Dataset Information

Normal Aging Brain Collection Amsterdam (NABCA): A comprehensive collection of postmortem high-field imaging, neuropathological and morphometric datasets of non-neurological controls.

ABSTRACT: Well-characterized, high-quality brain tissue of non-neurological control subjects is a prerequisite to study the healthy aging brain, and can serve as a control for the study of neurological disorders. The Normal Aging Brain Collection Amsterdam (NABCA) provides a comprehensive collection of post-mortem (ultra-)high-field MRI (3Tesla and 7?Tesla) and neuropathological datasets of non-neurological controls. By providing MRI within the pipeline, NABCA uniquely stimulates translational neurosciences; from molecular and morphometric tissue studies to the clinical setting. We describe our pipeline, including a description of our on-call autopsy team, donor selection, in situ and ex vivo post-mortem MRI protocols, brain dissection and neuropathological diagnosis. A demographic, radiological and pathological overview of five selected cases on all these aspects is provided. Additionally, information is given on data management, data and tissue application procedures, including review by a scientific advisory board, and setting up a material transfer agreement before distribution of tissue. Finally, we focus on future prospects, which includes laying the foundation for a unique platform for neuroanatomical, histopathological and neuro-radiological education, of professionals, students and the general (lay) audience.

SUBMITTER: Jonkman LE

PROVIDER: S-EPMC6360607 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Normal Aging Brain Collection Amsterdam (NABCA): A comprehensive collection of postmortem high-field imaging, neuropathological and morphometric datasets of non-neurological controls.

Jonkman Laura E LE Graaf Yvon Galis-de YG Bulk Marjolein M Kaaij Eliane E Pouwels Petra J W PJW Barkhof Frederik F Rozemuller Annemieke J M AJM van der Weerd Louise L Geurts Jeroen J G JJG van de Berg Wilma D J WDJ

NeuroImage. Clinical 20190129

Well-characterized, high-quality brain tissue of non-neurological control subjects is a prerequisite to study the healthy aging brain, and can serve as a control for the study of neurological disorders. The Normal Aging Brain Collection Amsterdam (NABCA) provides a comprehensive collection of post-mortem (ultra-)high-field MRI (3Tesla and 7 Tesla) and neuropathological datasets of non-neurological controls. By providing MRI within the pipeline, NABCA uniquely stimulates translational neuroscienc ...[more]

PMID: 30711684

Similar Datasets

Project description:Unravel the mechanisms underlying brain aging and Alzheimer´s disease (AD) has been difficult because of complexity of the networks that drive these aging-related changes. Analysis of the gene expression in the brain is a valuable tool to study the function of the brain under normal and pathological conditions. Gene microarray technology allows massively parallel analysis of most genes expressed in a tissue, and therefore is an important research tool that potentially can provide the investigative power needed to address the complexity of brain aging and neurodegenerative processes. One of the reasons that account for the resistance of AD pathogenesis to analysis is that clinically normal subjects may exhibit considerable AD pathology, blurring criteria for distinguishing subjects with normal aging or AD. Here, we analyzed hippocampal and cortex frontal gene expression from 32 subjects separated in individuals presenting, 1) both pathologic and clinical AD (definitive AD); 2) AD pathology and normal clinic (pathologic AD); 3) cognitive impairment, without AD pathology (others dementias); and 4) no cognitive impairment, without AD pathology (normal individuals). Our results show that based on gene expression profile these individuals we could verify similarity between the definitive AD group and the group that only had AD-type pathology (pathologic AD). Specimens of hippocampus and cortex frontal used in this study were obtained at autopsy from 32 subjects. Neuropathology, specifically NFT and NP, was assessed in accordance to the Braak staging and CERAD scores, respectively. Based on pathologic and clinic criteria, subjects were categorized into four groups: 1) nine subjects with AD neuropathologic (Braak = IV / V / VI and CERAD ≠ 0) presenting cognitive impairment (CDR ≥ 1), termed “definitive AD” (dAD); 2) five subjects with AD neuropathologic (Braak = IV / V / VI and CERAD ≠ 0) and without cognitive impairment (CDR = 0), termed “pathologic AD” (pAD); 3) nine subjects without AD neuropathologic (Braak = 0 / I / II and CERAD ≠ C) presenting cognitive impairment (CDR ≥ 1), termed “other dementias” (OD); 4) - nine subjects without AD neuropathologic (Braak = 0 / I / II and CERAD ≠ C) and without cognitive impairment (CDR = 0), termed “normal” (N). RNA isolation and Amplification. From total RNA, a two-round linear amplification procedure (T7-based protocol) was carried out for all samples and for a pool of RNAs obtained from 15 distinct human cell lines used as reference. Labeled cDNA was generated in a reverse transcriptase reaction using amplified RNA (aRNA). Equal amounts of test and reference cDNA reverse color Cy-labeled aRNA targets were competitively hybridized against the cDNA probes in a customized cDNA platform with 4,608 ORESTES representing human genes. Dye-swap was performed for each sample as control for dye bias and used as replicate.

Project description:Geometric morphometrics is routinely used in ecology and evolution and morphometric datasets are increasingly shared among researchers, allowing for more comprehensive studies and higher statistical power (as a consequence of increased sample size). However, sharing of morphometric data opens up the question of how much nonbiologically relevant variation (i.e., measurement error) is introduced in the resulting datasets and how this variation affects analyses. We perform a set of analyses based on an empirical 3D geometric morphometric dataset. In particular, we quantify the amount of error associated with combining data from multiple devices and digitized by multiple operators and test for the presence of bias. We also extend these analyses to a dataset obtained with a recently developed automated method, which does not require human-digitized landmarks. Further, we analyze how measurement error affects estimates of phylogenetic signal and how its effect compares with the effect of phylogenetic uncertainty. We show that measurement error can be substantial when combining surface models produced by different devices and even more among landmarks digitized by different operators. We also document the presence of small, but significant, amounts of nonrandom error (i.e., bias). Measurement error is heavily reduced by excluding landmarks that are difficult to digitize. The automated method we tested had low levels of error, if used in combination with a procedure for dimensionality reduction. Estimates of phylogenetic signal can be more affected by measurement error than by phylogenetic uncertainty. Our results generally highlight the importance of landmark choice and the usefulness of estimating measurement error. Further, measurement error may limit comparisons of estimates of phylogenetic signal across studies if these have been performed using different devices or by different operators. Finally, we also show how widely held assumptions do not always hold true, particularly that measurement error affects inference more at a shallower phylogenetic scale and that automated methods perform worse than human digitization.

Dataset Information

Normal Aging Brain Collection Amsterdam (NABCA): A comprehensive collection of postmortem high-field imaging, neuropathological and morphometric datasets of non-neurological controls.

Publications

Normal Aging Brain Collection Amsterdam (NABCA): A comprehensive collection of postmortem high-field imaging, neuropathological and morphometric datasets of non-neurological controls.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure