Magnetic resonance (MR) based shape analysis provides an opportunity to detect regional specificity of volumetric changes that may distinguish mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy elderly controls (CON), and predict future conversion to AD. We assessed the surface deformation of seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, body and temporal horn of the lateral ventricles) in 383 MRI volumes, based on data shared through the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI), to identify regionally-specific shape abnormalities in MCI and AD. Large deformation diffeomorphic metric mapping (LDDMM) was used to generate the shapes of seven structures based on template shapes injected into segmented subcortical volumes. LDDMM then constructed the surface deformation maps encoding the local shape variation of each subject relative to the template. Hierarchical models were developed to detect differences in local shape in MCI and AD relative to CON. Our findings revealed that surface inward-deformation in MCI and AD is most prominent in the anterior hippocampal segment and the basolateral complex of the amygdala. Most pronounced surface outward-deformation in MCI and AD occurs in the lateral ventricles. Mild surface inward-deformation in MCI and AD occurs in the anterior-lateral and ventral-lateral aspects of the thalamus, with no evidence of regionally-specific deformation in the putamen or globus pallidus. Although the locations of the shape abnormalities in MCI and AD are primarily within the mesial temporal region, analyses support distinct components of correlated shape variation that may help predict future MCI conversion.

The choroid plexus (ChP) is a major source of cerebrospinal fluid (CSF) production, with a direct and indirect role in protein clearance, and pathogenesis of Alzheimer's disease (AD). Here, we tested the link between the ChP volume and levels of CSF proteins in 2 data sets of (i) healthy controls, mild cognitive impairment (MCI), and AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 509), and (ii) healthy controls and Parkinson's disease (PD) patients from the Parkinson's Progression Markers Initiative (N = 302). All patients had baseline CSF proteins (amyloid-β, total and phosphorylated-tau and α-synuclein (only in Parkinson's Progression Markers Initiative)). ChP was automatically segmented on 3T structural T1-weighted MRIs. We found negative associations between ChP volume and CSF proteins, which were stronger in healthy controls, early-MCI patients, and PD patients compared with late-MCI and AD patients. Further grouping of patients of ADNI dataset into amyloid-positive and amyloid-negative based on their florbetapir (AV45) PET imaging showed that the association between ChP volume and CSF proteins (t/p-tau) was lower in amyloid-positive group. Our findings support the possible role of ChP in the clearance of CSF proteins, provide evidence for ChP dysfunction in AD, and suggest the need to account for the ChP volume in future studies of CSF-based biomarkers.

Ideal biomarkers of Alzheimer's disease (AD) should correlate with accepted measures of pathology in the cerebrospinal fluid (CSF); they should also correlate with, or predict, future clinical decline, and should be readily measured in hundreds to thousands of subjects. Here we explored the utility of automated 3D maps of the lateral ventricles as a possible biomarker of AD. We used our multi-atlas fluid image alignment (MAFIA) method, to compute ventricular models automatically, without user intervention, from 804 brain MRI scans with 184 AD, 391 mild cognitive impairment (MCI), and 229 healthy elderly controls (446 men, 338 women; age: 75.50 +/- 6.81 [SD] years). Radial expansion of the ventricles, computed pointwise, was strongly correlated with current cognition, depression ratings, Hachinski Ischemic scores, language scores, and with future clinical decline after controlling for any effects of age, gender, and educational level. In statistical maps ranked by effect sizes, ventricular differences were highly correlated with CSF measures of Abeta(1-42), and correlated with ApoE4 genotype. These statistical maps are highly automated, and offer a promising biomarker of AD for large-scale studies.

