16 research outputs found

    Bayesian segmentation of brainstem structures in MRI

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    VK: Lampinen, J.In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer.Peer reviewe

    Identifying cognitively healthy elderly individuals with subsequent memory decline by using automated MR temporoparietal volumes.

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    PURPOSE: To determine whether automated temporoparietal brain volumes can be used to accurately predict future memory decline among a multicenter cohort of cognitively healthy elderly individuals. MATERIALS AND METHODS: The study was approved by the institutional review board at each site and was HIPAA compliant, with written consent obtained from all participants. One hundred forty-nine cognitively healthy study participants were recruited through the Alzheimer\u27s Disease Neuroimaging Initiative and underwent a standardized baseline 1.5-T magnetic resonance (MR) imaging examination, as well as neuropsychological assessment at baseline and after 2 years of follow-up. A composite memory score for the 2-year change in the results of two delayed-recall tests was calculated, and memory decline was defined as a composite score that was at least 1 standard deviation below the group mean score. The predictive accuracy of the brain volumes was estimated by using areas under receiver operating characteristic curves and was further assessed by using leave-one-out cross validation. RESULTS: Use of the most accurate region model, which included the hippocampus; parahippocampal gyrus; amygdala; superior, middle, and inferior temporal gyri; superior parietal lobe; and posterior cingulate gyrus, resulted in a fitted accuracy of 94% and a cross-validated accuracy of 81%. CONCLUSION: Study results indicate that automated temporal and parietal volumes can be used to identify with high accuracy cognitively healthy individuals who are at risk for future memory decline. Further validation of this predictive model in a new cohort is required

    Identifying Cognitively Healthy Elderly Individuals with Subsequent Memory Decline by Using Automated MR Temporoparietal Volumes

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    Our study results demonstrate that automated temporal and parietal volumes measured at a single baseline MR examination can be used to accurately identify cognitively healthy individuals who are at risk for future memory decline

    The Effect of Subsyndromal Symptoms of Depression and White Matter Lesions on Disability for Individuals With Mild Cognitive Impairment

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    ObjectiveTo assess the effect of subsyndromal symptoms of depression (SSD) on ratings of disability for individuals with mild cognitive impairment (MCI).MethodsData from 405 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were analyzed. Participants were evaluated at baseline and at 6-month intervals over 2 years. Severity of depressive symptoms was rated utilizing the Geriatric Depression Scale. Disability was assessed utilizing the Functional Assessment Questionnaire (FAQ). Other clinical variables included white matter lesion (WML) and intracranial brain (ICV) volumes derived from magnetic resonance imaging, ratings of overall cognitive function (Alzheimer's Disease Assessment Scale, ADAS), and apolipoprotein E (ApoE) status. Demographic variables included age, education, and gender.ResultsSSD individuals had a lower volume of WML and higher frequency of ApoE Δ4 alleles than nondepressed participants but the two groups did not differ with respect to other clinical or demographic variables. At baseline, SSD individuals were 1.77 times more likely to have poorer FAQ scores than individuals with no symptoms of depression after controlling for the effect of cognitive functioning, ICV, WML, and ApoE status. The presence of SSD at baseline was not associated with a poorer course of disability outcomes, cognitive functioning, or conversion to dementia over 24 months.ConclusionsSSD demonstrated a significant impact on disability for MCI individuals, who are also at high risk for functional limitations related to neurodegenerative disease. Therefore, the treatment of SSD may represent a significant avenue to reduce the burden of disability in this vulnerable patient population

    Posterior Cingulate Glucose Metabolism, Hippocampal Glucose Metabolism, and Hippocampal Volume in Cognitively Normal, Late-Middle-Aged Persons at 3 Levels of Genetic Risk for Alzheimer Disease

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    ObjectiveTo characterize and compare measurements of the posterior cingulate glucose metabolism, the hippocampal glucose metabolism, and hippocampal volume so as to distinguish cognitively normal, late-middle-aged persons with 2, 1, or 0 copies of the apolipoprotein E (APOE) Δ4 allele, reflecting 3 levels of risk for late-onset Alzheimer disease.DesignCross-sectional comparison of measurements of cerebral glucose metabolism using 18F-fluorodeoxyglucose positron emission tomography and measurements of brain volume using magnetic resonance imaging in cognitively normal Δ4 homozygotes, Δ4 heterozygotes, and noncarriers.SettingAcademic medical center.ParticipantsA total of 31 Δ4 homozygotes, 42 Δ4 heterozygotes, and 76 noncarriers, 49 to 67 years old, matched for sex, age, and educational level.Main outcome measuresThe measurements of posterior cingulate and hippocampal glucose metabolism were characterized using automated region-of-interest algorithms and normalized for whole-brain measurements. The hippocampal volume measurements were characterized using a semiautomated algorithm and normalized for total intracranial volume.ResultsAlthough there were no significant differences among the 3 groups of participants in their clinical ratings, neuropsychological test scores, hippocampal volumes (P = .60), or hippocampal glucose metabolism measurements (P = .12), there were significant group differences in their posterior cingulate glucose metabolism measurements (P = .001). The APOE Δ4 gene dose was significantly associated with posterior cingulate glucose metabolism (r = 0.29, P = .0003), and this association was significantly greater than those with hippocampal volume or hippocampal glucose metabolism (P < .05, determined by use of pairwise Fisher z tests).ConclusionsAlthough our findings may depend in part on the analysis algorithms used, they suggest that a reduction in posterior cingulate glucose metabolism precedes a reduction in hippocampal volume or metabolism in cognitively normal persons at increased genetic risk for Alzheimer disease
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