A six-month longitudinal evaluation significantly improves accuracy of predicting incipient Alzheimer's disease in mild cognitive impairment.

RATIONALE AND OBJECTIVES: Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD. MATERIALS AND METHODS: Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change. RESULTS: The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%). CONCLUSION: Short-term longitudinal assessments significantly enhance the performance of AD prediction models.

Global and multi-focal changes in cerebral blood flow during subthalamic nucleus stimulation in Parkinson's disease.

Electrical stimulation of subthalamic nuclei (STN) is a widely used therapy in Parkinson's disease (PD). While deep brain stimulation (DBS) of the STN alters the neurophysiological activity in basal ganglia, the therapeutic mechanism has not been established. A positron emission tomography (PET) study of cerebral blood flow (CBF) during speech production in PD subjects treated with STN-DBS found significant increases in global (whole-brain) CBF.1 That study utilized a series of whole-slice regions of interest to obtain global CBF values. The present study examined this effect using a voxel-based principal component analysis (PCA) combined with Fisher's linear discriminant analysis (FLDA) to classify STN-DBS on versus STN-DBS off whole-brain images. The approach yielded wide-spread CBF changes that classified STN-DBS status with accuracy, sensitivity, and specificity approaching 90%. The PCA component of the analysis supported the observation of a global CBF change during STN-DBS. The FLDA component demonstrated wide-spread multi-focal CBF changes. Further, CBF measurements related to a number of subject characteristics when STN-DBS was off, but not when it was on, suggesting that the normal relationship between CBF and behavior may be disrupted by this form of neuromodulation.

Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment.

BACKGROUND: Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose. OBJECTIVE: To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm. METHODS: We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD. RESULTS: The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI. CONCLUSION: The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.