Assessment of Alzheimer's disease risk with functional magnetic resonance imaging: an arterial spin labeling study.

Functional magnetic resonance imaging (fMRI) of older adults at risk for Alzheimer's disease (AD) by virtue of their cognitive (i.e., mild cognitive impairment [MCI]) and/or genetic (i.e., apolipoprotein E [APOE] ε4 allele) status demonstrate divergent brain response patterns during memory encoding across studies. Using arterial spin labeling MRI, we examined the influence of AD risk on resting cerebral blood flow (CBF) as well as the CBF and blood oxygenation level dependent (BOLD) signal response to memory encoding in the medial temporal lobes (MTL) in 45 older adults (29 cognitively normal [14 APOE ε4 carriers and 15 noncarriers]; 16 MCI [8 APOE ε4 carriers, 8 noncarriers]). Risk groups were comparable in terms of mean age, years of education, gender distribution, and vascular risk burden. Individuals at genetic risk for AD by virtue of the APOE ε4 allele demonstrated increased MTL resting state CBF relative to ε4 noncarriers, whereas individuals characterized as MCI showed decreased MTL resting state CBF relative to their cognitively normal peers. For percent change CBF, there was a trend toward a cognitive status by genotype interaction. In the cognitively normal group, there was no difference in percent change CBF based on APOE genotype. In contrast, in the MCI group, APOE ε4 carriers demonstrated significantly greater percent change in CBF relative to ε4 noncarriers. No group differences were found for BOLD response. Findings suggest that abnormal resting state CBF and CBF response to memory encoding may be early indicators of brain dysfunction in individuals at risk for developing AD.

Identification of brain regions responsible for Alzheimer's disease using a Self-adaptive Resource Allocation Network.

In this paper, we present a novel approach for the identification of brain regions responsible for Alzheimer's disease using the Magnetic Resonance (MR) images. The approach incorporates the recently developed Self-adaptive Resource Allocation Network (SRAN) for Alzheimer's disease classification using voxel-based morphometric features of MR images. SRAN classifier uses a sequential learning algorithm, employing self-adaptive thresholds to select the appropriate training samples and discard redundant samples to prevent over-training. These selected training samples are then used to evolve the network architecture efficiently. Since, the number of features extracted from the MR images is large, a feature selection scheme (to reduce the number of features needed) using an Integer-Coded Genetic Algorithm (ICGA) in conjunction with the SRAN classifier (referred to here as the ICGA-SRAN classifier) have been developed. In this study, different healthy/Alzheimer's disease patient's MR images from the Open Access Series of Imaging Studies data set have been used for the performance evaluation of the proposed ICGA-SRAN classifier. We have also compared the results of the ICGA-SRAN classifier with the well-known Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. The study results clearly show that the ICGA-SRAN classifier produces a better generalization performance with a smaller number of features, lower misclassification rate and a compact network. The ICGA-SRAN selected features clearly indicate that the variations in the gray matter volume in the parahippocampal gyrus and amygdala brain regions may be good indicators of the onset of Alzheimer's disease in normal persons.

Temporal horn index and volume of medial temporal lobe atrophy using a new semiautomated method for rapid and precise assessment.

BACKGROUND AND PURPOSE: Quantitative markers of Alzheimer disease (AD), particularly in the early stages, are needed for clinical assessment and monitoring. We have evaluated a novel method to segment and visualize the ventricular system and obtain volumetric measures thereof. The temporal horn volume (THV) and index in patients with mild cognitive impairment (MCI) and in those with AD were evaluated. METHODS: High-resolution T1-weighted volume imaging was performed in 52 subjects (21 patients with MCI, 10 with AD, and 21 healthy control subjects). An interactive watershed transformation and semiautomated histogram analysis were implemented to produce segmented THV and temporal horn indices (THI) (ratio of THV to lateral ventricular volume). RESULTS: Cerebral ventricular and temporal horn size could be semiautomatically quantified from all 52 datasets. The method was fast and rater-independent. Qualitative ventricular inspections using surface rendering shading could uncover atrophic process with enlargement of the whole and especially temporal horn volume. Both THV and THI of patients with AD were significantly larger than those of patients with MCI or control subjects (P < .005). There was no significant difference in THV and THI between patients with MCI or control subjects (P > .05). There was a significant correlation between the neuropsychologic performance and both THI and THV across groups (P < .01). CONCLUSION: THV and THI could be used as markers of AD in the clinical environment and are expected to be helpful in monitoring therapeutic intervention.

Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas.

OBJECTIVES: To develop an automated imaging assessment tool that accommodates the anatomic variability of the elderly and demented population as well as the registration errors occurring during spatial normalization. METHODS: 20 subjects with Alzheimer's disease (AD), mild cognitive impairment, or normal cognition underwent MRI brain imaging and had their 3D volumetric datasets manually partitioned into 68 regions of interest (ROI) termed sub-volumes. Gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) voxel counts were then made in the subject's native space for comparison against automated volumetric measures within three sub-volume probabilistic atlas (SVPA) models. The three SVPAs were constructed using 12 parameter affine (12 p), 2nd order (2nd), and 6th order (6th) transforms derived from registering the manually partitioned scans into a Talairach compatible AD population-based target. The three SVPA automated measures were compared to the manually derived measures in the 20 subjects' native space with a "jack-knife" procedure in which each subject was assessed by an SVPA they did not contribute toward constructing. RESULTS: The mean left and right GM ratio (GM ratio = [GM + CSF] / CSF) "r values" for the 3 SVPAs compared to the manually derived ratios across the 68 ROIs were 0.85 for the 12p SVPA, 0.88 for the 2nd SVPA, and 0.89 for the 6th SVPA. The mean left and right WM ratio (WM ratio = [WM + CSF] / CSF) "r values" for the 3 SVPAs being 0.84 for the 12p SVPA, 0.86 for the 2nd SVPA, and 0.88 for the 6th SVPA. CONCLUSION: We have constructed, from an elderly and demented cohort, an automated brain volumetric tool that has excellent accuracy compared to a manual gold standard and is capable of regional hypothesis testing and individual patient assessment compared to a population.