Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging☆

MRI shows more severe hippocampal atrophy and shape deformation in hippocampal sclerosis than in Alzheimer's disease

While hippocampal atrophy is a key feature of both hippocampal sclerosis (HS) and Alzheimer's disease (AD), the pathology underlying this finding differs in these two conditions. In AD, atrophy is due primarily to loss of neurons and neuronal volume as a result of neurofibrillary tangle formation. While the etiology of HS is unknown, neuron loss in the hippocampus is severe to complete. We compared hippocampal volume and deformations from premortem MRI in 43 neuropathologically diagnosed cases of HS, AD, and normal controls (NC) selected from a longitudinal study of subcortical ischemic vascular disease (IVD Program Project). HS cases (n = 11) showed loss of neurons throughout the rostral-caudal extent of the hippocampus in one or both hemispheres. AD cases (n = 24) met NIA-Reagan criteria for high likelihood of AD. Normal control cases (n = 8) were cognitively intact and showed no significant AD or hippocampal pathology. The mean hippocampal volumes were significantly lower in HS versus AD groups (P < .001). Mean shape deformations in the CA1 and subiculum differed significantly between HS versus AD, HS versus NC, and AD versus NC (P < .0001). Additional study is needed to determine whether these differences will be meaningful for clinical diagnosis of individual cases.

Quantization and analysis of hippocampal morphometric changes due to dementia of Alzheimer type using metric distances based on large deformation diffeomorphic metric mapping

The metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm is used to quantize changes in morphometry of brain structures due to neuropsychiatric diseases. For illustrative purposes we consider changes in hippocampal morphometry (shape and size) due to very mild dementia of the Alzheimer type (DAT). LDDMM, which was previously used to calculate dense one-to-one correspondence vector fields between hippocampal shapes, measures the morphometric differences with respect to a template hippocampus by assigning metric distances on the space of anatomical images thereby allowing for direct comparison of morphometric differences. We characterize what information the metric distances provide in terms of size and shape given the hippocampal, brain and intracranial volumes. We demonstrate that metric distance is a measure of morphometry (i.e., shape and size) but mostly a measure of shape, while volume is mostly a measure of size. Moreover, we show how metric distances can be used in cross-sectional, longitudinal analysis, as well as left-right asymmetry comparisons, and provide how the metric distances can serve as a discriminative tool using logistic regression. Thus, we show that metric distances with respect to a template computed via LDDMM can be a powerful tool in detecting differences in shape.

Pharmacokinetics and tissue distribution of 14C-labeled grape polyphenols in the periphery and the central nervous system following oral administration

Grape polyphenols confer potential health benefits, including prevention of neurodegenerative diseases. To determine the absorption and tissue distribution of the complex grape polyphenol mixture, (14)C-labeled polyphenols were biosynthesized by grape cell suspension cultures, during co-incubation with radioisotopically labeled sucrose, and fractionated into polyphenolic subfractions. The pharmacokinetics and distribution of grape polyphenols into blood, brain, and peripheral interstitial fluid were determined by tracking the (14)C label. The blood peak (14)C concentration of the fractions ranged from 15 minutes to 4 hours. Absorption and tissue distribution varied greatly between fractions. Concentrations in interstitial fluid were lower than in blood. The amount of residual label in the brain at 24 hours ranged from 0.1% to 1.7% of the dose, depending on the fraction. (14)C label found in the brain tissue and brain microdialysate indicated that grape polyphenols or their metabolites are able to cross the blood-brain barrier. Using (14)C-labeled plant polyphenols it is possible to track the compounds or their metabolic products into any tissue and determine distribution patterns in spite of low concentrations. A central question regarding the potential role of dietary polyphenolics in neurodegenerative research is whether they are bioavailable in the brain. Our observations indicate that some grape-derived polyphenolics do reach the brain, which suggests their potential value for applications in neurodegenerative disorders.

Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults

The Open Access Series of Imaging Studies is a series of neuroimaging data sets that are publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 years all acquired on the same scanner using identical sequences. Each subject was scanned on two or more visits, separated by at least 1 year for a total of 373 imaging sessions. Subjects were characterized using the Clinical Dementia Rating (CDR) as either nondemented or with very mild to mild Alzheimer's disease. Seventy-two of the subjects were characterized as nondemented throughout the study. Sixty-four of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with CDR 0.5 similar level of impairment to individuals elsewhere considered to have "mild cognitive impairment." Another 14 subjects were characterized as nondemented at the time of their initial visit (CDR 0) and were subsequently characterized as demented at a later visit (CDR > 0). The subjects were all right-handed and include both men (n = 62) and women (n = 88). For each scanning session, three or four individual T1-weighted MRI scans were obtained. Multiple within-session acquisitions provide extremely high contrast to noise, making the data amenable to a wide range of analytic approaches including automated computational analysis. Automated calculation of whole-brain volume is presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.

Hippocampal sulcal cavities: prevalence, risk factors and relationship to memory impairment

While hippocampal volumes have been extensively examined in neuropsychiatric disorders and ageing, small areas of signal variation within the hippocampus commonly observed on MRI, described as hippocampal sulcal cavities (HSCs), have received less attention. We review the published literature on HSCs to examine their prevalence, putative aetiological factors such as hypertension, and possible cognitive correlates. HSCs were reported in 77% (66% weighted mean) of patients with memory disorders and 48% (47% weighted mean) of controls, and the prevalence increased with age in healthy subjects (r=0.64, p=0.047). A number of studies reported hypertension as a risk factor, and related their presence to poorer memory function. Further work is needed to fully understand the clinical significance of these lesions.

Learning Fourier descriptors for computer-aided diagnosis of the supraspinatus

RATIONALE AND OBJECTIVES

Supraspinatus muscle disorders are frequent and debilitating, resulting in pain and a limited range of shoulder motion. The gold standard for diagnosis involves an invasive surgical procedure. As part of a proposed clinical workflow for noninvasive computer-aided diagnosis (CAD) of the condition of the supraspinatus, we present a method to classify three-dimensional shapes of the muscle into relevant pathology groups, based on magnetic resonance (MR) images.

MATERIALS AND METHODS

We obtained MR images of the shoulder from 72 patients, separated into five pathology groups. The imaging protocol ensures that the supraspinatus is consistently oriented relative to the MR imaging plane for each scan. Next, we compute the Fourier coefficients of two-dimensional contours lying on parallel imaging planes and integrate the corresponding frequency components across all contours. To classify the shapes, we learn the Fourier coefficients that best distinguish the different classes.

RESULTS

We show that our method leads to significant improvement when compared to previous work. We are able to distinguish between normal shapes and shapes that possess a pathology with an accuracy of almost 100%. Moreover, we can differentiate between the different pathology groups with an average accuracy of 86%.

CONCLUSION

We confirm that analyzing the three-dimensional shape of the muscle has potential as a form of diagnosis reinforcement to assess the condition of the supraspinatus. Moreover, our proposed descriptor based on Fourier coefficients is able to distinguish the different pathology groups with accuracies higher than those obtained by previous work, indicating its potential application to support a system for CAD of the supraspinatus.

BDNF polymorphism rs6265 and hippocampal structure and memory performance in healthy control subjects

Brain-derived neurotrophic factor (BDNF) is highly expressed in the hippocampus of many species, including humans. The single-nucleotide polymorphism rs6265 on the BDNF gene is thought to alter activity-dependent secretion of the protein, and previous research suggests that the Met allele is associated with smaller hippocampal volumes and poorer memory performance in human populations. For this study, we genotyped 154 healthy human subjects for the Val66Met polymorphism. The effects of genotype upon hippocampal volume, as assessed using high resolution magnetic resonance imaging and high-dimensional brain mapping, and upon memory performance, as assessed using a battery of neuropsychological tests, were determined. We found that genotype had no significant effect on hippocampal structure, nor did it have a significant effect on memory performance, covarying for age. Age, however, was significantly related to changes in whole brain volume and performance on memory tasks. We concluded that in a large cohort of healthy human subjects, the Met allele of rs6265 is not associated with hippocampal structure or memory performance.

Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta-analyses of MRI studies

Numerous studies have reported a smaller hippocampal volume in Alzheimer's disease (AD) patients than in aging controls. However, in mild cognitive impairment (MCI), the results are inconsistent. Moreover, the left-right asymmetry of the hippocampus receives less research attention. In this article, meta-analyses are designed to determine the extent of hippocampal atrophy in MCI and AD, and to evaluate the asymmetry pattern of the hippocampal volume in control, MCI, and AD groups. From 14 studies including 365 MCI patients and 382 controls, significant atrophy is found in both the left [Effect size (ES), 0.92; 95% confidence interval (CI), 0.72-1.11] and right (ES, 0.78; 95% CI, 0.57-0.98) hippocampus, which is lower than that in AD (ES, 1.60, 95% CI, 1.37-1.84, in left; ES, 1.52, 95% CI, 1.31-1.72, in right). Comparing with aging controls, the average volume reduction weighted by sample size is 12.9% and 11.1% in left and right hippocampus in MCI, and 24.2% and 23.1% in left and right hippocampus in AD, respectively. The findings show a bilateral hippocampal volume loss in MCI and the extent of atrophy is less than that in AD. By comparing the left and right hippocampal volume, a consistent left-less-than-right asymmetry pattern is found, but with different extents in control (ES, 0.39), MCI (ES, 0.56), and AD (ES, 0.30) group.

A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline

Hippocampal atrophy is frequently observed on magnetic resonance images from patients with Alzheimer's disease and persons with mild cognitive impairment. Even in asymptomatic elderly, a small hippocampal volume on magnetic resonance imaging is a risk factor for developing Alzheimer's disease. However, not everyone with a small hippocampus develops dementia. With the increased interest in the use of sequential magnetic resonance images as potential surrogate biomarkers of the disease process, it has also been shown that the rate of hippocampal atrophy is higher in persons with Alzheimer's disease compared to those with mild cognitive impairment and the healthy elderly. Whether a higher rate of hippocampal atrophy also predicts Alzheimer's disease or subtle cognitive decline in non-demented elderly is unknown. We examine these associations in a group of 518 elderly (age 60-90 years, 50% female), taken from the population-based Rotterdam Scan Study. A magnetic resonance imaging examination was performed at baseline in 1995-96 that was repeated in 1999-2000 (in 244 persons) and in 2006 (in 185 persons). Using automated segmentation procedures, we assessed hippocampal volumes on all magnetic resonance imaging scans. All persons were free of dementia at baseline and followed over time for cognitive decline and incident dementia. Persons had four repeated neuropsychological tests at the research centre over a 10-year period. We also continuously monitored the medical records of all 518 participants for incident dementia. During a total follow-up of 4360 person-years, (mean 8.4, range 0.1-11.3), 50 people developed incident dementia (36 had Alzheimer's disease). We found an increased risk to develop incident dementia per standard deviation faster rate of decline in hippocampal volume [left hippocampus 1.6 (95% confidence interval 1.2-2.3, right hippocampus 1.6 (95% confidence interval 1.2-2.1)]. Furthermore, decline in hippocampal volume predicted onset of clinical dementia when corrected for baseline hippocampal volume. In people who remained free of dementia during the whole follow-up period, we found that decline in hippocampal volume paralleled, and preceded, specific decline in delayed word recall. No associations were found in this sample between rate of hippocampal atrophy, Mini Mental State Examination and tests of executive function. Our results suggest that rate of hippocampal atrophy is an early marker of incipient memory decline and dementia, and could be of additional value when compared with a single hippocampal volume measurement as a surrogate biomarker of dementia.

Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease

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< or =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.

Donepezil treatment and changes in hippocampal structure in very mild Alzheimer disease

OBJECTIVE

To compare longitudinal changes in the hippocampal structure in subjects with very mild dementia of the Alzheimer type (DAT) treated with donepezil hydrochloride, untreated subjects with very mild DAT, and controls without dementia.

DESIGN

MPRAGE sequences were collected approximately 2 years apart on two 1.5-T magnetic resonance imaging systems, yielding 2 cohorts. Large-deformation high-dimensional brain mapping was used to compute deformation of hippocampal subfields.

SETTING

A dementia clinic at Washington University School of Medicine.

PATIENTS OR OTHER PARTICIPANTS

Subjects came from 2 sources: 18 untreated subjects with DAT and 26 controls were drawn from a previous longitudinal study; 18 treated subjects with DAT were studied prospectively, and 44 controls were drawn from a longitudinal study from the same period. Intervention Patients were prescribed donepezil by their physician.

MAIN OUTCOME MEASURES

Hippocampal volume loss and surface deformation.

RESULTS

There was no significant cohort effect at baseline; therefore, the 2 groups of control subjects were combined. The potential confounding effect of cohort/scanner was dealt with by including it as a covariate in statistical tests. There was no significant group effect in the rate of change of hippocampal volume or subfield deformation. Further exploration showed that compared with the untreated subjects with DAT, the treated subjects with DAT did not differ in the rate of change in any of the hippocampal measures. They also did not differ from the controls, while the untreated subjects with DAT differed from the controls in the rates of change of hippocampal volume and CA1 and subiculum subfield deformations.

CONCLUSIONS

Treatment with donepezil did not alter the progression of hippocampal deformation in subjects with DAT in this study. Small sample size may have contributed to this outcome.

Multivariate tensor-based morphometry on surfaces: application to mapping ventricular abnormalities in HIV/AIDS

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics-these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain.

Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

We used a new method we developed for automated hippocampal segmentation, called the auto context model, to analyze brain MRI scans of 400 subjects from the Alzheimer's disease neuroimaging initiative. After training the classifier on 21 hand-labeled expert segmentations, we created binary maps of the hippocampus for three age- and sex-matched groups: 100 subjects with Alzheimer's disease (AD), 200 with mild cognitive impairment (MCI) and 100 elderly controls (mean age: 75.84; SD: 6.64). Hippocampal traces were converted to parametric surface meshes and a radial atrophy mapping technique was used to compute average surface models and local statistics of atrophy. Surface-based statistical maps visualized links between regional atrophy and diagnosis (MCI versus controls: P = 0.008; MCI versus AD: P = 0.001), mini-mental state exam (MMSE) scores, and global and sum-of-boxes clinical dementia rating scores (CDR; all P < 0.0001, corrected). Right but not left hippocampal atrophy was associated with geriatric depression scores (P = 0.004, corrected); hippocampal atrophy was not associated with subsequent decline in MMSE and CDR scores, educational level, ApoE genotype, systolic or diastolic blood pressure measures, or homocysteine. We gradually reduced sample sizes and used false discovery rate curves to examine the method's power to detect associations with diagnosis and cognition in smaller samples. Forty subjects were sufficient to discriminate AD from normal and correlate atrophy with CDR scores; 104, 200, and 304 subjects, respectively, were required to correlate MMSE with atrophy, to distinguish MCI from normal, and MCI from AD.

Comparing registration methods for mapping brain change using tensor-based morphometry

Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models to detect changes in TBM, and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed Unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large-deformation registration schemes (viscous fluid and inverse-consistent linear elastic registration methods versus Symmetric and Asymmetric Unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer's Disease scanned at 2-week and 1-year intervals. We also analyzed registration results when matching images corrupted with artificial noise. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps.

Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping

This article describes a large multi-institutional analysis of the shape and structure of the human hippocampus in the aging brain as measured via MRI. The study was conducted on a population of 101 subjects including nondemented control subjects (n = 57) and subjects clinically diagnosed with Alzheimer's Disease (AD, n = 38) or semantic dementia (n = 6) with imaging data collected at Washington University in St. Louis, hippocampal structure annotated at the Massachusetts General Hospital, and anatomical shapes embedded into a metric shape space using large deformation diffeomorphic metric mapping (LDDMM) at the Johns Hopkins University. A global classifier was constructed for discriminating cohorts of nondemented and demented subjects based on linear discriminant analysis of dimensions derived from metric distances between anatomical shapes, demonstrating class conditional structure differences measured via LDDMM metric shape (P < 0.01). Localized analysis of the control and AD subjects only on the coordinates of the population template demonstrates shape changes in the subiculum and the CA1 subfield in AD (P < 0.05). Such large scale collaborative analysis of anatomical shapes has the potential to enhance the understanding of neurodevelopmental and neuropsychiatric disorders.

