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The term 'mirror sign' refers to the inability to recognize the reflection of oneself in a mirror, while the ability to recognize others' faces often remains intact. In this article, we present a case of an 85-year-old woman, with probable Lewy body dementia, who stably exhibited a delusional 'mirror sign' for a period of 9 months. Following a straightforward, ecological, non-pharmacological intervention, her 'mirror sign' delusion was no longer present. [ABSTRACT FROM AUTHOR]

We conducted a cross-sectional study to investigate the clinical and anatomical correlates of gait dysfunction in advanced Alzheimer's disease (AD). A comprehensive clinical protocol that included cognitive, functional, behavioral, and motor variables was administered to patients with probable AD (n = 100), possible AD (n = 17), and AD with cerebrovascular disease (AD + CVD) (n = 27). Gait dysfunction was evaluated with the Rating Scale for Gait Evaluation in Cognitive Deterioration and magnetic resonance imaging was analyzed in 94 patients (volumetry study) and 78 patients (diffusion tensor imaging study). Univariate correlations, multivariate regression, and statistical parametric mapping analyses were conducted in the total sample and in the subsample of patients with probable AD. Mean age was 82.5 (SD 6.3, range 56 to 98), 83.3% were female patients, and 95.1% displayed moderate to severe dementia. Parkinsonism, patient setting (nursing home), dementia severity, apathy, and (worse) cognitive performance significantly predicted gait dysfunction in the total sample (p < 0.05, R2 = 0.58), whereas parkinsonism, patient setting, and limb weakness due to non-AD conditions predicted gait dysfunction in probable AD (p < 0.05, R2 = 0.57). Gait dysfunction was related to atrophy in the motor cortex, the middle cingulate, the anterior insula, the right caudate (total sample only), and the anterior lobe of the cerebellum (p < 0.01, corrected). Significant correlations were also observed between gait dysfunction and damage in several white matter locations (p < 0.001, uncorrected). The present results are congruent with a model of multi-system gray matter degeneration, with progressive damage to critical regions (i.e., motor cortex, cingulate, insula, and cerebellum) producing gait dysfunction and, eventually, gait loss in AD. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to an article about aceruloplasminemia and multiple motor system dysfunction of a 33 year-old woman by Juan Bernar in the 2007 issue.

Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological, biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of the normal ageing. Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and connections. The resulting graph-based structures are often very complex; there can be many kinds of connections between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study. This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different phenotypes in dementia, in particular, different stages of Alzheimer's disease.

Objective: To show the results of a device that generates automated olfactory stimuli suitable for functional magnetic resonance imaging (fMRI) experiments., Material and Methods: Ten normal volunteers, 5 women and 5 men, were studied. The system allows the programming of several sequences, providing the capability to synchronise the onset of odour presentation with acquisition by a trigger signal of the MRI scanner. The olfactometer is a device that allows selection of the odour, the event paradigm, the time of stimuli and the odour concentration. The paradigm used during fMRI scanning consisted of 15-s blocks. The odorant event took 2s with butanol, mint and coffee., Results: We observed olfactory activity in the olfactory bulb, entorhinal cortex (4%), amygdala (2.5%) and temporo-parietal cortex, especially in the areas related to emotional integration., Conclusions: The device has demonstrated its effectiveness in stimulating olfactory areas and its capacity to adapt to fMRI equipment., (Copyright © 2010 Elsevier España, S.L. All rights reserved.)