Effects of sex, age, and apolipoprotein E genotype on hippocampal parenchymal fraction in cognitively normal older adults.

Early detection of Alzheimer's disease (AD) is important for timely interventions and developing new treatments. Hippocampus atrophy is an early biomarker of AD. The hippocampal parenchymal fraction (HPF) is a promising measure of hippocampal structural integrity computed from structural MRI. It is important to characterize the dependence of HPF on covariates such as age and sex in the normal population to enhance its utility as a disease biomarker. We measured the HPF in 4239 structural MRI scans from 340 cognitively normal (CN) subjects aged 59-89 years from the AD Neuroimaging Initiative database, and studied its dependence on age, sex, apolipoprotein E (APOE) genotype, brain hemisphere, intracranial volume (ICV), and education using a linear mixed-effects model. In this CN cohort, HPF was inversely associated with ICV; was greater on the right hemisphere compared to left in both sexes with the degree of right > left asymmetry being slightly more pronounced in men; declined quadratically with age and faster in APOE ϵ4 carriers compared to non-carriers; and was significantly associated with cognitive ability. Consideration of HPF as an AD biomarker should be in conjunction with other subject attributes that are shown in this research to influence HPF levels in CN older individuals.

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.

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.

Stimulation-induced transient changes in neuronal activity, blood flow and N-acetylaspartate content in rat prefrontal cortex: a chemogenetic fMRS-BOLD study.

Brain activation studies in humans have shown the dynamic nature of neuronal N-acetylaspartate (NAA) and N-acetylaspartylglutamate (NAAG) based on changes in their MRS signals in response to stimulation. These studies demonstrated that upon visual stimulation there was a focal increase in cerebral blood flow (CBF) and a decrease in NAA or in the total of NAA and NAAG signals in the visual cortex, and that these changes were reversed upon cessation of stimulation. In the present study we have developed an animal model in order to explore the relationships between brain stimulation, neuronal activity, CBF and NAA. We use "designer receptor exclusively activated by designer drugs" (DREADDs) technology for site-specific neural activation, a local field potential electrophysiological method for measurement of changes in the rate of neuronal activity, functional MRS for measurement of changes in NAA and a blood oxygenation level-dependent (BOLD) MR technique for evaluating changes in CBF. We show that stimulation of the rat prefrontal cortex using DREADDs results in the following: (i) an increase in level of neuronal activity; (ii) an increase in BOLD and (iii) a decrease in the NAA signal. These findings show for the first time the tightly coupled relationships between stimulation, neuron activity, CBF and NAA dynamics in brain, and also provide the first demonstration of the novel inverse stimulation-NAA phenomenon in an animal model.

Hippocampal volume and integrity as predictors of cognitive decline in intact elderly.

The risk of Alzheimer's disease can be predicted by volumetric analyses of MRI data in the medial temporal lobe. The present study compared a volumetric measurement of the hippocampus with a novel measure of hippocampal integrity (HI) derived from the ratio of parenchyma volume over total volume. Participants were cognitively intact and aged 60 years or older at baseline, and were tested twice, roughly 3 years apart. Participants had been recruited for a study on late-life major depression (LLMD) and were evenly split between depressed patients and controls. Linear regression models were applied to the data with a cognitive composite score as the outcome, and HI and volume, together or separately, as predictors. Subsequent cognitive performance was predicted well by models that included an interaction between HI and LLMD status, such that lower HI scores predicted more cognitive decline in depressed patients. More research is needed, but tentative results from this study appear to suggest that the newly introduced measure HI is an effective tool for the purpose of predicting future changes in general cognitive ability, and especially so in individuals with LLMD.

Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy.

INTRODUCTION: We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS: Longitudinal (baseline + 1-year follow-up) MRI scans of 132 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used. A total of 54 subjects did not convert to AD over an average (±SD) follow-up of 5.46 (±1.63) years, whereas 78 converted to AD with an average conversion time of 2.56 (±1.65) years. Annual change in the corpus callosum thickness profile was calculated from the baseline and 1-year follow-up MRI. A logistic regression model with fused lasso regularization for prediction was applied to the annual changes. RESULTS: We found a sex difference. The accuracy of prediction was 84% in females and 61% in males. The discriminating regions of corpus callosum differed between sexes. In females, the genu, rostrum, and posterior body had predictive power, whereas the genu and splenium were relevant in males. DISCUSSION: Annual callosal atrophy predicts MCI-to-AD conversion in females more accurately than in males.

Analysis of the MIRIAD Data Shows Sex Differences in Hippocampal Atrophy Progression.

