Correlates of Theta and Gamma Activity during Visuospatial Incidental/Intentional Encoding and Retrieval Indicate Differences in Processing in Young and Elderly Healthy Participants.

Incidental visuospatial learning acquired under incidental conditions is more vulnerable to aging than in the intentional case. The theta and gamma correlates of the coding and retrieval of episodic memory change during aging. Based on the vulnerability of incidental coding to aging, different theta and gamma correlates could occur under the incidental versus intentional coding and retrieval of visuospatial information. Theta and gamma EEG was recorded from the frontotemporal regions, and incidental/intentional visuospatial learning was evaluated in young (25-60 years old) and elderly (60-85 years old) participants. The EEG recorded during encoding and retrieval was compared between incidental low-demand, incidental high-demand, and intentional conditions through an ANCOVA considering the patient's gender, IQ, and years of schooling as covariates. Older adults exhibited worse performances, especially in place-object associations. After the intentional study, older participants showed a further increase in false-positive errors. Higher power at the theta and gamma bands was observed for frontotemporal derivations in older participants for both encoding and retrieval. Under retrieval, only young participants had lower power in terms of errors compared with correct responses. In conclusion, the different patterns of power and coherence support incidental and intentional visuospatial encoding and retrieval in young and elderly individuals. The correlates of power with behavior are sensitive to age and performance.

Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis.

Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges. In this paper, a simple method that combines well-known segmentation algorithms is presented. This method is applied to detect acid-fast bacilli (AFB) in bacilloscopies used to diagnose pulmonary tuberculosis (TB). This diagnosis can be performed through different tests, and the most used worldwide is smear microscopy because of its low cost and effectiveness. This diagnosis technique is based on the analysis and counting of the bacilli in the bacilloscopy observed under an optical microscope. The proposed method is used to segment the bacilli in digital images from bacilloscopies processed using Ziehl-Neelsen (ZN) staining. The proposed method is fast, has a low computational cost and good efficiency compared to other methods. The bacilli image segmentation is performed by image processing and analysis techniques, probability concepts and classifiers. In this work, a Bayesian classifier based on a Gaussian mixture model (GMM) is used. The segmentations' results are validated by using the Jaccard index, which indicates the efficiency of the classifier.