Short Communications Proton MR Spectroscopy Correlates Diffuse Axonal Abnormalities with Post-Concussive Symptoms in Mild Traumatic Brain Injury

Abstract There are no established biomarkers for mild traumatic brain injury (mTBI), in part because post-concussive symptoms (PCS) are subjective and conventional imaging is typically unremarkable. To test whether diffuse axonal abnormalities quantified with three-dimensional (3D) proton magnetic resonance spectroscopic imaging ( 1 H-MRSI) correlated with patients' PCS, we retrospectively studied 26 mTBI patients (mean Glasgow Coma Scale [GCS] score of 14.7), 18-to 56-year-olds and 13 controls three to 55 days post-injury. All were scanned at 3 Tesla with T1-and T2-weighted MRI and 3D 1 H-MRSI (480 voxels over 360 cm 3 , *30% of the brain). On scan day, patients completed a symptom questionnaire, and those who indicated at least one of the most common subacute mTBI symptoms (headache, dizziness, sleep disturbance, memory deficits, blurred vision) were grouped as PCS-positive. Global gray matter and white matter (GM/WM) absolute concentrations of N-acetylaspartate (NAA), choline (Cho), creatine (Cr) and myo-inositol (mI) in PCS-positive and PCS-negative patients were compared to age-and gender-matched controls using two-way analysis of variance. The results showed that the PCS-negative group (n = 11) and controls (n = 8) did not differ in any GM or WM metabolite level. The PCS-positive patients (n = 15) had lower WM NAA than the controls (n = 12; 7.0 -0.6 versus 7.9 -0.5mM; p = 0.0007). Global WM NAA, therefore, showed sensitivity to the TBI sequelae associated with common PCS in patients with mostly normal neuroimaging, as well as GCS scores. This suggests a potential biomarker role in a patient population in which objective measures of injury and symptomatology are currently lacking

A modular implementation of the SAVaNT anticipatory route guidance algorithm

http://deepblue.lib.umich.edu/bitstream/2027.42/7238/5/bap9098.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/7238/4/bap9098.0001.001.tx

Efficient random algorithms for constrained global and convex optimization.

Several Markov chain sampling algorithms, including the Hit-and-Run algorithm, are unified within the framework of random walks taking steps inside balls centered at the current point. Several new Markov chain sampling algorithms are developed within this theoretical framework. Using existing results for the complexity of one random ball walk, the theory of Markov chain conductance is used to derive complexity results for the class of random ball walks, including the polynomiality of Hit-and-Run over certain convex regions. A technique based on filtering Markov chains is used to extend these results to any well-rounded convex body. These complexity bounds are validated through a series of empirical trials and statistical tests. Further results are presented bounding the complexity of generating samples from arbitrary distributions by filtering random ball walks. Using a result from coupling theory, we demonstrate how the random ball walks may be used to generate samples having exactly a specified distribution. The random ball walk sampling algorithms are applied to develop the first polynomial time implementation of the Pure Adaptive Search optimization algorithm for convex programming, as well as a quasi-polynomial implementation of the Adaptive Search algorithm. A new random optimization algorithm, called Relaxed Adaptive Search, is developed on the basis of relaxing the feasible region to obtain a region more suitable for rapid convergence of Markov chain samplers. Relaxed Adaptive Search is demonstrated to have quasi-polynomial complexity when applied to convex programming. An approximate version of the Adaptive Search algorithm called Hide-and-Seek is coupled with the Generalized Lagrange Multiplier method to develop an efficient global optimization procedure for nonlinear programs involving both equality and inequality constraints. When applied to a difficult automotive design problem, it is demonstrated that this version of Hide-and-Seek significantly outperforms a commercial optimization algorithm.Ph.D.Applied SciencesComputer scienceMathematicsOperations researchPure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/130571/2/9732168.pd

Anticipatory route guidance using rolling horizon procedures

http://deepblue.lib.umich.edu/bitstream/2027.42/7237/5/ban1324.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/7237/4/ban1324.0001.001.tx

A decentralized approach to system optimal routings in dynamic traffic networks

http://deepblue.lib.umich.edu/bitstream/2027.42/5000/5/ban0203.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/5000/4/ban0203.0001.001.tx

Implementing pure adaptive search for global optimization using Markov chain sampling

The Pure Adaptive Search (PAS) algorithm for global optimization yields a sequence of points, each of which is uniformly distributed in the level set corresponding to its predecessor. This algorithm has the highly desirable property of solving a large class of global optimization problems using a number of iterations that increases at most linearly in the dimension of the problem. Unfortunately, PAS has remained of mostly theoretical interest due to the difficulty of generating, in each iteration, a point uniformly distributed in the improving feasible region. In this article, we derive a coupling equivalence between generating an approximately uniformly distributed point using Markov chain sampling, and generating an exactly uniformly distributed point with a certain probability. This result is used to characterize the complexity of a PAS-implementation as a function of (a) the number of iterations required by PAS to achieve a certain solution quality guarantee, and (b) the complexity of the sampling algorithm used. As an application, we use this equivalence to show that PAS, using the so-called Random ball walk Markov chain sampling method for generating nearly uniform points in a convex region, can be used to solve most convex programming problems in polynomial time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44931/1/10898_2004_Article_336369.pd

Analysis of lawsuits related to diagnostic errors from point-of-care ultrasound in internal medicine, paediatrics, family medicine and critical care in the USA

PURPOSE: The purpose of this study is to identify the extent of diagnostic error lawsuits related to point-of-care ultrasound (POCUS) in internal medicine, paediatrics, family medicine and critical care, of which little is known. METHODS: We conducted a retrospective review of the Westlaw legal database for indexed state and federal lawsuits involving the diagnostic use of POCUS in internal medicine, paediatrics, family medicine and critical care. Retrieved cases were reviewed independently by three physicians to identify cases relevant to our study objective. A lawyer secondarily reviewed any cases with discrepancies between the three reviewers. RESULTS: Our search criteria returned 131 total cases. Ultrasound was mentioned in relation to the lawsuit claim in 70 of the cases returned. In these cases, the majority were formal ultrasounds performed and reviewed by the radiology department, echocardiography studies performed by cardiologists or obstetrical ultrasounds. There were no cases of internal medicine, paediatrics, family medicine or critical care physicians being subjected to adverse legal action for their diagnostic use of POCUS. CONCLUSION: Our results suggest that concerns regarding the potential for lawsuits related to POCUS in the fields of internal medicine, paediatrics, family medicine and critical care are not substantiated by indexed state and federal filed lawsuits

Characterization of Thalamo-cortical Association Using Amplitude and Connectivity of Functional MRI in Mild Traumatic Brain Injury

PurposeTo examine thalamic and cortical injuries using fractional amplitude of low-frequency fluctuations (fALFFs) and functional connectivity MRI (fcMRI) based on resting state (RS) and task-related fMRI in patients with mild traumatic brain injury (MTBI)