PROMO: Real-time prospective motion correction in MRI using image-based tracking

Run-time motion and first-order shim control by expanded servo navigation

PURPOSE

To provide a navigator-based run-time motion and first-order field correction for three-dimensional human brain imaging with high precision, minimal calibration and acquisition, and fast processing.

METHODS

A complex-valued linear perturbation model with feedback control is extended to estimate and correct for gradient shim fields using orbital navigators (2.3 ms). Two approaches for sensitizing the model to gradient fields are presented, one based on finite differences with three additional navigators, and another projection-based approximation requiring no additional navigators. A mechanism for noise decorrelation of the matrix and the data is proposed and evaluated to reduce unwanted parameter biases.

RESULTS

The rigid motion and first-order field control achieves robust motion and gradient shim corrections improving image quality in a series of phantom and in vivo experiments with varying field conditions. In phantom scans, magnet drifts, forced gradient field perturbations and field distortions from shifts of a second bottle phantom are successfully corrected. Field estimates of the magnet drifts are in good agreement with concurrent field probe measurements. For in vivo scans, the proposed method mitigates field variations from torso motions while being robust to head motion. In vivo gradient field precisions were30   nT / m $$ 30\;\mathrm{nT}/\mathrm{m} $$ along with single-digit micrometer and millidegree rigid precisions.

CONCLUSION

The navigator-based method achieves accurate, high-precision run-time motion and field corrections with low sequence impact and calibration requirements.

Prospective head motion correction at 3 Tesla with wireless NMR markers and ultrashort echo navigators

PURPOSE

Prospective motion correction (PMC) with inductively-coupled wireless NMR markers has been shown to be an effective plug-and-play method for dealing with head motion at 7 Tesla [29,30]. However, technical challenges such as one-to-one identification of three wireless markers, generation of hyper-intense marker artifacts and low marker peak SNR in the navigators has limited the adoption of this technique. The goal of this work is to introduce solutions to overcome these issues and extend this technique to PMC for brain imaging at 3 Tesla.

METHODS

PMC with 6 degrees of freedom (DOF) was implemented using a novel ∼8 ms, ultrashort echo time (UTE) navigator in concert with optimally chosen MnCl2 marker samples to minimize marker artifacts. Distinct head coil sensitivities were leveraged to enable identification and tracking of individual markers and a variable flip angle (VFA) scheme and real time filtering were used to boost marker SNR. PMC was performed in 3D T1 weighted brain imaging at 3 Tesla with voluntary head motions in adult volunteers.

RESULTS

PMC with wireless markers improved image quality in 3D T1 weighted images in all subjects compared to non-motion corrected images for similar motions with no noticeable marker artifacts. Precision of motion tracking was found to be in the range of 0.01-0.06 mm/degrees. Navigator execution had minimal impact on sequence duration.

CONCLUSIONS

Wireless NMR markers provide an accurate, calibration-free and economical option for 6 DOF PMC in brain imaging across field strengths. Challenges in this technique can be addressed by combining navigator design, sample selection and real time data processing strategies.

Quantitative evaluation of Scout Accelerated Motion Estimation and Reduction (SAMER) MPRAGE for morphometric analysis of brain tissue in patients undergoing evaluation for memory loss

BACKGROUND

Three-dimensional (3D) T1-weighted MRI sequences such as the magnetization prepared rapid gradient echo (MPRAGE) sequence are important for assessing regional cortical atrophy in the clinical evaluation of dementia but have long acquisition times and are prone to motion artifact. The recently developed Scout Accelerated Motion Estimation and Reduction (SAMER) retrospective motion correction method addresses motion artifact within clinically-acceptable computation times and has been validated through qualitative evaluation in inpatient and emergency settings.

METHODS

We evaluated the quantitative accuracy of morphometric analysis of SAMER motion-corrected compared to non-motion-corrected MPRAGE images by estimating cortical volume and thickness across neuroanatomical regions in two subject groups: (1) healthy volunteers and (2) patients undergoing evaluation for dementia. In part (1), we used a set of 108 MPRAGE reconstructed images derived from 12 healthy volunteers to systematically assess the effectiveness of SAMER in correcting varying degrees of motion corruption, ranging from mild to severe. In part (2), 29 patients who were scheduled for brain MRI with memory loss protocol and had motion corruption on their clinical MPRAGE scans were prospectively enrolled.

RESULTS

In part (1), SAMER resulted in effective correction of motion-induced cortical volume and thickness reductions. We observed systematic increases in the estimated cortical volume and thickness across all neuroanatomical regions and a relative reduction in percent error values compared to reference standard scans of up to 66 % for the cerebral white matter volume. In part (2), SAMER resulted in statistically significant volume increases across anatomical regions, with the most pronounced increases seen in the parietal and temporal lobes, and general reductions in percent error relative to reference standard clinical scans.

CONCLUSION

SAMER improves the accuracy of morphometry through systematic increases and recovery of the estimated cortical volume and cortical thickness following motion correction, which may affect the evaluation of regional cortical atrophy in patients undergoing evaluation for dementia.

Beat Pilot Tone (BPT): Simultaneous MRI and RF motion sensing at arbitrary frequencies

PURPOSE

To introduce a simple system exploitation with the potential to turn MRI scanners into general-purpose radiofrequency (RF) motion monitoring systems.

METHODS

Inspired by Pilot Tone (PT), this work proposes Beat Pilot Tone (BPT), in which two or more RF tones at arbitrary frequencies are transmitted continuously during the scan. These tones create motion-modulated standing wave patterns that are sensed by the receiver coil array, incidentally mixed by intermodulation in the receiver chain, and digitized simultaneously with the MRI data. BPT can operate at almost any frequency as long as the intermodulation products lie within the bandwidth of the receivers. BPT's mechanism is explained in electromagnetic simulations and validated experimentally.

RESULTS

Phantom and volunteer experiments over a range of transmit frequencies suggest that BPT may offer frequency-dependent sensitivity to motion. Using a semi-flexible anterior receiver array, BPT appears to sense cardiac-induced body vibrations at microwave frequencies (≥ $$ \ge $$ 1.2 GHz). At lower frequencies, it exhibits a similar cardiac signal shape to PT, likely due to blood volume changes. Other volunteer experiments with respiratory, bulk, and head motion show that BPT can achieve greater sensitivity to motion than PT and greater separability between motion types. Basic multiple-input multiple-output (4 × 22 $$ 4\times 22 $$ MIMO) operation with simultaneous PT and BPT in head motion is demonstrated using two transmit antennas and a 22-channel head-neck coil.

