"Fully automated segmentation of the corticospinal tract using the TractSeg algorithm in patients with brain tumors".

OBJECTIVE: Tractography has been used to define the presurgical location of white matter tracts, but this is subjective and time-intensive, making incorporation to imaging workflow at scale problematic. The objective is to validate a fully automated pipeline using the TractSeg algorithm (Wasserthal et al. NeuroImage 2018;183:239-253) to segment the corticospinal tract in patients with brain tumors adjacent to the corticospinal tract. METHODS: The process of importing a structural MPRAGE sequence and raw diffusion weighted images from PACS, executing the TractSeg algorithm, overlaying the resulting bilateral corticospinal tracts on the MPRAGE image, and exporting this composite image to PACS was automated. This procedure was used to segment the corticospinal tract in 28 patients with brain masses adjacent to or displacing the corticospinal tract. These segmentations were compared with both manual deterministic tractography performed with DSI Studio using seeds placed in the pons and an automated tractography method in DSI Studio. RESULTS: The automated algorithm was able to segment the bilateral corticospinal tracts in all 28 patients whereas the manual reference method and DSI Studio based automated tractography were unsuccessful in 2 and 1 patients, respectively. In all cases, the TractSeg segmentations very closely matched the manual segmentations. Also, TractSeg appeared to include larger portions of the lateral corticospinal tract fibers than the other 2 methods. CONCLUSION: The TractSeg algorithm demonstrated robust performance in segmenting the corticospinal tract in patients with brain tumors adjacent to this tract. The algorithm is fast to perform and has great potential for optimizing and streamlining neurosurgical planning.

Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury.

Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.

Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains.

The functional architecture of the brain can be described as a dynamical system where components interact in flexible ways, constrained by physical connections between regions. Using correlation, either in time or in space, as an abstraction of functional connectivity, we analyzed resting state fMRI data from 1003 subjects. We compared several data preprocessing strategies and found that independent component-based nuisance regression outperformed other strategies, with the poorest reproducibility in strategies that include global signal regression. We also found that temporal vs. spatial functional connectivity can encode different aspects of cognition and personality. Topological analyses using persistent homology show that persistence barcodes are significantly correlated to individual differences in cognition and personality, with high reproducibility. Topological data analyses, including approaches to model connectivity in the time domain, are promising tools for representing high-level aspects of cognition, development, and neuropathology.

Guidelines for resident teaching experiences.

Postgraduate year one (PGY1) and postgraduate year two (PGY2) residencies serve to develop pharmacists into skillful clinicians who provide advanced patient-centered care in various general and specialized areas of pharmacy practice. Pharmacy residencies are a minimum requirement for many clinical pharmacy positions, as well as for positions in academia. The role of clinical pharmacists typically includes teaching, regardless of whether they pursue an academic appointment. Common teaching duties of pharmacist-clinicians include giving continuing education or other invited presentations, providing education to colleagues regarding clinical initiatives, precepting pharmacy students (early and advanced experiences) and residents, and educating other health care professionals. Although ASHP provides accreditation standards for PGY1 and PGY2 residencies, the standards pertaining to teaching or education training are vague. Through the years, teaching certificate programs that develop residents' teaching skills and better prepare residents for a diverse pharmacy job market have increased in popularity; moreover, teaching certificate programs serve as an attractive recruitment tool. However, the consistency of requirements for teaching certificate programs is lacking, and standardization is needed. The Task Force on Residencies developed two sets of guidelines to define teaching experiences within residencies. The first guideline defines the minimum standards for teaching experiences in any residency-training program. The second guideline is for programs offering a teaching certificate program to provide standardization, ensuring similar outcomes and quality on program completion. One of the main differences between the guidelines is the recommendation that residency programs offering a teaching certificate program be affiliated with an academic institution to provide the pedagogy and variety of teaching experiences for the resident. Residency program directors should consider adopting these guidelines to offer consistent teaching experiences. In addition, residents should inquire about the elements of teaching in a program as an aid to selecting the training best suited to their needs.