Cognitive Correlates of Hippocampal Atrophy and Ventricular Enlargement in Adults with or without Mild Cognitive Impairment.

We analyzed structural magnetic resonance imaging data from 58 cognitively normal and 101 mild cognitive impairment subjects. We used a general linear regression model to study the association between cognitive performance with hippocampal atrophy and ventricular enlargement using the radial distance method. Bilateral hippocampal atrophy was associated with baseline and longitudinal memory performance. Left hippocampal atrophy predicted longitudinal decline in visuospatial function. The multidomain ventricular analysis did not reveal any significant predictors.

Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability.

BACKGROUND: Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. METHODS: We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. RESULTS: Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166). CONCLUSIONS: The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.

Associations between hippocampal morphometry and neuropathologic markers of Alzheimer's disease using 7 T MRI.

Hippocampal atrophy, amyloid plaques, and neurofibrillary tangles are established pathologic markers of Alzheimer's disease. We analyzed the temporal lobes of 9 Alzheimer's dementia (AD) and 7 cognitively normal (NC) subjects. Brains were scanned post-mortem at 7 Tesla. We extracted hippocampal volumes and radial distances using automated segmentation techniques. Hippocampal slices were stained for amyloid beta (Aß), tau, and cresyl violet to evaluate neuronal counts. The hippocampal subfields, CA1, CA2, CA3, CA4, and subiculum were manually traced so that the neuronal counts, Aß, and tau burden could be obtained for each region. We used linear regression to detect associations between hippocampal atrophy in 3D, clinical diagnosis and total as well as subfield pathology burden measures. As expected, we found significant correlations between hippocampal radial distance and mean neuronal count, as well as diagnosis. There were subfield specific associations between hippocampal radial distance and tau in CA2, and cresyl violet neuronal counts in CA1 and subiculum. These results provide further validation for the European Alzheimer's Disease Consortium Alzheimer's Disease Neuroimaging Initiative Center Harmonized Hippocampal Segmentation Protocol (HarP).

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

INTRODUCTION: The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD). METHOD: Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables. RESULTS: Classifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone. CONCLUSION: The brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD.

Common variants in ABCA7 and MS4A6A are associated with cortical and hippocampal atrophy.

The precise physiologic function of many of the recently discovered Alzheimer's disease risk variants remains unknown. The downstream effects of genetic variants remain largely unexplored. We studied the relationship between the top 10 non-APOE genes with cortical and hippocampal atrophy as markers of neurodegeneration using 1.5T magnetic resonance imaging, 1-million single nucleotide polymorphism Illumina Human Omni-Quad array and Illumina Human BeadChip peripheral blood expression array data on 50 cognitively normal and 98 mild cognitive impairment subjects. After explicit matching of cortical and hippocampal morphology, we computed in 3D, the cortical thickness and hippocampal radial distance measures for each participant. Associations between the top 10 non-APOE genome-wide hits and neurodegeneration were explored using linear regression. Map-wise statistical significance was determined with permutations using threshold of p < 0.01. MS4A6A rs610932 and ABCA7 rs3764650 demonstrated significant associations with cortical and hippocampal atrophy. Exploratory MS4A6A and ABCA7 peripheral blood expression analyses revealed a similar pattern of associations with cortical neurodegeneration. To our knowledge, this is the first report of the effect of ABCA7 and MS4A6A on neurodegeneration.

Relationship between hippocampal atrophy and neuropathology markers: a 7T MRI validation study of the EADC-ADNI Harmonized Hippocampal Segmentation Protocol.

OBJECTIVE: The pathologic validation of European Alzheimer's Disease Consortium Alzheimer's Disease Neuroimaging Initiative Center Harmonized Hippocampal Segmentation Protocol (HarP). METHODS: Temporal lobes of nine Alzheimer's disease (AD) and seven cognitively normal subjects were scanned post-mortem at 7 Tesla. Hippocampal volumes were obtained with HarP. Six-micrometer-thick hippocampal slices were stained for amyloid beta (Aß), tau, and cresyl violet. Hippocampal subfields were manually traced. Neuronal counts, Aß, and tau burden for each hippocampal subfield were obtained. RESULTS: We found significant correlations between hippocampal volume and Braak and Braak staging (ρ = -0.75, P = .001), tau (ρ = -0.53, P = .034), Aß burden (ρ = -0.61, P = .012), and neuronal count (ρ = 0.77, P < .001). Exploratory subfield-wise significant associations were found for Aß in Cornu Ammonis (CA)1 (ρ = -0.58, P = .019) and subiculum (ρ = -0.75, P = .001), tau in CA2 (ρ = -0.59, P = .016), and CA3 (ρ = -0.5, P = .047), and neuronal count in CA1 (ρ = 0.55, P = .028), CA3 (ρ = 0.65, P = .006), and CA4 (ρ = 0.76, P = .001). CONCLUSIONS: The observed associations provide pathological confirmation of hippocampal morphometry as a valid biomarker for AD and pathologic validation of HarP.

Age effects on cortical thickness in cognitively normal elderly individuals.

BACKGROUND/AIMS: Atrophy in both grey and white matter is found in normal aging. The prefrontal cortex and the frontal lobe white matter are thought to be the most affected regions. Our aim was to examine the effects of normal aging on cortical grey matter using a 3D quantitative cortical mapping method. METHODS: We analyzed 1.5-tesla brain magnetic resonance imaging data from 44 cognitively normal elderly subjects using cortical pattern matching and cortical thickness analyses. Linear regression analysis was used to study the effect of age on cortical thickness. 3D map-wide correction for multiple comparisons was conducted with permutation analyses using a threshold of p < 0.01. RESULTS: We found a significant negative association between age and cortical thickness in the right hemisphere (pcorrected = 0.009) and a trend level association in the left hemisphere (pcorrected = 0.081). Age-related changes were greatest in the sensorimotor, bilateral dorsal anterior cingulate and supplementary motor cortices, and the right posterior middle and inferior frontal gyri. Age effects greater in the medial than lateral visual association cortices were also seen bilaterally. CONCLUSION: Our novel method further validates that normal aging results in diffuse cortical thinning that is most pronounced in the frontal and visual association cortices.