Open Access

The study of multiple indices of diffusion, including axial (DA), radial (DR) and mean diffusion (MD), as well as fractional anisotropy (FA), enables WM damage in Alzheimer's disease (AD) to be assessed in detail. Here, tract-based spatial statistics (TBSS) were performed on scans of 40 healthy elders, 19 non-amnestic MCI (MCIna) subjects, 14 amnestic MCI (MCIa) subjects and 9 AD patients. Significantly higher DA was found in MCIna subjects compared to healthy elders in the right posterior cingulum/precuneus. Significantly higher DA was also found in MCIa subjects compared to healthy elders in the left prefrontal cortex, particularly in the forceps minor and uncinate fasciculus. In the MCIa versus MCIna comparison, significantly higher DA was found in large areas of the left prefrontal cortex. For AD patients, the overlap of FA and DR changes and the overlap of FA and MD changes were seen in temporal, parietal and frontal lobes, as well as the corpus callosum and fornix. Analysis of differences between the AD versus MCIna, and AD versus MCIa contrasts, highlighted regions that are increasingly compromised in more severe disease stages. Microstructural damage independent of gross tissue loss was widespread in later disease stages. Our findings suggest a scheme where WM damage begins in the core memory network of the temporal lobe, cingulum and prefrontal regions, and spreads beyond these regions in later stages. DA and MD indices were most sensitive at detecting early changes in MCIa.

Family members provide most of the patient care and administer most of the treatments to patients with Alzheimer’s disease (AD). Family caregivers have an important impact on clinical outcomes, such as quality of life (QoL). As a consequence of this service, family caregivers suffer high rates of psychological and physical illness as well as social and financial burdens. Hence, it is important to involve family caregivers in multimodal treatment settings and provide interventions that are both suitable and specifically tailored to their needs. In recent years, several clinical guidelines have been presented worldwide for evidence-based treatment of AD and other forms of dementia. Most of these guidelines have considered family advice as integral to the optimal clinical management of AD. This article reviews current and internationally relevant guidelines with emphasis on recommendations concerning family advice.

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

Introduction Complex psychopathological and behavioral symptoms, such as delusions and aggression against care providers, are often the primary cause of acute hospital admissions of elderly patients to emergency units and psychiatric departments. This issue resembles an interdisciplinary clinically highly relevant diagnostic and therapeutic challenge across many medical subjects and general practice. At least 50% of the dramatically growing number of patients with dementia exerts aggressive and agitated symptoms during the course of clinical progression, particularly at moderate clinical severity. Methods Commonly used rating scales for agitation and aggression are reviewed and discussed. Furthermore, we focus in this article on benefits and limitations of all available data of anticonvulsants published in this specific indication, such as valproate, carbamazepine, oxcarbazepine, lamotrigine, gabapentin and topiramate. Results To date, most positive and robust data are available for carbamazepine, however, pharmacokinetic interactions with secondary enzyme induction limit its use. Controlled data of valproate do not seem to support the use in this population. For oxcarbazepine only one controlled but negative trial is available. Positive small series and case reports have been reported for lamotrigine, gabapentin and topiramate. Conclusions So far, data of anticonvulsants in demented patients with behavioral disturbances are not convincing. Controlled clinical trials using specific, valid and psychometrically sound instruments of newer anticonvulsants with a better tolerability profile are mandatory to verify whether they can contribute as treatment option in this indication.

The E4 allele of the ApoE gene has consistently been shown to be related to an increased risk of Alzheimer's disease (AD). The E4 allele is also associated with functional and structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess volumes of deep grey matter structures of 22 healthy younger ApoE4 carriers and 22 non-carriers (20–38 years). Volumes of the nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen, thalamus and brain stem were calculated by FMRIB's Integrated Registration and Segmentation Tool (FIRST) algorithm. A significant drop in volume was found in the right hippocampus of ApoE4 carriers (ApoE4+) relative to non-carriers (ApoE4−), while there was a borderline significant decrease in the volume of the left hippocampus of ApoE4 carriers. The volumes of no other structures were found to be significantly affected by genotype. Atrophy has been found to be a sensitive marker of neurodegenerative changes, and our results show that within a healthy young population, the presence of the ApoE4+ carrier gene leads to volume reduction in a structure that is vitally important for memory formation. Our results suggest that the hippocampus may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age. Although volume reductions were noted bilaterally in the hippocampus, atrophy was more pronounced in the right hippocampus. This finding relates to previous work which has noted a compensatory increase in right hemisphere activity in ApoE4 carriers in response to preclinical declines in memory function. Possession of the ApoE4 allele may lead to greater predilection for right hemisphere atrophy even in healthy young subjects in their twenties.