Image-driven population analysis through mixture modeling

We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to construct an atlas. We derive the algorithm based on a generative model of an image population as a mixture of deformable template images. We validate and explore our method in four experiments. In the first experiment, we use synthetic data to explore the behavior of the algorithm and inform a design choice on parameter settings. In the second experiment, we demonstrate the utility of having multiple atlases for the application of localizing temporal lobe brain structures in a pool of subjects that contains healthy controls and schizophrenia patients. Next, we employ iCluster to partition a data set of 415 whole brain MR volumes of subjects aged 18 through 96 years into three anatomical subgroups. Our analysis suggests that these subgroups mainly correspond to age groups. The templates reveal significant structural differences across these age groups that confirm previous findings in aging research. In the final experiment, we run iCluster on a group of 15 patients with dementia and 15 age-matched healthy controls. The algorithm produces two modes, one of which contains dementia patients only. These results suggest that the algorithm can be used to discover subpopulations that correspond to interesting structural or functional "modes."

Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields

Histopathological studies and animal models suggest that different physiological and pathophysiological processes exert different subfield specific effects on the hippocampus. High-resolution images at 4T depict details of the internal structure of the hippocampus allowing for in vivo volumetry of hippocampal subfields. The aims of this study were (1) to determine patterns of hippocampal subfield volume loss due to normal aging and Apo e4 carrier state, (2) to determine subfield specific volume losses due to preclinical (MCI) and clinical Alzheimer's disease (AD) and their modification due to age and Apo e4 carrier state. One hundred fifty seven subjects (119 cognitively healthy elderly controls, 20 MCI and 18 AD) were studied with a high resolution T2 weighted imaging sequence obtained at 4T aimed at the hippocampus. Apo e4 carrier state was known in 95 subjects (66 controls, 14 MCI, 15 AD). Subiculum (SUB), CA1, CA1-CA2 transition zone (CA1-2 transition), CA3- dentate gyrus (CA3&DG) were manually marked. Multiple linear regression analysis was used to test for effects of age, Apo e4 carrier state and effects of MCI and AD on different hippocampal subfields. Age had a significant negative effect on CA1 and CA3&DG volumes in controls (P < 0.05). AD had significantly smaller volumes of SUB, CA1, CA1-2 transition, and MCI had smaller CA1-2 transition volumes than controls (P < 0.05). Apo e4 carrier state was associated with volume loss in CA3&DG compared to non-Apo e4 carriers in healthy controls and AD. Based on these findings, we conclude that subfield volumetry provides regional selective information that allows to distinguish between different normal and pathological processes affecting the hippocampus and thus for an improved differential diagnosis of neurodegenerative diseases affecting the hippocampus.

Disease classification with hippocampal shape invariants

We present an Alzheimer's detection study based on a global shape description of hippocampal surface models. With global descriptors forming our bag of features, Support Vector Machine classification of 49 Alzheimer (AD) and 63 elderly control subjects yielded 75.5% sensitivity and 87.3% specificity with 82.1% correct overall in a leave-one-out test. We show that our description contributes new information to simpler shape measures. Armed with a rigid shape registration tool, we also present a way to visualize variation in global shape description as a local displacement map, thus clarifying the descriptors' anatomical meaning.

Clinical application of measurement of hippocampal atrophy in degenerative dementias

Hippocampal atrophy is a characteristic and early feature of Alzheimer's disease. Volumetry of the hippocampus using T1-weighted magnetic resonance imaging (MRI) has been used not only to assess hippocampal involvement in different neurodegenerative diseases as a potential diagnostic biomarker, but also to understand the natural history of diseases, and to track changes in volume over time. Assessing change in structure circumvents issues surrounding interindividual variability and allows assessment of disease progression. Disease-modifying effects of putative therapies are important to assess in clinical trials and are difficult using clinical scales. As a result, there is increasing use of serial MRI in trials to detect potential slowing of atrophy rates as an outcome measure. Automated and yet reliable methods of quantifying such change in the hippocampus would therefore be very valuable. Algorithms capable of measuring such changes automatically have been developed and may be applicable to predict decline to a diagnosis of dementia in the future. This article details the progress in using MRI to understand hippocampal changes in the degenerative dementias and also describes attempts to automate hippocampal segmentation in these diseases.

Fully-automated, multi-stage hippocampus mapping in very mild Alzheimer disease

Landmark-based high-dimensional diffeomorphic maps of the hippocampus (although accurate) is highly-dependent on rater's anatomic knowledge of the hippocampus in the magnetic resonance images. It is therefore vulnerable to rater drift and errors if substantial amount of effort is not spent on quality assurance, training, and re-training. A fully-automated, FreeSurfer-initialized large-deformation diffeomorphic metric mapping procedure of small brain substructures, including the hippocampus, has been previously developed and validated in small samples. In this report, we demonstrate that this fully-automated pipeline can be used in place of the landmark-based procedure in a large-sample clinical study to produce similar statistical outcomes. Some direct comparisons of the two procedures are also presented.

Cardiac Motion Analysis in Ischemic and Non-Ischemic Cardiomyopathy Using Parallel Transport

In this study, we used the multi-detector computed tomographic (MDCT) images of heart left ventricles at end-diastole and end-systole to perform quantitative analysis and comparison of heart motion in patients with anterior wall myocardial infarction and ischemic cardiomyopathy (ICM) versus those with global non-ischemic cardiomyopathy (NICM). MDCT ventricular images of 25 subjects (13 with ICM) with ejection fraction (EF)< 35% were analyzed. We used parallel transport in diffeomorphism under the large deformation diffeomorphic metric mapping framework to translate within subject motion related deformation in a global template coordinate system. We then performed a hypothesis testing on the ventricular motion variation in the global template coordinate. Statistical analysis indicates that there are meaningful ventricular motion differences between ICM and NICM groups. Additionally, subjects with ICM demonstrated less wall thickening at ES in the anterior wall where the pathology is located.

