Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

Jack, Clifford R.; Barnes, Josephine; Bernstein, Matt A.; Borowski, Bret; Brewer, James B.; Clegg, Shona; Dale, Anders M.; Carmichael, Owen; Ching, Christopher R. K.; DeCarli, Charles; Desikan, Rahul S.; Fennema-Notestine, Christine; Fjell, Anders M.; Fletcher, Evan; Fox, Nick C.; Gunter, Jeff; Gutman, Boris A.; Holland, Dominic; Hou, Xue; Insel, Philip S.; Kantarci, Kejal; Killiany, Ronald J.; Krueger, Gunnar; Leung, Kelvin K.; Mackin, Scott; Maillard, Pauline; Malone, Ian B.; Mattsson, Niklas; McEvoy, Linda K.; Modat, Marc; Mueller, Susanne; Nosheny, Rachel L.; Ourselin, Sébastien; Schuff, Norbert; Senjem, Matthew L.; Simonson, Alix; Thompson, Paul M.; Rettmann, Dan; Vemuri, Prashanthi; Walhovd, Kristine B.; Zhao, Yongbo; Zuk, Samantha M.; Weiner, Michael W.

doi:10.1016/j.jalz.2015.05.002

Cited by 162 publications

(115 citation statements)

References 176 publications

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“…The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing large-scale, multi-center, longitudinal study designed to improve methods for clinical trials by identifying brain imaging, clinical, cognitive, and molecular biomarkers of AD and aging. Now in its third phase (ADNI3), ADNI continues to incorporate newer technologies as they become established (Jack et al, 2015); data from ADNI, collected at participating sites across the U.S. and Canada, is publicly available and has been used in a diverse range of publications (Veitch et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…ADNI's second phase (ADNI2) introduced, to the initiative, the use of diffusion-weighted MRI (dMRI) as an additional approach for tracking AD progression (Jack et al, 2015). dMRI has since been used in numerous studies to understand the effects of AD on white matter (WM) microstructure and brain connectivity (Daianu et al, 2013a,b;Nir et al, 2013;Prasad et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Initiative

2018

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Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4±7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FA DTI ) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FA TDF ), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FA DTI . We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and Alzheimer's disease: the Alzheimer's Disease Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampalcingulum and fornix (crus) / stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FA TDF detected robust widespread associations with clinical measures, while FA DTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Initiative

2018

Preprint

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“…The three‐dimensional (3D) MPRAGE volumes were acquired with the following parameters: repetition time (TR)/inversion time (TI)/echo time (TE)/α/time to acquisition (TA) = 2300 ms/900 ms/2.86 ms/9°/5:20, echo spacing = 7 ms, matrix 256 × 256 × 176, 1.0 mm 3 isotropic, 2 × iPAT with 24 reference lines, Cartesian sampling scheme, closely resembling the updated ADNI protocol . The FIDnav acquisition module (α = 9°, 64 points in 0.2 ms, TI = 1532 ms) and the IMGnav module (gradient echo (GRE), α/TA = 9°/1.2 s, echo spacing = 7 ms, six echoes, matrix size 64 × 64 × 32, voxel size 4.1 × 4.1 × 6.0 mm 3 ) were added to a prototype MPRAGE sequence.…”

Section: Methodsmentioning

confidence: 99%

Prospective head motion correction using FID‐guided on‐demand image navigators

Waszak

Falkovskiy

Hilbert

et al. 2016

Magnetic Resonance in Med

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Purpose: We suggest a motion correction concept that employs free-induction-decay (FID) navigator signals to continuously monitor motion and to guide the acquisition of image navigators for prospective motion correction following motion detection. Methods: Motion causes out-of-range signal changes in FID time series that, and in this approach, initiate the acquisition of an image navigator. Co-registration of the image navigator to a reference provides rigid-body-motion parameters to facilitate prospective motion correction. Both FID and image navigator are integrated into a prototype magnetization-prepared rapid gradient-echo (MPRAGE) sequence. The performance of the method is investigated using image quality metrics and the consistency of brain volume measurements. Results: Ten healthy subjects were scanned (a) while performing head movements (nodding, shaking, and moving in zdirection) and (b) to assess the co-registration performance. Mean absolute errors of 0.27 6 0.38 mm and 0.19 6 0.24 for translation and rotation parameters were measured. Image quality was qualitatively improved after correction. Significant improvements were observed in automated image quality measures and for most quantitative brain volume computations after correction. Conclusion:The presented method provides high sensitivity to detect head motion while minimizing the time invested in acquiring navigator images. Limits of this implementation arise from temporal resolution to detect motion, false-positive alarms, and registration accuracy. Magn Reson

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“…For comparison, all ADNI scans were reviewed by a quality control team at the Mayo clinic for issues such as protocol deviation or motion artifact (19), similar to what is being done within ON-DRI.…”

Section: Challenges Associated With Large Multi-site Imaging Studiesmentioning

confidence: 99%

An overview of the quality assurance and quality control of magnetic resonance imaging data for the Ontario Neurodegenerative Disease Research Initiative (ONDRI): pipeline development and neuroinformatics

Scott

Arnott²,

Chemparathy³

et al. 2020

Preprint

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Large scale research studies combining magnetic resonance imaging data generated at multiple sites on multiple vendor platforms are becoming more commonplace. The Ontario Neurodegenerative Disease Research Initiative (ONDRI -http://ondri.ca/), a project funded by the Ontario Brain Institute (OBI), is a recently established province-wide natural history study, which has recruited more than 500 participants from neurodegenerative disease groups including amyotrophic lateral sclerosis, fronto-temporal dementia, Parkinson's disease, Alzheimer's disease, mild cognitive impairment, and cerebrovascular disease (previously referred to as the vascular cognitive impairment cohort). Because of its multi-site nature, all captured data must be standardized and meet minimum quality standards to reduce variability. The goal of the ONDRI imaging platform is to maximize data quality by implementing vendor-specific harmonized MR imaging protocols (consistent with the Canadian Dementia Imaging Protocol -http://www.cdip-pcid.ca/), monitoring protocol adherence, qualitatively assessing image quality, measuring signal-to-noise and contrast-to-noise, monitoring system stability, and applying corrections based on the analysis of images from two different phantoms regularly acquired at each site. To maximize image quality, this work describes the use of various automatic pipelines and manual assessment steps, integrated within an established informatics and databasing platform, the Stroke Patient Recovery Research Database (SPReD) built on the Extensible Neuroimaging Archive Toolkit (XNAT), and contained within the Brain-CODE (Centre for Ontario Data Exploration) framework. The purpose of the current paper is to describe the steps undertaken by ONDRI to achieve this high standard of data integrity. Data have been successfully collected for the past 4 years with the pipelines and assessments identifying deviations, allowing for timely interventions and assessment of image quality.

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Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

Cited by 162 publications

References 176 publications

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Prospective head motion correction using FID‐guided on‐demand image navigators

An overview of the quality assurance and quality control of magnetic resonance imaging data for the Ontario Neurodegenerative Disease Research Initiative (ONDRI): pipeline development and neuroinformatics

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