Economic Strength and Micropolitan Statistical Areas: Looking Beyond Generalities

Facility location decisions serve an important role in setting up efficient and cost-effective supply chains. While metropolitan areas may appear an obvious choice for many companies, nonmetropolitan areas deserve consideration. In the past, nonmetropolitan areas have been broadly classified as “rural” with reports of economic decline. This research looks beyond the general category of nonmetropolitan by dividing the area into micropolitan statistical areas and non-core statistical areas. The authors use U.S. Census data from the years 2010 to 2016 to analyze changes in population, median household income, retail employment, and retail salaries in Alabama, Georgia, and Mississippi. Companies can use this more refined information approach to help identify specific counties outside metropolitan statistical areas that demonstrate growth and may provide suitable facility locations

A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

<div><p>Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.</p></div

MNI Coordinates for Sparse Logistic Regression Selected Voxels Weights Classifying Chronic Pain and Normal Individuals.

<p>L = Left; BA = Brodmann Area; S1 = Primary Somatosensory Cortex; IPC = Inferior Parietal Cortex.</p><p>*Denotes significance at p<0.05 correcting for multiple comparisons of the number of cross-validation iterations out of 26 a voxel is selected by the sparse logistic regression SLR relative to the distribution of the maximum time a voxel is select by SLR over 1000 permutations of randomly shuffled labels of the subjects in the training set. Note that the five selected weights form three separate clusters of brain regions. The clusters composed of two weights consist of neighboring voxels.</p

Performance of the Sparse Logistic Regression Classifier Chronic Pain versus Normal Group.

<p>*Significant at p<0.05 utilizing 1000 permutation tests of sparse logistic regression SLR classification over randomly shuffled labels of the subjects in the training set. Percent correct was determined by the mean performance of the 26 classifiers trained using the leave one out cross validation method. Measures of sensitivity, specificity, positive predictive value, negative predictive value, posterior mean accuracy, posterior probability interval (p<0.05) and D Prime are also given. Posterior mean accuracy and posterior probability intervals were computed using methods given in Brodersen et al. (2010). The mask of voxels included in the analysis consisted of brain regions composing pain related areas: Primary Somatosensory Cortex, Secondary Somatosensory Cortex, Inferior Parietal Cortex, Insula, and Anterior Cingulate Cortex. The mask consisted of 6686 voxels. After training, 22 of the classifiers selected 3 features and 4 or the classifiers selected 2 features. The mean number of extracted features was 2.85.</p

The three brain regions defined by the voxels selected by the sparse logistic regression located in the primary somatosensory cortex Brodmann Area BA 3 (MNI coordinates −42, −25,58 and −18,−43,61) consisting of negative weights (normal group greater than chronic pain group) and the inferior parietal cortex BA 40 (MNI coordinates −57,−49,25) consisting of positive weights (chronic pain group greater than normal group).

<p>A. Shows the three regions rendered on the surface of the brain. These three regions B. Somatosensory (MNI −42,−25,58), C. Somatosensory (MNI −18,−43,61), D. Inferior parietal cortex (MNI −57,−49,25) rendered on surface of MRI images sagittal, coronal, axial slices with MNI coordinates with an 8 mm sphere from center coordinate.</p