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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Neuropsychologia. Author manuscript; available in PMC Jul 20, 2009.
Published in final edited form as:
PMCID: PMC2713100
NIHMSID: NIHMS54899
Mapping Progressive Brain Structural Changes in Early Alzheimer’s Disease and Mild Cognitive Impairment
Liana G. Apostolova1,2 and Paul M. Thompson1,2
1 Department of Neurology, David Geffen School of Medicine, UCLA, CA
2 Laboratory of Neuro Imaging, David Geffen School of Medicine, UCLA, CA
Send correspondence to: Liana G. Apostolova, M.D., Alzheimer’s Disease Research Center, 10911 Weyburn, 2nd floor, Los Angeles, CA 90095, Phone: 310/794-2551, Fax: 310/794-3148, E-mail: lapostolova/at/mednet.ucla.edu
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 4–6-fold 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.
Alzheimer’s disease (AD), the most common cause of degenerative dementia, causes progressive brain atrophy. These atrophic changes are readily observed with structural neuroimaging. In the past three decades, several important technological leaps have allowed us to study the brain, as degeneration progresses. Magnetic resonance imaging (MRI), currently the structural neuroimaging method of choice for diagnostic and research efforts, revolutionized the field several decades ago. More recently, advanced analytic techniques have become available further empowering our ability to discover disease associated pathologic changes and clinical correlations in vivo. In this review we will provide a comprehensive overview of recent advances in MRI research on AD and related diseases, while critically appraising the methodology.
Alzheimer’s Disease
AD is the commonest form of dementia worldwide - it currently affects 4.9 million elderly over the age of 65 and as many as 500,000 people under the age of 65 in the Unites States alone.(1) It manifests with relentlessly progressive cognitive decline presenting initially as memory loss and then spreads to affect all other cognitive faculties and the patients’ ability to conduct an independent lifestyle. Pre-mortem, AD-associated brain changes can be clinically evaluated with the help of neuroimaging. They consist of global atrophy with an early predilection for the hippocampal region and the temporo-parietal cortical areas. Post-mortem examination reveals abundant cortical and hippocampal neuritic plaques (NP) and neurofibrillary tangles (NFT) as well as pancerebellar atrophy upon gross inspection of the brain.
Several risk factors influence the prevalence of AD. Age is by far the greatest risk factor: at the age of 65, one in eight elderly individuals carries the diagnosis, but after the age of 85, the ratio is close to one in every two persons. Genetic predisposition for late-onset sporadic AD seems to be primarily conveyed by the presence of the apolipoprotein E4 (ApoE4) allele in a dose-dependent fashion – subjects with one ApoE4 copy have increased risk (odds ratio, OR=2.6–3.2), and those with two copies have greatly increased risk (OR=14.9) for developing AD, while the ApoE2 allele appears to be protective (OR=0.6) (2, 3). Rare genetic variants of fully penetrant autosomal dominant forms of AD also exist and have been attributed to presenilin 1 and presenilin 2 gene mutations on chromosomes 14 and 1 and to an amyloid protein precursor gene mutation on chromosome 21. Even so, these autosomal dominant forms account for only 2% of all AD cases. (4)
The societal cost of AD is immense. AD is the 5th leading cause of death among the elderly. The total number of deaths caused by AD has increased by 33% between 2000 and 2004, but those from other major etiologies, such as heart disease, breast cancer, prostate cancer and stroke, have decreased by 3–10% each.(1) More than $148 billion are spent on AD related healthcare costs annually. (1)
Mild Cognitive Impairment
Mild cognitive impairment (MCI) is a relatively recent concept introduced to recognize the intermediate cognitive state where patients are neither cognitively intact nor demented (5). The current prevalence rate for MCI among those 65 years and older is 12–18% (6) and 10–15% of these patients progress to develop dementia annually. (7) Many subjects with MCI have cortical and hippocampal atrophy. Most show unequivocal signs of AD pathology, including plaque and tangle accumulation, postmortem. (810) Nevertheless, some MCI patients harbor an alternative pathological diagnosis such as dementia with Lewy bodies, vascular dementia, hippocampal sclerosis, frontotemporal dementia, progressive supranuclear palsy, argyrophilic brain disease or a nonspecific tauopathy. (6) Some MCI cases can also be attributed to nondegenerative pathology. (6)
In recent years, MCI has attracted increasing research interest. It is now widely accepted that MCI is the single most important at-risk state for AD. Two major research questions have captured most attention – how can we predict which MCI patients will develop AD and which treatment would offer neuroprotection from future progression to dementia.
