Method for retrospective estimation of natural head movement during structural MRI

Zacà, Domenico; Hasson, Uri; Minati, Ludovico; Jovicich, Jorge

doi:10.1002/jmri.25959

Cited by 20 publications

(16 citation statements)

References 40 publications

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“…Head motion was also estimated directly from the T1-weighted volume as the average edge strength (AES), following from Zacà et al (in press) ; higher AES values correspond to less motion. Here I calculated AES for each plane orientation.…”

Section: Resultsmentioning

confidence: 99%

“…(2) Estimated directly from the T1-weighted volume as ‘average edge strength’ (AES) ( Aksoy et al, 2012 ; Zacà et al, in press ). This approach measures the intensity of contrast at edges within an image.…”

Section: Methodsmentioning

confidence: 99%

“…Higher AES values correspond to less motion, with image blurring yielding decreased tissue contrast. AES was calculated using the toolbox provided by Zacà et al (in press) , on the skull-stripped volumes generated as an intermediate stage of the FreeSurfer processing pipeline. AES is calculated on two-dimensional image planes and was performed on each plane orientation (axial, sagittal, and coronal).…”

Section: Methodsmentioning

confidence: 99%

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Age differences in head motion and estimates of cortical morphology

Madan

2018

PeerJ

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Cortical morphology is known to differ with age, as measured by cortical thickness, fractal dimensionality, and gyrification. However, head motion during MRI scanning has been shown to influence estimates of cortical thickness as well as increase with age. Studies have also found task-related differences in head motion and relationships between body–mass index (BMI) and head motion. Here I replicated these prior findings, as well as several others, within a large, open-access dataset (Centre for Ageing and Neuroscience, CamCAN). This is a larger dataset than these results have been demonstrated previously, within a sample size of more than 600 adults across the adult lifespan. While replicating prior findings is important, demonstrating these key findings concurrently also provides an opportunity for additional related analyses: critically, I test for the influence of head motion on cortical fractal dimensionality and gyrification; effects were statistically significant in some cases, but small in magnitude.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Age differences in head motion and estimates of cortical morphology

Madan

2018

PeerJ

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“…Two linear models were fitted including the average left and right gray-matter dlPFC MP-RAGE signal as the dependent variable and age, sex and group as the independent variables. A retrospective motion-estimate (Average Edge Strength) was also calculated with the homonymous Matlab toolbox 51 and entered as an independent variable. The analyses were repeated for data acquired at both 3T scanners (Trio, Siemens Healthineers).…”

Section: Analysis Of the Dlpfc Gray-matter Mp-rage Signal And Correlamentioning

confidence: 99%

Key role for lipids in cognitive symptoms of schizophrenia

Maas

Martens²,

Priovoulos

et al. 2020

Transl Psychiatry

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Schizophrenia (SZ) is a psychiatric disorder with a convoluted etiology that includes cognitive symptoms, which arise from among others a dysfunctional dorsolateral prefrontal cortex (dlPFC). In our search for the molecular underpinnings of the cognitive deficits in SZ, we here performed RNA sequencing of gray matter from the dlPFC of SZ patients and controls. We found that the differentially expressed RNAs were enriched for mRNAs involved in the Liver X Receptor/Retinoid X Receptor (LXR/RXR) lipid metabolism pathway. Components of the LXR/RXR pathway were upregulated in gray matter but not in white matter of SZ dlPFC. Intriguingly, an analysis for shared genetic etiology, using two SZ genome-wide association studies (GWASs) and GWAS data for 514 metabolites, revealed genetic overlap between SZ and acylcarnitines, VLDL lipids, and fatty acid metabolites, which are all linked to the LXR/RXR signaling pathway. Furthermore, analysis of structural T1-weighted magnetic resonance imaging in combination with cognitive behavioral data showed that the lipid content of dlPFC gray matter is lower in SZ patients than in controls and correlates with a tendency towards reduced accuracy in the dlPFC-dependent task-switching test. We conclude that aberrations in LXR/RXR-regulated lipid metabolism lead to a decreased lipid content in SZ dlPFC that correlates with reduced cognitive performance.

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“…Although we have demonstrated the utility of the technique for controlling for the effects of motion in cortical thickness and subcortical volume analyses, the method is independent of image acquisition protocol and could therefore also be useful for other MRI modalities in which head motion is an issue, such as diffusion imaging [23]. Our technique may also be useful for validating proposed proxy measures of in-scanner head motion that are derived from analysis of the structural MRI scans such as average edge strength [24, 25] or Euler number [26].…”

Section: Discussionmentioning

confidence: 99%

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

Pardoe¹,

Martin²,

Zhao

et al. 2021

Preprint

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Introduction In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are currently no widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. Methods 21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R²). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. Results Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. Conclusions We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

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Method for retrospective estimation of natural head movement during structural MRI

Abstract: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:927-937.

Cited by 20 publications

References 40 publications

Age differences in head motion and estimates of cortical morphology

Age differences in head motion and estimates of cortical morphology

Key role for lipids in cognitive symptoms of schizophrenia

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

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