The emerging discipline of Computational Functional Anatomy

Computational Functional Anatomy (CFA) is the study of functional and physiological response variables in anatomical coordinates. For this we focus on two things: (i) the construction of bijections (via diffeomorphisms) between the coordinatized manifolds of human anatomy, and (ii) the transfer (group action and parallel transport) of functional information into anatomical atlases via these bijections. We review advances in the unification of the bijective comparison of anatomical submanifolds via point-sets including points, curves and surface triangulations as well as dense imagery. We examine the transfer via these bijections of functional response variables into anatomical coordinates via group action on scalars and matrices in DTI as well as parallel transport of metric information across multiple templates which preserves the inner product.

Aging

The normal adult brain undergoes considerable morphological changes with aging. Studying these changes is paramount to differentiate normal age-related brain variations from the effects of neurodegenerative diseases affecting brain structure in the elderly. Considerable progress has been made in this research area during the past few decades, given the availability of noninvasive imaging tools such as magnetic resonance (MR). In recent years image acquisition devices, computer technology and software development have also advanced, allowing sophisticated methods for analyzing brain images, at both the macro-and microstructural level. In this article we will review studies assessing the effect of aging on global and regional gray and white matter volume using advanced MR techniques.

Characterization of Atrophic Changes in the Cerebral Cortex Using Fractal Dimensional Analysis

The purpose of this project is to apply a modified fractal analysis technique to high-resolution T1 weighted magnetic resonance images in order to quantify the alterations in the shape of the cerebral cortex that occur in patients with Alzheimer's disease. Images were selected from the Alzheimer's Disease Neuroimaging Initiative database (Control N=15, Mild-Moderate AD N=15). The images were segmented using a semi-automated analysis program. Four coronal and three axial profiles of the cerebral cortical ribbon were created. The fractal dimensions (D(f)) of the cortical ribbons were then computed using a box-counting algorithm. The mean D(f) of the cortical ribbons from AD patients were lower than age-matched controls on six of seven profiles. The fractal measure has regional variability which reflects local differences in brain structure. Fractal dimension is complementary to volumetric measures and may assist in identifying disease state or disease progression.

Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

As one of the earliest structures to degenerate in Alzheimer's disease (AD), the hippocampus is the target of many studies of factors that influence rates of brain degeneration in the elderly. In one of the largest brain mapping studies to date, we mapped the 3D profile of hippocampal degeneration over time in 490 subjects scanned twice with brain MRI over a 1-year interval (980 scans). We examined baseline and 1-year follow-up scans of 97 AD subjects (49 males/48 females), 148 healthy control subjects (75 males/73 females), and 245 subjects with mild cognitive impairment (MCI; 160 males/85 females). We used our previously validated automated segmentation method, based on AdaBoost, to create 3D hippocampal surface models in all 980 scans. Hippocampal volume loss rates increased with worsening diagnosis (normal=0.66%/year; MCI=3.12%/year; AD=5.59%/year), and correlated with both baseline and interval changes in Mini-Mental State Examination (MMSE) scores and global and sum-of-boxes Clinical Dementia Rating scale (CDR) scores. Surface-based statistical maps visualized a selective profile of ongoing atrophy in all three diagnostic groups. Healthy controls carrying the ApoE4 gene atrophied faster than non-carriers, while more educated controls atrophied more slowly; converters from MCI to AD showed faster atrophy than non-converters. Hippocampal loss rates can be rapidly mapped, and they track cognitive decline closely enough to be used as surrogate markers of Alzheimer's disease in drug trials. They also reveal genetically greater atrophy in cognitively intact subjects.

Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes

Serial MRI human brain scans have facilitated the detection of brain development and of the earliest signs of neuropsychiatric and neurodegenerative diseases, monitoring disease progression, and resolving drug effects in clinical trials for preventing or slowing the rate of brain degeneration. To track anatomical shape changes in serial images, we introduce new point-based time sequence large deformation diffeomorphic metric mapping (TS-LDDMM) to infer the time flow of within-subject geometric shape changes that carry known observations through a period. Its Euler-Lagrange equation is generalized for anatomies whose shapes are characterized by point sets, such as landmarks, curves, and surfaces. The time-dependent momentum obtained from the TS-LDDMM encodes within-subject shape changes. For the purpose of across-subject shape comparison, we then propose a diffeomorphic analysis framework to translate within-subject deformation in a global template without incorporating across-subject anatomical variations via parallel transport technique. The analysis involves the retraction of the within-subject time-dependent momentum along the TS-LDDMM trajectory from each time to the baseline, the translation of the momentum in a global template, and the reconstruction of the TS-LDDMM trajectory starting from the global template.

Mapping local hippocampal changes in Alzheimer's disease and normal ageing with MRI at 3 Tesla

Histological studies have suggested differing involvement of the hippocampal subfields in ageing and in Alzheimer's disease. The aim of this study was to assess in vivo local hippocampal changes in ageing and Alzheimer's disease based on high resolution MRI at 3 Tesla. T(1)-weighted images were acquired from 19 Alzheimer's disease patients [age 76 +/- 6 years, three males, Mini-Mental State Examination 13 +/- 4] and 19 controls (age 74 +/- 5 years, 11 males, Mini-Mental State Examination 29 +/- 1). The hippocampal formation was isolated by manual tracing. Radial atrophy mapping was used to assess group differences and correlations by averaging hippocampal shapes across subjects using 3D parametric surface mesh models. Percentage difference, Pearson's r, and significance maps were produced. Hippocampal volumes were inversely correlated with age in older healthy controls (r = 0.56 and 0.6 to the right and left, respectively, P < 0.05, corresponding to 14% lower volume for every 10 years of older age from ages 65 to 85 years). Ageing-associated atrophy mapped to medial and lateral areas of the tail and body corresponding to the CA1 subfield and ventral areas of the head corresponding to the presubiculum. Significantly increased volume with older age mapped to a few small spots mainly located to the CA1 sector of the right hippocampus. Volumes were 35% and 30% smaller in Alzheimer's disease patients to the right and left (P < 0.0005). Alzheimer's disease-associated atrophy mapped not only to CA1 areas of the body and tail corresponding to those also associated with age, but also to dorsal CA1 areas of the head unaffected by age. Regions corresponding to the CA2-3 fields were relatively spared in both ageing and Alzheimer's disease. Hippocampal atrophy in Alzheimer's disease maps to areas in the body and tail that partly overlap those affected by normal ageing. Specific areas in the anterior and dorsal CA1 subfield involved in Alzheimer's disease were not in normal ageing. These patterns might relate to differential neural systems involved in Alzheimer's disease and ageing.