Neuroimaging is a powerful tool for creative exploration of the epidemiology, diagnostic sensitivity, progression and therapeutic efficacy in AD and MCI. Reliable biomarkers of the underlying pathology and such that can also predict disease progression in MCI are needed and several candidate brain measures have been examined in a wealth of cross-sectional and longitudinal neuroimaging studies. Neuroimaging has captured the interest of clinical trialists and may help establish disease-modifying effects in clinical trials by documenting slowing of brain atrophy rates or of amyloid accumulation. Structural measures such as total brain volume, hippocampal and entorhinal cortical volumes have been thoroughly evaluated and used as surrogate markers for clinical trials.(1116) New powerful techniques for more precise 3D disease and treatment effect localization are likewise being explored (1720), as are novel positron emission tomography tracers to label the hallmarks of AD in the living brain. (2123)
The extent of brain degeneration in dementia can be quantified by purely structural techniques such as MRI and diffusion tensor imaging (which can examine white fiber integrity), and tomographic approaches, such as PET and SPECT (24, 25), which can quantify cerebral blood flow and metabolism. The structural/functional distinction has been blurred to include PET studies with new ligands that label structural pathology (such as tracer compounds that bind to amyloid) (22, 2628), and MRI variants such as fMRI imaging of blood-oxygenation level dependent (BOLD) contrast (29), arterial spin labeling(30), relaxometry (31), spectroscopy (32), and magnetization transfer imaging(33). For structural MRI scans in particular, the oldest image analysis approach currently used in dementia research is the region of interest (ROI) technique. This measures the overall volume of specific brain substructures. It relies on manual delineation of the structures of interest on each successive image slice, followed by calculating the total volume of the structure, which is then used for statistical analyses. Manual volumetry is a powerful technique and has yielded a wealth of findings, but has several disadvantages. It requires proficient and knowledgeable tracers who can delineate the ROIs with high reliability and consistency. As an operator-dependent technique, the ROI method is most susceptible to subjective bias, although this can be reduced by blinding of analysts to disease status, and periodic assessments to avoid drift in tracing reliability over time. Additionally, it is time consuming and requires an accurate a priori hypothesis, so analyses often tend to be limited to one or two structures of interest. The ROI technique also requires a detailed and well-established tracing protocol that unambiguously defines segmentation criteria for each respective brain region. Several automated ROI segmentation techniques for the hippocampus, lateral ventricles, caudate nucleus and corpus callosum have been recently developed but have not yet been widely used. (17, 3438)
Voxel-based morphometry is a more recent methodology that can simultaneously visualize group differences or statistical effects on gray and white matter, throughout the whole brain. It is implemented in the widely used Statistical Parametric Mapping software package. (39) The ‘classical’ VBM approach segments the brain into three tissue classes – gray and white matter and cerebro-spinal fluid, then aligns the gray matter individual maps to a common 3D space and averages the data of all subjects while retaining information on the variability of each study group. Gray matter images are smoothed with a 8–12 mm kernel to reduce the confounding effects of individual morphological differences while retaining the global disease associated changes. The averaged gray matter maps are then compared at the voxel level via general linear modeling techniques. The resulting 3D maps show areas where the variable of interest (diagnosis, clinical score, clinical outcome, etc.) shows significant correlation with focal gray matter density.
The traditional VBM technique has been criticized for being nonspecific and possibly even biased as false-positive between-group disease effects could arise due to systematic image registration errors or systematic shifts in unaffected regions in close proximity to truly affected areas. (40) The newer generation VBM approaches addresses this problem by modulating the voxel intensity of the spatially normalized gray matter maps by the local expansion factor of a 3D deformation field that aligns each brain to a standard brain template. As a result, the final modulated voxel contains the same amount of gray matter as in the native pre-registered gray matter map. (4143) This improved methodology accounts for voxel expansion/contraction effects and removes potential sources of bias in estimating group differences in brain structure, provided that corresponding cortical areas are successfully matched, and not systematically misaligned from one group to another.
