Search

Background: An international Delphi panel has defined a harmonized protocol (HarP) for the manual segmentation of the hippocampus on MR. The aim of this study is to study the concurrent validity of the HarP toward local protocols, and its major sources of variance., Methods: Fourteen tracers segmented 10 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases scanned at 1.5 T and 3T following local protocols, qualified for segmentation based on the HarP through a standard web-platform and resegmented following the HarP. The five most accurate tracers followed the HarP to segment 15 ADNI cases acquired at three time points on both 1.5 T and 3T., Results: The agreement among tracers was relatively low with the local protocols (absolute left/right ICC 0.44/0.43) and much higher with the HarP (absolute left/right ICC 0.88/0.89). On the larger set of 15 cases, the HarP agreement within (left/right ICC range: 0.94/0.95 to 0.99/0.99) and among tracers (left/right ICC range: 0.89/0.90) was very high. The volume variance due to different tracers was 0.9% of the total, comparing favorably to variance due to scanner manufacturer (1.2), atrophy rates (3.5), hemispheric asymmetry (3.7), field strength (4.4), and significantly smaller than the variance due to atrophy (33.5%, P < .001), and physiological variability (49.2%, P < .001)., Conclusions: The HarP has high measurement stability compared with local segmentation protocols, and good reproducibility within and among human tracers. Hippocampi segmented with the HarP can be used as a reference for the qualification of human tracers and automated segmentation algorithms., (Copyright © 2015 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

Aims: We assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective memory complaints (SMC). We also examined the role of the C9ORF72-related genetic status in the differentiation sensitivity. Methods: The MRI scans of 50 patients with bvFTD (17 C9ORF72 expansion carriers) were analyzed using 6 quantification methods as follows: voxel-based morphometry (VBM), tensor-based morphometry, volumetry (VOL), manifold learning, grading, and white-matter hyperintensities. Each patient was then individually compared to an independent reference group in order to attain diagnostic suggestions. Results: Only VBM and VOL showed utility in correctly identifying bvFTD from our set of data. The overall classification sensitivity of bvFTD with VOL + VBM achieved a total sensitivity of 60%. Using VOL + VBM, 32% were misclassified as having LBD. There was a trend of higher values for classification sensitivity of the C9ORF72 expansion carriers than noncarriers. Conclusion: VOL, VBM, and their combination are effective in differential diagnostics between bvFTD and AD or SMC. However, MRI atrophy profiles for bvFTD and LBD are too similar for a reliable differentiation with the quantification methods tested in this study. [ABSTRACT FROM AUTHOR]

Background: CAIDE Dementia Risk Score is the first validated tool for estimating dementia risk based on a midlife risk profile.Objectives: This observational study investigated longitudinal associations of CAIDE Dementia Risk Score with brain MRI, amyloid burden evaluated with PIB-PET, and detailed cognition measures.Methods: FINGER participants were at-risk elderly without dementia. CAIDE Risk Score was calculated using data from previous national surveys (mean age 52.4 years). In connection to baseline FINGER visit (on average 17.6 years later, mean age 70.1 years), 132 participants underwent MRI scans, and 48 underwent PIB-PET scans. All 1,260 participants were cognitively assessed (Neuropsychological Test Battery, NTB). Neuroimaging assessments included brain cortical thickness and volumes (Freesurfer 5.0.3), visually rated medial temporal atrophy (MTA), white matter lesions (WML), and amyloid accumulation.Results: Higher CAIDE Dementia Risk Score was related to more pronounced deep WML (OR 1.22, 95% CI 1.05-1.43), lower total gray matter (β-coefficient -0.29, p = 0.001) and hippocampal volume (β-coefficient -0.28, p = 0.003), lower cortical thickness (β-coefficient -0.19, p = 0.042), and poorer cognition (β-coefficients -0.31 for total NTB score, -0.25 for executive functioning, -0.33 for processing speed, and -0.20 for memory, all p < 0.001). Higher CAIDE Dementia Risk Score including APOE genotype was additionally related to more pronounced MTA (OR 1.15, 95% CI 1.00-1.30). No associations were found with periventricular WML or amyloid accumulation.Conclusions: The CAIDE Dementia Risk Score was related to indicators of cerebrovascular changes and neurodegeneration on MRI, and cognition. The lack of association with brain amyloid accumulation needs to be verified in studies with larger sample sizes. [ABSTRACT FROM AUTHOR]

Purpose: To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI and CSF biomarkers. Methods: Altogether 391 MCI cases (158 AD converters) were selected from the ADNI cohort. All the cases had baseline cognitive tests, MRI and/or CSF levels of Aβ1–42 and Tau. Using baseline data, the status of MCI patients (AD or MCI) three years later was predicted using current diagnostic research guidelines and the PredictAD software tool designed for supporting clinical diagnostics. The data used were 1) clinical criteria for episodic memory loss of the hippocampal type, 2) visual MTA, 3) positive CSF markers, 4) their combinations, and 5) when the PredictAD tool was applied, automatically computed MRI measures were used instead of the visual MTA results. The accuracies of diagnosis were evaluated with the diagnosis made 3 years later. Results: The PredictAD tool achieved the overall accuracy of 72% (sensitivity 73%, specificity 71%) in predicting the AD diagnosis. The corresponding number for a clinician’s prediction with the assistance of the PredictAD tool was 71% (sensitivity 75%, specificity 68%). Diagnosis with the PredictAD tool was significantly better than diagnosis by biomarkers alone or the combinations of clinical diagnosis of hippocampal pattern for the memory loss and biomarkers (p≤0.037). Conclusion: With the assistance of PredictAD tool, the clinician can predict AD conversion more accurately than the current diagnostic criteria. [ABSTRACT FROM AUTHOR]