Dataset Information

Cloud computing for voxel-wise SEM analysis of MRI data.

ABSTRACT: As data collection costs fall and vast quantities of data are collected, data analysis time can become a bottleneck. For massively parallel analyses, cloud computing offers the short-term rental of ample processing power. Recent software innovations have reduced the offort needed to take advantage of cloud computing. To demonstrate, we replicate a voxel-wise examination of the genetic contributions to cortical development by age using evidence from 1,748 MRI scans. Specifically, we employ off-the-shelf Kubernetes software that permits us to re-run our analyses using almost the same computer code as was published in the original article. Large, well funded institutions may continue to maintain their own computing clusters. However, the modest cost of renting and ease of utilizing cloud computing services makes unprecedented compute power available to all researchers, whether or not affliated with a research institution. We expect this to spur innovation in the sophisticated modeling of large datasets.

SUBMITTER: Pritikin JN

PROVIDER: S-EPMC6534137 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Cloud computing for voxel-wise SEM analysis of MRI data.

Pritikin Joshua N JN Schmitt J Eric JE Neale Michael C MC

Structural equation modeling : a multidisciplinary journal 20181002 3

As data collection costs fall and vast quantities of data are collected, data analysis time can become a bottleneck. For massively parallel analyses, cloud computing offers the short-term rental of ample processing power. Recent software innovations have reduced the offort needed to take advantage of cloud computing. To demonstrate, we replicate a voxel-wise examination of the genetic contributions to cortical development by age using evidence from 1,748 MRI scans. Specifically, we employ off-th ...[more]

PMID: 31133771

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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

Cloud computing for voxel-wise SEM analysis of MRI data.

Publications

Cloud computing for voxel-wise SEM analysis of MRI data.

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