APOE related hippocampal shape alteration in geriatric depression

Late-onset depression often precedes the onset of dementia associated with the hippocampal degeneration. Using large deformation diffeomorphic metric mapping (LDDMM), we evaluated apolipoprotein E epsilon-4 allele (apoE E4) effects on hippocampal volume and shape in 38 depressed patients without the apoE E4, 14 depressed patients with one apoE E4, and 31 healthy comparison subjects without the apoE E4. The hippocampal volumes were manually assessed. We applied a diffeomorphic template generation procedure for creating the hippocampal templates based on a subset of the population. The LDDMM mappings were used to generate the hippocampal shape of each subject and characterize the surface deformation of each hippocampus relative to the template. Such deformation was modeled as random field characterized by the Laplace-Beltrami basis functions in the template coordinates. Linear regression was used to examine group differences in the hippocampal volume and shape. We found that there were significant hippocampal shape alternations in both depressed groups while the groups of depressed patients and the group of healthy subjects did not differ in the hippocampal volume. The depressed patients with one apoE E4 show more pronounced shape inward-compression in the anterior CA1 than the depressed patients without the apoE E4 when compared with the healthy controls without the apoE E4. Thus, hippocampal shape abnormalities in late-onset depressed patients with one apoE E4 may indicate future conversion of this group to AD at higher risk than depressed patients without the apoE E4.

Hippocampal head atrophy predominance in Parkinson's disease with hallucinations and with dementia

We studied regional gray matter density in the hippocampus in Parkinson's disease (PD) patients. We obtained magnetic resonance scans in 44 PD patients (PD patients with dementia (PDD) = 9, non-demented PD patients with visual hallucinations (PD + VH) = 16, and PD patients without dementia and without visual hallucinations (PD - VH) = 19) and 56 controls matched for age and years of education. A region of interest (ROI) of the hippocampus following voxel-based morphometry (VBM) procedures was used to perform group comparisons, single-case individual analysis and correlations with learning scores. Group comparisons showed that PDD patients and PD+VH patients had significant hippocampal gray matter loss compared to controls. In PDD patients, hippocampal gray matter loss involved the entire hippocampus and in PD+VH this reduction was mainly confined to the hippocampal head. 78 % of PDD patients, 31 % of PD+VH patients and 26 % of PD-VH patients had hippocampal head gray matter loss when compared to controls. These results suggest that in PD the neurodegenerative process in the hippocampus starts in the head of this structure and later spreads to the tail and that, in addition, memory impairment assessed by Rey's Auditory Verbal Learning Test (RAVLT) correlates with hippocampal head gray matter loss.

Subregional hippocampal atrophy predicts Alzheimer's dementia in the cognitively normal

Atrophic changes of the hippocampus are typically regarded as an early sign of Alzheimer's dementia (AD). Using the radial distance atrophy mapping approach, we compared the longitudinal MRI data of 10 cognitively normal elderly subjects who remained normal at 3-year and 6-year follow-up (NL-NL) and 7 cognitively normal elderly subjects who were diagnosed with mild cognitive impairment (MCI) 2.8 (range 2.0-3.9) and with AD 6.8 years (range 6.1-8.2) after baseline (NL-MCI(AD)). 3D statistical maps revealed greater hippocampal atrophy in the NL-MCI(AD) relative to the NL-NL group at baseline (left p=0.05; right p=0.06) corresponding to 10-15% CA1, and 10-25% subicular atrophy, and bilateral differences at 3-year follow-up (left p=0.001, right p<0.02) corresponding to 10-30% subicular, 10-20% CA1, and 10-20% newly developed CA2-3 atrophy. This preliminary study suggests that excess CA1 and subicular atrophy is present in cognitively normal individuals predestined to decline to amnestic MCI, while progressive involvement of the CA1 and subiculum, and atrophy spreading to the CA2-3 subfield in amnestic MCI, suggests future diagnosis of AD.

Tensor-based cortical surface morphometry via weighted spherical harmonic representation

We present a new tensor-based morphometric framework that quantifies cortical shape variations using a local area element. The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation, which generalizes the traditional SPHARM as a special case. For a specific choice of weights, the weighted-SPHARM is shown to be the least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims of this paper are to present the weighted-SPHARM and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in the group of high functioning autistic subjects.

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.