CONCLUSION

BPT may offer a rich source of motion information that is frequency-dependent, simultaneous, and complementary to PT and the MRI exam.

Improved motion correction in brain MRI using 3D radial trajectory and projection moment analysis

PURPOSE

To develop a generalized rigid body motion correction method in 3D radial brain MRI to deal with continuous motion pattern through projection moment analysis.

METHODS

An assumption was made that the multichannel coil moves with the head, which was achieved by using a flexible head coil. A two-step motion correction scheme was proposed to directly extract the motion parameters from the acquired k-space data using the analysis of center-of-mass with high noise robustness, which were used for retrospective motion correction. A recursive least-squares model was introduced to recursively estimate the motion parameters for every single spoke, which used the smoothness of motion and resulted in high temporal resolution and low computational cost. Five volunteers were scanned at 3 T using a 3D radial multidimensional golden-means trajectory with instructed motion patterns. The performance was tested through both simulation and in vivo experiments. Quantitative image quality metrics were calculated for comparison.

RESULTS

The proposed method showed good accuracy and precision in both translation and rotation estimation. A better result was achieved using the proposed two-step correction compared to traditional one-step correction without significantly increasing computation time. Retrospective correction showed substantial improvements in image quality among all scans, even for stationary scans.

CONCLUSIONS

The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients as well as scientific MRI researches.

Autism is associated with in vivo changes in gray matter neurite architecture

Postmortem investigations in autism have identified anomalies in neural cytoarchitecture across limbic, cerebellar, and neocortical networks. These anomalies include narrow cell mini-columns and variable neuron density. However, difficulty obtaining sufficient post-mortem samples has often prevented investigations from converging on reproducible measures. Recent advances in processing magnetic resonance diffusion weighted images (DWI) make in vivo characterization of neuronal cytoarchitecture a potential alternative to post-mortem studies. Using extensive DWI data from the Adolescent Brain Cognitive Developmentsm (ABCD®) study 142 individuals with an autism diagnosis were compared with 8971 controls using a restriction spectrum imaging (RSI) framework that characterized total neurite density (TND), its component restricted normalized directional diffusion (RND), and restricted normalized isotropic diffusion (RNI). A significant decrease in TND was observed in autism in the right cerebellar cortex (β = -0.005, SE =0.0015, p = 0.0267), with significant decreases in RNI and significant increases in RND found diffusely throughout posterior and anterior aspects of the brain, respectively. Furthermore, these regions remained significant in post-hoc analysis when the autism sample was compared against a subset of 1404 individuals with other psychiatric conditions (pulled from the original 8971). These findings highlight the importance of characterizing neuron cytoarchitecture in autism and the significance of their incorporation as physiological covariates in future studies.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

Altered development of the Hurst Exponent in medial prefrontal cortex in preschoolers with autism

BACKGROUND

Atypical balance of excitation (E) and inhibition (I) in the brain is thought to contribute to the emergence and symptomatology of autism spectrum disorders (ASD). E/I ratio can be estimated from resting state functional magnetic resonance imaging (fMRI) using the Hurst Exponent (H). A recent study reported decreased ventromedial prefrontal cortex (vmPFC) H in male adults with ASD. Part of the default mode network (DMN), vmPFC plays an important role in emotion regulation, decision making, and social cognition. It frequently shows altered function and connectivity in autistic individuals.

METHODS

The current study presents the first fMRI evidence of altered early development of vmPFC H and its link to DMN functional connectivity (FC) and emotional control in toddlers and preschoolers with ASD. 83 children (n=45 ASD), ages 1½ - 5 years, underwent natural sleep fMRI as part of a longitudinal study.

RESULTS

In a cross-sectional analysis, vmPFC H decreased with age in children with ASD, reflecting increasing E/I ratio, but not in typically developing children. This effect remained significant when controlling for gestational age at birth, socioeconomic status, or ethnicity. The same pattern was also observed in a subset of children with longitudinal fMRI data acquired two years apart on average. Lower vmPFC H was further associated with reduced FC within the DMN as well as with higher emotional control deficits (though only significant transdiagnostically).

CONCLUSIONS

These results suggest an early onset of E/I imbalances in vmPFC in ASD with likely consequences for the maturation of the DMN.

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation

PURPOSE

The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI.

METHODS

MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility.

RESULTS

In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC.

CONCLUSIONS

The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.

Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations

Population neuroscience datasets allow researchers to estimate reliable effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals. We employ motion-ordering and motion-ordering+resampling (bagging) to test if these methods preserve functional MRI (fMRI) data in the Adolescent Brain Cognitive Development Study ( N = 5,733 ). Black and Hispanic youth exhibited excess head motion relative to data collected from White youth, and were discarded disproportionately when using conventional approaches. Both methods retained more than 99% of Black and Hispanic youth. They produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data. The motion-ordering and bagging methods are two feasible approaches that can enhance sample representation for testing brain-behavior associations and fulfill the promise of consortia datasets to produce generalizable effect sizes across diverse populations.

Anatomy-aware and acquisition-agnostic joint registration with SynthMorph

Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner's reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Servo navigators: Linear regression and feedback control for rigid-body motion correction

PURPOSE

Navigator-based correction of rigid-body motion reconciling high precision with minimal acquisition, minimal calibration and simple, fast processing.

METHODS

A short orbital navigator (2.3 ms) is inserted in a three-dimensional (3D) gradient echo sequence for human head imaging. Head rotation and translation are determined by linear regression based on a complex-valued model built either from three reference navigators or in a reference-less fashion, from the first actual navigator. Optionally, the model is expanded by global phase and field offset. Run-time scan correction on this basis establishes servo control that maintains validity of the linear picture by keeping its expansion point stable in the head frame of reference. The technique is assessed in a phantom and demonstrated by motion-corrected imaging in vivo.

RESULTS

The proposed approach is found to establish stable motion control both with and without reference acquisition. In a phantom, it is shown to accurately detect motion mimicked by rotation of scan geometry as well as change in global B0 . It is demonstrated to converge to accurate motion estimates after perturbation well beyond the linear signal range. In vivo, servo navigation achieved motion detection with precision in the single-digit range of micrometers and millidegrees. Involuntary and intentional motion in the range of several millimeters were successfully corrected, achieving excellent image quality.

CONCLUSION

The combination of linear regression and feedback control enables prospective motion correction for head imaging with high precision and accuracy, short navigator readouts, fast run-time computation, and minimal demand for reference data.

