Faculty Bibliography

Trauma in childhood is a psychosocial, medical, and public policy problem with serious consequences for its victims and for society. Chronic interpersonal violence in children is common worldwide. Developmental traumatology, the systemic investigation of the psychiatric and psychobiological effects of chronic overwhelming stress on the developing child, provides a framework and principles when empirically examining the neurobiological effects of pediatric trauma. This article focuses on peer-reviewed literature on the neurobiological sequelae of childhood trauma in children and in adults with histories of childhood trauma.

Cognitive theories implicate information-processing biases in the etiology of anxiety disorders. Results of attention-bias studies in posttraumatic stress disorder (PTSD) have been inconsistent, suggesting biases towards and away from threat. Within-subject variability of attention biases in posttraumatic patients may be a useful marker for attentional control impairment and the development of posttrauma symptoms. This study reports 2 experiments investigating threat-related attention biases, mood and anxiety symptoms, and attention-bias variability following trauma. Experiment 1 included 3 groups in a cross-sectional design: (a) PTSD, (b) trauma-exposed without PTSD, and (c) healthy controls with no trauma or Axis I diagnoses. Greater attention-bias variability was found in the PTSD group compared to the other 2 groups (etap2=.23); attention-bias variability was significantly and positively correlated (r = .37) with PTSD symptoms. Experiment 2 evaluated combat-exposed and nonexposed soldiers before and during deployment. Attention-bias variability did not differentiate groups before deployment, but did differentiate groups during deployment (etap2=.16); increased variability was observed in groups with acute posttraumatic stress symptoms and acute depression symptoms only. Attention-bias variability could be a useful marker for attentional impairment related to threat cues associated with mood and anxiety symptoms after trauma exposure.

OBJECTIVE: As initial episode type can predict later morbidity in bipolar disorder, we tested the hypothesis that clinical antecedents might predict initial episode types. METHOD: We studied 263 first-episode, adult, DSM-IV-TR type I bipolar disorder (BD-I) subjects within the McLean-Harvard-International First-Episode Project. Based on blinded assessments of antecedents from SCID examinations and clinical records, we compared first lifetime manic vs. other (mixed, depressive, or non-affective) major psychotic episodes. RESULTS: We identified 32 antecedents arising at early, intermediate or later times, starting 12.3 +/- 10.7 years prior to first lifetime major psychotic episodes. Based on multivariate modeling, antecedents associated significantly and independently with other (n = 113) more than manic (n = 150) first lifetime major psychotic episodes ranked by odds ratio: more early attentional disturbances, more late depression, more early perplexity, more detoxification, more early unstable mixed affects, more antidepressants, more early dysphoria, more intermediate depression, more early impulsivity, more late anhedonia, longer early-to-intermediate intervals, more intermediate substance abuse, more family history of major depression, and younger at earliest antecedents. Antecedents selectively preceding manic more than other first psychotic episodes included more late behavioral problems and more risk of familial BD-I. CONCLUSION: Clinical antecedents in adult, BD-I patients, beginning a decade before first major episodes and progressing through sequential stages were dissimilar in manic vs. other first psychotic episodes.

Medical imaging studies increasingly use longitudinal images of individual subjects in order to follow-up changes due to development, degeneration, disease progression or efficacy of therapeutic intervention. Repeated image data of individuals are highly correlated, and the strong causality of information over time lead to the development of procedures for joint segmentation of the series of scans, called 4D segmentation. A main aim was improved consistency of quantitative analysis, most often solved via patient-specific atlases. Challenging open problems are contrast changes and occurance of subclasses within tissue as observed in multimodal MRI of infant development, neurodegeneration and disease. This paper proposes a new 4D segmentation framework that enforces continuous dynamic changes of tissue contrast patterns over time as observed in such data. Moreover, our model includes the capability to segment different contrast patterns within a specific tissue class, for example as seen in myelinated and unmyelinated white matter regions in early brain development. Proof of concept is shown with validation on synthetic image data and with 4D segmentation of longitudinal, multimodal pediatric MRI taken at 6, 12 and 24 months of age, but the methodology is generic w.r.t. different application domains using serial imaging.

Post-acquisition motion correction is widely performed in diffusion-weighted imaging (DWI) to guarantee voxel-wise correspondence between DWIs. Whereas this is primarily motivated to save as many scans as possible if corrupted by motion, users do not fully understand the consequences of different types of interpolation schemes on the final analysis. Nonetheless, interpolation might increase the partial volume effect while not preserving the volume of the diffusion profile, whereas excluding poor DWIs may affect the ability to resolve crossing fibers especially with small separation angles. In this paper, we investigate the effect of interpolating diffusion measurements as well as the elimination of bad directions on the reconstructed fiber orientation diffusion functions and on the estimated fiber orientations. We demonstrate such an effect on synthetic and real HARDI datasets. Our experiments demonstrate that the effect of interpolation is more significant with small fibers separation angles where the exclusion of motion-corrupted directions decreases the ability to resolve such crossing fibers.

Temporal modeling frameworks often operate on scalar variables by summarizing data at initial stages as statistical summaries of the underlying distributions. For instance, DTI analysis often employs summary statistics, like mean, for regions of interest and properties along fiber tracts for population studies and hypothesis testing. This reduction via discarding of variability information may introduce significant errors which propagate through the procedures. We propose a novel framework which uses distribution-valued variables to retain and utilize the local variability information. Classic linear regression is adapted to employ these variables for model estimation. The increased stability and reliability of our proposed method when compared with regression using single-valued statistical summaries, is demonstrated in a validation experiment with synthetic data. Our driving application is the modeling of age-related changes along DTI white matter tracts. Results are shown for the spatiotemporal population trajectory of genu tract estimated from 45 healthy infants and compared with a Krabbe's patient.

4D pathological anatomy modeling is key to understanding complex pathological brain images. It is a challenging problem due to the difficulties in detecting multiple appearing and disappearing lesions across time points and estimating dynamic changes and deformations between them. We propose a novel semi-supervised method, called 4D active cut, for lesion recognition and deformation estimation. Existing interactive segmentation methods passively wait for user to refine the segmentations which is a difficult task in 3D images that change over time. 4D active cut instead actively selects candidate regions for querying the user, and obtains the most informative user feedback. A user simply answers 'yes' or 'no' to a candidate object without having to refine the segmentation slice by slice. Compared to single-object detection of the existing methods, our method also detects multiple lesions with spatial coherence using Markov random fields constraints. Results show improvement on the lesion detection, which subsequently improves deformation estimation.

Touch plays a crucial role in social-emotional development. Slow, gentle touch applied to hairy skin is processed by C-tactile (CT) nerve fibers. Furthermore, 'social brain' regions, such as the posterior superior temporal sulcus (pSTS) have been shown to process CT-targeted touch. Research on the development of these neural mechanisms is scant, yet such knowledge may inform our understanding of the critical role of touch in development and its dysfunction in disorders involving sensory issues, such as autism. The aim of this study was to validate the ability of functional near-infrared spectroscopy (fNIRS), an imaging technique well-suited for use with infants, to measure temporal lobe responses to CT-targeted touch. Healthy adults received brushing to the right forearm (CT) and palm (non-CT) separately, in a block design procedure. We found significant activation in right pSTS and dorsolateral prefrontal cortex to arm > palm touch. In addition, individual differences in autistic traits were related to the magnitude of peak activation within pSTS. These findings demonstrate that fNIRS can detect brain responses to CT-targeted touch and lay the foundation for future work with infant populations that will characterize the development of brain mechanisms for processing CT-targeted touch in typical and atypical populations.