Publications by authors named "Mohamad Habes"

35 Publications

Associated factors of white matter hyperintensity volume: a machine-learning approach.

Sci Rep 2021 Jan 27;11(1):2325. Epub 2021 Jan 27.

Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.

To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.
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http://dx.doi.org/10.1038/s41598-021-81883-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840689PMC
January 2021

Cerebral small vessel disease genomics and its implications across the lifespan.

Nat Commun 2020 12 8;11(1):6285. Epub 2020 Dec 8.

University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35233, USA.

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
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http://dx.doi.org/10.1038/s41467-020-19111-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722866PMC
December 2020

Associations between sleep apnea and advanced brain aging in a large-scale population study.

Sleep 2021 03;44(3)

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.

Advanced brain aging is commonly regarded as a risk factor for neurodegenerative diseases, for example, Alzheimer's dementia, and it was suggested that sleep disorders such as obstructive sleep apnea (OSA) are significantly contributing factors to these neurodegenerative processes. To determine the association between OSA and advanced brain aging, we investigated the specific effect of two indices quantifying OSA, namely the apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI), on brain age, a score quantifying age-related brain patterns in 169 brain regions, using magnetic resonance imaging and overnight polysomnography data from 690 participants (48.8% women, mean age 52.5 ± 13.4 years) of the Study of Health in Pomerania. We additionally investigated the mediating effect of subclinical inflammation parameters on these associations via a causal mediation analysis. AHI and ODI were both positively associated with brain age (AHI std. effect [95% CI]: 0.07 [0.03; 0.12], p-value: 0.002; ODI std. effect [95% CI]: 0.09 [0.04; 0.13], p-value: < 0.0003). The effects remained stable in the presence of various confounders such as diabetes and were partially mediated by the white blood cell count, indicating a subclinical inflammation process. Our results reveal an association between OSA and brain age, indicating subtle but widespread age-related changes in regional brain structures, in one of the largest general population studies to date, warranting further examination of OSA in the prevention of neurodegenerative diseases.
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http://dx.doi.org/10.1093/sleep/zsaa204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953208PMC
March 2021

Smoking mediates the relationship between SES and brain volume: The CARDIA study.

PLoS One 2020 21;15(9):e0239548. Epub 2020 Sep 21.

National Institute on Aging, Bethesda, Maryland, United States of America.

Objective: Investigate whether socioeconomic status (SES) was related to brain volume in aging related regions, and if so, determine whether this relationship was mediated by lifestyle factors that are known to associate with risk of dementia in a population-based sample of community dwelling middle-aged adults.

Methods: We studied 645 (41% black) participants (mean age 55.3±3.5) from the Coronary Artery Risk Development in Young Adults (CARDIA) study who underwent brain magnetic resonance imaging. SES was operationalized as a composite measure of annual income and years of education. Gray matter volume was estimated within the insular cortex, thalamus, cingulate, frontal, inferior parietal, and lateral temporal cortex. These regions are vulnerable to age-related atrophy captured by the Spatial Pattern of Atrophy for Recognition of Brain Aging (SPARE-BA) index. Lifestyle factors of interest included physical activity, cognitive activity (e.g. book/newspaper reading), smoking status, alcohol consumption, and diet. Multivariable linear regressions tested the association between SES and brain volume. Sobel mediation analyses determined if this association was mediated by lifestyle factors. All models were age, sex, and race adjusted.

Results: Higher SES was positively associated with brain volume (β = .109 SE = .039; p < .01) and smoking status significantly mediated this relationship (z = 2.57). With respect to brain volume, smoking accounted for 27% of the variance (β = -.179 SE = .065; p < .01) that was previously attributed to SES.

Conclusion: Targeting smoking cessation could be an efficacious means to reduce the health disparity of low SES on brain volume and may decrease vulnerability for dementia.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239548PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505457PMC
November 2020

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Alzheimers Dement 2021 01 13;17(1):89-102. Epub 2020 Sep 13.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Introduction: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.

Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.
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http://dx.doi.org/10.1002/alz.12178DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923395PMC
January 2021

A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies.