Predicting dynamic, motion-related changes in B0 field in the brain at a 7T MRI using a subject-specific fine-trained U-net

PURPOSE

Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B0 ), which is a prerequisite for high quality data. Thus, characterization of changes to B0 , for example induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities.

METHODS

We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo gradient-echo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine-trained the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data.

RESULTS

Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method.

CONCLUSION

It is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of MR acquisitions without the use of navigators.

Rapid and accurate navigators for motion and B0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators

PURPOSE

To develop a framework that jointly estimates rigid motion and polarizing magnetic field (B0 ) perturbations (δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ ) for brain MRI using a single navigator of a few milliseconds in duration, and to additionally allow for navigator acquisition at arbitrary timings within any type of sequence to obtain high-temporal resolution estimates.

THEORY AND METHODS

Methods exist that match navigator data to a low-resolution single-contrast image (scout) to estimate either motion orδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . In this work, called QUEEN (QUantitatively Enhanced parameter Estimation from Navigators), we propose combined motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimation from a fast, tailored trajectory with arbitrary-contrast navigator data. To this end, the concept of a quantitative scout (Q-Scout) acquisition is proposed from which contrast-matched scout data is predicted for each navigator. Finally, navigator trajectories, contrast-matched scout, andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ are integrated into a motion-informed parallel-imaging framework.

RESULTS

Simulations and in vivo experiments show the need to modelδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ to obtain accurate motion parameters estimated in the presence of strongδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Simulations confirm that tailored navigator trajectories are needed to robustly estimate both motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Furthermore, experiments show that a contrast-matched scout is needed for parameter estimation from multicontrast navigator data. A retrospective, in vivo reconstruction experiment shows improved image quality when using the proposed Q-Scout and QUEEN estimation.

CONCLUSIONS

We developed a framework to jointly estimate rigid motion parameters andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ from navigators. Combing a contrast-matched scout with the proposed trajectory allows for navigator deployment in almost any sequence and/or timing, which allows for higher temporal-resolution motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimates.

Prospective motion correction for brain MRI using spherical navigators

PURPOSE

To demonstrate for the first time the feasibility of performing prospective motion correction using spherical navigators (SNAVs).

METHODS

SNAVs were interleaved in a 3D FLASH sequence with an additional short baseline scan (6.8 s) for fast rotation estimation. Assessment of SNAV-based prospective motion correction was performed in six volunteers. Participant motion was guided using randomly generated stepwise prompts as well as prompts derived from real motion cases. Experiments were performed on a 3 T MRI scanner using a 32-channel head coil.

RESULTS

When optimized for real-time application, SNAV-based motion estimates were computed in 25.8 ± 1.3 ms. Phantom-based quantification of rotation and translation accuracy indicated mean absolute errors of 0.10 ± 0.09° and 0.25 ± 0.14 mm, respectively. Implementing SNAV-based motion estimates for prospective motion correction led to a clear improvement in image quality with minimal increase in scan time (<5%).

CONCLUSION

Optimization of SNAV processing for real-time application enables prospective motion correction with low latency and minimal scan time requirements.

Human brain activity and functional connectivity associated with verbal long-term memory consolidation across 1 month

Introduction

Declarative memories are initially dependent on the hippocampus and become stabilized through the neural reorganization of connections between the medial temporal lobe and neocortex. The exact time-course of these neural changes is not well established, although time-dependent changes in retrieval-related brain function can be detected across relatively short time periods in humans (e.g., hours to months).

Methods

In a study involving older adults with normal cognition (N = 24), we investigated changes in brain activity and functional connectivity associated with the long-term memory consolidation of verbal material over one month. Participants studied fact-like, three-word sentences at 1-month, 1-week, 1-day, and 1-hour intervals before a recognition memory test inside an MRI scanner. Old/new recognition with confidence ratings and response times were recorded. We examined whole-brain changes in retrieval-related brain activity, as well as functional connectivity of the hippocampus and ventromedial prefrontal cortex (vmPFC), as memories aged from 1 hour to 1 month. Secondary analyses minimized the effect of confounding factors affected by memory age (i.e., changes in confidence and response time or re-encoding of targets).

Results

Memory accuracy, confidence ratings, and response times changed with memory age. A memory age network was identified where retrieval-related brain activity in cortical regions increased or decreased as a function of memory age. Hippocampal brain activity in an anatomical region of interest decreased with memory age. Importantly, these changes in retrieval-related activity were not confounded with changes in activity related to concomitant changes in behavior or encoding. Exploratory analyses of vmPFC functional connectivity as a function of memory age revealed increased connectivity with the posterior parietal cortex, as well as with the vmPFC itself. In contrast, hippocampal functional connectivity with the vmPFC and orbitofrontal cortex decreased with memory age.

Discussion

The observed changes in retrieval-related brain activity and functional connectivity align with the predictions of standard systems consolidation theory. These results suggest that processes consistent with long-term memory consolidation can be identified over short time periods using fMRI, particularly for verbal material.

Reduced covariation between brain morphometry and local spontaneous activity in young children with ASD

While disruptions in brain maturation in the first years of life in ASD are well documented, little is known about how the brain structure and function are related in young children with ASD compared to typically developing peers. We applied a multivariate pattern analysis to examine the covariation patterns between brain morphometry and local brain spontaneous activity in 38 toddlers and preschoolers with ASD and 31 typically developing children using T1-weighted structural MRI and resting-state fMRI data acquired during natural sleep. The results revealed significantly reduced brain structure-function correlations in ASD. The resultant brain structure and function composite indices were associated with age among typically developing children, but not among those with ASD, suggesting mistiming of typical brain maturational trajectories early in life in autism. Additionally, the brain function composite indices were associated with the overall developmental and adaptive behavior skills in the ASD group, highlighting the neurodevelopmental significance of early local brain activity in autism.

Longitudinal relationships between lifestyle risk factors and neurodevelopment in early adolescence

OBJECTIVE

The goal of this study is to investigate the cross-sectional and longitudinal relationships between clustered lifestyle risk factors (sleep, physical activity, body mass index [BMI], and screen time) and neurodevelopment over the early adolescent period.

METHOD

Data from the ABCD Study Data Release 3.0 consisted of 11,878 participants (aged 9-10 years) at baseline and 6,571 participants (aged 11-12 years) at 2-year follow-up. The interrelationships between lifestyle risk factors and brain structure were analyzed using bivariate multiple indicator latent change score models. Using confirmatory factor analysis, a single lifestyle risk factor domain (measured by sleep, physical activity, BMI, and screen time) was shown to fit the data well. Using exploratory and confirmatory factor analysis, seven brain structure domains were extracted and labeled as temporal-parietal, frontotemporal, occipital, orbitofrontal, temporal, cingulate, parietal, and cuneus domains. All bivariate latent change score models accounted for age, sex at birth, race/ethnicity, parental education, and marital status.