Even with the improved methodology there is another limitation posed by the VBM methods. Spatial smoothing is a step necessary to counterbalance the inter-individual differences contained in the data. Digital filtering of gray matter maps is applied to remove high-spatial frequency differences in anatomy that remain because anatomy cannot be aligned exactly across subjects, and it is also applied so that the residuals of the statistical model at each voxel are better approximations to a Gaussian random field, allowing standard parametric statistics to be applied (such as t tests or analysis of variance). Although this approach makes it easier to detect systematic, global disease-induced effects as opposed to individual variability, it occurs at the expense of blurring of focal changes. Newer computational anatomy techniques that control inter-subject variability by setting focal constraints (such as for instance sulcal lines) for the local warping forces have achieved much improved co-registration of cortical anatomy. (18) In addition, surface modeling techniques have been used to create explicit 3D models of the cortex and hippocampus in the form of computational meshes, and these meshes can be aligned precisely point-by-point across subjects, even allowing higher-order matching of important landmarks that lie within surfaces (e.g., CA fields of the hippocampus, gyral/sulcal boundaries) when making group comparisons. (4448)
Computational anatomy techniques are the newest generation of image analysis software. They view the brain or any ROI as a 3D shape or a complex geometrical pattern that can be modeled as a continuous 3D deformable mesh structure. These mesh models contain information on the focal geometry and the warping deformation vectors applied. They can be thus easily averaged across groups and subjected to statistical comparisons (see Gee and Thompson, 2007, for a recent review of the field). (49) Many are generally built upon the pre-existing ROI and VBM techniques. Most computational anatomy hippocampal analytic techniques rely on initial manual tracing of the hippocampus followed by complex mathematical approaches for 3D hippocampal modeling. (19, 50)One such method, the radial atrophy mapping approach, computes the 3D distance from the hippocampal centroid in each coronal section (also called a medial curve or core) to each hippocampal surface point. This provides an intuitive 3D measure of hippocampal thickness, and provides metric estimates of hippocampal atrophy that can be compared point-by-point across individuals and groups. (19)The related large-deformation high-dimensional brain mapping approach is also used to study the changes in hippocampal morphology in AD. (50) This approach is so-called because, rather than relying on manual tracings of the hippocampus, a template hippocampus is traced on a single subject and fluidly warped to match the anatomy of new subjects. The transformation involved is high-dimensional, i.e. involves millions of degrees of freedom to produce a deformation that captures shape differences in detail. It is also a ‘large-deformation’ approach, meaning that the deformation model follows a continuum-mechanical law that prevents folding or tearing of the deforming template (known mathematically as a diffeomorphism).
The radial atrophy mapping approach was originally developed for the hippocampus, and grew out of work on medial representations (‘M-reps’) or ‘skeletonization’, in the computer vision literature. Even so, the same approach is readily applicable to any structure that shrinks or expands over time, such as the lateral ventricles, for instance. One newly developed method for ventricular surface extraction and comparison (17, 37) consists of manual tracing of the ventricles in a few randomly selected subjects from any given study cohort, followed by fluid propagation of the resulting parametric mesh models over the ventricles of the rest of the subjects in the study. This approach results in several automated ventricular models for each subject where the number is equal to the number of the individual ventricular “primers” used. These models are not identical due to registration variability. However, these models can be averaged to a single automated optimized individual ventricular model. This automated approach saves time and manual effort, reduces the possibility of subjective bias and reduces segmentation errors that invariably result from fluid propagation of only one ventricular model to the ventricles of all subjects in the study. These steps are then followed by the radial distance measurement technique.(19) The outputs are group-specific 3D ventricular expansion maps that can be statistically compared between the groups.
Similar to VBM many cortical computational anatomy techniques use the segmented gray matter maps and the gray matter density approach (18, 51) or a more advanced gray matter thickness approach. (5254)Some cortical mapping techniques are conceptually related to the VBM approach, in that they do not use intrinsic cortical surface features to guide the matching of cortical anatomy across subjects. (52, 53) Other approaches such as the cortical pattern matching technique (18) specifically use primary and some secondary cortical sulci for optimized warping of the cortical gyral anatomy.(55) The latter technique allows for 3D mapping and visualization of cortical gray matter density or thickness, PET, fMRI, amyloid-imaging data. Its goal is to combine data across subjects from homologous cortical regions as far as possible (by matching gross anatomic landmarks). This greatly improved cortical registration can increase the signal-to-noise ratio and therefore the statistical power to detect disease-associated changes. In a sense, approaches that do not explicitly match cortical surfaces across subjects incur a substantial loss of power as similar features are misaligned when data from different individuals is integrated.
