There has been a long controversy concerning whether the amygdala's response to emotional stimuli is automatic or dependent on attentional load. Using magnoencephalography and an advanced beamformer source localization technique, we found that amygdala automaticity was a function of time: while early amygdala responding to emotional stimuli (40 -140 ms) was unaffected by attentional load, later amygdala response (280 -410 ms), subsequent to frontoparietal cortex activity, was modulated by attentional load.

This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3-D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and a experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to a state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function.

What makes us become aware? A popular hypothesis is that if cortical neurons fire in synchrony at a certain frequency band (gamma), we become aware of what they are representing. We tested this hypothesis adopting brain-imaging techniques with good spatiotemporal resolution and frequency-specific information. Specifically, we examined the degree to which increases in event-related synchronization (ERS) in the gamma band were associated with awareness of a stimulus (its detectability) and/or the emotional content of the stimulus. We observed increases in gamma band ERS within prefrontal–anterior cingulate, visual, parietal, posterior cingulate, and superior temporal cortices to stimuli available to conscious awareness. However, we also observed increases in gamma band ERS within the amygdala, visual, prefrontal, parietal, and posterior cingulate cortices to emotional relative to neutral stimuli, irrespective of their availability to conscious access. This suggests that increased gamma band ERS is related to, but not sufficient for, consciousness.

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

We report on a quantitative elastography technique achieved by combining phase-sensitive optical coherence tomography (PhS-OCT) with the surface acoustic wave (SAW) method. Different from traditional optical coherence elastography, the elastography is achieved by impulse-stimulated SAW, rather than by shear waves. PhS-OCT serves not only as a detector to measure SAW signals but also as a means to provide a cross-sectional image of the sample. The experimental results indicate that the combination of PhS-OCT with SAW is feasible to provide quantitative elastography of heterogeneous tissue samples.

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This paper presents a method for intensity inhomogeniety removal in fMRI studies of a moving subject. In such studies, subtle changes in signal as the subject moves in the presence of a bias field can be a significant confound for BOLD signal analysis. The proposed method avoids the need for a specific tissue model or assumptions about tissue homogeneity by making use of the multiple views of the underlying bias field provided by the subject's motion. A parametric bias field model is assumed and a regression model is used to estimate the basis function weights of this model. Quantitative evaluation of the effects of motion and noise in motion estimates are performed using simulated data. Results demonstrate the strength and robustness of the new method compared to explicit segmentation based methods that estimate bias within individual timeframes, as well as the state of the art 4D nonparametric bias estimator (N4ITK). We also qualitatively demonstrate the impact of the method on resting state neuroimage analysis of a moving adult brain with simulated motion and bias fields, as well as on in-vivo moving fetal fMRI.

Diagnosis of mild traumatic brain injuries (TBIs) has been difficult because of the absence of obvious focal brain lesions, using conventional computed tomography (CT) or magnetic resonance imaging (MRI) scans, in a large percentage of TBIs. One useful measure that can characterize potential tissue and neural network damage objectively is Lempel-Ziv complexity (LZC) applied to magnetoencephalography (MEG) signals. LZC is a model-independent estimator of system complexity that estimates the number of different patterns in a sequence. We hypothesized that because of the potential network damage, TBIs would show a reduced level of complexity in regions that are impaired. We included 18 healthy controls and 18 military veterans with TBI in the study. Resting state MEG data were acquired, and the LZCs were analyzed across the whole brain. Our results indicated reduced complexity in multiple brain areas in TBI patients relative to the healthy controls. In addition, we detected several neuropsychological measures associated with motor responses, visual perception, and memory, correlated with LZC, which likely explains some of the cognitive deficits in TBI patients.