Differentiating high-grade gliomas and intracranial metastases through non-invasive imaging has been challenging. Here, we retrospectively compared both intratumoral and peritumoral fractional anisotropy (FA), mean diffusivity (MD), and fluid-attenuated inversion recovery (FLAIR) measurements between high-grade gliomas and metastases. Two methods were utilized to select peritumoral region of interest (ROI). The first method utilized the manual placement of four ROIs adjacent to the lesion. The second method utilized a semiautomated and proprietary MATLAB script to generate an ROI encompassing the entire tumor. The average peritumoral FA, MD, and FLAIR values were determined within the ROIs for both methods. Forty patients with high-grade gliomas and 44 with metastases were enrolled in this study. Thirty-five patients with high-grade glioma and 30 patients with metastases had FLAIR images. There was no significant difference in age, gender, or race between the two patient groups. The high-grade gliomas had a significantly higher tumor-to-brain area ratio compared to the metastases. There were no differences in average intratumoral FA, MD, and FLAIR values between the two groups. Both the manual sample method and the semiautomated peritumoral ring method resulted in significantly higher peritumoral FA and significantly lower peritumoral MD in high-grade gliomas compared to metastases (p < 0.05). No significant difference was found in FLAIR values between the two groups peritumorally. Receiver operating curve analysis revealed FA to be a more sensitive and specific metric to differentiate high-grade gliomas and metastases than MD. The differences in the peritumoral FA and MD values between high-grade gliomas and metastases seemed due to the infiltration of glioma to the surrounding brain parenchyma.

Implanted gradient index lenses have extended the reach of standard multiphoton microscopy from the upper layers of the mouse cortex to the lower cortical layers and even subcortical regions. These lenses have the clarity to visualize dynamic activities, such as calcium transients, with subcellular and millisecond resolution and the stability to facilitate repeated imaging over weeks and months. In addition, behavioral tests can be used to correlate performance with observed changes in network function and structure that occur over time. Yet, this raises the questions, does an implanted microlens have an effect on behavioral tests, and if so, what is the extent of the effect? To answer these questions, we compared the performance of three groups of mice in three common behavioral tests. A gradient index lens was implanted in the prefrontal cortex of experimental mice. We compared their performance with mice that had either a cranial window or a sham surgery. Three presurgical and five postsurgical sets of behavioral tests were performed over seven weeks. Behavioral tests included rotarod, foot fault, and Morris water maze. No significant differences were found between the three groups, suggesting that microlens implantation did not affect performance. The results for the current study clear the way for combining behavioral studies with gradient index lens imaging in the prefrontal cortex, and potentially other regions of the mouse brain, to study structural, functional, and behavioral relationships in the brain.

Stretch-attend posture (SAP) occurs during risk assessment and is prevalent in common rodent behavioral tests. Measuring this behavior can enhance behavioral tests. For example, stretch-attend posture is a more sensitive measure of the effects of anxiolytics than traditional spatiotemporal indices. However, quantifying stretch-attend posture using human observers is time consuming, somewhat subjective, and prone to errors. We have developed MATLAB-based software, MATSAP, which is a quick, consistent, and open source program that provides objective automated analysis of stretch-attend posture in rodent behavioral experiments. Unlike human observers, MATSAP is not susceptible to fatigue or subjectivity. We assessed MATSAP performance with videos of male Swiss mice moving in an open field box and in an elevated plus maze. MATSAP reliably detected stretch-attend posture on par with human observers. This freely-available program can be broadly used by biologists and psychologists to accelerate neurological, pharmacological, and behavioral studies.

Stretch-attend posture (SAP) occurs during risk assessment and is prevalent in common rodent behavioral tests. Measuring this behavior can enhance behavioral tests. For example, stretch-attend posture is a more sensitive measure of the effects of anxiolytics than traditional spatiotemporal indices. However, quantifying stretch-attend posture using human observers is time consuming, somewhat subjective, and prone to errors. We have developed MATLAB-based software, MATSAP, which is a quick, consistent, and open source program that provides objective automated analysis of stretchattend posture in rodent behavioral experiments. Unlike human observers, MATSAP is not susceptible to fatigue or subjectivity. We assessed MATSAP performance with videos of male Swiss mice moving in an open field box and in an elevated plus maze. MATSAP reliably detected stretch-attend posture on par with human observers. This freely-available program can be broadly used by biologists and psychologists to accelerate neurological, pharmacological, and behavioral studies.Rodent behavioral analysis is often used to assess the effects of pharmaceuticals, implanted devices, or surgical procedures in preclinical research. The development of image analysis tools has enabled researchers to quantitatively assess various rodent behaviors quickly and objectively 1,2 . However, most automated scoring programs track patterns in spatial locomotor exploration and neglect ethological behaviors, such as head dipping and stretch-attend posture (SAP) 1 . Currently, there is no accurate tracking and scoring software that can directly detect SAP 3 . SAP, which is generally associated with anxiety, occurs when the rodent lowers its back, elongates its body, and is either standing still or moving forward very slowly 4 . SAP is a naturally occurring behavior found in rodents, such as mice, etc. that can be reliably intensified by certain experimental paradigms, such as placing the rodent in an open-field test 4,5 . In mice, the SAP behavior occurs when the mouse is undergoing risk-assessment specifically due to an internal exploratory-anxiety conflict. It can also occur under fearful risk-assessment where SAP would be an ambivalent element reflecting an approach-avoidance tendency 4,6 . When SAP is present during a passive avoidance situation, mice approach or avoid the object at nearly equal rates, which indicates they are undergoing risk-assessment during an approach-avoidance conflict 5,6 . SAP is a good identifier for conflict behavior in mice and can be used to evaluate the effects of drugs at reducing these internal conflicts 7 . During exploratory-anxiety conflict situations, SAP can be used as a valid measure of anxiety as anxiolytic drugs have successfully reduced SAP 5,7-9 .SAP has been evaluated in elevated plus maze (EPM) 10 , open field (OF) 11 , rat exposure test 12 , and canopy stretch attend posture test 8 . Increased SAP behavior of rodents near the entrance of the open arms in EPM and along the border of the canopy in the canopy stretch attend po...

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The Semantic Web uses a data model called a triple, which consists of a subject-predicate-object structure. When represented as triples, geospatial data require a spatial relation term to serve as the predicate linking two spatial features. This document summarizes the approaches and procedures used during the identification of spatial relationships common between topographic features using terms from topographic data standards. This project identified verb-predicate arguments that could be used in the creation of data triples and ontologies for The National Map of the U.S. Geological Survey and also investigated the possibility of deriving ontology from predefined textual definitions. The primary purpose of this report is to present the data used for subsequent analysis. A summary of terms organized by basic categories is provided.

BackgroundFollowing mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction. Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy.Methods16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation.ResultsGroup comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity.ConclusionsOur multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.