information processing speed and global cognition. In addition, diffusivity within white matter lesions related to memory, while in normal-appearing white matter, it furthermore related to executive function. Lower mean fractional anisotropy in white matter lesions or normal-appearing white matter related to worse information processing speed and motor speed., Conclusions: Microstructural integrity of both white matter lesions and normal-appearing white matter is associated with cognitive function, regardless of white matter atrophy and white matter lesion volume. This suggests that measuring white matter integrity has added value beyond macrostructural assessment of white matter changes to study the relation between white matter and cognition.

Brain atrophy and small vessel disease increase the risk of dementia and stroke. In a population-based cohort study (n=490; 60-90 years) we investigated how volumetric measures of atrophy and small vessel disease were related to mortality and whether this was independent of incident dementia or stroke. Brain volume and hippocampal volume were considered as measures of atrophy, whereas white matter lesions (WML) and lacunar infarcts reflected small vessel disease. We first investigated all-cause mortality in the whole cohort. In subsequent analyses we censored persons at incident dementia or incident stroke. Finally, we separately investigated cardiovascular mortality. The average follow-up was 8.4 years, during which 191 persons died. Brain atrophy and hippocampal atrophy, as well as WML increased the risk of death. The risks associated with hippocampal atrophy attenuated when censoring persons at incident dementia, but not at incident stroke. Censoring at either incident dementia or stroke did not change the risk associated with brain atrophy and WML. Moreover, WML were particularly associated with cardiovascular mortality.

Background and Purpose: Cerebral white matter lesions and lacunar infarcts are small vessel disease-related lesions, which are associated with cognitive decline and dementia. We aimed to assess the relationship between risk factors, effect modifiers, and progression of these lesions. Furthermore, we studied the cognitive consequences of lesion progression., Methods: Six hundred sixty-eight people, aged 60 to 90 years, underwent repeated MRI scanning and neuropsychological testing within 3-year follow-up. We rated incident lacunar infarcts and change in periventricular and subcortical white matter lesion severity with a semiquantitative scale. We assessed the relationships between age, sex, baseline lesion load, risk factors, lesion progression, and change in cognitive function by multivariate regression analyses and additional stratified analyses., Results: Baseline lesion load, higher age, high blood pressure, and current smoking were independently associated with progression of white matter lesions. Women had more marked progression of subcortical white matter lesions and incident lacunar infarcts compared with men. Carotid atherosclerosis was associated with incident lacunar infarcts. Higher blood pressure did not contribute to lesion progression in people with already severe lesions at baseline nor in the very old. Lesion progression was associated with a paralleled decline in general cognitive function and in particular with a decreased information processing speed., Conclusions: Higher age, female sex, cigarette smoking, elevated blood pressure, and baseline lesion load were associated with small vessel disease progression. Age and baseline lesion load influenced the risk relations with blood pressure. Progression of small vessel disease was related to a paralleled decline in cognitive function.

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.

Reduced cerebral perfusion may contribute to the development of cerebrovascular and neurodegenerative diseases. Little is known on cerebral perfusion in the general population, as most measurement techniques are too invasive for application in large groups of healthy individuals. Total cerebral blood flow (tCBF) can be noninvasively measured by magnetic resonance imaging (MRI) but is highly correlated with brain volume. We calculated total brain perfusion by dividing tCBF by brain volume, and we investigated determinants of total brain perfusion in comparison with tCBF. Secondly, we studied whether persons with a low tCBF or low total brain perfusion have a larger volume of white matter lesions (WML). This study is based on 892 persons aged 60 to 91 years from the Rotterdam Study, a population-based cohort study. We performed two-dimensional (2D) phase-contrast MRI for tCBF measurement. Brain volume and WML volume were quantitatively assessed. Cardiovascular determinants were assessed by interview and physical examination. We assessed associations between cardiovascular determinants and flow measures with linear regression models, adjusted for age and sex. Associations between tCBF or total brain perfusion and WML volume were assessed using general linear models. We found that determinants of tCBF and total brain perfusion differed largely due to the large influence of brain volume on tCBF values. Persons with low total brain perfusion had a significantly larger WML volume compared with those with high total brain perfusion. Prospective studies are required to unravel whether hypoperfusion contributes to WML formation or that tissue damage, manifested by WML, leads to brain hypoperfusion.

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.

Objective: To evaluate the concordance of a volumetric method for measuring white matter lesion (WML) change with visual rating scales., Methods: The authors selected a stratified sample of 20 elderly people (mean age 72 years, range 61 to 88 years) with an MRI examination at baseline and at 3-year follow-up from the community-based Rotterdam Scan Study (RSS). Four raters assessed WML change with four different visual rating scales: the Fazekas scale, the Scheltens scale, the RSS scale, and a new visual rating scale that was designed to measure change in WML. The authors assessed concordance with a volumetric method with scatter plots and correlations, and interobserver agreement with intraclass correlation coefficients., Results: For assessment of change in WML, the Fazekas, Scheltens, and periventricular part of the RSS scale showed little correlation with volumetrics, and low interobserver agreement. The authors' new WML change scale and the subcortical part of the RSS scale showed good correlation with volumetrics. After additional training, the new WML change scale showed good interobserver agreement for measuring WML change., Conclusions: Commonly used visual rating scales are not well suited for measuring change in white matter lesion severity. The authors' new white matter lesion change scale is more accurate and precise, and may be of use in studies focusing on progression of white matter lesions.

Background and Purpose: In the developing brain, myelination occurs in an orderly and predetermined sequence. The aim of this study was to determine whether such changes can be tracked using volumetric magnetization transfer imaging., Methods: Three-dimensional magnetization transfer imaging was performed in 50 children (age range, 0.6-190 months) with no evidence of developmental delay or structural abnormalities. Volumetric magnetization transfer ratio (MTR) parameters generated of the whole brain were mean MTR and height and location of the MTR histogram peak. Relationships between volumetric MTR parameters and age were assessed using nonlinear regression analysis., Results: With age, all volumetric MTR parameters changed exponentially in a way that was best expressed by the function y = a + b.exp(-x/c) (P < .0001). The peak height of the MTR histogram was the parameter that changed most predictably and that continued to change for the longest period of time., Conclusion: With this preliminary study, we show that by using volumetric MTR analysis, it is possible to monitor changes in the developing brain, presumably the myelination progress. This method has a potential role for detecting myelination disorders in the pediatric population, for studying the natural history of these diseases, and for monitoring the effects of treatment.