Quality Control of Structural MRI Images Applied Using FreeSurfer—A Hands-On Workflow to Rate Motion Artifacts

In structural magnetic resonance imaging motion artifacts are common, especially when not scanning healthy young adults. It has been shown that motion affects the analysis with automated image-processing techniques (e.g. FreeSurfer). This can bias results. Several developmental and adult studies have found reduced volume and thickness of gray matter due to motion artifacts. Thus, quality control is necessary in order to ensure an acceptable level of quality and to define exclusion criteria of images (i.e. determine participants with most severe artifacts). However, information about the quality control workflow and image exclusion procedure is largely lacking in the current literature and the existing rating systems differ. Here we propose a stringent workflow of quality control steps during and after acquisition of T1-weighted images, which enables researchers dealing with populations that are typically affected by motion artifacts to enhance data quality and maximize sample sizes. As an underlying aim we established a thorough quality control rating system for T1-weighted images and applied it to the analysis of developmental clinical data using the automated processing pipeline FreeSurfer. This hands-on workflow and quality control rating system will aid researchers in minimizing motion artifacts in the final data set, and therefore enhance the quality of structural magnetic resonance imaging studies

Retrospective Motion Correction in Magnetic Resonance Imaging of the Brain

Magnetic Resonance Imaging (MRI) is a tremendously useful diagnostic imaging modality that provides outstanding soft tissue contrast. However, subject motion is a significant unsolved problem; motion during image acquisition can cause blurring and distortions in the image, limiting its diagnostic utility. Current techniques for addressing head motion include optical tracking which can be impractical in clinical settings due to challenges associated with camera cross-calibration and marker fixation. Another category of techniques is MRI navigators, which use specially acquired MRI data to track the motion of the head. This thesis presents two techniques for motion correction in MRI: the first is spherical navigator echoes (SNAVs), which are rapidly acquired k-space navigators. The second is a deep convolutional neural network trained to predict an artefact-free image from motion-corrupted data. Prior to this thesis, SNAVs had been demonstrated for motion measurement but not motion correction, and they required the acquisition of a 26s baseline scan during which the subject could not move. In this work, a novel baseline approach is developed where the acquisition is reduced to 2.6s. Spherical navigators were interleaved into a spoiled gradient echo sequence (SPGR) on a stand-alone MRI system and a turbo-FLASH sequence (tfl) on a hybrid PET/MRI system to enable motion measurement throughout image acquisition. The SNAV motion measurements were then used to retrospectively correct the image data. While MRI navigator methods, particularly SNAVs that can be acquired very rapidly, are useful for motion correction, they do require pulse sequence modifications. A deep learning technique may be a more general solution. In this thesis, a conditional generative adversarial network (cGAN) is trained to perform motion correction on image data with simulated motion artefacts. We simulate motion in previously acquired brain images and use the image pairs (corrupted + original) to train the cGAN. MR image data was qualitatively and quantitatively improved following correction using the SNAV motion estimates. This was also true for the simultaneously acquired MR and PET data on the hybrid system. Motion corrected images were more similar than the uncorrected to the no-motion reference images. The deep learning approach was also successful for motion correction. The trained cGAN was evaluated on 5 subjects; and artefact suppression was observed in all images

Associations Between Prenatal Selective Serotonin Reuptake Inhibitor Exposure, Depression and Brain Morphology in Middle Childhood

Objective: Selective Serotonin Reuptake Inhibitors (SSRIs) are one of the most widely used prescribed medicine by pregnant women. A mixed literature suggests that prenatal SSRI exposure may increase depression risk among offspring. Method: Using data from children (n=11,076) who completed the baseline session of the Adolescent Brain and Cognitive Development (ABCD) study, we examined whether prenatal exposure to SSRIs is associated with child depression and variability in depression-related brain structures (i.e., hippocampus, amygdala, nucleus accumbens, caudate, putamen; rostral anterior cingulate; rostral and caudal middle frontal, superior frontal, and lateral and medical orbitofrontal cortices). Analyses were cross-sectional and included the following covariates: sex, race, ethnicity, age, birthweight, household income, maternal education, whether pregnancy was planned, gestational age when mother aware of pregnancy, prenatal exposure to prenatal vitamins, tobacco, marijuana, and alcohol. Lifetime maternal depression was included when not the independent variable of interest, and intracranial volume was included for brain structure analyses. Results: Prenatal SSRI exposure and maternal depression were each independently associated with depressive symptoms among children. No gray matter-based imaging metrics were associated with SSRI exposure following correction for multiple testing. SSRI exposure was nominally associated with reduced caudate, amygdala, and hippocampal volumes (all ps \u3c0.045). Conclusion: We find evidence that prenatal SSRI exposure is associated with elevated depressive symptoms among children, even after accounting for lifetime maternal depression. Imaging derived metrics of gray matter (i.e., subcortical volume, cortical thickness of brain regions associated with depression) may not play a mechanistic role in prenatal SSRI exposure-related offspring depression risk

Tourette syndrome research highlights 2015 [version 1; referees: 3 approved]

We present selected highlights from research that appeared during 2015 on Tourette syndrome and other tic disorders. Topics include phenomenology, comorbidities, developmental course, genetics, animal models, neuroimaging, electrophysiology, pharmacology, and treatment. We briefly summarize articles whose results we believe may lead to new treatments, additional research or modifications in current models of TS

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

Image acquisition and quality assurance in the Boston Adolescent Neuroimaging of Depression and Anxiety study

The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14–17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and dif-ferences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA)

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works