Neuroimage 2020 12 27;223:117248. Epub 2020 Aug 27.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Richards Building, 3700 Hamilton Walk, 7th Floor, Philadelphia, PA 19104, United States.

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117248DOI Listing
December 2020

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Brain 2020 07;143(7):2312-2324

Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.
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http://dx.doi.org/10.1093/brain/awaa160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7364766PMC
July 2020

Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods.

Biol Psychiatry 2020 07 31;88(1):70-82. Epub 2020 Jan 31.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.

Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.
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http://dx.doi.org/10.1016/j.biopsych.2020.01.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305953PMC
July 2020

Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.

IEEE Trans Biomed Eng 2020 10 27;67(10):2735-2744. Epub 2020 Jan 27.

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.
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http://dx.doi.org/10.1109/TBME.2020.2969839DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048106PMC
October 2020

A Biomarker for Alzheimer's Disease Based on Patterns of Regional Brain Atrophy.

Front Psychiatry 2019 14;10:953. Epub 2020 Jan 14.

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.

It has been shown that Alzheimer's disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for detection of AD using a supervised machine learning approach. Based on an individual's pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD. The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend). Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen's f = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen's f = 0.009). This association was mainly driven by the immediate recall performance. In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too.
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http://dx.doi.org/10.3389/fpsyt.2019.00953DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6970941PMC
January 2020

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.

Neuroimage 2020 03 9;208:116450. Epub 2019 Dec 9.

Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USA. Electronic address:

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.
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http://dx.doi.org/10.1016/j.neuroimage.2019.116450DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980790PMC
March 2020

Estimating regional cerebral blood flow using resting-state functional MRI via machine learning.

J Neurosci Methods 2020 02 19;331:108528. Epub 2019 Nov 19.

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Electronic address:

Background: Perfusion MRI is an important modality in many brain imaging protocols, since it probes cerebrovascular changes in aging and many diseases; however, it may not be always available.

New Method: We introduce a new method that seeks to estimate regional perfusion properties using spectral information of resting-state functional MRI (rsfMRI) via machine learning. We used pairs of rsfMRI and arterial spin labeling (ASL) images from the same individuals with normal cognition and mild cognitive impairment (MCI), and built support vector machine models aiming to estimate regional cerebral blood flow (CBF) from the rsfMRI signal alone.

Results: This method demonstrated higher associations between the estimated CBF and actual CBF (ASL-CBF) at the total lobar gray matter (r = 0.40; FDR-p = 1.9e-03), parietal lobe (r = 0.46, FDR-p = 8e-04), and occipital lobe (r = 0.35; FDR-p = 0.01) using rsfMRI signals of frequencies [0.01-0.15] Hertz compared to frequencies [0.01-0.10] Hertz and [0.01-0.20] Hertz. We further observed significant associations between the estimated CBF and actual CBF in 24 regions of interest (p < 0.05), with the highest association observed in the superior parietal lobule (r = 0.50, FDR-p = 0.002). Moreover, the estimated CBF at superior parietal lobule showed significant correlation with the mini-mental state exam (MMSE) score (r = 0.27; FDR-p = 0.04) and decreased in MCI with lower MMSE score compared to NC group (FDR-p = 0.04).

Comparison With Existing Methods: Consistent with previous findings, this new method also suggests that rsfMRI signals contain perfusion information.

Conclusion: The proposed framework can obtain estimates of regional perfusion from rsfMRI, which can serve as surrogate perfusion measures in the absence of ASL.
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http://dx.doi.org/10.1016/j.jneumeth.2019.108528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216635PMC
February 2020

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

Alzheimers Dement 2019 08 11;15(8):1059-1070. Epub 2019 Jun 11.

Section for Biomedical Image Analysis (SBIA), Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address:

Introduction: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.

Methods: A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.

Results: The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning-based progression risk was combined with baseline clinical measures.

Discussion: Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.
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http://dx.doi.org/10.1016/j.jalz.2019.02.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719787PMC
August 2019

APOE Effect on Amyloid-β PET Spatial Distribution, Deposition Rate, and Cut-Points.

