Method for retrospective estimation of natural head movement during structural MRI

Diagnostic utility of 7T neuromelanin imaging of the substantia nigra in Parkinson's disease

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that presents a diagnostic challenge due to symptom overlap with other disorders. Neuromelanin (NM) imaging is a promising biomarker for PD, but adoption has been limited, in part due to subpar performance at standard MRI field strengths. We aimed to evaluate the diagnostic utility of ultra-high field 7T NM-sensitive imaging in the diagnosis of PD versus controls and essential tremor (ET), as well as NM differences among PD subtypes. A retrospective case-control study was conducted including PD patients, ET patients, and controls. 7T NM-sensitive 3D-GRE was acquired, and substantia nigra pars compacta (SNpc) volumes, contrast ratios, and asymmetry indices were calculated. Statistical analyses, including general linear models and ROC curves, were employed. Twenty-one PD patients, 13 ET patients, and 18 controls were assessed. PD patients exhibited significantly lower SNpc volumes compared to non-PD subjects. SNpc total volume showed 100% sensitivity and 96.8% specificity (AUC = 0.998) for differentiating PD from non-PD and 100% sensitivity and 95.2% specificity (AUC = 0.996) in differentiating PD from ET. Contrast ratio was not significantly different between PD and non-PD groups (p = 0.07). There was also significantly higher asymmetry index in SNpc volume in PD compared to non-PD cohorts (p < 0.001). NM signal loss in PD predominantly involved the inferior, posterior, and lateral aspects of SNpc. Akinetic-rigid subtype showed more significant NM signal loss compared to tremor dominant subtype (p < 0.001). 7T NM imaging demonstrates potential as a diagnostic tool for PD, including potential distinction between subtypes, allowing improved understanding of disease progression and subtype-related characteristics.

Improved diagnostic performance of susceptibility-weighted imaging with compressed sensing-sensitivity encoding and neuromelanin-sensitive MRI for Parkinson's disease and atypical Parkinsonism

AIM

To verify the diagnostic performance of the loss of nigrosome-1 on susceptibility-weighted imaging (SWI) with compressed sensing-sensitivity encoding (CS-SENSE) and neuromelanin on neuromelanin-sensitive (NM) magnetic resonance imaging (MRI) for the diagnosis of Parkinson's disease (PD) and atypical Parkinsonism.

MATERIALS AND METHODS

A total of 195 patients who underwent MRI between October 2019 and February 2020, including SWI, with or without CS-SENSE, and NM-MRI, were reviewed retrospectively. Two neuroradiologists assessed the loss of nigrosome-1 on SWI and neuromelanin on the NM-MRI. The result of N-3-fluoropropyl-2-beta-carbomethoxy-3-beta-(4-iodophenyl) nortropane positron-emission tomography (PET) was set as the reference standard.

RESULTS

When CS-SENSE was applied for nigrosome-1 imaging on SWI, the non-diagnostic scan rate was lowered significantly from 19.3% (17/88) to 5.6% (6/107; p=0.004). Diagnosis of PD and atypical Parkinsonism based on the loss of nigrosome-1 on SWI and based on NM-MRI showed good diagnostic value (area under the curve [AUC] 0.821, 95% confidence interval [CI] = 0.755-0.875: AUC 0.832, 95% CI = 0.771-0.882, respectively) with a substantial inter-reader agreement (κ = 0.791 and 0.681, respectively). Combined SWI and neuromelanin had a similar discriminatory ability (AUC 0.830, 95% CI = 0.770-0.880). Similarly, the diagnosis of PD was excellent.

CONCLUSIONS

CS-SENSE may add value to the diagnostic capability of nigrosome-1 on SWI to reduce the nondiagnostic scan rates. Furthermore, loss of nigrosome-1 on SWI or volume loss of neuromelanin on NM-MRI may be helpful for diagnosing PD.

Feasibility of online non-rigid motion correction for high-resolution supine breast MRI

PURPOSE

Conventional breast MRI is performed in the prone position with a dedicated coil. This allows high-resolution images without breast motion, but the patient position is inconsistent with that of other breast imaging modalities or interventions. Supine breast MRI may be an interesting alternative, but respiratory motion becomes an issue. Motion correction methods have typically been performed offline, for instance, the corrected images were not directly accessible from the scanner console. In this work, we seek to show the feasibility of a fast, online, motion-corrected reconstruction integrated into the clinical workflow.