Another technique known as tensor-based morphometry (TBM) accomplishes time-efficient spatial registration. It first uses a linear registration for spatial alignment of the scans followed by a fully automated fluid warping algorithm of cortical and subcortical structures to maximize the mutual information (e.g., anatomical correspondence) between the images (for detailed description of the technique please see Chiang et al, 2007 (56, 57)). The final product is the first image transformed into the shape of the second one, and, in addition, the Jacobian transformation necessary to deform one voxel to its counterpart in the other scan is saved and visually displayed in the color-coded “voxel compression” or “tensor map” on the follow-up scan. TBM allows efficient mapping and analysis of very large datasets and works quite well for subcortical analyses. For cortical analysis, most current implementations of TBM are comparable to VBM in that the automated whole-brain registration does not provide an explicit fine-scale cortical pattern registration as the sulcal-based warping technique does. Clearly, future studies will exploit the benefits of cortical surface-based and fully volumetric registration to capture changes cortically and subcortically with optimal power. (5860) Given its great sensitivity and time efficiency TBM could present a promising surrogate marker tool for clinical trials. (Figure 1)
Figure 1
Figure 1
Tensor based morphometry analysis showing the progression of brain atrophy in posterior cortical atrophy patient scanned at 6-month intervals. The images show progressive tissue loss (purple) in the right temporal and parietal and left frontal lobes and (more ...)
The ROI technique has been heavily used in many AD and more recently MCI neuroimaging studies. Subtle hippocampal atrophy, on brain MRI, is also associated with normal aging. It affects 15% of 60–75-year-old population and 48% of 76–90-year-old population. (61) Hippocampal volume loss is strikingly more prevalent in both MCI and AD where 78 % and 96 % of patients are affected respectively.(61) Cross-sectional ROI studies have shown that the hippocampal (6266) and entorhinal volumes (62, 6467) can reliably differentiate AD subjects from normal elderly. The absolute volumetric difference in entorhinal volume between amnestic MCI and normal elderly is approximately 13–17% and between mild to moderate AD and MCI from 30–38%, while for the hippocampus the differences are 7–11 % and 19–39 %, respectively.(68, 69) The sensitivity of hippocampal volume measures for differentiating AD from controls ranges from 77–92% and the specificity is approximately 80–92%. (64, 66, 7072) The precision of differentiating MCI from normal controls and MCI from AD is substantially less accurate when using hippocampal volume (MCI vs. controls sensitivity 52–80 % and specificity 79–80% and MCI vs. AD sensitivity 45–60 % and specificity 80%). (66, 68, 70) The diagnostic accuracy of the entorhinal volume in the comparison of AD vs. normal controls has been reported as high as 92% (sensitivity 76–80% and specificity 80–90%). (64, 66, 73) Which structure is the more powerful discriminator when distinguishing MCI patients from normal controls is currently debated, with some studies siding with the hippocampus(62) and others with the entorhinal cortex.(69)
Longitudinal ROI studies have allowed researchers to estimate the rates of structural atrophy in normal aging, MCI and AD and to research the risk, protective and predictive factors for clinical conversion between these cognitive states. The annual atrophy of the hippocampus in healthy elderly subjects is about 1.6 to 1.7 % per year (74, 75) while that of the entorhinal cortex is about 1.4 % per year.(73) Much higher hippocampal rates are observed in longitudinal epidemiologic studies in MCI and AD. Elevated atrophic rates have been reported in MCI subjects who decline to AD relative to those who remain stable (annual hippocampal atrophy rate for MCI patients who remain stable = 2.8%, for MCI converters =3.7% and for AD =3.5–4%). (74, 75) The yearly volume loss for the entorhinal cortex in AD is about 7%.(73) Mutation carriers with the autosomal dominant genetic variants of AD with 100% penetrance were reported to have 6.7-fold greater annual atrophy rates 5.5 years before the clinical manifestation of dementia. (76) The odds ratio for conversion to sporadic AD over 1.2 – 4.8 years was reported to be 1.75 in MCI subjects with smaller relative to those with larger hippocampal volumes.(13)
The recently published results of a 3 year-long donepezil/vitamin E/placebo study in MCI reported somewhat higher atrophy rates for the hippocampus and entorhinal cortex in the placebo group (5.44% and 11.58, respectively) than the ones reported above.(11) The most likely contributing factor is that most of the studies above used less stringent criteria for MCI than the clinical trial did. For instance, a delayed recall score of 1.5 SD below the age- and education-adjusted mean was an enrollment criterion for the clinical trial while most of the studies above did not use a neuropsychological score as a cut-off and based their MCI diagnosis on clinical grounds alone.