J Alzheimers Dis 2019 ;69(3):783-793

Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

There are conflicting results regarding how APOE genotype, the strongest genetic risk factor for Alzheimer's disease (AD), influences spatial and longitudinal amyloid-β (Aβ) deposition and its impact on the selection of biomarker cut-points. In our study, we sought to determine the impact of APOE genotype on cross-sectional and longitudinal florbetapir positron emission tomography (PET) amyloid measures and its impact in classification of patients and interpretation of clinical cohort results. We included 1,019 and 1,072 Alzheimer's Disease Neuroimaging Initiative participants with cerebrospinal fluid Aβ1 - 42 and florbetapir PET values, respectively. 623 of these subjects had a second florbetapir PET scans two years after the baseline visit. We evaluated the effect of APOE genotype on Aβ distribution pattern, pathological biomarker cut-points, cross-sectional clinical associations with Aβ load, and longitudinal Aβ deposition rate measured using florbetapir PET scans. 1) APOEɛ4 genotype influences brain amyloid deposition pattern; 2) APOEɛ4 genotype does not modify Aβ biomarker cut-points estimated using unsupervised mixture modeling methods if white matter and brainstem references are used (but not when cerebellum is used as a reference); 3) findings of large differences in Aβ biomarker value differences based on APOE genotype are due to increased probability of having AD neuropathology and are most significant in mild cognitive impairment subjects; and 4) APOE genotype and age (but not gender) were associated with increased Aβ deposition rate. APOEɛ4 carrier status affects rate and location of brain Aβ deposition but does not affect choice of biomarker cut-points if adequate references are selected for florbetapir PET processing.
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http://dx.doi.org/10.3233/JAD-181282DOI Listing
September 2020

Precision diagnostics based on machine learning-derived imaging signatures.

Magn Reson Imaging 2019 12 6;64:49-61. Epub 2019 May 6.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America.

The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.
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http://dx.doi.org/10.1016/j.mri.2019.04.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832825PMC
December 2019

Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings.

Alzheimers Dement (Amst) 2019 Dec 30;11:286-290. Epub 2019 Mar 30.

Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.

Advances in technology enable increasing amounts of data collection from individuals for biomedical research. Such technologies, for example, in genetics and medical imaging, have also led to important scientific discoveries about health and disease. The combination of multiple types of high-throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high-dimensional data sets. In our previous EU Joint Programme-Neurodegenerative Disease Research (JPND) Working Group, called HD-READY, we developed methods that allowed successful combination of omics data with neuroimaging. Still, several issues remained to fully leverage high-dimensional multimodality data. For instance, high-dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize. In this Full-HD Working Group, we focused on such harmonization of high-dimensional neuroimaging phenotypes in combination with other omics data and how to make the resulting ultra-high-dimensional data easily accessible in neurodegeneration research.
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http://dx.doi.org/10.1016/j.dadm.2019.02.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441785PMC
December 2019

Inflammatory markers and imaging patterns of advanced brain aging in the general population.

Brain Imaging Behav 2020 Aug;14(4):1108-1117

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Ferdinand-Sauerbruch-Straße, 17475, Greifswald, Germany.

