Pooja Gaur

Functional MRI of Sentence Comprehension in Children with Dyslexia: Beyond Word Recognition

Sentence comprehension (SC) studies in typical and impaired readers suggest that reading for meaning involves more extensive brain activation than reading isolated words. Thus far, no reading disability/dyslexia (RD) studies have directly controlled for the word recognition (WR) components of SC tasks, which is central for understanding comprehension processes beyond WR. This experiment compared SC to WR in 29, 9-14 year olds (15 typical and 14 impaired readers). The SC-WR contrast for each group showed activation in left inferior frontal and extrastriate regions, but the RD group showed significantly more activation than Controls in areas associated with linguistic processing (left middle/superior temporal gyri), and attention and response selection (bilateral insula, right cingulate gyrus, right superior frontal gyrus, and right parietal lobe). Further analyses revealed this overactivation was driven by the RD groups response to incongruous sentences. Correlations with out-of-scanner measures showed that better word- and text-level reading fluency was associated with greater left occipitotemporal activation, whereas worse performance on WR, fluency, and comprehension (reading and oral) were associated with greater right hemisphere activation in a variety of areas, including supramarginal and superior temporal gyri. Results provide initial foundations for understanding the neurobiological correlates of higher-level processes associated with reading comprehension.

Visuospatial processing in children with neurofibromatosis type 1

Neuroimaging studies investigating the neural network of visuospatial processing have revealed a right hemisphere network of activation including inferior parietal lobe, dorsolateral prefrontal cortex, and extrastriate regions. Impaired visuospatial processing, indicated by the Judgment of Line Orientation (JLO), is commonly seen in individuals with neurofibromatosis type 1 (NF-1). Nevertheless, few studies have examined the neural activity associated with visuospatial processing in NF-1, in particular, during a JLO task. This study used functional neuroimaging to explore differences in volume of activation in predefined regions of interest between 13 individuals with NF-1 and 13 controls while performing an analogue JLO task. We hypothesized that participants with NF-1 would show anomalous right hemisphere activation and therefore would recruit regions within the left hemisphere to complete the task. Multivariate analyses of variance were used to test for differences between groups in frontal, temporal, parietal, and occipital regions. Results indicate that, as predicted, controls utilized various right hemisphere regions to complete the task, while the NF-1 group tended to recruit left hemisphere regions. These results suggest that the NF-1 group has an inefficient right hemisphere network. An additional unexpected finding was that the NF-1 group showed decreased volume of activation in primary visual cortex (BA 17). Future studies are needed to examine whether the decrease in primary visual cortex is related to a deficit in basic visual processing; findings could ultimately lead to a greater understanding of the nature of deficits in NF-1 and have implications for remediation.

Accelerated MRI thermometry by direct estimation of temperature from undersampled k-space data

Acceleration of magnetic resonance (MR) thermometry is desirable for several applications of MR‐guided focused ultrasound, such as those requiring greater volume coverage, higher spatial resolution, or higher frame rates.

Preschool anxiety disorders predict different patterns of amygdala-prefrontal connectivity at school-age.

Objective In this prospective, longitudinal study of young children, we examined whether a history of preschool generalized anxiety, separation anxiety, and/or social phobia is associated with amygdala-prefrontal dysregulation at school-age. As an exploratory analysis, we investigated whether distinct anxiety disorders differ in the patterns of this amygdala-prefrontal dysregulation. Methods Participants were children taking part in a 5-year study of early childhood brain development and anxiety disorders. Preschool symptoms of generalized anxiety, separation anxiety, and social phobia were assessed with the Preschool Age Psychiatric Assessment (PAPA) in the first wave of the study when the children were between 2 and 5 years old. The PAPA was repeated at age 6. We conducted functional MRIs when the children were 5.5 to 9.5 year old to assess neural responses to viewing of angry and fearful faces. Results A history of preschool social phobia predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces. Preschool generalized anxiety predicted less functional connectivity between the amygdala and dorsal prefrontal cortices in response to fearful faces. Finally, a history of preschool separation anxiety predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces and greater school-age functional connectivity between the amygdala and dorsal prefrontal cortices to angry faces. Conclusions Our results suggest that there are enduring neurobiological effects associated with a history of preschool anxiety, which occur over-and-above the effect of subsequent emotional symptoms. Our results also provide preliminary evidence for the neurobiological differentiation of specific preschool anxiety disorders.

