The purpose of this study was to investigate whether the Framingham Cardiovascular Risk Profile and carotid artery intima-media thickness are associated with cortical volume and thickness.
...Consecutive subjects participating in a prospective cohort study of aging and mild cognitive impairment enriched for vascular risk factors for atherosclerosis underwent structural MRI scans at 3-T and 4-T MRI at 3 sites. Freesurfer (Version 5.1) was used to obtain regional measures of neocortical volumes (mm3) and thickness (mm). Multiple linear regression was used to determine the association of Framingham Cardiovascular Risk Profile and carotid artery intima-media thickness with cortical volume and thickness.
One hundred fifty-two subjects (82 men) were aged 78 (±7) years, 94 had a clinical dementia rating of 0, 58 had a clinical dementia rating of 0.5, and the mean Mini-Mental State Examination was 28±2. Framingham Cardiovascular Risk Profile score was inversely associated with total gray matter volume and parietal and temporal gray matter volume (adjusted P<0.04). Framingham Cardiovascular Risk Profile was inversely associated with parietal and total cerebral gray matter thickness (adjusted P<0.03). Carotid artery intima-media thickness was inversely associated with thickness of parietal gray matter only (adjusted P=0.04). Including history of myocardial infarction or stroke and radiological evidence of brain infarction, or apolipoprotein E genotype did not alter relationships with Framingham Cardiovascular Risk Profile or carotid artery intima-media thickness.
Increased cardiovascular risk was associated with reduced gray matter volume and thickness in regions also affected by Alzheimer disease independent of infarcts and apolipoprotein E genotype. These results suggest a "double hit" toward developing dementia when someone with incipient Alzheimer disease also has high cardiovascular risk.
Criteria for the diagnosis of vascular dementia (VaD) that are reliable, valid, and readily applicable in a variety of settings are urgently needed for both clinical and research purposes. To address ...this need, the Neuroepidemiology Branch of the National Institute of Neurological Disorders and Stroke (NINDS) convened an International Workshop with support from the Association Internationale pour la Recherche et l'Enseignement en Neurosciences (AIREN), resulting in research criteria for the diagnosis of VaD. Compared with other current criteria, these guidelines emphasize (1) the heterogeneity of vascular dementia syndromes and pathologic subtypes including ischemic and hemorrhagic strokes, cerebral hypoxic-ischemic events, and senile leukoencephalopathic lesions; (2) the variability in clinical course, which may be static, remitting, or progressive; (3) specific clinical findings early in the course (eg, gait disorder, incontinence, or mood and personality changes) that support a vascular rather than a degenerative cause; (4) the need to establish a temporal relationship between stroke and dementia onset for a secure diagnosis; (5) the importance of brain imaging to support clinical findings; (6) the value of neuropsychological testing to document impairments in multiple cognitive domains; and (7) a protocol for neuropathologic evaluations and correlative studies of clinical, radiologic, and neuropsychological features. These criteria are intended as a guide for case definition in neuroepidemiologic studies, stratified by levels of certainty (definite, probable, and possible). They await testing and validation and will be revised as more information becomes available.
Abstract Introduction Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve ...methods for clinical trials in Alzheimer's disease (AD) and related disorders. Methods We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. Results Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. Discussion Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
Revised diagnostic criteria for Alzheimer disease (AD) acknowledge a key role of imaging biomarkers for early diagnosis. Diagnostic accuracy depends on which marker (i.e., amyloid imaging, ...F-fluorodeoxyglucose FDG-PET, SPECT, MRI) as well as how it is measured (“metric”visual, manual, semiautomated, or automated segmentation/computation). We evaluated diagnostic accuracy of marker vs metric in separating AD from healthy and prognostic accuracy to predict progression in mild cognitive impairment. The outcome measure was positive (negative) likelihood ratio, LR+ (LR−), defined as the ratio between the probability of positive (negative) test outcome in patients and the probability of positive (negative) test outcome in healthy controls. Diagnostic LR+ of markers was between 4.4 and 9.4 and LR− between 0.25 and 0.08, whereas prognostic LR+ and LR− were between 1.7 and 7.5, and 0.50 and 0.11, respectively. Within metrics, LRs varied up to 100-foldLR+ from approximately 1 to 100; LR− from approximately 1.00 to 0.01. Markers accounted for 11% and 18% of diagnostic and prognostic variance of LR+ and 16% and 24% of LR−. Across all markers, metrics accounted for an equal or larger amount of variance than markers13% and 62% of diagnostic and prognostic variance of LR+, and 29% and 18% of LR−. Within markers, the largest proportion of diagnostic LR+ and LR− variability was within F-FDG-PET and MRI metrics, respectively. Diagnostic and prognostic accuracy of imaging AD biomarkers is at least as dependent on how the biomarker is measured as on the biomarker itself. Standard operating procedures are key to biomarker use in the clinical routine and drug trials.
Abstract Background This study aimed to have international experts converge on a harmonized definition of whole hippocampus boundaries and segmentation procedures, to define standard operating ...procedures for magnetic resonance (MR)-based manual hippocampal segmentation. Methods The panel received a questionnaire regarding whole hippocampus boundaries and segmentation procedures. Quantitative information was supplied to allow evidence-based answers. A recursive and anonymous Delphi procedure was used to achieve convergence. Significance of agreement among panelists was assessed by exact probability on Fisher's and binomial tests. Results Agreement was significant on the inclusion of alveus/fimbria ( P = .021), whole hippocampal tail ( P = .013), medial border of the body according to visible morphology ( P = .0006), and on this combined set of features ( P = .001). This definition captures 100% of hippocampal tissue, 100% of Alzheimer’s disease-related atrophy, and demonstrated good reliability on preliminary intrarater (0.98) and inter-rater (0.94) estimates. Discussion Consensus was achieved among international experts with respect to hippocampal segmentation using MR resulting in a harmonized segmentation protocol.
OBJECTIVES
To study the interactive effect of white matter hyperintensities (WMH) and hippocampal atrophy on cognition in the oldest old.
DESIGN
Ongoing longitudinal study.
SETTING
In Southern ...California, brain magnetic resonance imaging (MRI) scans were conducted between May 2014 and December 2017.
PARTICIPANTS
Individuals from The 90+ Study with a valid brain MRI scan (N = 141; 94 cognitively normal and 47 with cognitive impairment).
MEASUREMENTS
Cognitive testing was performed every 6 months with a mean follow‐up of 2 years and included these tests: Mini‐Mental State Examination (MMSE), modified MMSE (3MS), California Verbal Learning Test (CVLT) immediate recall over four trials and delayed recall, Digit Span Backward, Animal Fluency, and Trail Making Test (TMT) A, B, and C. We used one linear mixed model for each cognitive test to study the baseline and longitudinal association of WMH and hippocampal volume (HV) with cognition. Models were adjusted for age, sex, and education.
RESULTS
Mean age was 94.3 years (standard deviation SD = 3.2 y). At baseline, higher WMH volumes were associated with worse scores on the 3MS, CVLT immediate and delayed recall, and TMT B. Lower HVs were associated with worse baseline scores on all cognitive tests, except for the Digit Span Backward. Longitudinally, higher WMH and lower HVs were associated with faster decline in the 3MS and MMSE, and lower HV was also associated with faster decline in the CVLT immediate recall. No association was observed between WMH and HV and no interaction between WMH and HV in their association with baseline cognition or cognitive decline.
CONCLUSION
We show that WMH and hippocampal atrophy have an independent, negative effect on cognition that make these biomarkers relevant to evaluate in the diagnostic work‐up of the oldest‐old individuals with cognitive complaints. However, the predictive value of WMH for cognitive decline seems to be less evident in the oldest‐old compared with a younger group of older adults. J Am Geriatr Soc 67:1827–1834, 2019
MRI segmentation and mapping techniques were used to assess evidence in support of categorical distinctions between periventricular white matter hyperintensities (PVWMH) and deep WMH (DWMH). ...Qualitative MRI studies generally identify 2 categories of WMH on the basis of anatomical localization. Separate pathophysiologies and behavioral consequences are often attributed to these 2 classes of WMH. However, evidence to support these empirical distinctions has not been rigorously sought.
MRI analysis of 55 subjects included quantification of WMH volume, mapping onto a common anatomical image, and spatial localization of each WMH voxel. WMH locations were then divided into PVWMH and DWMH on the basis of distance from the lateral ventricles and correlations, with total WMH volume determined. Periventricular distance histograms of WMH voxels were also calculated.
PVWMH and DWMH were highly correlated with total WMH (R2>0.95) and with each other (R2>0.87). Mapping of all WMH revealed smooth expansion from around central cerebrospinal fluid spaces into more distal cerebral white matter with increasing WMH volume.
PVWMH, DWMH, and total WMH are highly correlated with each other. Moreover, spatial analysis failed to identify distinct subpopulations for PVWMH and DWMH. These results suggest that categorical distinctions between PVWMH and DWMH may be arbitrary, and conclusions regarding individual relationships between causal factors or behavior for PVWMH and DWMH may more accurately reflect total WMH volume relationships.
In later adulthood brain pathology becomes common and trajectories of cognitive change are heterogeneous. Among the multiple determinants of late-life cognitive course, cognitive reserve has been ...proposed as an important factor that modifies or buffers the impact of brain pathology on cognitive function. This article presents and investigates a novel method for measuring and investigating such factors. The core concept is that in a population where pathology is common and variably present, ‘reserve’ may be defined as the difference between the cognitive performance predicted by an individual's level of pathology and that individual's actual performance. By this definition, people whose measured cognitive performance is better than predicted by pathology have high reserve, whereas those who perform worse than predicted have low reserve. To test this hypothesis, we applied a latent variable model to data from a diverse ageing cohort and decomposed the variance in a measure of episodic memory into three components, one predicted by demographics, one predicted by pathology as measured by structural MRI and a ‘residual’ or ‘reserve’ term that included all remaining variance. To investigate the plausibility of this approach, we then tested the residual component as an operational measure of reserve. Specific predictions about the effects of this putative reserve measure were generated from a general conceptual model of reserve. Each was borne from the results. The results show that the current level of reserve, as measured by this decomposition approach, modifies rates of conversion from mild cognitive impairment to dementia, modifies rates of longitudinal decline in executive function and, most importantly, attenuates the effect of brain atrophy on cognitive decline such that atrophy is more strongly associated with cognitive decline in subjects with low reserve than in those with high reserve. Decomposing the variance in cognitive function scores offers a promising new approach to the measure and study of cognitive reserve.
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