Inflammaging describes the complexity between low-grade chronic inflammation with the pathogenesis of brain aging and Alzheimer´s disease (AD). We aimed to find associations of inflammatory markers: i) white blood cell count (WBC), ii) high-sensitivity C-reactive protein (hs-CRP), and iii) fibrinogen with brain structures, sensitive neuroimaging markers of advanced brain aging and AD-like atrophy, and cognitive aging scores. We analyzed magnetic resonance imaging (MRI) scans of 2204 participants from the Study of Health in Pomerania-2 (SHIP-2) and SHIP-Trend (55.6% women, mean age 52.4±13.7 years). Associations of the inflammatory markers with specific brain signatures of brain aging (SPARE-BA), AD-like brain atrophy (SPARE-AD) and white matter disease (white matter hyperintensities volume (WMHV)) were investigated. Furthermore we explored their association with general brain structures including total brain volume (TBV), gray matter volume (GMV), and white matter volume (WMV), as well as cognitive scores (Nurnberger Age Inventory (NAI); Verbal Learning and Memory Test (VLMT). We adjusted for multiple vascular risk factors (VRF; e.g. smoking and blood pressure) and corresponding medication use to take their brain aging effects into account and corrected for false-discovery rate (FDR). Results:WBC was inversely associated with SPARE-BA (FDR-adjusted p=0.003), TBV (FDR-adjusted p=0.019) and GMV (FDR-adjusted p= 0.017). GMV was also inversely associated with hs-CRP (FDR-adjusted p=0.039) and fibrinogen (FDR-adjusted p=0.039). None of the inflammatory markers was associated with WMHV. Regression analysis also revealed a trend-level interaction between intake of antiinflammatory medication and hs-CRP with brain aging (SPARE-BA; FDR-adjusted p=0.062). Inflammatatory markers are associated with neuroimaging markers, with elevated WBC leading to significant acceleration in brain aging patterns but not with AD-like imaging structural changes. Given the overlap between accelerated brain aging and AD-like atrophy, increased WBC might be associated with global dementia symptoms due to this overlap in atrophy patterns. Elevated WBC may be not causal to preclinical AD dementia, but an accessory symptom of inflammaging. At population level, our results support the relevant roles of inflammatory markers on brain aging related atrophy.
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http://dx.doi.org/10.1007/s11682-019-00058-yDOI Listing
August 2020

Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods.

Neurobiol Aging 2018 11 15;71:41-50. Epub 2018 Jun 15.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns.
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http://dx.doi.org/10.1016/j.neurobiolaging.2018.06.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162110PMC
November 2018

White matter lesions: Spatial heterogeneity, links to risk factors, cognition, genetics, and atrophy.

Neurology 2018 09 3;91(10):e964-e975. Epub 2018 Aug 3.

From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD.

Objectives: To investigate spatial heterogeneity of white matter lesions or hyperintensities (WMH).

Methods: MRI scans of 1,836 participants (median age 52.2 ± 13.16 years) encompassing a wide age range (22-84 years) from the cross-sectional Study of Health in Pomerania (Germany) were included as discovery set identifying spatially distinct components of WMH using a structural covariance approach. Scans of 307 participants (median age 73.8 ± 10.2 years, with 747 observations) from the Baltimore Longitudinal Study of Aging (United States) were included to examine differences in longitudinal progression of these components. The associations of these components with vascular risk factors, cortical atrophy, Alzheimer disease (AD) genetics, and cognition were then investigated using linear regression.

Results: WMH were found to occur nonuniformly, with higher frequency within spatially heterogeneous patterns encoded by 4 components, which were consistent with common categorizations of deep and periventricular WMH, while further dividing the latter into posterior, frontal, and dorsal components. Temporal trends of the components differed both cross-sectionally and longitudinally. Frontal periventricular WMH were most distinctive as they appeared in the fifth decade of life, whereas the other components appeared later in life during the sixth decade. Furthermore, frontal WMH were associated with systolic blood pressure and with pronounced atrophy including AD-related regions. AD polygenic risk score was associated with the dorsal periventricular component in the elderly. Cognitive decline was associated with the dorsal component.

Conclusions: These results support the hypothesis that the appearance of WMH follows age and disease-dependent regional distribution patterns, potentially influenced by differential underlying pathophysiologic mechanisms, and possibly with a differential link to vascular and neurodegenerative changes.
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http://dx.doi.org/10.1212/WNL.0000000000006116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6139818PMC
September 2018

Enlarged perivascular spaces and cognition: A meta-analysis of 5 population-based studies.

Neurology 2018 08 1;91(9):e832-e842. Epub 2018 Aug 1.