Hippocampus shape analysis and late-life depression

Major depression in the elderly is associated with brain structural changes and vascular lesions. Changes in the subcortical regions of the limbic system have also been noted. Studies examining hippocampus volumetric differences in depression have shown variable results, possibly due to any volume differences being secondary to local shape changes rather than differences in the overall volume. Shape analysis offers the potential to detect such changes. The present study applied spherical harmonic (SPHARM) shape analysis to the left and right hippocampi of 61 elderly subjects with major depression and 43 non-depressed elderly subjects. Statistical models controlling for age, sex, and total cerebral volume showed a significant reduction in depressed compared with control subjects in the left hippocampus (F_1,103 = 5.26; p = 0.0240) but not right hippocampus volume (F_1,103 = 0.41; p = 0.5213). Shape analysis showed significant differences in the mid-body of the left (but not the right) hippocampus between depressed and controls. When the depressed group was dichotomized into those whose depression was remitted at time of imaging and those who were unremitted, the shape comparison showed remitted subjects to be indistinguishable from controls (both sides) while the unremitted subjects differed in the midbody and the lateral side near the head. Hippocampal volume showed no difference between controls and remitted subjects but nonremitted subjects had significantly smaller left hippocampal volumes with no significant group differences in the right hippocampus. These findings may provide support to other reports of neurogenic effects of antidepressants and their relation to successful treatment for depressive symptoms.

A meta-analysis of hippocampal atrophy rates in Alzheimer's disease

Hippocampal atrophy rates are useful in both diagnosing and tracking Alzheimer's disease (AD). However, cohorts and methods used to determine such rates are heterogeneous, leading to differences in reported annualised rates. We performed a meta-analysis of hippocampal atrophy rates in AD patients and matched controls from studies reported in the peer-reviewed literature. Studies reporting longitudinal volume change in hippocampi in AD subjects together with controls were systematically identified and appraised. All authors were contacted either to confirm the results or to provide missing data. Meta-analysis and meta-regression were then performed on this data. Nine studies were included from seven centres, with data from a total of 595 AD and 212 matched controls. Mean (95% CIs) annualised hippocampal atrophy rates were found to be 4.66% (95% CI 3.92, 5.40) for AD subjects and 1.41% (0.52, 2.30) for controls. The difference between AD and control subject in this rate was 3.33% (1.73, 4.94).

Hippocampal morphology and distinguishing late-onset from early-onset elderly depression

OBJECTIVE

Despite evidence for hippocampal abnormalities in elderly depression, it is unknown whether these changes are regionally specific. This study used three-dimensional mapping techniques to identify regional hippocampal abnormalities in early- and late-onset depression. Neuropsychological correlates of hippocampal morphology were also investigated.

METHOD

With high-resolution magnetic resonance imaging, hippocampal morphology was compared among elderly patients with early- (N=24) and late-onset (N=22) depression and comparison subjects (N=34). Regional structural abnormalities were identified by comparing distances, measured from homologous hippocampal surface points to the central core of each individual's hippocampal surface model, between groups.

RESULTS

Hippocampal volumes differed between depressed patients and comparison subjects but not between patients with early- and late-onset depression. However, statistical mapping results showed that regional surface contractions were significantly pronounced in late- compared to early-onset depression in the anterior of the subiculum and lateral posterior of the CA1 subfield in the left hemisphere. Significant shape differences were observed bilaterally in anterior CA1-CA3 subfields and the subiculum in patients in relation to comparison subjects. These results were similar when each disease group was separately compared to comparison subjects. Hippocampal surface contractions significantly correlated with memory measures among late- but not early-onset depressed patients or comparison subjects.

CONCLUSIONS

More pronounced regional volume deficits and their associations with memory in late-onset depression may suggest that these patients are more likely to develop cognitive impairment over time than individuals with early-onset depression. Mapping regional hippocampal abnormalities and their cognitive correlates may help guide research in defining risk profiles and treatment strategies.

Age effects on hippocampal structural changes in old men: the HAAS

Understanding the variability of the hippocampus in human brain research is essential. The effect of age on the hippocampus has been explored in several studies that have been focused on either normal aging or neural degeneration. Shape analysis of magnetic resonance imaging (MRI) provides morphological measures for brain structures. This study further investigates the age effects on hippocampal morphology in three groups (104 normal controls, 24 Alzheimer's disease (AD) and 14 vascular dementia (VaD) patients). By utilizing a parametric shape analysis of hippocampal MRI scans, each individual distance map is generated and analyzed statistically. Specifically, after eliminating similarity parameters (rotation, translation, and scaling) effects for each hippocampus, an individual distance map is generated from parametric hippocampal surfaces and medial axes. Then statistical methods, including regression, and permutation tests, are applied to detect the differences in hippocampal distance maps and volumes under the effect of age in each group. Statistical analyses reveal that the loss of hippocampal volume and changes in shape are more significantly related to aging in the control group than in AD/VaD. The results also show that the asymmetry of hippocampus in healthy subjects is greater than that in either of the disease groups. Our study shows that 3D statistical shape analysis could enhance the understanding of age effects on local areas of hippocampi. However, the sample sizes of disease groups are relatively low; further studies with more AD/VaD data are needed.

High-resolution fMRI investigation of the medial temporal lobe

The medial temporal lobe (MTL) is critical for declarative memory formation. Several theories of MTL function propose functional distinctions between the different structures of the MTL, namely the hippocampus and the surrounding cortical areas. Furthermore, computational models and electrophysiological studies in animals suggest distinctions between the subregions of the hippocampus itself. Standard fMRI resolution is not sufficiently fine to resolve activity on the scale of hippocampal subregions. Several approaches to scanning the MTL at high resolutions have been made, however there are limitations to these approaches, namely difficulty in conducting group-level analyses. We demonstrate here techniques for scanning the MTL at high resolution and analyzing the high-resolution fMRI data at the group level. To address the issue of cross-participant alignment, we employ the ROI-LDDMM alignment technique, which is demonstrated to result in smaller alignment errors when compared with several other common normalization techniques. Finally, we demonstrate that the pattern of activation obtained in the high-resolution functional data is similar to that obtained at lower resolution, although the spatial extent is smaller and the percent signal change is greater. This difference in the pattern of activation may be due to less partial volume sampling in the high-resolution data, resulting in more accentuated regions of activation.

