Early stressful experiences are associated with reduced neural responses to naturalistic emotional and social content in children

How do children’s experiences relate to their naturalistic emotional and social processing? Because children can struggle with tasks in the scanner, we collected fMRI data while 4-to-11-year-olds watched a short film with positive and negative emotional events, and rich parent-child interactions (n = 70). We captured broad, normative stressful experiences by examining socioeconomic status (SES) and stressful life events, as well as children’s more proximal experiences with their parents. For a sub-sample (n = 30), parenting behaviors were measured during a parent-child interaction, consisting of a picture book, a challenging puzzle, and free play with novel toys. We characterized positive parenting behaviors (e.g., warmth, praise) and negative parenting behaviors (e.g., harsh tone, physical control). We found that higher SES was related to greater activity in medial orbitofrontal cortex during parent-child interaction movie events. Negative parenting behaviors were associated with less activation of the ventral tegmental area and cerebellum during positive emotional events. In a region-of-interest analysis, we found that stressful life events and negative parenting behaviors were associated with less activation of the amygdala during positive emotional events. These exploratory results demonstrate the promise of using movie fMRI to study how early experiences may shape emotional, social, and motivational processes

Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI

ObjectiveWe demonstrate and evaluate the first markerless motion tracker compatible with PET, MRI, and simultaneous PET/MRI systems for motion correction (MC) of brain imaging.MethodsPET and MRI compatibility is achieved by careful positioning of in-bore vision extenders and by placing all electronic components out-of-bore. The motion tracker is demonstrated in a clinical setup during a pediatric PET/MRI study including 94 pediatric patient scans. PET MC is presented for two of these scans using a customized version of the Multiple Acquisition Frame method. Prospective MC of MRI acquisition of two healthy subjects is demonstrated using a motion-aware MRI sequence. Real-time motion estimates are accompanied with a tracking validity parameter to improve tracking reliability.ResultsFor both modalities, MC shows that motion induced artifacts are noticeably reduced and that motion estimates are sufficiently accurate to capture motion ranging from small respiratory motion to large intentional motion. In the PET/MRI study, a time-activity curve analysis shows image improvements for a patient performing head movements corresponding to a tumor motion of ±5-10 mm with a 19% maximal difference in standardized uptake value before and after MC.ConclusionThe first markerless motion tracker is successfully demonstrated for prospective MC in MRI and MC in PET with good tracking validity.SignificanceAs simultaneous PET/MRI systems have become available for clinical use, an increasing demand for accurate motion tracking and MC in PET/MRI scans has emerged. The presented markerless motion tracker facilitate this demand

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upen

Neuroanatomical and cellular degeneration associated with a social disorder characterized by new ritualistic belief systems in a TDP-C patient vs. a Pick patient

Frontotemporal dementia (FTD) is a spectrum of clinically and pathologically heterogenous neurodegenerative dementias. Clinical and anatomical variants of FTD have been described and associated with underlying frontotemporal lobar degeneration (FTLD) pathology, including tauopathies (FTLD-tau) or TDP-43 proteinopathies (FTLD-TDP). FTD patients with predominant degeneration of anterior temporal cortices often develop a language disorder of semantic knowledge loss and/or a social disorder often characterized by compulsive rituals and belief systems corresponding to predominant left or right hemisphere involvement, respectively. The neural substrates of these complex social disorders remain unclear. Here, we present a comparative imaging and postmortem study of two patients, one with FTLD-TDP (subtype C) and one with FTLD-tau (subtype Pick disease), who both developed new rigid belief systems. The FTLD-TDP patient developed a complex set of values centered on positivity and associated with specific physical and behavioral features of pigs, while the FTLD-tau patient developed compulsive, goal-directed behaviors related to general themes of positivity and spirituality. Neuroimaging showed left-predominant temporal atrophy in the FTLD-TDP patient and right-predominant frontotemporal atrophy in the FTLD-tau patient. Consistent with antemortem cortical atrophy, histopathologic examinations revealed severe loss of neurons and myelin predominantly in the anterior temporal lobes of both patients, but the FTLD-tau patient showed more bilateral, dorsolateral involvement featuring greater pathology and loss of projection neurons and deep white matter. These findings highlight that the regions within and connected to anterior temporal lobes may have differential vulnerability to distinct FTLD proteinopathies and serve important roles in human belief systems

MRI denoising via phase error estimation

If the phase error at each pixel in a complex-valued MRI image is known the noise in the image can be reduced resulting in improved detection of medically significant details. However, given a complex-valued MRI image, estimating the phase error at each pixel is a difficult problem. Several approaches have previously been suggested including non-linear least squares fitting and smoothing filters. We propose a new scheme based on iteratively applying a series of non-linear filters, each used to modify the estimate into greater agreement with one piece of knowledge about the problem, until the output converges to a stable estimate. We compare our results with other phase estimation and MRI denoising schemes using synthetic data

M.S.: Perception of dim targets on dark backgrounds

Some diagnostic tasks in MRI involve determining the presence of a faint feature (target) relative to a dark background. In MR images produced by taking pixel magnitudes it is well known that the contrast between faint features and dark backgrounds is reduced due to the Rician noise distribution. In an attempt to enhance detection we implemented three different MRI reconstruction algorithms: the normal magnitude, phase-corrected real, and a wavelet thresholding algorithm designed particularly for MRI noise suppression and contrast enhancement. To compare these reconstructions, we had volunteers perform a two-alternative forced choice (2AFC) signal detection task. The stimuli were produced from high-field head MRI images with synthetic thermal noise added to ensure realistic backgrounds. Circular targets were located in regions of the image that were dark, but next to bright anatomy. Images were processed using one of the three reconstruction techniques. In addition we compared a channelized Hotelling observer (CHO) to the human observers in this task. We measured the percentage correct in both the human and model observer experiments. Our results showed better performance with the use of magnitude or phase-corrected real images compared to the use of the wavelet algorithm. In particular, artifacts induced by the wavelet algorithm seem to distract some users and produce significant inter-subject variability. This contradicts predictions based only on SNR. The CHO matched the mean human results quite closely, demonstrating that this model observer may be used to simulate human response in MRI target detection tasks

Maximum Likelihood Estimators in Magnetic Resonance Imaging

Abstract. Images of the MRI signal intensity are normally constructed by taking the magnitude of the complex-valued data. This results in a biased estimate of the true signal intensity. We consider this as a problem of parameter estimation with a nuisance parameter. Using several standard techniques for this type of problem, we derive a variety of estimators for the MRI signal, some previously published and some novel. Using Monte Carlo experiments we compare the estimators we derive with others previously published. Our results suggest that one of the novel estimators we derive may strike a desirable trade-off between bias and mean squared error.

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