Branching Pattern of the Cerebral Arterial Tree

Quantitative data on branching patterns of the human cerebral arterial tree are lacking in the 1.0–0.1 mm radius range. We aimed to collect quantitative data in this range, and to study if the cerebral artery tree complies with the principle of minimal work (Law of Murray). To enable easy quantification of branching patterns a semi-automatic method was employed to measure 1,294 bifurcations and 2,031 segments on 7 T-MRI scans of two corrosion casts embedded in a gel. Additionally, to measure segments with a radius smaller than 0.1 mm, 9.4 T-MRI was used on a small cast section to characterize 1,147 bifurcations and 1,150 segments. Besides MRI, traditional methods were employed. Seven hundred thirty-three bifurcations were manually measured on a corrosion cast and 1,808 bifurcations and 1,799 segment lengths were manually measured on a fresh dissected cerebral arterial tree. Data showed a large variation in branching pattern parameters (asymmetry-ratio, area-ratio, length-radius-ratio, tapering). Part of the variation may be explained by the variation in measurement techniques, number of measurements and location of measurement in the vascular tree. This study confirms that the cerebral arterial tree complies with the principle of minimum work. These data are essential in the future development of more accurate mathematical blood flow models. Anat Rec, 302:1434–1446, 2019. © 2018 The Authors. The Anatomical Record published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists.     

The effects of varying levels of hemispheric activation on episodic memory

Bilateral saccadic eye movements enhance episodic memory retrieval; however, this usually only occurs for consistent-handed, not inconsistent-handed, individuals. It was hypothesized that inconsistent-handers begin closer to the peak of a Yerkes–Dodson-type inverted-U curve and increasing activation pushes them along the curve eventually decreasing performance, while consistent-handers start at a lower baseline and therefore increasing activation increases their performance. The current study tested this hypothesis by using hand clenching (grip strength) to increase activation at 5 different levels for both consistent- and inconsistent-handers. A total of 316 participants were presented with a list of 36 words after which they squeezed a hand dynamometer to induce cortical activation, and then recalled as many of the words as they could. Results showed that, as predicted, both inconsistent- and consistent-handers demonstrated an inverted-U pattern of memory performance as the strength of squeeze increased with inconsistent-handers peaking at a lower level of grip strength than consistent-handers. These results may help explain past findings, not only with episodic memory but also a variety of other cognitive tasks. They may also have interesting theoretical and real-world implications, which are discussed.     

Computer-Based Identification of Type 2 Diabetic Subjects with and Without Neuropathy Using Dynamic Planter Pressure and Principal Component Analysis

Diabetes is a chronic disease that is characterized by an increased blood glucose level due to insulin resistance. Type 2 diabetes is common in middle aged and old people. In this work, we present a technique to analyze dynamic foot pressures images and classify them into normal, diabetes type 2 with and without neuropathy classes. Plantar pressure images were obtained using the F-Scan (Tekscan, USA) in-shoe measurement system. We used Principal Component Analysis (PCA) and extracted the eigenvalues from different regions of the foot image. The features extracted from region 1 of the foot pressure image, which were found to be clinically significant, were fed into the Fuzzy classifier (Sugeno model) for automatic classification. Our results show that the proposed method is able to identify the unknown class with an accuracy of 93.7%, sensitivity of 100%, and specificity of 83.3%. Moreover, in this work, we have proposed an integrated index using the eigenvalues to differentiate the normal subjects from diabetes with and without neuropathy subjects using just one number. This index will help the clinicians in easy and objective daily screening, and it can also be used as an adjunct tool to cross check their diagnosis.