RESULTS

Higher lifestyle risk was associated with smaller brain volume at baseline. Higher baseline lifestyle risk was also associated with a greater rate of change (i.e., greater decreases) in brain volume for the temporal-parietal, frontotemporal, orbitofrontal, parietal, and cuneus domains. Effects were not reciprocal; baseline brain volume did not predict changes in lifestyle behaviors over time.

CONCLUSION

These findings are important for understanding the biological mechanisms underpinning health risk factors and can be used to target interventions and improve brain health during this critical developmental phase. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Simultaneous multislice EPI prospective motion correction by real-time receiver phase correction and coil sensitivity map interpolation

PURPOSE

To improve the image reconstruction for prospective motion correction (PMC) of simultaneous multislice (SMS) EPI of the brain, an update of receiver phase and resampling of coil sensitivities are proposed and evaluated.

METHODS

A camera-based system was used to track head motion (3 translations and 3 rotations) and dynamically update the scan position and orientation. We derived the change in receiver phase associated with a shifted field of view (FOV) and applied it in real-time to each k-space line of the EPI readout trains. Second, for the SMS reconstruction, we adapted resampled coil sensitivity profiles reflecting the movement of slices. Single-shot gradient-echo SMS-EPI scans were performed in phantoms and human subjects for validation.

RESULTS

Brain SMS-EPI scans in the presence of motion with PMC and no phase correction for scan plane shift showed noticeable artifacts. These artifacts were visually and quantitatively attenuated when corrections were enabled. Correcting misaligned coil sensitivity maps improved the temporal SNR (tSNR) of time series by 24% (p = 0.0007) for scans with large movements (up to ˜35 mm and 30°). Correcting the receiver phase improved the tSNR of a scan with minimal head movement by 50% from 50 to 75 for a United Kingdom biobank protocol.

CONCLUSION

Reconstruction-induced motion artifacts in single-shot SMS-EPI scans acquired with PMC can be removed by dynamically adjusting the receiver phase of each line across EPI readout trains and updating coil sensitivity profiles during reconstruction. The method may be a valuable tool for SMS-EPI scans in the presence of subject motion.

Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques

PURPOSE

This study investigated the artifacts arising from different types of head motion in brain MR images and how well these artifacts can be compensated using retrospective correction based on two different motion-tracking techniques.

METHODS

MPRAGE images were acquired using a 3 T MR scanner on a cohort of nine healthy participants. Subjects moved their head to generate circular motion (4 or 6 cycles/min), stepwise motion (small and large) and "simulated realistic" motion (nodding and slow diagonal motion), based on visual instructions. One MPRAGE scan without deliberate motion was always acquired as a "no motion" reference. Three dimensional fat-navigator (FatNavs) and a Tracoline markerless device (TracInnovations) were used to obtain motion estimates and images were separately reconstructed retrospectively from the raw data based on these different motion estimates.

RESULTS

Image quality was recovered from both motion tracking techniques in our stepwise and slow diagonal motion scenarios in almost all cases, with the apparent visual image quality comparable to the no-motion case. FatNav-based motion correction was further improved in the case of stepwise motion using a skull masking procedure to exclude non-rigid motion of the neck from the co-registration step. In the case of circular motion, both methods struggled to correct for all motion artifacts.

CONCLUSION

High image quality could be recovered in cases of stepwise and slow diagonal motion using both motion estimation techniques. The circular motion scenario led to more severe image artifacts that could not be fully compensated by the retrospective motion correction techniques used.

Improved Brain Tumor Conspicuity at 3 T Using Dark Blood, Fat-Suppressed, Dixon Unbalanced T1 Relaxation-Enhanced Steady-State MRI

OBJECTIVES

Contrast-enhanced magnetic resonance imaging (MRI) is the cornerstone for brain tumor diagnosis and treatment planning. We have developed a novel dual-echo volumetric dark blood pulse sequence called Dixon unbalanced T1 relaxation-enhanced steady-state (uT 1 RESS) that improves the visibility of contrast-enhancing lesions while suppressing the tissue signals from blood vessels and fat. The purpose of this study was to test the hypothesis that Dixon uT 1 RESS would significantly improve the conspicuity of brain tumors compared with magnetization-prepared rapid gradient echo (MPRAGE), as well as to determine potential limitations of the technique.

MATERIALS AND METHODS

This retrospective study was approved by the hospital institutional review board. Forty-seven adult patients undergoing an MRI scan for a brain tumor indication were included. Contrast-enhanced MRI of the brain was performed at 3 T using both MPRAGE and Dixon uT 1 RESS. To control for any impact of contrast agent washout during the scan procedure, Dixon uT 1 RESS was acquired in approximately half the subjects immediately after MPRAGE, and in the other half immediately before MPRAGE. Image quality, artifacts, and lesion detection were scored by 3 readers, whereas lesion apparent signal-to-noise ratio and lesion-to-background Weber contrast were calculated from region-of-interest measurements.

RESULTS

Image quality was not rated significantly different between MPRAGE and Dixon uT 1 RESS, whereas motion artifacts were slightly worse with Dixon uT 1 RESS. Comparing Dixon uT 1 RESS with MPRAGE, the respective values for mean lesion apparent signal-to-noise ratio were not significantly different (199.31 ± 99.05 vs 203.81 ± 110.23). Compared with MPRAGE, Dixon uT 1 RESS significantly increased the tumor-to-brain contrast (1.60 ± 1.18 vs 0.61 ± 0.47 when Dixon uT1RESS was acquired before MPRAGE and 1.94 ± 0.97 vs 0.82 ± 0.55 when Dixon uT 1 RESS was acquired after MPRAGE). In patients with metastatic disease, Dixon uT 1 RESS detected at least 1 enhancing brain lesion that was missed by MPRAGE on average in 24.7% of patients, whereas Dixon uT 1 RESS did not miss any lesions that were demonstrated by MPRAGE. Dixon uT 1 RESS better detected vascular and dural invasion in a small number of patients.

CONCLUSIONS

In conclusion, brain tumors were significantly more conspicuous at 3 T using Dixon uT 1 RESS compared with MPRAGE, with an approximately 2.5-fold improvement in lesion-to-background contrast irrespective of sequence order. It outperformed MPRAGE for the detection of brain metastases, dural or vascular involvement. These results suggest that Dixon uT 1 RESS could prove to be a useful adjunct or alternative to existing neuroimaging techniques for the postcontrast evaluation of intracranial tumors.

