Brendon M. Nacewicz

Gaze fixation and the neural circuitry of face processing in autism

Diminished gaze fixation is one of the core features of autism and has been proposed to be associated with abnormalities in the neural circuitry of affect. We tested this hypothesis in two separate studies using eye tracking while measuring functional brain activity during facial discrimination tasks in individuals with autism and in typically developing individuals. Activation in the fusiform gyrus and amygdala was strongly and positively correlated with the time spent fixating the eyes in the autistic group in both studies, suggesting that diminished gaze fixation may account for the fusiform hypoactivation to faces commonly reported in autism. In addition, variation in eye fixation within autistic individuals was strongly and positively associated with amygdala activation across both studies, suggesting a heightened emotional response associated with gaze fixation in autism.

Behavioral Problems After Early Life Stress: Contributions of the Hippocampus and Amygdala

BACKGROUND Early life stress (ELS) can compromise development, with higher amounts of adversity linked to behavioral problems. To understand this linkage, a growing body of research has examined two brain regions involved with socioemotional functioning-amygdala and hippocampus. Yet empirical studies have reported increases, decreases, and no differences within human and nonhuman animal samples exposed to different forms of ELS. This divergence in findings may stem from methodological factors, nonlinear effects of ELS, or both. METHODS We completed rigorous hand-tracing of the amygdala and hippocampus in three samples of children who experienced different forms of ELS (i.e., physical abuse, early neglect, or low socioeconomic status). Interviews were also conducted with children and their parents or guardians to collect data about cumulative life stress. The same data were also collected in a fourth sample of comparison children who had not experienced any of these forms of ELS. RESULTS Smaller amygdala volumes were found for children exposed to these different forms of ELS. Smaller hippocampal volumes were also noted for children who were physically abused or from low socioeconomic status households. Smaller amygdala and hippocampal volumes were also associated with greater cumulative stress exposure and behavioral problems. Hippocampal volumes partially mediated the relationship between ELS and greater behavioral problems. CONCLUSIONS This study suggests ELS may shape the development of brain areas involved with emotion processing and regulation in similar ways. Differences in the amygdala and hippocampus may be a shared diathesis for later negative outcomes related to ELS.

General Multivariate Linear Modeling of Surface Shapes Using SurfStat

Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper presents a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used to parameterize, smooth out, and normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using the SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects.

Robust Automated Amygdala Segmentation via Multi-Atlas Diffeomorphic Registration

Here, we describe a novel method for volumetric segmentation of the amygdala from MRI images collected from 35 human subjects. This approach is adapted from open-source techniques employed previously with the hippocampus (Suh et al., 2011; Wang et al., 2011a,b). Using multi-atlas segmentation and machine learning-based correction, we were able to produce automated amygdala segments with high Dice (Mean = 0.918 for the left amygdala; 0.916 for the right amygdala) and Jaccard coefficients (Mean = 0.850 for the left; 0.846 for the right) compared to rigorously hand-traced volumes. This automated routine also produced amygdala segments with high intra-class correlations (consistency = 0.830, absolute agreement = 0.819 for the left; consistency = 0.786, absolute agreement = 0.783 for the right) and bivariate (r = 0.831 for the left; r = 0.797 for the right) compared to hand-drawn amygdala. Our results are discussed in relation to other cutting-edge segmentation techniques, as well as commonly available approaches to amygdala segmentation (e.g., Freesurfer). We believe this new technique has broad application to research with large sample sizes for which amygdala quantification might be needed.

