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Purpose: While laparoscopic simulation-based training is a well-established component of general surgery training, no such requirement or standardized curriculum exists for robotic surgery. Furthermore, there is a lack of high-fidelity electrocautery simulation training exercises in the literature. Using Messick’s validity framework, we sought to determine the content, response process, internal content and construct validity of a novel inanimate tissue model that utilizes electrocautery for potential incorporation in such curricula. Methods: Multi-institutional, prospective study involving medical students (MS) and general surgery residents (PGY1-3). Participants performed an exercise using a biotissue bowel model on the da Vinci Xi robotic console during which they created an enterotomy using electrocautery followed by approximation with interrupted sutures. Participant performance was recorded and then scored by crowd-sourced assessors of technical skill along with three of the authors. Construct validity was determined via difference in Global Evaluative Assessment of Robotic Skills (GEARS) score, time to completion and total number of errors between the two cohorts. Upon completion of the exercise, participants were surveyed on their perception of the exercise and its impact on their robotic training to determine content validity. Results: 31 participants were enrolled and separated into two cohorts: MS + PGY1 vs PGY2-3. Time spent on the robotic trainer (0.8 vs 8.13 hours, p=0.002), number of bedside robotic assists (5.7 vs 14.8, pConclusions: This study confirmed the content, response process, internal structure and construct validity of a novel, high-fidelity and cost-effective inanimate tissue exercise which successfully incorporates electrocautery. Consideration should be given to its addition to robotic surgery training programs.

OBJECTIVES/GOALS: To evaluate whether the default mode network experiences age-related changes in functional connectivity and to identify these patterns of progression across seven decades of life. The overall goal is to evaluate whether quantifying these functional changes can serve as potential neurobiomarkers of aging for further quantitative genetic analyses. METHODS/STUDY POPULATION: Scans were performed at the RII on a 3T Siemens Trio scanner with an 8-channel head coil. Whole-brain, rsfMR imaging was performed using a gradient-echo EPI sequence sensitive to the BOLD effect (TE/TR = 30/3000 ms; flip angle = 90°; isotropic 1.72 mm2). Subjects were instructed to lie in dimmed light with their eyes open and try not to fall asleep. Image analysis was performed with FMRIB’s Software Library tools (www.fmrib.ox.ac.uk/fsl). Preprocessing of resting state data includes motion correction, brain extraction, spatial smoothing, and high-pass temporal filtering. Time series data was extracted from 9 DMN ROIs using FSL’s Featquery tool with 6mm radius spherical ROI masks created in Mango. After extraction, DMN connectivity was assess using structural equation modeling implemented in Amos 22.0 (IBM, Inc.). RESULTS/ANTICIPATED RESULTS: The exploratory SEM (eSEM) default mode network (DMN) model used consists of 9 regions of interest and 13 functional connectivity paths. The eSEM DMN model exhibited exceptional model fit to a primary resting state data set of 1169 subjects from the Genetics of Brain Structure project (1R01MH078111-01, David Glahn PI) with an RMSEA of 0.037. This model also had excellent model fit in 7 cohorts that were grouped by decade age (10s – RMSEA: 0.058, 20s – 0.051, 30s – 0.045, 40s – 0.048, 50s – 0.043, 60s – 0.035, 70s – 0.037). Analysis of the decade group-wise path coefficients identified 7 of the 13 paths (pC -> LMTG, pC -> PCC, PCC -> MPFG, PCC -> vACC, MPFG -> vACC, LIPL -> RIPL, LMTG -> RMTG) significantly negatively correlated with age-related changes. As early as the 1st decade of life, the functional connectivity within the DMN decreases. DISCUSSION/SIGNIFICANCE OF IMPACT: The DMN experiences progressive age-related decreases in connectivity, beginning in the first decade of life. Our results suggest that DMN path coefficients can serve as biomarkers of cognitive aging, which can then be used as quantitative traits for genetic analyses to identify genes associated with normal aging and age-related cognitive diseases.

Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case-control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen's d=-0.232; P=3.50 × 10(-7)) and thalamus (d=-0.148; P=4.27 × 10(-3)) and enlarged lateral ventricles (d=-0.260; P=3.93 × 10(-5)) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.Molecular Psychiatry advance online publication, 9 February 2016; doi:10.1038/mp.2015.227. ispartof: Molecular Psychiatry vol:21 issue:12 pages:1710-1716 ispartof: location:England status: published

Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD.

We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains' continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer's patients and controls. Our measures show significant differences between the two groups.

Neuroimaging offers a powerful means to assess the trajectory of brain degeneration in a variety of disorders, including Alzheimer’s disease (AD). Here we describe how multi-modal imaging can be used to study the changing brain during the different stages of AD. We integrate findings from a range of studies using magnetic resonance imaging (MRI), positron emission tomography (PET), functional MRI (fMRI) and diffusion weighted imaging (DWI). Neuroimaging reveals how risk genes for degenerative disorders affect the brain, including several recently discovered genetic variants that may disrupt brain connectivity. We review some recent neuroimaging studies of genetic polymorphisms associated with increased risk for late-onset Alzheimer’s disease (LOAD). Some genetic variants that increase risk for drug addiction may overlap with those associated with degenerative brain disorders. These common associations offer new insight into mechanisms underlying neurodegeneration and addictive behaviors, and may offer new leads for treating them before severe and irreversible neurological symptoms appear.