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.

Objective: To investigate the population-based interaction between a biological variable (APOE e4), neuropsychiatric symptoms, and the risk of incident dementia among subjects with prevalent mild cognitive impairment (MCI). Methods:We prospectively followed 332 participants with prevalent MCI (aged 70 years and older) enrolled in the Mayo Clinic Study of Aging for a median of 3 years. The diagnoses of MCI and dementia were made by an expert consensus panel based on published criteria, after reviewing neurologic, cognitive, and other pertinent data. Neuropsychiatric symptoms were determined at baseline using the Neuropsychiatric Inventory Questionnaire. We used Cox proportional hazards models, with age as a time scale, to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Models were adjusted for sex, education, and medical comorbidity.Results: Baseline agitation, nighttime behaviors, depression, and apathy significantly increased the risk of incident dementia. We observed additive interactions between APOE e4 and depression (joint effect HR 5 2.21; 95% CI 5 1.24-3.91; test for additive interaction, p , 0.001); and between APOE e4 and apathy (joint effect HR 5 1.93; 95% CI 5 0.93-3.98; test for additive interaction, p 5 0.031). Anxiety, irritability, and appetite/eating were not associated with increased risk of incident dementia.Conclusions: Among prevalent MCI cases, baseline agitation, nighttime behaviors, depression, and apathy elevated the risk of incident dementia. There was a synergistic interaction between depression or apathy and APOE e4 in further elevating the risk of incident dementia. Dementia is one of the leading causes of morbidity and mortality in late life. It presents several challenges, not least of which are the economic consequences.1 Therefore, it is critical to prevent or delay dementia.2 Identification of high-risk groups is a key step toward the prevention of dementia. Mild cognitive impairment (MCI) is the intermediate stage between cognitive aging and dementia and is associated with an increased risk of dementia. Clinic-based samples have indicated that neuropsychiatric symptoms in prevalent MCI increase the risk of incident dementia.4-6 However, only a few studies were derived from population-based settings. 7,8 In addition, studies derived from clinical samples including our own team have reported the synergistic interaction between a neuropsychiatric symptom (e.g., depression) and APOE e4 in increasing the risk of incident dementia. 9-11While APOE e4 and neuropsychiatric symptoms are independent risk factors for incident dementia, little is known about the interaction between APOE e4 and a broad spectrum of neuropsychiatric symptoms in increasing the risk of incident dementia in a population-based

Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical.