Identifying brain changes related to cognitive aging using VBM and visual rating scales

Aging is often associated with changes in brain structures as well as in cognitive functions. Structural changes can be visualized with Magnetic Resonance Imaging (MRI) using voxel-based grey matter morphometry (VBM) and visual rating scales to assess atrophy level. Several MRI studies have shown that possible neural correlates of cognitive changes can be seen in normal aging. It is still not fully understood how cognitive function as measured by tests and demographic factors are related to brain changes in the MRI. We recruited 55 healthy elderly subjects aged 50–79 years. A battery of cognitive tests was administered to all subjects prior to MRI scanning. Our aim was to assess correlations between age, sex, education, cognitive test performance, and the said two MRI-based measures. Our results show significant differences in VBM grey matter volume for education level (≤ 12 vs. > 12 years), with a smaller amount of grey matter volume in subjects with lower educational levels, and for age in interaction with education, indicating larger grey matter volume for young, higher educated adults. Also, grey matter volume was found to be correlated with working memory function (Digit Span Backward). Furthermore, significant positive correlations were found between visual ratings and both age and education, showing larger atrophy levels with increasing age and decreasing level of education. These findings provide supportive evidence that MRI-VBM detects structural differences for education level, and correlates with educational level and age, and working memory task performance.</p

Hierarchical neural learning for object recognition

We present a neural-based learning system for object recognition in still gray-scale images, The system comprises several hierarchical levels of increasing complexity modeling the feed-forward path of the ventral stream in the visual cortex, The system learns typical shape patterns of objects as these appear in images from experience alone without any prior labeling. Ascending in the hierarchy, spatial information about the exact origin of parts of the stimulus is systematically discarded while the shape-related object identity information is preserved, resulting in strong compression of the original image data. On the highest level of the hierarchy, the decision on the class of an object is taken by a linear classifier depending solely on the object's shape, We train the system and the classifier on a publicly available natural image data set to test the learning capability and the influence of system parameters. The neural system performs respectably when recognizing objects in novel images

Learning Eye Vergence Control from a Distributed Disparity Representation

We present two neural models for vergence angle control of a robotic head, a simplified and a more complex one. Both models work in a closed-loop manner and do not rely on explicitly computed disparity, but extract the desired vergence angle from the post-processed response of a population of disparity tuned complex cells, the actual gaze direction and the actual vergence angle. The first model assumes that the gaze direction of the robotic head is orthogonal to its baseline and the stimulus is a frontoparallel plane orthogonal to the gaze direction. The second model goes beyond these assumptions, and operates reliably in the general case where all restrictions on the orientation of the gaze, as well as the stimulus position, type and orientation, are dropped

Gaining insight in domestic violence with emergent self organizing maps

Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the past years, new types of Self Organizing Maps (SOM) were introduced in the literature, including the recent Emergent SOM. The ESOM tool is used here to analyze a large set of police reports describing a wide range of violent incidents that occurred during the year 2007 in the Amsterdam-Amstelland police region (the Netherlands). This article aims to demonstrate that the ESOM tool provides a valuable exploratory instrument for examining unstructured text in police reports. First, it is shown how ESOM was used to discover a range of new features that better distinguish domestic from non-domestic violence cases. Second, it is demonstrated how this resulted in a significant improvement in classification accuracy. Third, the ESOM tool facilitates an in-depth investigation of the nature and scope of domestic violence, which is particularly useful for the domain expert. Interestingly, it was discovered that the definition of domestic violence employed by the management was much broader than the definition employed by police officers. Fourth, the ESOM tool enables an accurate and automated assignment of either a domestic or a non-domestic violence label to unclassified cases. Finally, ESOM is a highly accurate and comprehensible case triage model for detecting and retrieving wrongly classified cases