Saturday, November 7, 2015

7 Tesla MRI in Alzheimer's disease

T2*-weighted (a and b), T2-weighted (c and d), and FLAIR (e and f) images of the medial temporal lobe obtained at 1.5 T (a, c, and e) and 7 T (b, d, and f), illustrating the strikingly improved resolution that high-field MRI offers. Reprinted from Theysohn et al.: The human hippocampus at 7 T-in vivo MRI, Hippocampus 19:1–7, 2009, copyright 2008, Wiley-Liss, Inc.
Five AD hippocampal specimens (A1–A5) and one normal control (N4) are shown. Note the signal voids in AD specimens along the hippocampus compared with the lack of such signal voids in the normal control. The border between field CA1 and the subiculum is indicated by the white line derived from coregistered acetylcholine, myelin, and Nissl staining. The variability in their locations relative to the medial aspect of the hippocampal body illustrates the challenges inherent in in vivo imaging studies of hippocampal subregions. Reprinted from Neurobiology of Aging, vol 36, Zeineh M, Chen Y, Kitzler HH, Hammond R, Vogel H, Rutt BK, “Activated iron-containing microglia in the human hippocampus identified by magnetic resonance imaging in Alzheimer’s disease,” pp 2483–2500, 2015, with permission from Elsevier.
7-T FLAIR MRI in the (left to right) transverse (left), sagittal (center), and coronal (right) views. The arrow is pointing to a microinfarct. Reprinted with permission from van Rooden S, Goos JD, van Opstal AM, Versluis MJ, Webb AG, Blauw GJ, et al: Increased number of microinfarcts in Alzheimer disease at 7-T MR imaging. Radiology 270:205–211, 2014.

Saturday, October 3, 2015

Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease

Objective: To compare the diagnostic accuracy of CSF biomarkers and amyloid PET for diagnosing early-stage Alzheimer disease (AD).
Methods: From the prospective, longitudinal BioFINDER study, we included 122 healthy elderly and 34 patients with mild cognitive impairment who developed AD dementia within 3 years (MCI-AD). β-Amyloid (Aβ) deposition in 9 brain regions was examined with [18F]-flutemetamol PET. CSF was analyzed with INNOTEST and EUROIMMUN ELISAs. The results were replicated in 146 controls and 64 patients with MCI-AD from the Alzheimer's Disease Neuroimaging Initiative study.
Results: The best CSF measures for identifying MCI-AD were Aβ42/total tau (t-tau) and Aβ42/hyperphosphorylated tau (p-tau) (area under the curve [AUC] 0.93–0.94). The best PET measures performed similarly (AUC 0.92–0.93; anterior cingulate, posterior cingulate/precuneus, and global neocortical uptake). CSF Aβ42/t-tau and Aβ42/p-tau performed better than CSF Aβ42 and Aβ42/40 (AUC difference 0.03–0.12, p < 0.05). Using nonoptimized cutoffs, CSF Aβ42/t-tau had the highest accuracy of all CSF/PET biomarkers (sensitivity 97%, specificity 83%). The combination of CSF and PET was not better than using either biomarker separately.
Conclusions: Amyloid PET and CSF biomarkers can identify early AD with high accuracy. There were no differences between the best CSF and PET measures and no improvement when combining them. Regional PET measures were not better than assessing the global Aβ deposition. The results were replicated in an independent cohort using another CSF assay and PET tracer. The choice between CSF and amyloid PET biomarkers for identifying early AD can be based on availability, costs, and doctor/patient preferences since both have equally high diagnostic accuracy.
Classification of evidence: This study provides Class III evidence that amyloid PET and CSF biomarkers identify early-stage AD equally accurately.
Reference: Neurology10.1212/WNL.0000000000001991 Full text

Saturday, April 4, 2015

Nonlinear Association Between Cerebrospinal Fluid and Florbetapir F-18 β-Amyloid Measures Across the Spectrum of Alzheimer Disease

Cerebrospinal fluid (CSF) and positron emission tomographic (PET) amyloid biomarkers have been proposed for the detection of Alzheimer disease (AD) pathology in living patients and for the tracking of longitudinal changes, but the relation between biomarkers needs further study. Indeed a study aimed at determining the association between CSF and PET amyloid biomarkers (cross-sectional and longitudinal measures) and compare the cutoffs for these measures has just been published:

Design, Setting, and Participants Longitudinal clinical cohort study from 2005 to 2014 including 820 participants with at least 1 florbetapir F-18 (hereafter referred to as simply florbetapir)–PET scan and at least 1 CSF β-amyloid 1-42 (Aβ1-42) sample obtained within 30 days of each other (501 participants had a second PET scan after 2 years, including 150 participants with CSF Aβ1-42 measurements). Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative database.

