Faculty Bibliography

Aversive Pavlovian memory coordinates the defensive behavioral response to learned threats. The amygdala is a key locus for the acquisition and storage of aversive associations. Information about conditioned and unconditioned stimuli converge in the lateral amygdala, which is a hot spot for the plasticity induced by associative learning. Central amygdala uses Pavlovian memory to coordinate the conditioned reaction to an aversive conditioned stimulus. Aversive associations can also access the brain networks of instrumental action. The offset of an aversive conditioned stimulus can reinforce behavior, recruiting a pathway that includes the lateral and basal amygdala, as opposed to the lateral and central amygdala circuit for Pavlovian reactions. Aversive conditioned stimuli can also modulate ongoing behavior, suppressing appetitive actions and facilitating aversive actions. Facilitation depends on an amygdalar network involving the lateral and central, as well as medial, nuclei. Thus, aversive Pavlovian memory has wide-reaching effects on defensive behavior, coordinating reactive to active responses to environmental threats.

Alzheimer's disease is a neurodegenerative disorder that is the most common cause of dementia in the elderly today. One of the earliest reported signs of Alzheimer's disease is olfactory dysfunction, which may manifest in a variety of ways. The present study sought to address this issue by investigating odor coding in the anterior piriform cortex, the primary cortical region involved in higher order olfactory function, and how it relates to performance on olfactory behavioral tasks. An olfactory habituation task was performed on cohorts of transgenic and age-matched wild-type mice at 3, 6 and 12 months of age. These animals were then anesthetized and acute, single-unit electrophysiology was performed in the anterior piriform cortex. In addition, in a separate group of animals, a longitudinal odor discrimination task was conducted from 3-12 months of age. Results showed that while odor habituation was impaired at all ages, Tg2576 performed comparably to age-matched wild-type mice on the olfactory discrimination task. The behavioral data mirrored intact anterior piriform cortex single-unit odor responses and receptive fields in Tg2576, which were comparable to wild-type at all age groups. The present results suggest that odor processing in the olfactory cortex and basic odor discrimination is especially robust in the face of amyloid beta precursor protein (AbetaPP) over-expression and advancing amyloid beta (Abeta) pathology. Odor identification deficits known to emerge early in Alzheimer's disease progression, therefore, may reflect impairments in linking the odor percept to associated labels in cortical regions upstream of the primary olfactory pathway, rather than in the basic odor processing itself.

This report evaluates whether classification tree algorithms (CTA) may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD). Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS) cohort (629 youth, 148 with BPSD and 481 without BPSD). Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4%) relative to logistic regression (77.6%). However, CTA showed increased sensitivity (0.28 vs. 0.18) at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%). High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%). Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data); these may increase the clinical utility of CTA models further.

Longitudinal MR imaging during early brain development provides important information about growth patterns and the development of neurological disorders. We propose a new framework for studying brain growth patterns within and across populations based on MRI contrast changes, measured at each time point of interest and at each voxel. Our method uses regression in the LogOdds space and an information-theoretic measure of distance between distributions to capture contrast in a manner that is robust to imaging parameters and without requiring intensity normalization. We apply our method to a clinical neuroimaging study on early brain development in autism, where we obtain a 4D spatiotemporal model of contrast changes in multimodal structural MRI.

Quantitative imaging biomarkers are important for assessment of impact, recovery and treatment efficacy in patients with traumatic brain injury (TBI). To our knowledge, the identification of such biomarkers characterizing disease progress and recovery has been insufficiently explored in TBI due to difficulties in registration of baseline and follow-up data and automatic segmentation of tissue and lesions from multimodal, longitudinal MR image data. We propose a new methodology for computing imaging biomarkers in TBI by extending a recently proposed spatiotemporal 4D modeling approach in order to compute quantitative features of tissue change. The proposed method computes surface-based and voxel-based measurements such as cortical thickness, volume changes, and geometric deformation. We analyze the potential for clinical use of these biomarkers by correlating them with TBI-specific patient scores at the level of the whole brain and of individual regions. Our preliminary results indicate that the proposed voxel-based biomarkers are correlated with clinical outcomes.

This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.

We present a new framework for spatiotemporal analysis of parameterized functions attributed by properties of 4D longitudinal image data. Our driving application is the measurement of temporal change in white matter diffusivity of fiber tracts. A smooth temporal modeling of change from a discrete-time set of functions is obtained with an extension of the logistic growth model to time-dependent spline functions, capturing growth with only a few descriptive parameters. An unbiased template baseline function is also jointly estimated. Solution is demonstrated via energy minimization with an extension to simultaneous modeling of trajectories for multiple subjects. The new framework is validated with synthetic data and applied to longitudinal DTI from 15 infants. Interpretation of estimated model growth parameters is facilitated by visualization in the original coordinate space of fiber tracts.