BACKGROUND: Hippocampus (HC) atrophy is a hallmark of early Alzheimer's disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and cognitive deficits and rates of brain atrophy in early AD dementia. It is important to corroborate these findings on independent datasets. OBJECTIVE: To study temporal rates of HC atrophy over a one-year period in probable AD patients and cognitively normal (CN) subjects by longitudinal MRI scans obtained from the Minimal Interval Resonance Imaging in AD (MIRIAD) database. METHODS: We used a novel algorithm to compute an index of hippocampal (volumetric) integrity (HI) at baseline and one-year follow-up in 43 mild-moderate probable AD patients and 22 CN subjects in MIRIAD. The diagnostic power of longitudinal HI measurement was assessed using a support vector machines (SVM) classifier. RESULTS: The HI was significantly reduced in the AD group (p <  10(-20)). In addition, the annualized percentage rate of reduction in HI was significantly greater in the AD group (p <  10(-13)). Within the AD group, the annual reduction of HI in women was significantly greater than in men (p = 0.008). The accuracy of SVM classification between AD and CN subjects was estimated to be 97% by 10-fold cross-validation. CONCLUSION: In the MIRIAD patients with probable AD, the HC atrophies at a significantly faster rate in women as compared to men. Female sex is a risk factor for faster descent into AD. The HI measure has potential for AD diagnosis, as a biomarker of AD progression and a therapeutic target in clinical trials.

Corpus callosum atrophy rate in mild cognitive impairment and prodromal Alzheimer's disease.

BACKGROUND: Corpus callosum (CC) size and shape have been previously studied in Alzheimer's disease (AD) with the majority of studies having been cross-sectional. Due to the large variance in normal CC morphology, cross-sectional studies are limited in statistical power. Determining individual rates of change requires longitudinal data. Physiological changes are particularly relevant in mild cognitive impairment (MCI), in which CC morphology has not been previously studied longitudinally. OBJECTIVE: To study temporal rates of change in CC morphology in MCI patients over a one-year period, and to determine whether these rates differ between MCI subjects who converted to AD (MCI-C) and those who did not (MCI-NC) over an average (±SD) observation period of 5.4 (±1.6) years. METHODS: We used a novel multi-atlas based algorithm to segment the mid-sagittal cross-sectional area of the CC in longitudinal MRI scans. Rates of change of CC circularity, total area, and five sub-areas were compared between 57 MCI-NC and 81 MCI-C subjects. RESULTS: The CC became less circular (-0.89% per year in MCI-NC, -1.85% per year in MCI-C) with time, with faster decline in MCI-C (p = 0.0002). In females, atrophy rates were higher in MCI-C relative to MCI-NC in total CC area (p = 0.0006), genu/rostrum (p = 0.005), and splenium (0.002). In males, these rates did not differ between groups. CONCLUSION: A greater than normal decline in CC circularity was shown to be an indicator of prodromal AD in MCI subjects. This measure is potentially useful as an imaging biomarker of disease and a therapeutic target in clinical trials.

Application of fused lasso logistic regression to the study of corpus callosum thickness in early Alzheimer's disease.

We propose a fused lasso logistic regression to analyze callosal thickness profiles. The fused lasso regression imposes penalties on both the l1-norm of the model coefficients and their successive differences, and finds only a small number of non-zero coefficients which are locally constant. An iterative method of solving logistic regression with fused lasso regularization is proposed to make this a practical procedure. In this study we analyzed callosal thickness profiles sampled at 100 equal intervals between the rostrum and the splenium. The method was applied to corpora callosa of elderly normal controls (NCs) and patients with very mild or mild Alzheimer's disease (AD) from the Open Access Series of Imaging Studies (OASIS) database. We found specific locations in the genu and splenium of AD patients that are proportionally thinner than those of NCs. Callosal thickness in these regions combined with the Mini Mental State Examination scores differentiated AD from NC with 84% accuracy.

Corpus callosum shape changes in early Alzheimer's disease: an MRI study using the OASIS brain database.

The corpus callosum (CC) is the largest fiber bundle connecting the left and right cerebral hemispheres. It has been a region examined extensively for indications of various pathologies, including Alzheimer's disease (AD). Almost all previous studies of the CC in AD have been concerned with its size, particularly its mid-sagittal cross-sectional area (CCA). In this study, we show that the CC shape, characterized by its circularity (CIR), may be affected more profoundly than its size in early AD. MRI scans (n = 196) were obtained from the publicly available Open Access Series of Imaging Studies database. The CC cross-sectional region on the mid-sagittal section of the brain was automatically segmented using a novel algorithm. The CCA and CIR were compared in 98 normal controls (NC) subjects, 70 patients with very mild AD (AD-VM), and 28 patients with mild AD (AD-M). Statistical analysis of covariance controlling for age and intracranial capacity showed that both the CIR and the CCA were significantly reduced in the AD-VM group relative to the NC group (CIR: p = 0.004; CCA: p = 0.005). However, only the CIR was significantly different between the AD-M and AD-VM groups (p = 0.006) being smaller in the former. The CCA was not significantly different between the AD-M and AD-VM groups. The results suggest that CC shape may be a more sensitive marker than its size for monitoring the progression of AD. In order to facilitate independent analyses, the CC segmentations and the CCA and CIR data used in this study have been made publicly available (http://www.nitrc.org/projects/art).