From the Departments of Radiology and Nuclear Medicine (S.H., H.H.H.A., M.W.V., M.A.I.), Epidemiology (S.H., H.H.H.A., M.K.I., M.W.V., M.A.I.), and Neurology (M.K.I., M.A.I.), Erasmus Medical Center, Rotterdam, the Netherlands; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory, Aging and Cognition Center (S.H., C.C.), National University Health System; Saw Swee Hock School of Public Health (C.S.T.), National University of Singapore; Department of Radiology (M.H.), University of Pennsylvania, Philadelphia; Department of Psychiatry and Psychotherapy (M.H., H.J.G.), Institute for Community Medicine (M.H., H.V., H.J.G.), and Institute of Diagnostic Radiology and Neuroradiology (N.H.), University Medicine Greifswald, Germany; Therese Pei Fong Chow Research Center for Prevention of Dementia (V.M.), LuiChe Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Centre; Department of Medicine and Therapeutics (V.M.) and Department of Imaging & Interventional Radiology (J.A.), Chinese University of Hong Kong, China; Raffles Neuroscience Centre (N.V.), Raffles Hospital, Singapore; Department of Neurology (E.H.), Medical University of Graz, Austria; and German Center for Neurodegenerative Diseases (DZNE) (R.S.), Site Rostock/Greifswald, Germany.

Objective: To investigate the association of enlarged perivascular spaces (ePVS) with cognition in elderly without dementia.

Methods: We included 5 studies from the Uniform Neuro-Imaging of Virchow-Robin Space Enlargement (UNIVRSE) consortium, namely the Austrian Stroke Prevention Family Study, Study of Health in Pomerania, Rotterdam Study, Epidemiology of Dementia in Singapore study, and Risk Index for Subclinical Brain Lesions in Hong Kong study. ePVS were counted in 4 regions (mesencephalon, hippocampus, basal ganglia, and centrum semiovale) with harmonized rating across studies. Mini-Mental State Examination (MMSE) and general fluid cognitive ability factor (G-factor) were used to assess cognitive function. For each study, a linear regression model was performed to estimate the effect of ePVS on MMSE and G-factor. Estimates were pooled across studies with the use of inverse variance meta-analysis with fixed- or random-effect models when appropriate.

Results: The final sample size consisted of 3,575 persons (age range 63.4-73.2 years, 50.6% women). Total ePVS counts were not significantly associated with MMSE score (mean difference per ePVS score increase 0.001, 95% confidence interval [CI] -0.007 to 0.008, = 0.885) or G-factor (mean difference per ePVS score increase 0.002, 95% CI -0.001 to 0.006, = 0.148) in age-, sex-, and education-adjusted models. Adjustments for cardiovascular risk factors and MRI markers did not change the results. Repeating the analyses with region-specific ePVS rendered similar results.

Conclusions: In this study, we found that ePVS counts were not associated with cognitive dysfunction in the general population. Future studies with longitudinal designs are warranted to examine whether ePVS contribute to cognitive decline.
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http://dx.doi.org/10.1212/WNL.0000000000006079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6133622PMC
August 2018

Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging.

Stroke 2018 08;49(8):1812-1819

Department of Biochemistry (D.W.B., N.D.P.), Wake Forest School of Medicine, Winston-Salem, NC.

Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P=4.5×10) partially independent of known common signal ( P=1.4×10). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; P=1.9×10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P=2.8×10). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.
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http://dx.doi.org/10.1161/STROKEAHA.118.020689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202149PMC
August 2018

Regional tract-specific white matter hyperintensities are associated with patterns to aging-related brain atrophy via vascular risk factors, but also independently.

Alzheimers Dement (Amst) 2018 5;10:278-284. Epub 2018 Mar 5.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Introduction: We sought to investigate associations of regional white matter hyperintensities (WMHs) within white matter (WM) tracts with cardiovascular risk and brain aging-related atrophy throughout adulthood in the general population, leveraging state of the art pattern analysis methods.

Methods: We analyzed a large sample (n = 2367) from the Study of Health in Pomerania, Germany (range 20-90 years). WMHs were automatically segmented on T1-weighted and fluid-attenuated inversion recovery magnetic resonance images, and WMH volumes were calculated in WM regions defined using the John Hopkins University WM tractography atlas. Regions with the highest average WMH volume were selected. We calculated a subject-specific index, Spatial Pattern of Alteration for Recognition of Brain Aging, to measure age-related atrophy patterns. The Framingham cardiovascular disease risk score summarized the individual cardiovascular risk profile. We used structural equation models, independently for each region, using Spatial Pattern of Alteration for Recognition of Brain Aging as a dependent variable, age as an independent variable, and cardiovascular disease risk score and regional WMH volumes as mediators.

