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Voxel-based morphometry focusing on medial temporal lobe structures has a limited capability to detect amyloid ?, an Alzheimer's disease pathology.

ABSTRACT: Voxel-based morphometry (VBM) analysis of nuclear Magnetic Resonance Imaging (MRI) data allows the identification of medial temporal lobe (MTL) atrophy and is widely used to assist the diagnosis of Alzheimer's disease (AD). However, its reliability in the clinical environment has not yet been confirmed. To determine the credibility of VBM, amyloid positron emission tomography (PET) and VBM studies were compared retrospectively. Patients who underwent Pittsburgh Compound B (PiB) PET were retrospectively recruited. Ninety-seven patients were found to be amyloid negative and 116 were amyloid positive. MTL atrophy in the PiB positive group, as quantified by thin sliced 3D MRI and VBM software, was significantly more severe (p =0.0039) than in the PiB negative group. However, data histogram showed a vast overlap between the two groups. The area under the ROC curve (AUC) was 0.646. MMSE scores of patients in the amyloid negative and positive groups were also significantly different (p = 0.0028), and the AUC was 0.672. Thus, MTL atrophy could not reliably differentiate between amyloid positive and negative patients in a clinical setting, possibly due to the wide array of dementia-type diseases that exist other than AD.

SUBMITTER: Kameyama M

PROVIDER: S-EPMC7732322 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Voxel-based morphometry focusing on medial temporal lobe structures has a limited capability to detect amyloid β, an Alzheimer's disease pathology.

Kameyama Masashi M Ishibashi Kenji K Toyohara Jun J Wagatsuma Kei K Umeda-Kameyama Yumi Y Shimoji Keigo K Kanemaru Kazutomi K Murayama Shigeo S Ogawa Sumito S Tokumaru Aya M AM Ishii Kenji K

Aging 20201005 19

Voxel-based morphometry (VBM) analysis of nuclear Magnetic Resonance Imaging (MRI) data allows the identification of medial temporal lobe (MTL) atrophy and is widely used to assist the diagnosis of Alzheimer's disease (AD). However, its reliability in the clinical environment has not yet been confirmed. To determine the credibility of VBM, amyloid positron emission tomography (PET) and VBM studies were compared retrospectively. Patients who underwent Pittsburgh Compound B (PiB) PET were retrospe ...[more]

PMID: 33024054

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Project description:BackgroundMagnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer's disease (AD) pathophysiologic continuum constituting what has been established as "AD signature". To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration.MethodLongitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), and 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cutoffs (<?192?pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these Jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting.ResultsThe optimal follow-up time for classification of Ctrls vs PreAD was ?t?>?2.5?years, and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC?=?0.87 (95%CI 0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior, and lateral temporal lobes; precuneus; caudate heads; basal forebrain; and lateral ventricles.ConclusionsOur work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, the presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid-positive individuals, this longitudinal voxel-wise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%.

Dataset Information

Voxel-based morphometry focusing on medial temporal lobe structures has a limited capability to detect amyloid ?, an Alzheimer's disease pathology.

Publications

Voxel-based morphometry focusing on medial temporal lobe structures has a limited capability to detect amyloid β, an Alzheimer's disease pathology.

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