The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

Snoek, Lukas; Miesen, Maite M. van der; Beemsterboer, Tinka; Leij, Andries van der; Eigenhuis, Annemarie; Scholte, H. Steven

doi:10.1038/s41597-021-00870-6

Cited by 90 publications

(119 citation statements)

References 88 publications

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“…As with FD, most tSNR variance occurs between people (e.g., higher tSNR for f1552xo than f1342ku) rather than for tasks within each person, suggesting that the differing task demands did not systematically affect image quality. The median tSNR of surface vertices across all participants and tasks is 103.68, which compares favorably to other multiband task fMRI datasets 108–110 .…”

Section: Technical Validationmentioning

confidence: 62%

The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery

Etzel

Freund

et al. 2021

Preprint

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Cognitive control is a critical higher mental function, which is subject to considerable individual variation and is impaired in a range of mental health disorders. We describe here the initial release of Dual Mechanisms of Cognitive Control (DMCC) project data, the DMCC55B dataset, with 55 healthy unrelated young adult participants. Each participant performed four well-established cognitive control tasks (Stroop, AX-CPT, Cued Task-Switching, and Sternberg Working Memory) while undergoing moderately high-resolution functional MRI scanning. The dataset includes a range of state and trait self-report questionnaires, as well as behavioural tasks assessing individual differences in cognitive ability. The DMCC project is on-going and features additional components (e.g., related participants, manipulations of cognitive control mode, resting state fMRI, longitudinal testing) that will be publicly released following study completion. This DMCC55B subset is released early with the aim of encouraging wider use and greater benefit to the scientific community. The DMCC55B dataset is suitable for benchmarking and methods exploration, as well as analyses of task performance and individual differences.

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Section: Technical Validationmentioning

confidence: 62%

The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery

Etzel

Freund

et al. 2021

Preprint

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“…Nevertheless, the presented data’s slope, α , is smaller than the previously published results. When comparing our data with a public image set, Amsterdam Open MRI Collection, AOMIC - PIOP01 (28), acquired with the same scanner and processed with the same setup, the results suggest that the slope’s variation is mainly related to age. Our supposed abnormal reported smaller α is probably due to the elder subjects present in the data.…”

Section: Discussionmentioning

confidence: 88%

Independent morphological variables correlate with Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

Moraes

Sudo

Monteiro

et al. 2022

Preprint

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This manuscript presents a study with recruited volunteers that comprehends three sorts of events present in Alzheimer's Disease (AD) evolution (structural, biochemical, and cognitive) to propose an update in neurodegeneration biomarkers for AD. The novel variables, K, I, and S, suggested based on physics properties and empirical evidence, are defined by power-law relations between cortical thickness, exposed and total area, and natural descriptors of brain morphology. Our central hypothesis is that variable K, almost constant in healthy human subjects, is a better discriminator of a diseased brain than the current morphological biomarker, Cortical Thickness, due to its aggregated information. We extracted morphological features from 3T MRI T1w images of 123 elderly subjects: 77 Healthy Cognitive Unimpaired Controls (CTL), 33 Mild Cognitive Impairment (MCI) patients, and 13 Alzheimer's Disease (AD) patients. Moreover, Cerebrospinal Fluid (CSF) biomarkers and clinical data scores were correlated with K, intending to characterize health and disease in the cortex with morphological criteria and cognitive-behavioral profiles. K distinguishes Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Cognitive Unimpaired Controls globally and locally with reasonable accuracy (CTL-AD, 0.82; CTL-MCI, 0.58). Correlations were found between global and local K associated with clinical behavioral data (executive function and memory assessments) and CSF biomarkers (t-Tau, Aβ-40, and Aβ-42). The results suggest that the cortical folding component, K, is a premature discriminator of healthy aging, Mild Cognitive Impairment, and Alzheimer's Disease, with significant differences within diagnostics. Despite the non-concomitant events, we found correlations between brain structural degeneration (K), cognitive tasks, and biochemical markers.

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“…Data from an open-access neuroimaging initiative, the Amsterdam Open MRI Collection (AOMIC) (Snoek et al, 2021) was used to investigate numerical and spatial reliability of FreeSurfer’s amygdala and hippocampal subregion segmentation algorithms. AOMIC includes structural and functional neuroimaging scans from participants, repeating scans in the same session to see the stability of MRI-based metrics.…”

Section: Methodsmentioning

confidence: 99%

“…T1-weighted MR images were acquired using a sagittal 3D-MPRAGE sequence (TR/TE = 8.1ms/3.7ms, 1mm 3 voxel, matrix size = 64×64). Additional details about the scanning parameters are described by Snoek and colleagues (2021). MRI Images were visually inspected to determine if a participant’s scans should be included in subsequent processing steps (e.g., FreeSurfer).…”

Section: Methodsmentioning

confidence: 99%

“…Neuroimaging data used in our analyses were sourced from the Amsterdam Open MRI Collection (AOMIC, Snoek et al, 2021). FreeSurfer software is publicly and freely available from the FreeSurferWiki resource (http://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferWiki), which is developed and maintained at the Martinos Center for Biomedical Imaging (http://www.nmr.mgh.harvard.edu/martinos/noFlashHome.php).…”

Section: Data Availability Statementmentioning

confidence: 99%

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Quantifying Numerical and Spatial Reliability of Amygdala and Hippocampal Subdivisions in FreeSurfer

Buser

Madan

Hanson

2020

Preprint

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On-going, large-scale neuroimaging initiatives have produced many MRI datasets with hundreds, even thousands, of individual participants and scans. These databases can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important factors. While volumetric quantification of brain structures can be completed by expert handtracing, automated segmentations are becoming the only truly tractable approach for particularly large datasets. Here, we assessed the spatial and numerical reliability for newly-deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer. In a sample of participants with repeated structural imaging scans (N=118), we found numerical reliability (as assessed by intraclass correlations) to be generally high, with 92% of the subregions having ICCs above 0.90 and the remainder still above 0.75. Spatial reliability was lower with only 11% of regions having Dice coefficients above 0.90, but 70% with Dice coefficients above 0.75. Of particular concern, three regions, the hippocampal fissure, the anterior amygdaloid area, and the paralaminar nucleus, had only moderate spatial reliability (0.50-0.75). We also examined correlations between spatial reliability and person-level factors (e.g., age, inter-scan interval, and difference in image quality). For these factors, interscan interval and image quality were related to variations in spatial reliability. Examined collectively, our work suggests strong numerical and spatial reliability for the majority of hippocampal and amygdala subdivisions; however, caution should be exercised for a few regions with more variable reliability.

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The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

Cited by 90 publications

References 88 publications

The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery

The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery

Independent morphological variables correlate with Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

Quantifying Numerical and Spatial Reliability of Amygdala and Hippocampal Subdivisions in FreeSurfer

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