Adaptive model-based Magnetic Resonance

PURPOSE

Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time.

METHODS

We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T₂ s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T₂ , which was used to guide the selection of sequence parameters in real time.

RESULTS

Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T₂ for n-acetyl-aspartate by a factor of 2.5.

CONCLUSION

Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.

Motion guidance lines for robust data consistency-based retrospective motion correction in 2D and 3D MRI

PURPOSE

To develop a robust retrospective motion-correction technique based on repeating k-space guidance lines for improving motion correction in Cartesian 2D and 3D brain MRI.

METHODS

The motion guidance lines are inserted into the standard sequence orderings for 2D turbo spin echo and 3D MPRAGE to inform a data consistency-based motion estimation and reconstruction, which can be guided by a low-resolution scout. The extremely limited number of required guidance lines are repeated during each echo train and discarded in the final image reconstruction. Thus, integration within a standard k-space acquisition ordering ensures the expected image quality/contrast and motion sensitivity of that sequence.

RESULTS

Through simulation and in vivo 2D multislice and 3D motion experiments, we demonstrate that respectively 2 or 4 optimized motion guidance lines per shot enables accurate motion estimation and correction. Clinically acceptable reconstruction times are achieved through fully separable on-the-fly motion optimizations (˜1 s/shot) using standard scanner GPU hardware.

CONCLUSION

The addition of guidance lines to scout accelerated motion estimation facilitates robust retrospective motion correction that can be effectively introduced without perturbing standard clinical protocols and workflows.

Longitudinal Structural and Diffusion-Weighted Neuroimaging of Young Children Born Preterm

BACKGROUND

Children born preterm are at risk for diffuse injury to subcortical gray and white matter.

METHODS

We used a longitudinal cohort study to examine the development of subcortical gray matter and white matter volumes, and diffusivity measures of white matter tracts following preterm birth. Our participants were 47 children born preterm (24 to 32 weeks gestational age) and 28 children born at term. None of the children born preterm had significant neonatal brain injury. Children received structural and diffusion weighted magnetic resonance imaging scans at ages five, six, and seven years. We examined volumes of amygdala, hippocampus, caudate nucleus, putamen, thalamus, brainstem, cerebellar white matter, intracranial space, and ventricles, and volumes, fractional anisotropy, and mean diffusivity of anterior thalamic radiation, cingulum, corticospinal tract, corpus callosum, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, temporal and parietal superior longitudinal fasciculus, and uncinate fasciculus.

RESULTS

Children born preterm had smaller volumes of thalamus, brainstem, cerebellar white matter, cingulum, corticospinal tract, inferior frontal occipital fasciculus, uncinate fasciculus, and temporal superior longitudinal fasciculus, whereas their ventricles were larger compared with term-born controls. We found no significant effect of preterm birth on diffusivity measures. Despite developmental changes and growth, group differences were present and similarly strong at all three ages.

CONCLUSION

Even in the absence of significant neonatal brain injury, preterm birth has a persistent impact on early brain development. The lack of a significant term status by age interaction suggests a delayed developmental trajectory.

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors

We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat.

METHODS

The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers.

RESULTS

MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG.

CONCLUSIONS

The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

Uncovering and mitigating bias in large, automated MRI analyses of brain development

Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk 1,2. However, MRI studies of youth are especially susceptible to motion and other artifacts 3,4. These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies, 5 and can potentially introduce non-random noise into clinical association analyses. Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality images, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study6. Compared to the best-rated images (44.9% of the sample), lower-quality images generally associated with decreased cortical thickness and increased cortical surface area measures (Cohen's d 0.14-2.84). Variable image quality led to counterintuitive patterns in analyses that associated structural MRI and clinical measures, as inclusion of lower-quality scans altered apparent effect sizes in ways that increased risk for both false positives and negatives. Quality-related biases were partially mitigated by controlling for surface hole number, an automated index of topological complexity that differentiated lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00). However, even among the highest-rated images, subtle topological errors occurred during image preprocessing, and their correction through manual edits significantly and reproducibly changed thickness measurements across much of the cortex (d 0.15-0.92). These findings demonstrate that inadequate QC of youth structural MRI scans can undermine advantages of large sample size to detect meaningful associations.

Motion robust magnetic resonance imaging via efficient Fourier aggregation

We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T₂-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3 T and 7 T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods.

Prospective motion correction in kidney MRI using FID navigators

PURPOSE

Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospectively, in real-time.

METHODS

FID navigators were inserted into a 3D radial sequence with stack-of-stars sampling. MRI experiments were conducted on 6 healthy volunteers. A calibration scan was first acquired to create a linear motion model that estimates the kidney displacement due to respiration from the FID navigator signal. This model was then applied to predict and prospectively correct for motion in real time during deep and continuous deep breathing scans. Resultant images acquired with the proposed technique were compared with those acquired without motion correction. Dice scores were calculated between inhale/exhale motion states. Furthermore, images acquired using the proposed technique were compared with images from extra-dimensional golden-angle radial sparse parallel, a retrospective motion state binning technique.

RESULTS

Images reconstructed for each motion state show that the kidneys' position could be accurately tracked and corrected with the proposed method. The mean of Dice scores computed between the motion states were improved from 0.93 to 0.96 using the proposed technique. Depiction of the kidneys was improved in the combined images of all motion states. Comparing results of the proposed technique and extra-dimensional golden-angle radial sparse parallel, high-quality images can be reconstructed from a fraction of spokes using the proposed method.

CONCLUSION

The proposed technique reduces blurriness and motion artifacts in kidney imaging by prospectively correcting their position both in-plane and through-slice.

Sleep Problems in Preschoolers With Autism Spectrum Disorder Are Associated With Sensory Sensitivities and Thalamocortical Overconnectivity

BACKGROUND

Projections between the thalamus and sensory cortices are established early in development and play an important role in regulating sleep as well as in relaying sensory information to the cortex. Atypical thalamocortical functional connectivity frequently observed in children with autism spectrum disorder (ASD) might therefore be linked to sensory and sleep problems common in ASD.

METHODS

Here, we investigated the relationship between auditory-thalamic functional connectivity measured during natural sleep functional magnetic resonance imaging, sleep problems, and sound sensitivities in 70 toddlers and preschoolers (1.5-5 years old) with ASD compared with a matched group of 46 typically developing children.