Results: Selected 12 WM regions included 75% of the total WMH burden in average. Structural equation models showed that the age effect on Spatial Pattern of Alteration for Recognition of Brain Aging was mediated by WMHs to a different extent in the superior frontal WM, anterior corona radiata, inferior frontal WM, superior corona radiata, superior longitudinal fasciculus, middle temporal WM, posterior corona radiata, superior parietal WM, splenium of corpus callosum, posterior thalamic radiation, and middle occipital WM (variance explained between 2.8% and 10.3%,  < .0001 Bonferroni corrected), but not in precentral WM.

Conclusions: Our results indicate that WMHs, in most WM tracts, might accelerate the brain aging process throughout adulthood in the general population as a result of vascular risk factors, but also independent of them. Preventive strategies against WMHs (such as controlling vascular risk factors or microglia depletion) could delay brain aging.
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http://dx.doi.org/10.1016/j.dadm.2018.02.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5889709PMC
March 2018

Microglia ablation alleviates myelin-associated catatonic signs in mice.

J Clin Invest 2018 02 18;128(2):734-745. Epub 2017 Dec 18.

DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany.

The underlying cellular mechanisms of catatonia, an executive "psychomotor" syndrome that is observed across neuropsychiatric diseases, have remained obscure. In humans and mice, reduced expression of the structural myelin protein CNP is associated with catatonic signs in an age-dependent manner, pointing to the involvement of myelin-producing oligodendrocytes. Here, we showed that the underlying cause of catatonic signs is the low-grade inflammation of white matter tracts, which marks a final common pathway in Cnp-deficient and other mutant mice with minor myelin abnormalities. The inhibitor of CSF1 receptor kinase signaling PLX5622 depleted microglia and alleviated the catatonic symptoms of Cnp mutants. Thus, microglia and low-grade inflammation of myelinated tracts emerged as the trigger of a previously unexplained mental condition. We observed a very high (25%) prevalence of individuals with catatonic signs in a deeply phenotyped schizophrenia sample (n = 1095). Additionally, we found the loss-of-function allele of a myelin-specific gene (CNP rs2070106-AA) associated with catatonia in 2 independent schizophrenia cohorts and also associated with white matter hyperintensities in a general population sample. Since the catatonic syndrome is likely a surrogate marker for other executive function defects, we suggest that microglia-directed therapies may be considered in psychiatric disorders associated with myelin abnormalities.
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http://dx.doi.org/10.1172/JCI97032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5785265PMC
February 2018

Association between serum neuron-specific enolase, age, overweight, and structural MRI patterns in 901 subjects.

Transl Psychiatry 2017 12 8;7(12):1272. Epub 2017 Dec 8.

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.

Serum neuron-specific enolase (sNSE) is considered a marker for neuronal damage, related to gray matter structures. Previous studies indicated its potential as marker for structural and functional damage in conditions with adverse effects to the brain like obesity and dementia. In the present study, we investigated the putative association between sNSE levels, body mass index (BMI), total gray matter volume (GMV), and magnetic resonance imaging-based indices of aging as well as Alzheimer's disease (AD)-like patterns.

Subjects/methods: sNSE was determined in 901 subjects (499 women, 22-81 years, BMI 18-48 kg/m), participating in a population-based study (SHIP-TREND). We report age-specific patterns of sNSE levels between males and females. Females showed augmenting, males decreasing sNSE levels associated with age (males: p = 0.1052, females: p = 0.0363). sNSE levels and BMI were non-linearly associated, showing a parabolic association and decreasing sNSE levels at BMI values >25 (p = 0.0056). In contrast to our hypotheses, sNSE levels were not associated with total GMV, aging, or AD-like patterns. Pathomechanisms discussed are: sex-specific hormonal differences, neuronal damage/differentiation, or impaired cerebral glucose metabolism. We assume a sex-dependence of age-related effects to the brain. Further, we propose in accordance to previous studies an actual neuronal damage in the early stages of obesity. However, with progression of overweight, we assume more profound effects of excess body fat to the brain.
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http://dx.doi.org/10.1038/s41398-017-0035-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802579PMC
December 2017

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.

Neuroimage 2017 07 13;155:530-548. Epub 2017 Apr 13.

Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA. Electronic address:

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
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http://dx.doi.org/10.1016/j.neuroimage.2017.03.057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5511557PMC
July 2017

Estimating effects of craniofacial morphology on gingival recession and clinical attachment loss.

J Clin Periodontol 2017 Apr 18;44(4):363-371. Epub 2017 Feb 18.

Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany.

Objectives: Evidence on possible associations between facial morphology, attachment loss and gingival recession is lacking. We analysed whether the facial type, which can be described by the ratio of facial width and length (facial index), is related to periodontal loss of attachment, hypothesizing that a broad face might be associated with less gingival recession (GR) and less clinical attachment loss (CAL) than a long face.

Materials And Methods: Data from the 11-year follow-up of the population-based Study of Health in Pomerania were used. Periodontal loss of attachment was assessed by GR and CAL. Linear regression models, adjusted for age and gender, were used to assess associations between specific landmark based distances extracted from magnetic resonance imaging head scans and clinically assessed GR or CAL (N = 556).

Results: Analysing all teeth, a higher maximum cranial width was associated with a lower mean GR (B = -0.016, 95% CI: -0.030; -0.003, p = 0.02) and a lower mean CAL (B = -0.023, 95% CI: -0.040; -0.005, p = 0.01). Moreover, a long narrow face was significantly associated with increased mean GR and CAL (facial index, P for trend = 0.02 and p = 0.01, respectively). Observed associations were more pronounced for incisors and canines than for premolars and molars.

Conclusion: This study revealed craniofacial morphology, specifically the cranial width and the facial index, as a putative risk factor for periodontal loss of attachment.
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http://dx.doi.org/10.1111/jcpe.12661DOI Listing
April 2017

A priori collaboration in population imaging: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium.

Alzheimers Dement (Amst) 2015 Dec 31;1(4):513-20. Epub 2015 Oct 31.

Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands; Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Department of Neurology, Erasmus MC, Rotterdam, The Netherlands.

Introduction: Virchow-Robin spaces (VRS), or perivascular spaces, are compartments of interstitial fluid enclosing cerebral blood vessels and are potential imaging markers of various underlying brain pathologies. Despite a growing interest in the study of enlarged VRS, the heterogeneity in rating and quantification methods combined with small sample sizes have so far hampered advancement in the field.

Methods: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement (UNIVRSE) consortium was established with primary aims to harmonize rating and analysis (www.uconsortium.org). The UNIVRSE consortium brings together 13 (sub)cohorts from five countries, totaling 16,000 subjects and over 25,000 scans. Eight different magnetic resonance imaging protocols were used in the consortium.

Results: VRS rating was harmonized using a validated protocol that was developed by the two founding members, with high reliability independent of scanner type, rater experience, or concomitant brain pathology. Initial analyses revealed risk factors for enlarged VRS including increased age, sex, high blood pressure, brain infarcts, and white matter lesions, but this varied by brain region.

Discussion: Early collaborative efforts between cohort studies with respect to data harmonization and joint analyses can advance the field of population (neuro)imaging. The UNIVRSE consortium will focus efforts on other potential correlates of enlarged VRS, including genetics, cognition, stroke, and dementia.
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http://dx.doi.org/10.1016/j.dadm.2015.10.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879491PMC
December 2015

White matter hyperintensities and imaging patterns of brain ageing in the general population.

Brain 2016 Apr 24;139(Pt 4):1164-79. Epub 2016 Feb 24.

Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, USA.

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.
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http://dx.doi.org/10.1093/brain/aww008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5006227PMC
April 2016

Effect of the interaction between childhood abuse and rs1360780 of the FKBP5 gene on gray matter volume in a general population sample.