Mapping progressive brain structural changes in early Alzheimer's disease and mild cognitive impairment

Alzheimer's disease (AD), the most common neurodegenerative disorder of the elderly, ranks third in health care cost after heart disease and cancer. Given the disproportionate aging of the population in all developed countries, the socio-economic impact of AD will continue to rise. Mild cognitive impairment (MCI), a transitional state between normal aging and dementia, carries a four- to sixfold increased risk of future diagnosis of dementia. As complete drug-induced reversal of AD symptoms seems unlikely, researchers are now focusing on the earliest stages of AD where a therapeutic intervention is likely to realize the greatest impact. Recently neuroimaging has received significant scientific consideration as a promising in vivo disease-tracking modality that can also provide potential surrogate biomarkers for therapeutic trials. While several volumetric techniques laid the foundation of the neuroimaging research in AD and MCI, more precise computational anatomy techniques have recently become available. This new technology detects and visualizes discrete changes in cortical and hippocampal integrity and tracks the spread of AD pathology throughout the living brain. Related methods can visualize regionally specific correlations between brain atrophy and important proxy measures of disease such as neuropsychological tests, age of onset or factors that may influence disease progression. We describe extensively validated cortical and hippocampal mapping techniques that are sensitive to clinically relevant changes even in the single individual, and can identify group differences in epidemiological studies or clinical treatment trials. We give an overview of some recent neuroimaging advances in AD and MCI and discuss strengths and weaknesses of the various analytic approaches.

Parallel transport in diffeomorphisms distinguishes the time-dependent pattern of hippocampal surface deformation due to healthy aging and the dementia of the Alzheimer's type

Hippocampal surface structure was assessed at twice 2 years apart in 26 nondemented subjects (CDR 0), in 18 subjects with early dementia of Alzheimer type (DAT, CDR 0.5), and in 9 subjects who converted from the nondemented (CDR 0) to the demented (CDR 0.5) state using magnetic resonance (MR) imaging. We used parallel transport in diffeomorphisms under the large deformation diffeomorphic metric mapping framework to translate within-subject deformation of the hippocampal surface as represented in the MR images between the two time points in a global template coordinate system. We then performed hypothesis testing on the longitudinal variation of hippocampal shape in the global template. Both subjects with early DAT and converters showed greater rates of hippocampal deformation across time than nondemented controls within every subfield of the hippocampus. In a random field analysis, inward surface deformation across time occurred in a non-uniform manner across the hippocampal surface in subjects with early DAT relative to the nondemented controls. Also, compared to the controls, the lateral aspect of the left hippocampal tail showed inward surface deformation in the converters. Using surface deformation patterns as features in a linear discriminant analysis, we were able to respectively distinguish converters and patients with early DAT from healthy nondemented controls at classification rates of 0.77 and 0.87, which were obtained in the same training set using the leave-one-out cross validation approach.

Automated measurement of hippocampal atrophy using fluid-registered serial MRI in AD and controls

OBJECTIVE

To assess hippocampal atrophy rates calculated from fluid registration methods.

METHODS

Hippocampi were segmented on baseline and registered-repeat scans of 32 probable Alzheimer disease (AD) subjects and 55 controls. Fluid-based atrophy rates were calculated.

RESULTS

In AD patients, the mean (SD) atrophy rates for manual, fluidly propagated, and Jacobian methods were 5.09 (3.59), 5.34 (3.43), and 3.55 (2.70) (percentage per year). In controls, atrophy rates were 1.31 (2.00), 0.89 (0.75), and 0.56 (1.12) (percentage per year). In AD, fluid propagation and manual rates were similar in means (P = 0.55) and variances (P = 0.71). Jacobian rates were smaller in mean (P = 0.002) and variance (P = 0.026) than in manual rates. In controls, fluid-propagated rates were similar in mean to manual rates (P = 0.12), but less variable (P < 0.0001). Jacobian rates were smaller in mean (P = 0.014) and less variable (P < 0.0001) than in manual rates. Both fluid methods were superior to manual measures in separating AD from controls (P < 0.0001).

CONCLUSIONS

Fluid-based methods may be useful in large serial hippocampal studies.

Overcoming interference: an fMRI investigation of pattern separation in the medial temporal lobe

The medial temporal lobe (MTL) supports the formation and retrieval of long-term declarative memories, or memories for facts and everyday events. One challenge posed for this type of memory stems from the highly overlapping nature of common episodes. Within cognitive psychology, it is widely accepted that interference between information learned at different times is a major limitation on memory. In spite of several decades of intense research in the fields of interference theory and the neurobiological underpinnings of declarative memory, there is little direct evidence bearing on how the MTL resolves this interference to form accurate memories of everyday facts and events. Computational models of MTL function have proposed a mechanism in which the MTL, specifically the hippocampus, performs pattern separation, whereby overlapping representations are made less similar. However, there is little evidence bearing on how this process is carried out in the intact human MTL. Using high-resolution fMRI, we conducted a set of experiments that taxed behavioral pattern separation by using highly similar, interfering stimuli in a modified continuous recognition task. Regions within the parahippocampal gyrus demonstrated activity consistent with a "recall to reject" strategy. In contrast and critical to performing the task, activity within the hippocampus distinguished between correctly identified true stimulus repetitions, correctly rejected presentations of similar lure stimuli, and false alarms to similar lures. These data support the computational models' assertion that the hippocampus plays a key role in pattern separation.