Main Outcomes and Measures Four different PET scans processing pipelines from 2 different laboratories were compared. The PET cutoff values were established using a mixture-modeling approach, and different mathematical models were applied to define the association between CSF and PET amyloid measures.

Results The values of the CSF Aβ1-42 samples and florbetapir-PET scans showed a nonlinear association (R2 = 0.48-0.66), with the strongest association for values in the middle range. The presence of a larger dynamic range of florbetapir-PET scan values in the higher range compared with the CSF Aβ1-42 plateau explained the differences in correlation with cognition (R2 = 0.36 and R2 = 0.25, respectively). The APOEgenotype significantly modified the association between both biomarkers. The PET cutoff values derived from an unsupervised classifier converged with previous PET cutoff values and the established CSF Aβ1-42 cutoff levels. There was no association between longitudinal Aβ1-42 levels and standardized uptake value ratios during follow-up.

Conclusions and Relevance The association between both biomarkers is limited to a middle range of values, is modified by the APOE genotype, and is absent for longitudinal changes; 4 different approaches in 2 different platforms converge on similar pathological Aβ cutoff levels; and different pipelines to process PET scans showed correlated but not identical results. Our findings suggest that both biomarkers measure different aspects of AD Aβ pathology.

Reference JAMA Neurol. Published online March 30, 2015. doi:10.1001/jamaneurol.2014.4829

Thursday, April 2, 2015

Florbetaben PET imaging to detect amyloid plaques in Alzheimer disease: Phase 3 study


Evaluation of brain β-amyloid by positron emission tomography (PET) imaging can assist in the diagnosis of Alzheimer disease (AD) and other dementias.


Open-label, nonrandomized, multicenter, phase 3 study to validate the 18F-labeled β-amyloid tracer florbetaben by comparing in vivo PET imaging with post-mortem histopathology.


Brain images and tissue from 74 deceased subjects (of 216 trial participants) were analyzed. Forty-six of 47 neuritic β-amyloid-positive cases were read as PET positive, and 24 of 27 neuritic β-amyloid plaque-negative cases were read as PET negative (sensitivity 97.9% [95% confidence interval or CI 93.8–100%], specificity 88.9% [95% CI 77.0–100%]). In a subgroup, a regional tissue-scan matched analysis was performed. In areas known to strongly accumulate β-amyloid plaques, sensitivity and specificity were 82% to 90%, and 86% to 95%, respectively.


Florbetaben PET shows high sensitivity and specificity for the detection of histopathology-confirmed neuritic β-amyloid plaques and may thus be a valuable adjunct to clinical diagnosis, particularly for the exclusion of AD.

Trial registration

Saturday, March 21, 2015

Predicting the risk of mild cognitive impairment

Objective: We sought to develop risk scores for the progression from cognitively normal (CN) to mild cognitive impairment (MCI).
Methods: We recruited into a longitudinal cohort study a randomly selected, population-based sample of Olmsted County, MN, residents, aged 70 to 89 years on October 1, 2004. At baseline and subsequent visits, participants were evaluated for demographic, clinical, and neuropsychological measures, and were classified as CN, MCI, or dementia. Using baseline demographic and clinical variables in proportional hazards models, we derived scores that predicted the risk of progressing from CN to MCI. We evaluated the ability of these risk scores to classify participants for MCI risk.
Results: Of 1,449 CN participants, 401 (27.7%) developed MCI. A basic model had a C statistic of 0.60 (0.58 for women, 0.62 for men); an augmented model resulted in a C statistic of 0.70 (0.69 for women, 0.71 for men). Both men and women in the highest vs lowest sex-specific quartiles of the augmented model's risk scores had an approximately 7-fold higher risk of developing MCI. Adding APOE ε4 carrier status improved the model (p = 0.002).
Conclusions: We have developed MCI risk scores using variables easily assessable in the clinical setting and that may be useful in routine patient care. Because of variability among populations, validation in independent samples is required. These models may be useful in identifying patients who might benefit from more expensive or invasive diagnostic testing, and can inform clinical trial design. Inclusion of biomarkers or other risk factors may further enhance the models.
Reference: Neurology10.1212/WNL.0000000000001437

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