Hum Brain Mapp 2016 Apr 27;37(4):1602-13. Epub 2016 Jan 27.

Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany.

Objective: The FKBP5 gene codes for a co-chaperone that regulates glucocorticoid receptor sensitivity and thereby impacts the reactivity of the hypothalamic-pituitary-adrenal (HPA)-axis. Evidence suggested that subjects exposed to childhood abuse and carrying the TT genotype of the FKBP5 gene single nucleotide polymorphism (SNP) rs1360780 have an increased susceptibility to stress-related disorders.

Method: The hypothesis that abused TT genotype carriers show changes in gray matter (GM) volumes in affect-processing brain areas was investigated. About 1,826 Caucasian subjects (age ≤ 65 years) from the general population [Study of Health in Pomerania (SHIP)] in Germany were investigated. The interaction between rs1360780 and child abuse (Childhood Trauma Questionnaire) and its effect on GM were analyzed.

Results: Voxel-based whole-brain interaction analysis revealed three large clusters (FWE-corrected) of reduced GM volumes comprising the bilateral insula, the superior and middle temporal gyrus, the bilateral hippocampus, the right amygdala, and the bilateral anterior cingulate cortex in abused TT carriers. These results were not confounded by major depressive disorders. In region of interest analyses, highly significant volume reductions in the right hippocampus/parahippocampus, the bilateral anterior and middle cingulate cortex, the insula, and the amygdala were confirmed in abused TT carriers compared with abused CT/CC carriers.

Conclusion: The results supported the hypothesis that the FKBP5 rs1360780 TT genotype predisposes subjects who have experienced childhood abuse to widespread structural brain changes in the subcortical and cortical emotion-processing brain areas. Those brain changes might contribute to an increased vulnerability of stress-related disorders in TT genotype carriers.
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http://dx.doi.org/10.1002/hbm.23123DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6867563PMC
April 2016

Association between waist circumference and gray matter volume in 2344 individuals from two adult community-based samples.

Neuroimage 2015 Nov 6;122:149-57. Epub 2015 Aug 6.

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany; German Center for Neurodegenerative Diseases DZNE, Site Rostock/Greifswald, Germany; Department of Psychiatry and Psychotherapy, University Medicine Greifswald, HELIOS Hospital Stralsund, Germany.

We analyzed the putative association between abdominal obesity (measured in waist circumference) and gray matter volume (Study of Health in Pomerania: SHIP-2, N=758) adjusted for age and gender by applying volumetric analysis and voxel-based morphometry (VBM) with VBM8 to brain magnetic resonance (MR) imaging. We sought replication in a second, independent population sample (SHIP-TREND, N=1586). In a combined analysis (SHIP-2 and SHIP-TREND) we investigated the impact of hypertension, type II diabetes and blood lipids on the association between waist circumference and gray matter. Volumetric analysis revealed a significant inverse association between waist circumference and gray matter volume. VBM in SHIP-2 indicated distinct inverse associations in the following structures for both hemispheres: frontal lobe, temporal lobes, pre- and postcentral gyrus, supplementary motor area, supramarginal gyrus, insula, cingulate gyrus, caudate nucleus, olfactory sulcus, para-/hippocampus, gyrus rectus, amygdala, globus pallidus, putamen, cerebellum, fusiform and lingual gyrus, (pre-) cuneus and thalamus. These areas were replicated in SHIP-TREND. More than 76% of the voxels with significant gray matter volume reduction in SHIP-2 were also distinct in TREND. These brain areas are involved in cognition, attention to interoceptive signals as satiety or reward and control food intake. Due to our cross-sectional design we cannot clarify the causal direction of the association. However, previous studies described an association between subjects with higher waist circumference and future cognitive decline suggesting a progressive brain alteration in obese subjects. Pathomechanisms may involve chronic inflammation, increased oxidative stress or cellular autophagy associated with obesity.
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http://dx.doi.org/10.1016/j.neuroimage.2015.07.086DOI Listing
November 2015