RESULTS

In children with ASD, sleep problems and sensory sensitivities were positively correlated, and increased sleep latency was associated with overconnectivity between the thalamus and auditory cortex in a subsample with high-quality magnetic resonance imaging data (n = 29). In addition, auditory cortex blood oxygen level-dependent signal amplitude was elevated in children with ASD, potentially reflecting reduced sensory gating or a lack of auditory habituation during natural sleep.

CONCLUSIONS

These findings indicate that atypical thalamocortical functional connectivity can be detected early in development and may play a crucial role in sleep problems and sensory sensitivities in ASD.

Volumetric development of hippocampal subfields and hippocampal white matter connectivity: Relationship with episodic memory

The hippocampus is a complex structure composed of distinct subfields. It has been central to understanding neural foundations of episodic memory. In the current cross-sectional study, using a large sample of 830, 3- to 21-year-olds from a unique, publicly available dataset we examined the following questions: (1) Is there elevated grey matter volume of the hippocampus and subfields in late compared to early development? (2) How does hippocampal volume compare with the rest of the cerebral cortex at different developmental stages? and (3) What is the relation between hippocampal volume and connectivity with episodic memory performance? We found hippocampal subfield volumes exhibited a nonlinear relation with age and showed a lag in volumetric change with age when compared to adjacent cortical regions (e.g., entorhinal cortex). We also observed a significant reduction in cortical volume across older cohorts, while hippocampal volume showed the opposite pattern. In addition to age-related differences in gray matter volume, dentate gyrus/cornu ammonis 3 volume was significantly related to episodic memory. We did not, however, find any associations with episodic memory performance and connectivity through the uncinate fasciculus, fornix, or cingulum. The results are discussed in the context of current research and theories of hippocampal development and its relation to episodic memory.

Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T

PURPOSE

To develop a prospective motion correction (MC) method for phase contrast (PC) MRI of penetrating arteries (PAs) in centrum semiovale at 7 T and to evaluate its performance using automatic PA segmentation.

METHODS

Head motion was monitored and corrected during the scan based on fat navigator images. Two convolutional neural networks (CNN) were developed to automatically segment PAs and exclude surface vessels. Real-life scans with MC and without MC (NoMC) were performed to evaluate the MC performance. Motion score was calculated from the ranges of translational and rotational motion parameters. MC versus NoMC pairs with similar motion scores during MC and NoMC scans were compared. Data corrupted by motion were reacquired to further improve PA visualization.

RESULTS

PA counts (N_PA ) and PC and magnitude contrasts (MgC) relative to neighboring tissue were significantly correlated with motion score and were higher in MC than NoMC images at motion scores above 0.5-0.8 mm. Data reacquisition further increased PC but had no significant effect on N_PA and MgC. CNNs had higher sensitivity and Dice similarity coefficient for detecting PAs than a threshold-based method.

CONCLUSIONS

Prospective MC can improve the count and contrast of segmented PAs in the presence of severe motion. CNN-based PA segmentation has improved performance in delineating PAs than the threshold-based method.

Accuracy investigations for volumetric head-motion navigators with and without EPI at 7 T

PURPOSE

Accuracy investigation of volumetric navigators for motion correction, with emphasis on geometric EPI distortions at ultrahigh field.

METHODS

High-resolution Dixon images were collected in different head positions and reconstructed to water, fat, T2 *, and B0 maps. Resolution reduction was performed, and the T2 * and B0 maps were used to apply effects of TE and EPI distortions to simulate various volumetric water and fat navigators. Registrations of the simulated navigators were compared with registrations of the original high-resolution images.

RESULTS

Increased accuracy was observed with increased spatial resolution for non-EPI navigators. When using EPI, the distortions had a negative effect on registration accuracy, which was most noticeable for high-resolution navigators. Parallel imaging helped to alleviate those caveats to a certain extent, and 5-fold acceleration gave close to similar accuracy to non-EPI in most cases. Shortening the TE by partial Fourier sampling was shown to be mostly beneficial, except for water navigators with long readout durations. The EPI blip direction had an influence on navigator accuracy, and positive blip gradient polarities (yielding mostly image stretching frontally) typically gave the best accuracy for water navigators, whereas no clear recommendation could be made for fat navigators. Generally, fat EPI navigators had lower accuracy than water EPI navigators with otherwise similar parameters.

CONCLUSIONS

Echo planar imaging has been widely used for MRI navigators, but the induced distortions reduce navigator accuracy at ultrahigh field. This study can help protocol optimization and guide the complex tradeoff between resolution and EPI acceleration in navigator parameter setup.

A prospective investigation of youth alcohol experimentation and reward responsivity in the ABCD study

Rationale

Greater risk-taking behaviors, such as alcohol experimentation, are associated with different patterns of brain functioning in regions implicated in reward (nucleus accumbens, NA) and cognitive control (inferior frontal gyrus, IFG). These neural features have been observed in youth with greater risk-taking tendencies prior to substance use initiation, suggesting NA-IFG disruption may serve as an early marker for subsequent substance use disorders. Prospective studies are needed to determine if NA-IFG neural disruption predicts future substance use in school-age children, including those with minimal use of alcohol (e.g., sipping). The present large-sample prospective study sought to use machine learning to: (1) examine alcohol sipping at ages 9, 10 as a potential behavioral indicator of concurrent underlying altered neural responsivity to reward, and (2) determine if alcohol sipping and NA-IFG activation at ages 9, 10 can be used to predict which youth reported increased alcohol use at ages 11, 12. Additionally, low-level alcohol use and brain functioning at ages 9, 10 were examined as predictors of substance use and brain functioning at ages 11, 12.

Design and methods

This project used data from the baseline (Time 1) and two-year follow-up (Time 2) assessments of the Adolescent Brain Cognitive Development (ABCD) Study (Release 3.0). Support Vector Machine (SVM) learning determined if: (1) NA-IFG neural activity could correctly identify youth who reported alcohol sipping at Time 1 (n = 7409, mean age = 119.34 months, SD = 7.53; 50.27% female), and (2) NA-IFG and alcohol sipping frequency at Time 1 could correctly identify youth who reported drinking alcohol at Time 2 (n = 4000, mean age = 143.25 months, SD = 7.63; 47.53% female). Linear regression was also used to examine the relationship between alcohol sipping and NA-IFG activity at Time 1 and substance use and NA-IFG activity at Time 2. Data were also examined to characterize the environmental context in which youth first tried sips of alcohol (e.g., with or without parental permission, as part of a religious experience).