Comparing the power of various ROIs for predicting conversion from MCI to AD, two research groups have independently reported additional predictive value for measures from some structures outside of the mesial temporal lobe. Convit et al. reported that a logistic regression model including volumes of the fusiform, inferior and middle temporal gyri achieved the best predictive value, while Jack et al. reported that whole brain and ventricular volume are good predictors for future cognitive decline across the spectrum from normal aging to AD.(13) Direct comparisons of hippocampus and entorhinal cortex have revealed that the entorhinal cortex volume may be a better predictor of future conversion from MCI to AD during follow-up of 12–77 months. (77, 78) Other researchers have implicated the entorhinal cortex as predictive of impending cognitive deterioration from normal aging and both the hippocampus and entorhinal cortex from the MCI/early AD stage. (79)
VBM has been increasingly used to study AD progression and has also been used as a clinical trial surrogate measure (see section Neuroimaging techniques as promising surrogate markers for clinical trials). Relative to normal controls, the gray matter volume of even mild AD subjects is greatly reduced in multiple brain regions that are known to be heavily affected by AD pathology. Using the classical VBM technique to compare mild AD and normal elderly subjects, greater atrophy was evident in the mesial temporal lobe structures and precuneus followed by the perisylvian and temporopolar cortices. (80) Comparing mild to moderate AD and normal controls, another research group reported additional changes in the inferior and lateral temporal, posterior cingulate and insular cortices. (81) Cortical structural differences between MCI and normal controls localize to the mesial temporal, middle temporal and anterior subcallosal cingulate, while those between MCI and mild AD also spread to left precuneus, left parietal lobe, left superior and middle temporal gyri and the right middle temporal gyrus. (82)
Using the so-called ‘optimized’ VBM approach (which multiplies the spatially normalized gray matter volumes by the local warping factors required to match them to a common template brain), a comparative cross-sectional study of AD and normal elderly controls revealed differential atrophy of the bilateral parahippocampal, the fusiform and the left inferior temporal gyri. (83) Another optimized VBM study reported 6.5% less total gray matter volume in MCI relative to normal controls and 6.2% more total gray matter volume relative to AD patients from the full range of disease spectrum (MMSE range 4–28).(84) AD subjects showed greater parietal, anterior and posterior cingulate atrophy relative to MCI, while MCI subjects demonstrated greater superior temporal, left insular and posterior hippocampal atrophy relative to normal controls.