We proposed a diffeomorphometry-based statistical pipeline to study the regional shape change rates of the bilateral hippocampus, amygdala, and ventricle in mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared with healthy controls (HC), using sequential magnetic resonance imaging (MRI) scans of 713 subjects (3,123 scans in total). The subgroup shape atrophy rates of the bilateral hippocampus and amygdala, as well as the expansion rates of the bilateral ventricles, for a majority of vertices were found to follow the order of AD>MCI>HC. The bilateral hippocampus and the left amygdala were subsegmented into multiple functionally meaningful subregions with the help of high-field MRI scans. The largest group differences in localized shape atrophy rates on the hippocampus were found to occur in CA1, followed by subiculum, CA2, and finally CA3/dentate gyrus, which is consistent with the neurofibrillary tangle accumulation trajectory. Highly nonuniform group differences were detected on the amygdala; vertices on the core amygdala (basolateral and lateral nucleus) revealed much larger atrophy rates, whereas those on the noncore amygdala (mainly centromedial) displayed similar or even smaller atrophy rates in AD relative to HC. The temporal horns of the ventricles were observed to have the largest localized ventricular expansion rate differences; with the AD group showing larger localized expansion rates on the anterior horn and the body part of the ventricles as well. Significant correlations were observed between the localized shape change rates of each of these six structures and the cognitive deterioration rates as quantified by the Alzheimer's Disease Assessment Scale-Cognitive Behavior Section increase rate and the Mini Mental State Examination decrease rate.

This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects.

In a previous report, we proposed a method for combining multiple markers of atrophy caused by Alzheimer's disease into a single atrophy score that is more powerful than any one feature. We applied the method to expansion rates of the lateral ventricles, achieving the most powerful ventricular atrophy measure to date. Here, we expand our method's application to tensor-based morphometry measures. We also combine the volumetric tensor-based morphometry measures with previously computed ventricular surface measures into a combined atrophy score. We show that our atrophy scores are longitudinally unbiased with the intercept bias estimated at 2 orders of magnitude below the mean atrophy of control subjects at 1 year. Both approaches yield the most powerful biomarker of atrophy not only for ventricular measures but also for all published unbiased imaging measures to date. A 2-year trial using our measures requires only 31 (22, 43) Alzheimer's disease subjects or 56 (44, 64) subjects with mild cognitive impairment to detect 25% slowing in atrophy with 80% power and 95% confidence.

In neurodegeneration research, normalization of regional volumes by intracranial volume (ICV) is important to estimate the extent of disease-driven atrophy. There is little agreement as to whether raw volumes, volume-to-ICV fractions or regional volumes from which the ICV factor has been regressed out should be used for volumetric brain imaging studies. Using multiple regional cortical and subcortical volumetric measures generated by Freesurfer (51 in total), the main aim of this study was to elucidate the implications of these adjustment approaches. Magnetic resonance imaging (MRI) data were analyzed from two large cohorts, the population-based PIVUS cohort (N = 406, all subjects age 75) and the Alzheimer disease Neuroimaging Initiative (ADNI) cohort (N = 724). Further, we studied whether the chosen ICV normalization approach influenced the relationship between hippocampus and cognition in the three diagnostic groups of the ADNI cohort (Alzheimer's disease, mild cognitive impairment, and healthy individuals). The ability of raw vs. adjusted hippocampal volumes to predict diagnostic status was also assessed. In both cohorts raw volumes correlate positively with ICV, but do not scale directly proportionally with it. The correlation direction is reversed for all volume-to-ICV fractions, except the lateral and third ventricles. Most gray matter fractions are larger in females, while lateral ventricle fractions are greater in males. Residual correction effectively eliminated the correlation between the regional volumes and ICV and removed gender differences. The association between hippocampal volumes and cognition was not altered by ICV normalization. Comparing prediction of diagnostic status using the different approaches, small but significant differences were found. The choice of normalization approach should be carefully considered when designing a volumetric brain imaging study.

There is considerable heterogeneity in the association between increasing β-amyloid (Aβ) pathology and early cognitive dysfunction in preclinical Alzheimer disease (AD). At this stage, some individuals show no signs of cognitive dysfunction, while others show clear signs of decline. The factors explaining this heterogeneity are particularly important for understanding progression in AD but remain largely unknown. In this study, we examined an array of genetic variants that may influence the relationships among Aβ, brain structure, and cognitive performance in 2 large cohorts.