Posterior cortical atrophy (PCA) is a neurodegenerative syndrome characterized by predominant visual deficits and parieto-occipital atrophy, and is typically associated with Alzheimer's disease (AD) pathology. In AD, assessment of hippocampal atrophy is widely used in diagnosis, research, and clinical trials; its utility in PCA remains unclear. Given the posterior emphasis of PCA, we hypothesized that hippocampal shape measures may give additional group differentiation information compared with whole-hippocampal volume assessments. We investigated hippocampal volume and shape in subjects with PCA (n = 47), typical AD (n = 29), and controls (n = 48). Hippocampi were outlined on MRI scans and their 3D meshes were generated. We compared hippocampal volume and shape between disease groups. Mean adjusted hippocampal volumes were ∼ 8% smaller in PCA subjects (P < 0.001) and ∼ 22% smaller in tAD subject (P < 0.001) compared with controls. Significant inward deformations in the superior hippocampal tail were observed in PCA compared with controls even after adjustment for hippocampal volume. Inward deformations in large areas of the hippocampus were seen in tAD subjects compared with controls and PCA subjects, but only localized shape differences remained after adjusting for hippocampal volume. The shape differences observed, even allowing for volume differences, suggest that PCA and tAD are each associated with different patterns of hippocampal tissue loss that may contribute to the differential range and extent of episodic memory dysfunction in the two groups.

Hippocampal volumetric measures may be useful for Alzheimer's disease (AD) diagnosis and disease tracking; however, manual segmentation of the hippocampus is labour-intensive. Therefore, automated techniques are necessary for large studies and to make hippocampal measures feasible for clinical use. As large studies and clinical centres are moving from using 1.5 Tesla (T) scanners to higher field strengths it is important to assess whether specific image processing techniques can be used at these field strengths. This study investigated whether an automated hippocampal segmentation technique (HMAPS: hippocampal multi-atlas propagation and segmentation) and volume change measures (BSI: boundary shift integral) were as accurate at 3T as at 1.5T. Eighteen Alzheimer's disease patients and 18 controls with 1.5T and 3T scans at baseline and 12-month follow-up were used from the Alzheimer's Disease Neuroimaging Initiative cohort. Baseline scans were segmented manually and using HMAPS and their similarity was measured by the Jaccard index. BSIs were calculated for serial image pairs. We calculated pair-wise differences between manual and HMAPS rates at 1.5T and 3T and compared the SD of these differences at each field strength. The difference in mean Jaccards (manual and HMAPS) between 1.5T and 3T was small with narrow confidence intervals (CIs) and did not appear to be segmentor dependent. The SDs of the difference between volumes from manual and automated segmentations were similar at 1.5T and 3T, with a relatively narrow CI for their ratios. The SDs of the difference between BSIs from manual and automated segmentations were also similar at 1.5T and 3T but with a wider CI for their ratios. This study supports the use of our automated hippocampal voluming methods, developed using 1.5T images, with 3T images.

There is considerable interest in designing therapeutic studies of individuals at risk of Alzheimer disease (AD) to prevent the onset of symptoms. Cortical β-amyloid plaques, the first stage of AD pathology, can be detected in vivo using positron emission tomography (PET), and several studies have shown that ~1/3 of healthy elderly have significant β-amyloid deposition. Here we assessed whether asymptomatic amyloid-PET-positive controls have increased rates of brain atrophy, which could be harnessed as an outcome measure for AD prevention trials. We assessed 66 control subjects (age = 73.5±7.3 yrs; MMSE = 29±1.3) from the Australian Imaging Biomarkers & Lifestyle study who had a baseline Pittsburgh Compound B (PiB) PET scan and two 3T MRI scans ~18-months apart. We calculated PET standard uptake value ratios (SUVR), and classified individuals as amyloid-positive/negative. Baseline and 18-month MRI scans were registered, and brain, hippocampal, and ventricular volumes and annualized volume changes calculated. Increasing baseline PiB-PET measures of β-amyloid load correlated with hippocampal atrophy rate independent of age (p = 0.014). Twenty-two (1/3) were PiB-positive (SUVR>1.40), the remaining 44 PiB-negative (SUVR≤1.31). Compared to PiB-negatives, PiB-positive individuals were older (76.8±7.5 vs. 71.7±7.5, p<0.05) and more were APOE4 positive (63.6% vs. 19.2%, p<0.01) but there were no differences in baseline brain, ventricle or hippocampal volumes, either with or without correction for total intracranial volume, once age and gender were accounted for. The PiB-positive group had greater total hippocampal loss (0.06±0.08 vs. 0.02±0.05 ml/yr, p = 0.02), independent of age and gender, with non-significantly higher rates of whole brain (7.1±9.4 vs. 4.7±5.5 ml/yr) and ventricular (2.0±3.0 vs. 1.1±1.0 ml/yr) change. Based on the observed effect size, recruiting 384 (95%CI 195-1080) amyloid-positive subjects/arm will provide 80% power to detect 25% absolute slowing of hippocampal atrophy rate in an 18-month treatment trial. We conclude that hippocampal atrophy may be a feasible outcome measure for secondary prevention studies in asymptomatic amyloidosis.

