Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations.
OBJECTIVE
An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1–3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol.
METHOD
MRI scans of a single healthy adult human subject were acquired both at 3 Tesla and 7 Tesla. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement.
RESULTS
The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail.
CONCLUSIONS
The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.
The methods and formulas presented in this article are intended to assist epidemiologists to calculate interaction on an additive scale between two variables on a certain outcome. The proposed methods are included in a spreadsheet which is freely available at: http://www.juliuscenter.nl/additive-interaction.xls.
White matter hyperintensities are frequent on neuroimaging of older people and are a key feature of cerebral small vessel disease. They are commonly attributed to chronic hypoperfusion, although whether low cerebral blood flow is cause or effect is unclear. We systematically reviewed studies that assessed cerebral blood flow in small vessel disease patients, performed meta-analysis and sensitivity analysis of potential confounders. Thirty-eight studies (n = 4006) met the inclusion criteria, including four longitudinal and 34 cross-sectional studies. Most cerebral blood flow data were from grey matter. Twenty-four cross-sectional studies (n = 1161) were meta-analysed, showing that cerebral blood flow was lower in subjects with more white matter hyperintensity, globally and in most grey and white matter regions (e.g. mean global cerebral blood flow: standardised mean difference-0.71, 95% CI -1.12, -0.30). These cerebral blood flow differences were attenuated by excluding studies in dementia or that lacked age-matching. Four longitudinal studies (n = 1079) gave differing results, e.g., more baseline white matter hyperintensity predated falling cerebral blood flow (3.9 years, n = 575); cerebral blood flow was low in regions that developed white matter hyperintensity (1.5 years, n = 40). Cerebral blood flow is lower in subjects with more white matter hyperintensity cross-sectionally, but evidence for falling cerebral blood flow predating increasing white matter hyperintensity is conflicting. Future studies should be longitudinal, obtain more white matter data, use better age-correction and stratify by clinical diagnosis.
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