Faculty Bibliography

Neuroscience is increasingly focusing on developmental factors related to human structural and functional connectivity. Unfortunately, to date, diffusion-based imaging approaches have only contributed modestly to these broad objectives, despite the promise of diffusion-based tractography. Here, we report a novel data-driven approach to detect similarities and differences among white matter tracts with respect to their developmental trajectories, using 64-direction diffusion tensor imaging. Specifically, using a cross-sectional sample comprising 144 healthy individuals (7 to 48 years old), we applied k-means cluster analysis to separate white matter voxels based on their age-related trajectories of fractional anisotropy. Optimal solutions included 5-, 9- and 14-clusters. Our results recapitulate well-established tracts (e.g., internal and external capsule, optic radiations, corpus callosum, cingulum bundle, cerebral peduncles) and subdivisions within tracts (e.g., corpus callosum, internal capsule). For all but one tract identified, age-related trajectories were curvilinear (i.e., inverted 'U-shape'), with age-related increases during childhood and adolescence followed by decreases in middle adulthood. Identification of peaks in the trajectories suggests that age-related losses in fractional anisotropy occur as early as 23 years of age, with mean onset at 30 years of age. Our findings demonstrate that data-driven analytic techniques may be fruitfully applied to extant diffusion tensor imaging datasets in normative and neuropsychiatric samples

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

Personality describes persistent human behavioral responses to broad classes of environmental stimuli. Investigating how personality traits are reflected in the brain's functional architecture is challenging, in part due to the difficulty of designing appropriate task probes. Resting-state functional connectivity (RSFC) can detect intrinsic activation patterns without relying on any specific task. Here we use RSFC to investigate the neural correlates of the five-factor personality domains. Based on seed regions placed within two cognitive and affective 'hubs' in the brain-the anterior cingulate and precuneus-each domain of personality predicted RSFC with a unique pattern of brain regions. These patterns corresponded with functional subdivisions responsible for cognitive and affective processing such as motivation, empathy and future-oriented thinking. Neuroticism and Extraversion, the two most widely studied of the five constructs, predicted connectivity between seed regions and the dorsomedial prefrontal cortex and lateral paralimbic regions, respectively. These areas are associated with emotional regulation, self-evaluation and reward, consistent with the trait qualities. Personality traits were mostly associated with functional connections that were inconsistently present across participants. This suggests that although a fundamental, core functional architecture is preserved across individuals, variable connections outside of that core encompass the inter-individual differences in personality that motivate diverse responses

Rational emotive behavioral therapy (REBT) is a form of cognitive behavioral therapy developed by Albert Ellis. Developed in the 1950s, REBT was one of the earliest forms of what became known as cognitive behavioral therapy. It was successfully adapted for children and adolescents in the 1980s (Barnard & Joyce, 1984) and has been studied extensively and shown to be effective in this population (Gonzales, Nelson, & Gutkin, 2004). This article provides a selective review of the literature, and brief overview of REBT, and discusses its advantages and disadvantages.

On September 11, 2001, the author, a clinical assistant professor of child and adolescent psychiatry at New York University Medical Center, worked as a psychologist in private practice in close proximity to New York University and the twin towers. She and her young children directly witnessed the attack. She was actively involved in postattack responding to children's and their parents' needs through private practice and disaster relief work. The essay is a narrative that describes her experience of the attacks, professionally and personally, and lessons learned as a result.

In this review, we trace the parallel development of scientific understanding of parasomnias and the jurisprudence of violent behavior during sleep. Before sleep physiology was studied, it was generally self-evident that sleeping persons would not be criminally responsible. Though the problem of malingering was acknowledged in early American forensic psychiatry, the defendant with sleepwalking or sleep drunkenness was treated leniently. Over the past half century, sleep physiology has been examined and there is a developing nomenclature for sleep disorders. Though there is no clear-cut correlation between violence and sleep, several parasomnias have been implicated. We discuss contemporary concepts of parasomnias and suggest how expert witnesses might apply this knowledge to the difficult matter of assessing criminal intent and responsibility. 2011 by Federal Legal Publications, Inc

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

School psychology research focused on child outcomes is critical for understanding which social and behavioral interventions affect children in schools. Yet effective interventions fulfill their promise when they fit their implementation contexts, are implemented well with existing resources, and can be sustained or scaled up to new populations. Process research to inform the work that occurs before outcome evaluation and during scale-up efforts remains under-specified. Toward this aim, we provide a conceptual model and guiding themes for conducting rigorous and responsive intervention development in school contexts. We introduce how the articles in this special series exemplify these themes and illuminate the methodological and conceptual approaches to this work. Documenting the research steps of intervention development, implementation, and dissemination may help to advance theoretical models of intervention science as well as guide school psychologists to build and install programs that allow more children to succeed in school.