Reliable non-invasive measurement of human neurochemistry using proton spectroscopy with an anatomically defined amygdala-specific voxel

Given the central role of the amygdala in fear perception and expression and its likely abnormality in affective disorders and autism, there is great demand for a technique to measure differences in neurochemistry of the human amygdala. Unfortunately, it is also a technically complex target for magnetic resonance spectroscopy (MRS) due to a small volume, high field inhomogeneity and a shared boundary with hippocampus, which can undergo opposite changes in response to stress. We attempted to achieve reliable PRESS-localized single-voxel MRS at 3T of the isolated human amygdala by using anatomy to guide voxel size and location. We present data from 106 amygdala-MRS sessions from 58 volunteers aged 10 to 52 years, including two tests of one-week stability and a feasibility study in an adolescent sample. Our main outcomes were indices of spectral quality, repeated measurement variability (within- and between-subject standard deviations), and sensitivity to stable individual differences measured by intra-class correlation (ICC). We present metrics of amygdala-MRS reliability for n-acetyl-aspartate, creatine, choline, myo-Inositol, and glutamate+glutamine (Glx). We found that scan quality suffers an age-related difference in field homogeneity and modified our protocol to compensate. We further identified an effect of anatomical inclusion near the endorhinal sulcus, a region of high synaptic density, that contributes up to 29% of within-subject variability across 4 sessions (n=14). Remaining variability in line width but not signal-to-noise also detracts from reliability. Statistical correction for partial inclusion of these strong neurochemical gradients decreases n-acetyl-aspartate reliability from an intraclass correlation of 0.84 to 0.56 for 7-minute acquisitions. This suggests that systematic differences in anatomical inclusion can contribute greatly to apparent neurochemical concentrations and could produce false group differences in experimental studies. Precise, anatomically-based prescriptions that avoid age-related sources of inhomogeneity and use longer scan times may permit study of individual differences in neurochemistry throughout development in this late-maturing structure.

Amygdala Surface Modeling with Weighted Spherical Harmonics

Although there are numerous publications on amygdala volumetry, so far there has not been many studies on modeling local amygdala surface shape variations in a rigorous framework. This paper present a systematic framework for modeling local amygdala shape. Using a novel surface flattening technique, we obtain a smooth mapping from the amygdala surface to a sphere. Then taking the spherical coordinates as a reference frame, amygdala surfaces are parameterized as a weighted linear combination of smooth basis functions using the recently developed weighted spherical harmonic representation. This new representation is used for parameterizing, smoothing and nonlinearly registering a group of amygdala surfaces. The methodology has been applied in detecting abnormal local shape variations in 23 autistic subjects compared against 24 normal controls. We did not detect any statistically significant abnormal amygdala shape variations in autistic subjects. The complete amygdala surface modeling codes used in this study is available at http://www.stat.wisc.edu/~mchung/research/amygdala .

Intrinsic functional connectivity of the central extended amygdala

The central extended amygdala (EAc)—including the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce)—plays a critical role in triggering fear and anxiety and is implicated in the development of a range of debilitating neuropsychiatric disorders. Although it is widely believed that these disorders reflect the coordinated activity of distributed neural circuits, the functional architecture of the EAc network and the degree to which the BST and the Ce show distinct patterns of functional connectivity is unclear. Here, we used a novel combination of imaging approaches to trace the connectivity of the BST and the Ce in 130 healthy, racially diverse, community‐dwelling adults. Multiband imaging, high‐precision registration techniques, and spatially unsmoothed data maximized anatomical specificity. Using newly developed seed regions, whole‐brain regression analyses revealed robust functional connectivity between the BST and Ce via the sublenticular extended amygdala, the ribbon of subcortical gray matter encompassing the ventral amygdalofugal pathway. Both regions displayed coupling with the ventromedial prefrontal cortex (vmPFC), midcingulate cortex (MCC), insula, and anterior hippocampus. The BST showed stronger connectivity with the thalamus, striatum, periaqueductal gray, and several prefrontal territories. The only regions showing stronger functional connectivity with the Ce were neighboring regions of the dorsal amygdala, amygdalohippocampal area, and anterior hippocampus. These observations provide a baseline against which to compare a range of special populations, inform our understanding of the role of the EAc in normal and pathological fear and anxiety, and showcase image registration techniques that are likely to be useful for researchers working with “deidentified” neuroimaging data.