Impact of APOE-ɛ4 and family history of dementia on gray matter atrophy in cognitively healthy middle-aged adults.

The apolipoprotein E ε4 allele (APOE4) and family history of dementia (FH) are well-known risk factors for the development of sporadic Alzheimer's disease. We assessed the effects of these risk factors on gray matter (GM) volume in 295 cognitively healthy middle-aged community-dwelling subjects. Voxel-based morphometry was used to study GM volume differences between high- and low-risk subjects, based on APOE4 carriership (n = 74), first-degree FH (n = 228), or both (n = 62). No significant results were found using a corrected p value. Using a more lenient threshold (p < 0.001 and minimum cluster size of 100 voxels), APOE4 carriers had reduced GM in the striatum compared to noncarriers. Subjects with FH had reduced GM in right precuneus compared to subjects without FH. Maternal and paternal FH provided similar atrophy patterns. APOE4 carriers with FH had GM reductions in bilateral insula compared to subjects with neither APOE4 nor FH. We conclude that a family history of dementia and APOE4 carriership are both associated with regional GM decreases in cognitively healthy middle-aged subjects, with differential effects on brain regions typically affected in Alzheimer's disease.

On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis.

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.