Several recent longitudinal VBM studies have increased our understanding of the AD-related cortical atrophy progression in vivo. Scahill et al. (85) used the classical VBM approach in a longitudinal study of 4 presymptomatic autosomal dominant AD, 10 mild and 12 moderate AD subjects. The presymptomatic and the mild AD subjects showed mesial, inferior and lateral temporal and posterior cingulate atrophy. Two studies used the optimized VBM approach: Bozzali et al. (86) clinically followed 26 MCI subjects, who all underwent a baseline structural MRI scan every six months for an average of 28.7 months. They compared the MCI to a normal control and an AD group. At baseline, MCI patients who converted to AD showed significantly more atrophy of the bilateral anterior cingulate, bilateral lateral temporal, left inferior temporal, left parietal and bilateral frontal association cortices relative to normal controls (pcorr<0.05). The atrophy pattern seen in the stable MCI group relative to controls was restricted to the bilateral rectus and middle frontal gyri, and the left fusiform and inferior temporal gyri. AD subjects showed more atrophy than MCI converters in the right superior temporal, left paracentral and precuneal and the bilateral fusiform cortex. Chetelat et al. (87) enrolled 18 MCI subjects of whom 7 converted to AD during an 18-month follow-up interval. The annualized regional gray matter loss in MCI converters ranged from 0–4.5% and in MCI nonconverters from 0–4 %. Fastest atrophy rates were identified in the temporal pole, entorhinal and lateral temporal cortices (2.5–4.5%). The prefrontal cortex appeared to be more affected in nonconverters. At baseline, greater cortical involvement was seen in the parahipppocampal, fusiform, lingual and posterior cingulate cortices in MCI subjects who later converted to AD relative to those who did not. (Figure 2)
Figure 2
Figure 2
Optimized voxel-based morphometry analysis of gray matter atrophy progression in MCI who converted to AD (converters) and MCI who remained stable (non-converters). The color-coded maps represent the percent annual volume loss projected on the 3D hemispheric (more ...)
One very recent optimized VBM study(88) prospectively followed 136 cognitively normal elderly and 5 MCI subjects for an average of 5.4 years. 23 of them converted to MCI and 9 of the 23 further deteriorated to meet criteria for AD. At baseline, the group consisting of the 23 pre-MCI/pre-AD and the 5 MCI subjects demonstrated significant gray matter atrophy in the anteromedial temporal and left angular gyri, and the left lateral temporal lobe.
Among the most recent advances in neuroimaging is the development of computational anatomy techniques for brain morphometry. These methods include newer metrics to create maps of structural differences throughout the brain without manual interaction with images. More subtle descriptors such as complexity or shape measures have also been employed. In the case of the hippocampus, the new proxy measures for focal hippocampal atrophy include radial atrophy measurements and surface deformation analyses. For cortex, the more elegant cortical thickness mapping techniques have recently been employed.(18, 52, 8992)
NFT neuropathology does not affect the hippocampus uniformly. Tangles affect the entorhinal cortex first, and then the subiculum and CA1 subfield of the hippocampus, followed by a spread to the CA2/3 and finally CA4 region before invading the neocortex.(93) 3D computational modeling techniques have offered a novel insight into these regional and sequential changes in the hippocampus. (19, 9496) Using the hippocampal radial atrophy mapping approach, Thompson et al. showed profound hippocampal differences between normal controls and AD patients, that were also linked with MMSE scores.(19) Atrophy of the CA1 area was 15–20% greater (97) in AD versus cognitively normal elderly subjects. In one longitudinal(95) and one cross-sectional(94) study, Apostolova et al. sequenced the progressive spread of atrophy through the hippocampal subfields. MCI subjects who later converted to AD had significantly more atrophy of the subiculum and CA1 areas of the hippocampus relative to MCI subjects who either remained stable or improved (95) (Figure 3 and Figure 4 middle row). Furthermore, the cross-sectional comparison of MCI and AD patients showed that in subjects who meet criteria for AD hippocampal atrophy has spread to the CA2/3 areas as well (Figure 4 bottom row).(94) An additional cross-sectional hippocampal radial atrophy study also demonstrated subicular atrophy in single domain amnestic MCI and AD relative to normal controls. (98)
Figure 3
Figure 3
The hippocampal radial atrophy technique was applied to identify regions of hippocampal atrophy that predict future decline from amnestic MCI to AD.(95) Illustrated are the statistical and percent difference surface map comparisons between the three MCI (more ...)
Figure 4
Figure 4
Using the hippocampal radial atrophy mapping technique Apostolova et al. revealed the stage wise spread of hippocampal atrophy through the hippocampus in amnestic MCI and AD. MCI converters show significantly more atrophy in the subiculum and CA1 areas (more ...)