METHODS

Fully sampled T2 -weighted (T2 w) and accelerated T1 -weighted (T1 w) breast supine MR images were acquired during free-breathing and were reconstructed using a non-rigid motion correction technique (generalized reconstruction by inversion of coupled systems). Online reconstruction was implemented using a dedicated system combining the MR raw data and respiratory signals from an external motion sensor. Reconstruction parameters were optimized on a parallel computing platform, and image quality was assessed by objective metrics and by radiologist scoring.

RESULTS

Online reconstruction time was 2 to 2.5 min. The metrics and the scores related to the motion artifacts significantly improved for both T2 w and T1 w sequences. The overall quality of T2 w images was approaching that of the prone images, whereas the quality of T1 w images remained significantly lower.

CONCLUSION

The proposed online algorithm allows a noticeable reduction of motion artifacts and an improvement of the diagnostic quality for supine breast imaging with a clinically acceptable reconstruction time. These findings serve as a starting point for further development aimed at improving the quality of T1 w images.

Evaluation of Image Quality of Motion-Corrected Supine Breast MRI

Breast MRI is the most performant modality for breast cancer diagnosis and could be widespread in the future. The gold standard breast MRI is performed in the prone position, but comfort and correlation with surgery or biopsy positioning can be problematic, while supine MRI could be an interesting alternative. In this work, we evaluated the image quality of T2-weighted supine breast MRI in healthy volunteers after online correction of respiratory motion artifacts compared to standard vendor's reconstruction and to standard prone MRI. T2-weighted images were acquired in the prone and free-breathing supine position in 10 volunteers. Two types of reconstructions were evaluated for supine acquisitions: the standard vendor's reconstruction and an online version of a nonrigid motion correction technique (generalized reconstruction by inversion of coupled system). Image quality criteria, including overall quality, sharpness, uniformity, and different types of artifacts, were assessed and scored by 2 radiologists in a randomized fashion. Interobserver agreement was verified by Weighted Cohen's Kappa calculation and a comparison between the different acquisitions was made by Wilcoxon signed-rank test. Generalized Reconstruction by Inversion of Coupled Systems (GRICS) reconstruction method significantly increased image quality in comparison to the standard reconstruction of supine acquisition. It allows a comparable quality, slightly lower than the gold standard prone MRI in T2-weighted images but it needs to be assessed with more patients and with target lesions before it can be used in clinical practice.

Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts

Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.

Tracking of rigid head motion during MRI using an EEG system

Purpose

To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients.

Theory and Methods

Gradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high‐impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators.

Results

Head‐pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]‐mm translation, [0.30 0.27 0.22]‐degree rotation). Retrospective correction of 3D gradient‐echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees).

Conclusion

Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

Gradient-Guided Isotropic MRI Reconstruction from Anisotropic Acquisitions

The trade-off between image resolution, signal-to-noise ratio (SNR), and scan time in any magnetic resonance imaging (MRI) protocol is inevitable and unavoidable. Super-resolution reconstruction (SRR) has been shown effective in mitigating these factors, and thus, has become an important approach in addressing the current limitations of MRI. In this work, we developed a novel, image-based MRI SRR approach based on anisotropic acquisition schemes, which utilizes a new gradient guidance regularization method that guides the high-resolution (HR) reconstruction via a spatial gradient estimate. Further, we designed an analytical solution to propagate the spatial gradient fields from the low-resolution (LR) images to the HR image space and exploited these gradient fields over multiple scales with a dynamic update scheme for more accurate edge localization in the reconstruction. We also established a forward model of image formation and inverted it along with the proposed gradient guidance. The proposed SRR method allows subject motion between volumes and is able to incorporate various acquisition schemes where the LR images are acquired with arbitrary orientations and displacements, such as orthogonal and through-plane origin-shifted scans. We assessed our proposed approach on simulated data as well as on the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 14 subjects. Our experimental results demonstrate that our approach achieved superior reconstructions compared to state-of-the-art methods, both in terms of local spatial smoothness and edge preservation, while, in parallel, at reduced, or at the same cost as scans delivered with direct HR acquisition.

