Quantifying MR head motion in the Rhineland Study – A robust method for population cohorts

Clemens, Pollak,; Kügler, David; Breteler, Monique M.B.; Reuter, Martin

doi:10.1016/j.neuroimage.2023.120176

Cited by 7 publications

(4 citation statements)

References 101 publications

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“…Third, in most scan protocols, when a standard scan is corrupted by motion and the scan cannot be repeated, that participant must be excluded from morphometric analyses because head motion can bias morphometric estimates and lead to spurious inferences (Reuter et al, 2015). This is inefficient and undermines generalizability because participants who move tend to be different from those who are still (Greene et al, 2018; Makowski et al, 2019; Pollak et al, 2023; Reuter et al, 2015). When multiple rapid scans are collected from each participant, morphometric analyses can move forward even when individual scans are unusable because of motion.…”

Section: Discussionmentioning

confidence: 99%

Precision Brain Morphometry Using Cluster Scanning

Elliott,

Nielsen,

Hanford

et al. 2023

Preprint

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Measurement error limits the statistical power to detect group differences and longitudinal change in structural MRI morphometric measures (e.g., hippocampal volume, prefrontal thickness). Recent advances in scan acceleration enable extremely fast T1-weighted scans (∼1 minute) to achieve morphometric errors that are close to the errors in longer traditional scans. As acceleration allows multiple scans to be acquired in rapid succession, it becomes possible to pool estimates to increase measurement precision, a strategy known as “cluster scanning.” Here we explored brain morphometry using cluster scanning in a test-retest study of 40 individuals (12 younger adults, 18 cognitively unimpaired older adults, and 10 adults diagnosed with mild cognitive impairment or Alzheimer’s Dementia). Morphometric errors from a single compressed sensing (CS) 1.0mm scan with 6x acceleration (CSx6) were, on average, 12% larger than a traditional scan using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) protocol. Pooled estimates from four clustered CSx6 acquisitions led to errors that were 34% smaller than ADNI despite having a shorter total acquisition time. Given a fixed amount of time, a gain in measurement precision can thus be achieved by acquiring multiple rapid scans instead of a single traditional scan. Errors were further reduced when estimates were pooled from eight CSx6 scans (51% smaller than ADNI). Neither pooling across a break nor pooling across multiple scan resolutions boosted this benefit. We discuss the potential of cluster scanning to improve morphometric precision, boost statistical power, and produce more sensitive disease progression biomarkers.

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Section: Discussionmentioning

confidence: 99%

Precision Brain Morphometry Using Cluster Scanning

Elliott,

Nielsen,

Hanford

et al. 2023

Preprint

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“…Several different methods have been applied to tackle the issue of head motion during MR image acquisition, however, no universal solution exists to this problem. Apart from motion restriction, shortening scanning duration may attenuate movement-related artifacts, as head motion has been shown to increase over time in the scanner, and this effect is amplified in older age [82]. Extremely rapid T1-weighted brain scans (~1 min scanning time) have been demonstrated to produce reliable morphometric measures in healthy older adults and in individuals suffering from neurodegenerative disease [83], providing a potential avenue for improving the robustness of structural brain age prediction in populations that are more prone to motion.…”

Section: Discussionmentioning

confidence: 99%

The effect of head motion on brain age prediction using deep convolutional neural networks

Vakli,

Weiss,

Rozmann

et al. 2023

Preprint

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Deep learning can be used effectively to predict participants’ age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age—referred to as brain-predicted age difference (brain-PAD)—is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.HighlightsTwo 3D CNNs trained from scratch and validated to predict age from T1-w brain MRITesting on motion-free and motion-corrupted scans from the same participantsImage quality assessed by neuroradiologists and using standard image quality metricsSystematic increase in brain-predicted age difference with worsening image qualitySpurious advanced brain aging effect in scans deemed usable for clinical diagnostics

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“…Given the negative association of age with brain volume and cortical thickness, both of these effects will result in higher age estimation and consequently higher BAGs. During adulthood (Madan, 2018; Pardoe et al, 2016; Pollak et al, 2023), motion levels are also higher in older populations and particularly in individuals with obesity (Beyer et al, 2020; Zeighami et al, 2021), PD (Makowski et al, 2019; Torres and Denisova, 2016), and other neurodegenerative disorders. Therefore, systematic biases in the estimated BAGs in these populations can confound the biologically relevant portions of BAG.…”

Section: Discussionmentioning

confidence: 99%

“…While in pediatric populations, age is negatively associated with presence of motion in the scanner (Baum et al, 2018; Pardoe et al, 2016), motion levels are significantly higher in the older populations (Madan, 2018; Pardoe et al, 2016; Pollak et al, 2023) as well as in individuals with obesity (Beyer et al, 2020; Zeighami et al, 2021), PD, and other disorders (Makowski et al, 2019; Torres and Denisova, 2016). Previous studies have indirectly shown that head motion is associated with alterations in T1w structural brain measure estimations.…”

Section: Introductionmentioning

confidence: 99%

Investigating the impact of motion in the scanner on brain age predictions

Moqadam

Dadar

Zeighami

2023

Preprint

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Introduction: Brain Age Gap (BAG) is defined as the difference between the brain's predicted age and the chronological age of an individual. Magnetic resonance imaging (MRI)-based BAG can quantify acceleration of brain aging, and is used to measure brain health as aging and disease interact. Motion in the scanner is a common occurrence that can affect the acquired MRI data and act as a major confound in the derived models. As such, age related changes in head motion may impact the observed age-related differences. However, the relationship between head motion and BAG has not been directly examined. The aim of this study is to assess the impact of motion on voxel-based morphometry (VBM) based BAG. Methods: Data were obtained from two sources: i) T1-weighted (T1w) MRIs from the Cambridge Centre for Ageing and Neuroscience (CamCAN) were used to train the brain age prediction model, and ii) T1w MRIs from the Movement-related artifacts (MR-ART) dataset were used to assess the impact of motion on BAG. MR-ART includes one motion-free and two motion-affected (one low and one high) 3D T1w MRIs. We also visually rated the motion levels of the MR-ART MRIs from 0 to 5, with 0 meaning no motion and 5 high motion levels. All images were pre-processed through a standard VBM pipeline. GM density across cortical and subcortical regions were then used to train the brain age prediction model and assess the relationship between BAG and MRI motion. Principal component analysis was used to perform dimension reduction and extract the VBM-based features. BAG was estimated by regressing out the portion of delta age explained by chronological age. Linear mixed effects models were used to investigate the relationship between BAG and motion session as well as motion severity, including participant IDs as random effects. Results: In contrast with the session with no induced motion, predicted delta age was significantly higher for high motion sessions (t-value = 5.06, p < 0.0001), with marginal effect for low motion sessions (t-value = 1.95, p = 0.051). In addition, delta age was significantly associated with motion severity as evaluated by visual rating (t = 4.46, p < 0.0001). Conclusion: Motion in the scanner can significantly impact brain age estimates, and needs to be accounted for as a confound, particularly when studying populations that are known to have higher levels of motion in the scanner. These results have significant implications for brain age studies in aging and neurodegeneration. Based on these findings, we recommend assessment and inclusion of visual motion ratings in such studies.

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Quantifying MR head motion in the Rhineland Study – A robust method for population cohorts

Cited by 7 publications

References 101 publications

Precision Brain Morphometry Using Cluster Scanning

Precision Brain Morphometry Using Cluster Scanning

The effect of head motion on brain age prediction using deep convolutional neural networks

Investigating the impact of motion in the scanner on brain age predictions

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