Corpus callosum shape and size changes in early Alzheimer's disease: a longitudinal MRI study using the OASIS brain database.

BACKGROUND: Alzheimer's disease (AD) has been shown to be associated with shrinkage of the corpus callosum mid-sagittal cross-sectional area (CCA). OBJECTIVE: To study temporal rates of corpus callosum atrophy not previously reported for early AD. METHODS: We used longitudinal MRI scans to study the rates of change of CCA and circularity (CIR), a measure of its shape, in normal controls (NC, n = 75), patients with very mild AD (AD-VM, n = 51), and mild AD (AD-M, n = 21). RESULTS: There were significant reduction rates in CCA and CIR in all three groups. While CCA reduction rates were not statistically different between groups, the CIR declined faster in AD-VM (p < 0.03) and AD-M (p < 0.0001) relative to NC, and in AD-M relative to AD-VM (p < 0.0004). CONCLUSION: CIR declines at an accelerated rate with AD severity. Its rate of change is more closely associated with AD progression than CCA or any of its sub-regions. CIR may be a useful group biomarker for objective assessment of treatments that aim to slow AD progression.

Model-based automatic detection of the anterior and posterior commissures on MRI scans.

The projections of the anterior and posterior commissures (AC/PC) on the mid-sagittal plane of the human brain are important landmarks in neuroimaging. They can be used, for example, during MRI scanning for acquiring the imaging sections in a standard orientation. In post-acquisition image processing, these landmarks serve to establish an anatomically-based frame of reference within the brain that can be extremely useful in designing automated image analysis algorithms such as image segmentation and registration methods. This paper presents a fully automatic model-based algorithm for AC/PC detection on MRI scans. The algorithm utilizes information from a number of model images on which the locations of the AC/PC and a reference point (the vertex of the superior pontine sulcus) are known. This information is then used to locate the landmarks on test scans by template matching. The algorithm is designed to be fast, robust, and accurate. The method is flexible in that it can be trained to work on different image contrasts, optimized for different populations, or scanning modes. To assess the effectiveness of this technique, we compared automatically and manually detected landmark locations on 84 T(1)-weighted and 42 T(2)-weighted test scans. Overall, the average Euclidean distance between automatically and manually detected landmarks was 1.1 mm. A software implementation of the algorithm is freely available online at www.nitrc.org/projects/art.

A new image similarity measure with reduced sensitivity to interpolation and generalizability to multispectral image registration.

Mutual information (MI) has proven to be a useful similarity measure for spatial registration between related pairs of images in various medical imaging applications. Image registration algorithms that utilize the MI assume that the best alignment between a pair of images is reached when their MI is at its maximum. However, this assumption is not always valid because the MI is not only sensitive to dissimilarity between images, but also to the image interpolation operations performed during the optimization process in image registration algorithms. When the images that are being registered are close to their optimum spatial alignment, MI's sensitivity to interpolation may become dominant over its sensitivity to image misalignment, hence limiting the accuracy of the image registration method. In this paper, we present an entropy-based cost function, closely related to MI, that can be made relatively insensitive to interpolation effects, and can be generalized to registration of multispectral images.

Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans.

The objective of inter-subject registration of three-dimensional volumetric brain scans is to reduce the anatomical variability between the images scanned from different individuals. This is a necessary step in many different applications such as voxelwise group analysis of imaging data obtained from different individuals. In this paper, the ability of three different image registration algorithms in reducing inter-subject anatomical variability is quantitatively compared using a set of common high-resolution volumetric magnetic resonance imaging scans from 17 subjects. The algorithms are from the automatic image registration (AIR; version 5), the statistical parametric mapping (SPM99), and the automatic registration toolbox (ART) packages. The latter includes the implementation of a non-linear image registration algorithm, details of which are presented in this paper. The accuracy of registration is quantified in terms of two independent measures: (1) post-registration spatial dispersion of sets of homologous landmarks manually identified on images before or after registration; and (2) voxelwise image standard deviation maps computed within the set of images registered by each algorithm. Both measures showed that the ART algorithm is clearly superior to both AIR and SPM99 in reducing inter-subject anatomical variability. The spatial dispersion measure was found to be more sensitive when the landmarks were placed after image registration. The standard deviation measure was found sensitive to intensity normalization or the method of image interpolation.