MRI Super-Resolution Through Generative Degradation Learning

Spatial resolution plays a critically important role in MRI for the precise delineation of the imaged tissues. Unfortunately, acquisitions with high spatial resolution require increased imaging time, which increases the potential of subject motion, and suffers from reduced signal-to-noise ratio (SNR). Super-resolution reconstruction (SRR) has recently emerged as a technique that allows for a trade-off between high spatial resolution, high SNR, and short scan duration. Deconvolution-based SRR has recently received significant interest due to the convenience of using the image space. The most critical factor to succeed in deconvolution is the accuracy of the estimated blur kernels that characterize how the image was degraded in the acquisition process. Current methods use handcrafted filters, such as Gaussian filters, to approximate the blur kernels, and have achieved promising SRR results. As the image degradation is complex and varies with different sequences and scanners, handcrafted filters, unfortunately, do not necessarily ensure the success of the deconvolution. We sought to develop a technique that enables accurately estimating blur kernels from the image data itself. We designed a deep architecture that utilizes an adversarial scheme with a generative neural network against its degradation counterparts. This design allows for the SRR tailored to an individual subject, as the training requires the scan-specific data only, i.e., it does not require auxiliary datasets of high-quality images, which are practically challenging to obtain. With this technique, we achieved high-quality brain MRI at an isotropic resolution of 0.125 cubic mm with six minutes of imaging time. Extensive experiments on both simulated low-resolution data and clinical data acquired from ten pediatric patients demonstrated that our approach achieved superior SRR results as compared to state-of-the-art deconvolution-based methods, while in parallel, at substantially reduced imaging time in comparison to direct high-resolution acquisitions.

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

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 few 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.

Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity

During wakeful rest, individuals make small eye movements during fixation. We examined how these endogenously driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye tracking. The eye-tracking data indicated minor movements during rest, which correlated modestly with RS BOLD data. However, eye-tracking data correlated well with echo-planar imaging time series sampled from the area of the eye-orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. Further analyses showed that EO-EPI data were correlated with activity in an extensive motor and sensorimotor network, including components of the dorsal attention network and the frontal eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensorimotor and visual areas. It also produced networks with higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations or as a function of different experimental conditions.

We studied how subtle eye movements made during fixation, in absence of any other task, are related to resting-state connectivity measured using fMRI. We used a dataset for which eye tracking and BOLD resting-state were acquired simultaneously. We correlated brain activity with both eye-tracking metrics as well as time series sampled from the area of the eye orbits (EO-EPI). Eye-tracking data correlated well with the EO-EPI data. Furthermore, EO-EPI correlated with BOLD signal in sensorimotor and visual brain systems. Removing variance related to EO-EPI reduced connectivity between sensorimotor and visual areas and resulted in more modular resting-state networks. Our findings show that oculomotor-related contributions are an important component of resting-state network topology, and that they can be studied using EPI data from the eye orbits.

Key role for lipids in cognitive symptoms of schizophrenia

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.

Learning a Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction

In MRI practice, it is inevitable to appropriately balance between image resolution, signal-to-noise ratio (SNR), and scan time. It has been shown that super-resolution reconstruction (SRR) is effective to achieve such a balance, and has obtained better results than direct high-resolution (HR) acquisition, for certain contrasts and sequences. The focus of this work was on constructing images with spatial resolution higher than can be practically obtained by direct Fourier encoding. A novel learning approach was developed, which was able to provide an estimate of the spatial gradient prior from the low-resolution (LR) inputs for the HR reconstruction. By incorporating the anisotropic acquisition schemes, the learning model was trained over the LR images themselves only. The learned gradients were integrated as prior knowledge into a gradient-guided SRR model. A closed-form solution to the SRR model was developed to obtain the HR reconstruction. Our approach was assessed on the simulated data as well as the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 15 subjects. The experimental results demonstrated that our approach led to superior SRR over state-of-the-art methods, and obtained better images at lower or the same cost in scan time than direct HR acquisition.

Age differences in head motion and estimates of cortical morphology

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.