Results

Approximately 24% of the sample reported having tried sips of alcohol by ages 9, 10. On average, youth reported trying sips of alcohol 4.87 times (SD = 23.19) with age of first sip occurring at 7.36 years old (SD = 1.91). The first SVM model classified youth according to alcohol sipping status at Time 1 no better than chance with an accuracy of 0.35 (balanced accuracy = 0.52, sensitivity = 0.24, specificity = 0.80). The second SVM model classified youth according to alcohol drinking status at Time 2 with an accuracy of 0.76 (balanced accuracy = 0.56, sensitivity = 0.21, specificity = 0.91). Linear regression demonstrated that frequency of alcohol sipping at Time 1 predicted frequency of alcohol use at Time 2 (p < 0.001, adjusted R 2 = 0.075). Alcohol sipping at Time 1 was not linearly associated with NA or IFG activity at Time 2 (all ps > 0.05), and NA activity at Time 1 and Time 2 were not related (all ps > 0.05). Activity in the three subsections of the IFG at Time 1 predicted activity in those same regions at Time 2 (all ps < 0.02).

Conclusions and implications

Early sips of alcohol appear to predict alcohol use in early adolescence. Findings do not provide strong evidence for minimal early alcohol use (sipping) as a behavioral marker of underlying alterations in NA-IFG neural responsivity to reward. Improving our understanding of the neural and behavioral factors that indicate a greater propensity for future substance use is crucial for identifying at-risk youth and potential targets for preventative efforts.

Parental socioeconomic status is linked to cortical microstructure and language abilities in children and adolescents

Gradients in parental socioeconomic status (SES) are closely linked to important life outcomes in children and adolescents, such as cognitive abilities, school achievement, and mental health. Parental SES may also influence brain development, with several magnetic resonance imaging (MRI) studies reporting associations with youth brain morphometry. However, MRI signal intensity metrics have not been assessed, but could offer a microstructural correlate, thereby increasing our understanding of SES influences on neurobiology. We computed a parental SES score from family income, parental education and parental occupation, and assessed relations with cortical microstructure as measured by T1w/T2w ratio (n = 504, age = 3-21 years). We found negative age-stabile relations between parental SES and T1w/T2w ratio, indicating that youths from lower SES families have higher ratio in widespread frontal, temporal, medial parietal and occipital regions, possibly indicating a more developed cortex. Effect sizes were small, but larger than for conventional morphometric properties i.e. cortical surface area and thickness, which were not significantly associated with parental SES. Youths from lower SES families had poorer language related abilities, but microstructural differences did not mediate these relations. T1w/T2w ratio appears to be a sensitive imaging marker for further exploring the association between parental SES and child brain development.

Reduced asymmetry of the hand knob area and decreased sensorimotor u-fiber connectivity in middle-aged adults with autism

Individuals with autism spectrum disorder (ASD) frequently present with impairments in motor skills (e.g., limb coordination, handwriting and balance), which are observed across the lifespan but remain largely untreated. Many adults with ASD may thus experience adverse motor outcomes in aging, when physical decline naturally occurs. The 'hand knob' of the sensorimotor cortex is an area that is critical for motor control of the fingers and hands. However, this region has received little attention in ASD research, especially in adults after midlife. The hand knob area of the precentral (PrC_hand) and postcentral (PoC_hand) gyri was semi-manually delineated in 49 right-handed adults (25 ASD, 24 typical comparison [TC] participants, aged 41-70 years). Using multimodal (T1-weighted, diffusion-weighted, and resting-state functional) MRI, we examined the morphology, ipsilateral connectivity and laterality of these regions. We also explored correlations between hand knob measures with motor skills and autism symptoms, and between structural and functional connectivity measures. Bayesian analyses indicated moderate evidence of group effects with greater right PrC_hand volume and reduced leftward laterality of PrC_hand and PoC_hand volume in the ASD relative to TC group. Furthermore, the right PoC-PrC_hand u-fibers showed increased mean diffusivity in the ASD group. In the ASD group, right u-fiber volume positively correlated with corresponding functional connectivity but did not survive multiple comparisons correction. Correlations of hand knob measures and behavior were observed in the ASD group but did not survive multiple comparisons correction. Our findings suggest that morphological laterality and u-fiber connectivity of the sensorimotor network, putatively involved in hand motor/premotor function, may be diminished in middle-aged adults with ASD, perhaps rendering them more vulnerable to motor decline in old age. The altered morphology may relate to atypical functional motor asymmetries found in ASD earlier in life, possibly reflecting altered functional asymmetries over time.

Behavioral features in child and adolescent huntingtin gene-mutation carriers

INTRODUCTION

We compared neuropsychiatric symptoms between child and adolescent huntingtin gene-mutation carriers and noncarriers. Given previous evidence of atypical striatal development in carriers, we also assessed the relationship between neuropsychiatric traits and striatal development.

METHODS

Participants between 6 and 18 years old were recruited from families affected by Huntington's disease and tested for the huntingtin gene expansion. Neuropsychiatric traits were assessed using the Pediatric Behavior Scale and the Behavior Rating Inventory of Executive Function. Striatal volumes were extracted from 3T neuro-anatomical images. Multivariable linear regression models were conducted to evaluate the impact of group (i.e., gene nonexpanded [GNE] or gene expanded [GE]), age, and trajectory of striatal growth on neuropsychiatric symptoms.

RESULTS

There were no group differences in any behavioral measure with the exception of depression/anxiety score, which was higher in the GNE group compared to the GE group (estimate = 4.58, t(129) = 2.52, FDR = 0.051). The growth trajectory of striatal volume predicted depression scores (estimate = 0.429, 95% CI 0.15:0.71, p = .0029), where a negative slope of striatal volume over time was associated with lower depression/anxiety.

CONCLUSIONS

The current findings show that GE children may have lower depression/anxiety compared to their peers. Previously, we observed a unique pattern of early striatal hypertrophy and continued decrement in volume over time among GE children and adolescents. In contrast, GNE individuals largely show striatal volume growth. These findings suggest that the lower scores of depression and anxiety seen in GE children and adolescents may be associated with differential growth of the striatum.

Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.

Associations between neurofilament light-chain protein, brain structure, and chronic kidney disease

BACKGROUND

Neurofilament light-chain (NfL) protein is a blood-based marker of neuroaxonal injury. We sought to (1) compare plasma NfL levels in children with chronic kidney disease (CKD) and healthy peers, (2) characterize the relationship between NfL level and kidney function, and (3) evaluate NfL as a predictor of abnormal brain structure in CKD.