Hippocampal pathology occurs early in Alzheimer disease (AD), and atrophy, measured by volumes and volume changes, may predict which subjects will develop AD. Measures of the temporal horn (TH), which is situated adjacent to the hippocampus, may also indicate early changes in AD. Previous studies suggest that these metrics can predict conversion from amnestic mild cognitive impairment (MCI) to AD with conversion and volume change measured concurrently. However, the ability of these metrics to predict future conversion has not been investigated. We compared the abilities of hippocampal, TH, and global measures to predict future conversion from MCI to AD. TH, hippocampi, whole brain, and ventricles were measured using baseline and 12-month scans. Boundary shift integral was used to measure the rate of change. We investigated the prediction of conversion between 12 and 24 months in subjects classified as MCI from baseline to 12 months. All measures were predictive of future conversion. Local and global rates of change were similarly predictive of conversion. There was evidence that the TH expansion rate is more predictive than the hippocampal atrophy rate (P=0.023) and that the TH expansion rate is more predictive than the TH volume (P=0.036). Prodromal atrophy rates may be useful predictors of future conversion to sporadic AD from amnestic MCI.

A significant proportion of asymptomatic elderly individuals have brain amyloid deposition and may be in the earliest stages of Alzheimer disease. We assessed whether amyloid deposition measured using PiB-PET correlated with subsequent neurodegeneration. We analyzed controls from the Australian Imaging Biomarkers & Lifestyle flagship study of ageing (AIBL, www.aibl.csiro.au) who had a baseline PIB scan and two serial 3T MRI scans. We calculated mean neocortical grey matter PIB uptake, which was normalized for uptake in cerebellar grey matter to produce standard uptake value ratios (SUVR). We performed a cluster analysis of the baseline SUVR to determine PIB-positive/negative individuals. We used automated techniques with manual editing to outline baseline and follow-up whole brain (Brain-MAPS) and hippocampal volumes (Hippo-MAPS); ventricles were delineated manually. Rates of whole brain, ventricular and hippocampal change were determined using the boundary shift integral. We assessed relationships between neocortical SUVR and rates of cerebral atrophy using regression analyses; and between the PIB-positive/negative groups using t-tests and Fisher's exact test. Sixty-nine individuals (mean ± SD age = 73.3 ± 7.3yrs, MMSE score = 29 ± 1.2, inter-MRI interval = 556 ± 94 days; 47.8% male; 33.3% ApoE4 positive) were included. All bar 7(10.1%) had a CDR = 0. SUVRs ranged between 1.00 and 2.45. Annualized rates of change were 5.6 ± 6.9ml/yr for whole brain loss, 1.4 ± 1.9ml/yr for ventricular expansion and 0.036 ± 0.06ml/yr for combined hippocampal atrophy. Increasing SUVR was associated with increasing age (P<0.05) and with hippocampal atrophy rate (P<0.01), with some evidence for an association with ventricular expansion rate (P = 0.08). The correlation between hippocampal atrophy rate and baseline SUVR was independent of both age and baseline MMSE. At an SUVR cut-off of 1.35, 22/69 (31.9%) subjects were PIB-positive. Compared to the PIB-negative group, PIB-positive individuals were older (76.8 ± 7.5 vs. 71.7 ± 7.5, P <0.05) and more likely to be ApoE E4 positive (63.6% vs. 19.2%, P <0.01) but there were no differences in baseline brain, ventricular, or hippocampal volumes. The PIB-positive group had greater hippocampal loss (0.06 ± 0.07 vs. 0.02 ± 0.06ml/yr, p = 0.05) with consistent trends towards higher rates of whole brain (7.1 ± 9.3 vs. 4.9 ± 5.3ml/yr) and ventricular (2.0 ± 3.0 vs. 1.1 ± 1.0ml/yr) change. In elderly controls, increasing PIB-PET amyloid load correlates with subsequent increased rates of hippocampal atrophy.

