Intentional enterotomies: validation of a novel robotic surgery training exercise.

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. A multi-institutional, prospective study involving medical students (MS) and general surgery residents (PGY1-3) was conducted. 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. 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 h, p = 0.002), number of bedside robotic assists (5.7 vs. 14.8, p < 0.001), and number of robotic cases as primary surgeon (0.3 vs. 13.1, p < 0.001) were statistically significant between the two groups. Differences in GEARS scores (18.5 vs. 19.9, p = 0.001), time to completion (26.1 vs. 14.4 min, p < 0.001), and total errors (21.5 vs. 11.9, p = 0.018) between the groups were statistically significant as well. Of the 23 participants that completed the post-exercise survey, 87% and 91.3% reported improvement in robotic surgical ability and confidence, respectively. On a 10-point Likert scale, respondents rated the realism of the exercise 7.5, educational benefit 9.1, and effectiveness in teaching robotic skills 8.7. Controlling for the upfront investment of certain training materials, each exercise iteration cost ~ $30. 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.

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease.

Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods - four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one "ball-and-stick" approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.

Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease.

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.

Neuroimaging and genetic risk for Alzheimer's disease and addiction-related degenerative brain disorders.

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.