Maintenance and Representation of Mind Wandering during Resting-State fMRI

Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.

Correcting heat-induced chemical shift distortions in proton resonance frequency-shift thermometry

To reconstruct proton resonance frequency‐shift temperature maps free of chemical shift distortions.

Interleaved EPI Based fMRI Improved by Multiplexed Sensitivity Encoding (MUSE) and Simultaneous Multi-Band Imaging

Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful imaging tool for detecting brain activities. The majority of fMRI studies are performed with single-shot echo-planar imaging (EPI) due to its high temporal resolution. Recent studies have demonstrated that, by increasing the spatial-resolution of fMRI, previously unidentified neuronal networks can be measured. However, it is challenging to improve the spatial resolution of conventional single-shot EPI based fMRI. Although multi-shot interleaved EPI is superior to single-shot EPI in terms of the improved spatial-resolution, reduced geometric distortions, and sharper point spread function (PSF), interleaved EPI based fMRI has two main limitations: 1) the imaging throughput is lower in interleaved EPI; 2) the magnitude and phase signal variations among EPI segments (due to physiological noise, subject motion, and B0 drift) are translated to significant in-plane aliasing artifact across the field of view (FOV). Here we report a method that integrates multiple approaches to address the technical limitations of interleaved EPI-based fMRI. Firstly, the multiplexed sensitivity-encoding (MUSE) post-processing algorithm is used to suppress in-plane aliasing artifacts resulting from time-domain signal instabilities during dynamic scans. Secondly, a simultaneous multi-band interleaved EPI pulse sequence, with a controlled aliasing scheme incorporated, is implemented to increase the imaging throughput. Thirdly, the MUSE algorithm is then generalized to accommodate fMRI data obtained with our multi-band interleaved EPI pulse sequence, suppressing both in-plane and through-plane aliasing artifacts. The blood-oxygenation-level-dependent (BOLD) signal detectability and the scan throughput can be significantly improved for interleaved EPI-based fMRI. Our human fMRI data obtained from 3 Tesla systems demonstrate the effectiveness of the developed methods. It is expected that future fMRI studies requiring high spatial-resolvability and fidelity will largely benefit from the reported techniques.

A Kernel Machine-based fMRI Physiological Noise Removal Method

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach.

Improved k-space-based MR thermometry by joint PRF phase shift and T1/T2* attenuation estimation

MR temperature mapping based on the proton resonance frequency (PRF) shift is used in MR-guided focused ultrasound procedures for dosimetry and safety monitoring. While conventional PRF-shift thermometry is based on calculating a phase difference between two reconstructed MR images, Gaur et al [1,2] have recently described two algorithms that estimate temperature-induced phase shifts directly from MR k-space data, prior to image reconstruction. The approach enables large dynamic scan acceleration factors[1] and the correction of chemical-shift (CS) effects that geometrically distort the temperature maps.[2] However, that work neglected image attenuation that accompanies the PRF phase shift and is primarily caused by increasing T1 with temperature.[3] Here it is shown that attenuation degrades the accuracy of k-space-based reconstructions, but that it can be accounted for in the reconstructions.

Identification and attenuation of physiological noise in fMRI using kernel techniques

Functional magnetic resonance imaging (fMRI) techniques enable noninvasive studies of brain functional activity under task and resting states. However, the analysis of brain activity could be significantly affected by the cardiac- and respiration-induced physiological noise in fMRI data. In most multi-slice fMRI experiments, the temporal sampling rates are not high enough to critically sample the physiological noise, and the noise is aliased into frequency bands where useful brain functional signal exists, compromising the analysis. Most existing approaches cannot distinguish between the aliased noise and signal if they overlap in the frequency domain. In this work, we further developed a kernel principal component analysis based physiological removal method based on our previous work. Specifically, two kernel functions were evaluated based on a newly proposed criterion that can measure the capability of a kernel to separate the aliased physiological noise from fMRI signal. In addition, a mutual information based criterion was designed to select principal components for noise removal. The method was evaluated by human experimental fMRI studies, and the results demonstrate that the proposed method can effectively identify and attenuate the aliased physiological noise in fMRI data.