The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI‐3 updates and global perspectives

Weber, Christopher J.; Carrillo, María C.; Jack, Clifford R.; Shaw, Leslie M.; Trojanowski, John Q.; Saykin, Andrew J.; Beckett, Laurel A.; Sur, Cyrille; Rao, Naren P.; Méndez, Patricio Chrem; Black, Sandra E.; Iwatsubo, Takeshi; Chang, Chiung‐Chih; Sosa, Ana Luisa; Rowe, Christopher C.; Perrin, Richard J.; Morris, John C.; Healan, Amanda M.B.; Hall, Stephen E.; Weiner, Michael W.

doi:10.1002/trc2.12226

Cited by 40 publications

(39 citation statements)

References 62 publications

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“…This work focussed on developing a pipeline that can solve the systematic reduction in the FOV of T1w MRI scans ("cropping") seen in real-world data from a memory clinic in the UK. This was motivated by allowing such data to be used in quantitative research and development, which is critical for addressing the current lack of ethnic and sociodemographic diversity [32,33] in the large neuroimaging data sets such as ADNI that are currently driving innovations including data-driven disease progression modelling [34,35]. The cropping issue is essentially a missing data problem, and thus the approach implemented was to recover the data in a sensible way.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Crop Filling: a pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Castro-Leal

Whitfield

Praharaju

et al. 2023

Preprint

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Data-driven solutions offer great promise for improving healthcare. However standard clinical neuroimaging data is subject to real-world imaging artefacts that can render the data unusable for computational research. T1 weighted structural MRI is used in research to obtain volumetric measurements from cortical and subcortical brain regions. However, clinical radiologists often prioritise T2 weighted or FLAIR scans for visual assessment. As such, T1 weighted scans are often acquired but may not be a priority. This can result in artefacts such as partial brain coverage being systematically present in memory clinic data. Here we present a neuroimaging pipeline to ameliorate such situations by filling the missing regions with synthetic data. We validate on artificially cropped scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI), showing that our pipeline largely removes the artefact, improving volumetric biomarker accuracy while also retaining statistical differences between diagnostic groups. We demonstrate utility by achieving diagnostic classification performance comparable to uncorrupted data. This is an important contribution towards moving research from the lab into the real world.

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Section: Discussion and Limitationsmentioning

confidence: 99%

Crop Filling: a pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Castro-Leal

Whitfield

Praharaju

et al. 2023

Preprint

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“…For example, the third phase of the initiative has recruited an additional 1200 volunteers to donate their data, to assess cognitive function through computer tests at home and in the clinic measuring changes in ability to handle money, and early signs of the disease. 30,31 Years of research, progress of establishing validation, and POC for beta amyloid PET imaging took place under the umbrella of the ADNI. 32 Recently, the FDA has rendered beta-amyloid PET as a reasonably likely surrogate of cognitive decline, based on analysis of publicly available neuroimaging data and clinical outcomes from multiple independent trials which tested drug candidates targeting beta amyloid.…”

Section: Imaging Biomarkers In Alzheimer's Diseasementioning

confidence: 99%

“…Their contributions range from patient recruitment to data contribution, to understanding the disease from the patient's perspective. For example, the third phase of the initiative has recruited an additional 1200 volunteers to donate their data, to assess cognitive function through computer tests at home and in the clinic measuring changes in ability to handle money, and early signs of the disease 30,31 …”

Section: Case Studiesmentioning

confidence: 99%

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

Izmailova¹,

Maguire

McCarthy

et al. 2022

Clinical Translational Sci

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The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor‐based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross‐validating algorithms. Data standardization and creation of shared databases will be vital. Pre‐competitive consortia (public‐private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.

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“…The second and third phase, ADNI-GO and ADNI-2, with additional 200 early MCI participants. Since 2016 we have ADNI-3 that is actively enrolling up to 1200 additional participants in normal cognitive (NC), mild cognitive impairment (MCI) and Alzheimer's Disease group (AD) [41]…”

Section: The Tadpole Data Study Participantsmentioning

confidence: 99%

Marital Status with Rey Auditory Verbal Learning Test Cognition Performance Is Associated with Mild Cognitive Impairment

Bautista¹,

Messeha²,

Theran³

et al. 2022

Preprint

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Introduction:Few robust studies have analyzed association between cognitive tests and the marital status of Mild Cognitive Impairment (MCI) group with ADNI dataset. To test the hypothesis that Rey Auditory Verbal Learning test (RAVLT) cognition performance with marital status is associated with greater odds of MCI group than either RAVLT independently, we used TADPOLE data to evaluate cross-sectional associations between RAVLT performance in immediate response, learning, forgetting, and perception of forgetting with marital status and MCI. Methods: Subjects with MCI and Normal Cognition were included. Logistic regression models indicate associations between four RAVLT subgroups (low and high performance of immediate response, immediate response with learning, performance of immediate response with learning and forgetting, performance of immediate response with learning, forgetting and perception of forgetting) and MCI group. Models adjust for age, sex, race, marital status, ethnicity, education, APOE4 genotype, hippocampus, whole-brain, ventricles and ICV. Results: The sample (n=6560) had a mean age of 77 / 67 years, 44% were female, 58% in MCI group. Only all RAVLT subgroups test with age 61 to 70(OR 0.26, 95% CI 0.15-0.45), age 71 or older(OR 0.07, 95% CI,0.04-0.12), race:black/african american(OR 0.13, 95% CI 0.03-0.52)race:more than one(OR 0.05, 95% CI 0.01-0.24), marital status:never married(OR 0.2, 95% CI 0.12-0.34). Conclusion: Studies are needed to evaluate other cognitive test with missing data within TADPOLE dataset as modifiable risk factor for MCI.

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The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI‐3 updates and global perspectives

Cited by 40 publications

References 62 publications

Crop Filling: a pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Crop Filling: a pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

Marital Status with Rey Auditory Verbal Learning Test Cognition Performance Is Associated with Mild Cognitive Impairment

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