Volume and change in volume of the hippocampus are both important markers of Alzheimer's disease (AD). Delineation of the structure on MRI is time-consuming and therefore reliable automated methods are required. We describe an improvement (multiple-atlas propagation and segmentation (MAPS)) to our template library-based segmentation technique. The improved technique uses non-linear registration of the best-matched templates from our manually segmented library to generate multiple segmentations and combines them using the simultaneous truth and performance level estimation (STAPLE) algorithm. Change in volume over 12months (MAPS–HBSI) was measured by applying the boundary shift integral using MAPS regions. Methods were developed and validated against manual measures using subsets from Alzheimer's Disease Neuroimaging Initiative (ADNI). The best method was applied to 682 ADNI subjects, at baseline and 12-month follow-up, enabling assessment of volumes and atrophy rates in control, mild cognitive impairment (MCI) and AD groups, and within MCI subgroups classified by subsequent clinical outcome. We compared our measures with those generated by Surgical Navigation Technologies (SNT) available from ADNI. The accuracy of our volumes was one of the highest reported (mean(SD) Jaccard Index 0.80(0.04) (N=30)). Both MAPS baseline volume and MAPS–HBSI atrophy rate distinguished between control, MCI and AD groups. Comparing MCI subgroups (reverters, stable and converters): volumes were lower and rates higher in converters compared with stable and reverter groups (p≤0.03). MAPS–HBSI required the lowest sample sizes (78 subjects) for a hypothetical trial. In conclusion, the MAPS and MAPS–HBSI methods give accurate and reliable volumes and atrophy rates across the clinical spectrum from healthy aging to AD.

Studies assessing clinical utility of MRI-based measures to discriminate specific neurodegenerative diseases from one another and from normal ageing are often limited by a lack of pathological confirmation. In this study we investigate the ability of volumes and change in volume to discriminate between groups using controls and confirmed disease cases. We included 55 individuals of which nine were controls and the remainder had either a genetic or post-mortem diagnosis of a neurodegenerative disease including: 22 Alzheimer's disease (AD), 16 cases with frontotemporal lobar degeneration and eight other dementias. All subjects had two useable serial MRI scans. We outlined the total intracranial volumes (TIVs), brains, lateral ventricles, temporal lobes, and temporal horns on all scans blinded to diagnosis, scan laterality (if appropriate) and time-point. We then assessed the ability of cross-sectional volumes to discriminate: a) control from all dementia cases, b) AD from controls and c) AD from non-AD dementias. Area under the curve (AUC) following logistic regression (including TIV) was used to measure discriminatory power. To assess whether a second scan added diagnostic value we compared AUCs for models including change in volume with the model containing cross-sectional volume within each structure. AUCs for each structure and comparison are shown in the Table. For normal vs. dementia cases, change in volume added to the discriminatory power of each volume (statistically significant apart from left temporal lobe). For AD vs. normals, adding volume change increased discrimination (significant apart from temporal horns). Comparing AD and non-AD dementias, although AUCs were higher after adding volume change, these differences were not statistically significant for any region. Change in volume significantly aids discriminatory power in determining normals from neurodegenerative cases and normals from AD cases. Change in volume does not necessarily help distinguish AD subjects from non-AD dementia cases.

Volume and change in volume of the hippocampus are both important markers of Alzheimer's disease (AD). Delineation of the structure is time-consuming and therefore reliable automated methods are required. We describe an improvement (multiple-atlas propagation and segmentation (MAPS)) to our template library-based automated segmentation technique and the combination with a recently-developed doubly-windowed boundary shift integral (MAPS-HBSI) to measure hippocampal volume and atrophy rate over 12 months. We downloaded baseline and 12-month MR scans of 682 subjects (200 control, 335 MCI and 147 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) website (www.adni-info.org). We sub-divided the MCI subjects into three subgroups (8 reverters (to normal), 204 stable and 123 converters (to AD)) based on their follow-up clinical diagnoses determined up to 36 months after baseline (Table). MAPS uses non-linear registration of the eight best-matched templates from our manually-segmented library to generate multiple segmentations and combines them using the simultaneous truth and performance level estimation (STAPLE) algorithm. MAPS was validated using manual hippocampal regions from 30 randomly selected baseline images (10 controls, 10 MCI and 10 AD). Changes in hippocampal volume were calculated using the boundary shift integral over the baseline hippocampal regions (MAPS-HBSI) on the baseline and repeat images. Linear regression was used to test for differences in age and MMSE between MCI-subgroups. Fisher's exact test was used to test for differences in gender. Linear regression was used to test for differences in baseline hippocampal volume adjusted for age, gender and total intra-cranial volume, and test for differences in annualised percentage loss between MCI sub-groups adjusted for age and gender. The mean (SD) Jaccard index between the automated and manual hippocampal regions was 0.80 (0.04). Mean (SD) unadjusted total hippocampal volumes and atrophy rates, together with the adjusted mean differences are shown in the Table for the MCI subgroups. We found differences across MCI subgroups based on follow-up diagnosis determined up to 36 months from baseline, with hippocampal volumes statistically significantly lower in converters compared to stable and reverters. Atrophy rates from MAPS-HBSI also showed the expected pattern (MCI reverters < MCI stable < MCI converters).

Hippocampal atrophy and temporal horn (TH) enlargement on MRI are early features of Alzheimer's disease (AD) and may predict progression. Volume changes may be measured from volumes at each time-point (indirect) or using image subtraction (direct). We a) assessed whether hippocampal atrophy or TH expansion over 12 months predicts subsequent conversion from mild cognitive impairment (MCI) to AD b) compared direct and indirect measures as predictors of conversion and c) estimated sample sizes. We included 167 ADNI subjects (45 controls, 88 MCI and 34 AD) with hippocampal semi-automated volumes available (from UCSF) for baseline and 12-month scans. The MCI group included 49 who remained stable 24 months from baseline (MCI stable), 17 who converted within 12 months (early converters) and 22 who converted between 12 and 24 months (MCI late-converters). THs were traced on all scans. Indirect (volume differences) and direct (boundary shift integral, BSI) measures of change for hippocampus and TH (expressed as %/year loss for hippocampus and mm3/year expansion for TH) were calculated. Logistic regression was used to compare the association of direct and indirect measures for each region with late conversion versus stable MCI. Sample size calculations were performed for each measure using all MCI subjects to detect a 20% reduction in atrophy rate with 90% power at the 5% significance level. All rates of change were associated with late conversion from MCI to AD (p < 0.04). Comparing hippocampal measures showed indirect measures could independently predict late conversion (p = 0.01), although this was not the case for direct measures (p = 0.09). Neither TH measure was independently predictive of late conversion (p > 0.6). Comparing direct methods showed neither was independently predictive of late conversion (p> 0.1). Comparing indirect methods showed hippocampal rates were independently predictive of late conversion (p = 0.01), but not TH rates (p = 0.1). Direct measures required smaller sample sizes than indirect measures for hippocampus, but not TH (see table). Hippocampal and TH atrophy rates predict a diagnosis of AD in MCI subjects and may be useful in monitoring progression in trials. Indirect hippocampal measures (volume at each time-point) had greater predictive ability but also larger sample size estimates.