METHODS

Sixteen children with CKD due to congenital kidney anomalies and 23 typically developing peers were included. Plasma NfL was quantified using single-molecule array immunoassay. Participants underwent structural magnetic resonance imaging. Multiple linear regression models were used to evaluate the association between plasma NfL levels, kidney function, and brain structure.

RESULTS

An age × group interaction was identified whereby NfL levels increased with age in the CKD group only (estimate = 0.65; confidence interval (CI) = 0.08-1.22; p = 0.026). Decreased kidney function was associated with higher NfL levels (estimate = -0.10; CI = -0.16 to -0.04; p = 0.003). Lower cerebellar gray matter volume predicted increased plasma NfL levels (estimate = -0.00024; CI = -0.00039 to 0.00009; p = 0.004) within the CKD group.

CONCLUSIONS

Children with CKD show accelerated age-related increases in NfL levels. NfL level is associated with lower kidney function and abnormal brain structure in CKD.

IMPACT

NfL is a component of the neuronal cytoskeleton providing structural axonal support. Elevated NfL has been described in relation to gray and white matter brain volume loss. We have previously described the abnormal cerebellar gray matter in CKD. We explored the relationship between NfL, CKD, and brain volume. There is an accelerated, age-related increase in NfL level in CKD. Within the CKD sample, NfL level is associated with abnormal kidney function and brain structure. Decreased kidney function may be linked to abnormal neuronal integrity in pediatric CKD.

In-scanner head motion and structural covariance networks

In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and low-motion whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion-affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

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.

A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging

Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.

Motion corrected silent ZTE neuroimaging

Purpose

To develop self‐navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion.

Methods

The proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self‐navigation by using interleaved 3D phyllotaxis k‐space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co‐registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k‐space trajectory and raw data and reconstructed into a final, high‐resolution ZTE image.

Results

MERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self‐navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA).

Conclusion

Silent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.

Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain

PURPOSE

To develop a rigid real-time prospective motion-corrected multiparametric mapping technique and to test the performance of quantitative estimates.

METHODS

Motion tracking and correction were performed by integrating single-shot spiral navigators into a multiparametric imaging technique, three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). The spiral navigator was optimized, and quantitative measurements were validated using a standard system phantom. The effect of motion correction on whole-brain T1 and T2 mapping under different types of head motion during the scan was evaluated in 10 healthy volunteers. Finally, six patients with Parkinson's disease, which is known to be associated with a high prevalence of motion artifacts, were scanned to evaluate the effectiveness of our method in the real world.

RESULTS

The phantom study demonstrated that the proposed motion correction method did not introduce quantitative bias. Improved parametric map quality and repeatability were shown in volunteer experiments with both in-plane and through-plane motions, comparable to the no-motion ground truth. In real-life validation in patients, the approach showed improved parametric map quality compared to images obtained without motion correction.

CONCLUSIONS

Real-time prospective motion-corrected multiparametric relaxometry based on 3D-QALAS provided robust and repeatable whole-brain multiparametric mapping.

Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T

PURPOSE

Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction.

METHODS

A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired.

RESULTS

The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899.

CONCLUSION

The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.

Do aggregate, multimodal structural neuroimaging measures replicate regional developmental differences observed in highly cited cellular histological studies?

Influential investigations of postmortem human brain tissue showed regional differences in tissue properties at early phases of development, such as between prefrontal and primary sensory cortical regions. Large-scale neuroimaging studies enable characterization of age-related trajectories with much denser sampling of cortical regions, assessment ages, and demographic variables than postmortem tissue analyses, but no single imaging measure perfectly captures what is measured with histology. Using publicly available data from the Pediatric Imaging, Neurocognition, and Genetics (PING) study, including 951 participants with ages ranging from 3 to 21 years, we characterized cortical regional variability in developmental trajectories of multimodal brain imaging measures. Multivariate analyses integrated morphometric and microstructural cortical surface measures. To replicate foundational histological work showing delayed synapse elimination in middle frontal gyrus relative to primary sensory areas, we tested whether developmental trajectories differ between prefrontal and visual or auditory cortex. We extended this to a whole-cortex analysis of interregional differences, producing cortical parcellations with maximally different developmental trajectories. Consistent with the general conclusions of postmortem analyses, our imaging results suggest that prefrontal regions show a protracted period of greater developmental change; however, they also illustrate the challenges of drawing conclusions about the relative maturational phases of different brain regions.

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Multimodal, multivariate, nonlinear modeling, integrating morphometric and microstructural measures.

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Tested regional developmental differences previously found in highly influential cellular histological studies.

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Produced cortical parcellations with maximally different, multimodal, developmental trajectories.

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Findings converge with evidence from histological studies showing delayed prefrontal cortical development.

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Interregional differences vary by measure and illustrate complexities of defining which regions mature first.

Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5-10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.

Cortical Myelination in Toddlers and Preschoolers with Autism Spectrum Disorder

Intracortical myelin is thought to play a significant role in the development of neural circuits and functional networks, with consistent evidence of atypical network connectivity in children with autism spectrum disorders (ASD). However, little is known about the development of intracortical myelin in the first years of life in ASD, during the critical neurodevelopmental period when autism symptoms first emerge. Using T1-weighted (T1w) and T2-weighted (T2w) structural magnetic resonance imaging (MRI) in 21 young children with ASD and 16 typically developing (TD) children, ages 1.5 to 5.5 years, we demonstrate the feasibility of estimating intracortical myelin in vivo using the T1w/T2w ratio as a proxy. The resultant T1w/T2w maps were largely comparable with those reported in prior T1w/T2w studies in typically developing children and adults, and revealed no group differences between TD children and those with ASD. However, differential associations between T1w/T2w and age were identified in several early myelinated regions (e.g., visual, posterior cingulate, precuneus cortices) in the ASD and TD groups, with age-related increase in estimated myelin content across the toddler and preschool years detected in TD children, but not in children with ASD. The atypical age-related effects in intracortical myelin, suggesting a disrupted myelination in the first years of life in ASD, may be related to the aberrant brain network connectivity reported in young children with ASD in some of the same cortical regions and circuits. This article is protected by copyright. All rights reserved.

Learning-based motion artifact removal networks for quantitative R 2 ∗ mapping

PURPOSE

To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data.

METHODS

We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B 0 -inhomogeneity-corrected R 2 ∗ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R 2 ∗ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and B 0 -inhomogeneity-corrected quantitative R 2 ∗ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay.

RESULTS

We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R 2 ∗ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models.

CONCLUSION

Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R 2 ∗ maps, while LEARN-BIO directly performs motion- and B 0 -inhomogeneity-corrected R 2 ∗ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.