Two cross-sectional large-deformation high-dimensional brain mapping hippocampal studies have implicated the CA1 subfield to discriminate between normal aging and questionable AD (Clinical Dementia Rating Scale (CDR) = 0.5). (50, 99) Longitudinal studies have demonstrated that the CA1 area predicts conversion from cognitively normal status to questionable AD (an AD subcategory inclusive of MCI and mild AD subjects) over a follow-up interval ranging from 0.9–7.1 years (100). Both CA1 and subicular atrophy were detected in questionable AD over a follow-up of 2 years.(99)
Computational anatomy has contributed significantly to our understanding of cortical changes that accompany AD. The first AD cortical pattern matching study, by Thompson et al., compared the baseline and 1.5-year follow-up cortical atrophy patterns between a mild to moderate AD and a normal group.(18) At baseline AD subjects had >15% greater atrophy in the lateral temporal and parietal as well as the mesial left frontal, parietal and occipital cortices. At follow-up differences relative to the control group were also seen in the frontal association fields and the mesial right hemisphereic surface. The researchers also developed an animated movie depicting the disease-associated pathology spread through the brain; this can be viewed at http://www.loni.ucla.edu/~thompson/AD_4D/dynamic.html. Recently, the same group compared the cortical atrophy pattern between amnestic MCI (mean MMSE=28.2± 1.6) and mild AD (mean MMSE=23.8±3.2). Despite the small absolute cognitive difference (MMSE difference=4.4) there was a highly significant difference in global right and left hemispheric cortical atrophy (p<0.001 for the whole hemisphere comparison bilaterally, corrected for multiple comparisons). Greatest absolute differences (15%) were seen in the entorhinal, right more than left lateral temporal, right parietal and bilateral precuneus followed by 10–15% atrophy throughout the rest of the cortex. The observed changes closely resemble Braak and Braak amyloid stages A and B (Figure 5).
Figure 5
Figure 5
Cortical atrophy differences between amnestic MCI and mild AD subjects can be visualized using the cortical pattern matching technique. (51) The 3D maps on the left demonstrate significantly greater atrophy in mild AD relative to MCI in most cortical (more ...)
In another study, Ringman et al.(101) created 3D maps of cortical gray matter thickness in MRI brain scans of 12 (10 female, 2 male) subjects at increased genetic risk for familial AD (4 with the L235V, and 4 with the A431E PS1 mutation, and 2 with the V717I substitution in APP). In mutation carriers, mean cortical thickness was 15% lower in the right medial parietal lobe, left medial frontal lobe, and in the lateral occipital cortex adjacent to the parietal lobe on the left. This demonstrated that quantitative comparisons of cortical thickness using our cortical pattern matching techniques (18) are sensitive to early atrophy during the preclinical phase of familial AD.
Using a related cortical mapping approach, Lerch et al. reported that mild to moderate AD subjects (MMSE range 10–29) have on average 18% thinner cortices relative to healthy controls (AD=3.1±0.28 mm, controls=3.74±0.32 mm).(52) The thickness computation differed slightly, in that it was based on computing the distance between explicitly computed inner and outer cortical surface meshes. By contrast, other studies, including those by Thompson et al., 2003(18), Apostolova et al. (51), and Ringman et al.(101) use a method where gray matter thickness is computed with a voxel-coding technique from the gray/white matter segmentation, progressively coding cortical voxels with increasing 3D distance from the inner cortical mantle. The regional pattern in the Lerch et al. study agreed closely with the one reported by Apostolova et al. in their mild AD to amnestic MCI cortical pattern matching study. (51) A larger follow-up study also included an MCI group and reported as expected that MCI subjects fall between normal aging and AD by their average cortical thickness – MCI cortex was 0.18 mm thinner than normal controls and 0.26 mm thicker than AD subjects.(53) The entorhinal and lateral occipito-temporal cortices were driving the effects in the MCI to normal control comparisons.
Using a novel automated ventricular extraction approach, our group recently mapped the ventricular differences between AD and cognitively normal subjects and between healthy normal ApoE4 carriers and non-carriers. (37) AD subjects showed significant expansions of the posterior and superior horns of the lateral ventricle relative to controls. ApoE4 carriers demonstrated mainly superior horn dilations relative to ApoE4 non-carriers; this is generally consistent with the progressing trajectory of lobar atrophy in AD. (Figure 6) Using a related technique, but using a single surface template and an optical flow registration approach for ventricular segmentation, Carmichael et al. (102) compared the ventricular shape differences between MCI, AD and cognitively normal subjects. As expected progressive ventricular expansion was evident where MCI subjects had intermediate and AD subjects largest ventricles. (Figure 7)
Figure 6
Figure 6
The ventricular mapping technique was used to study the effects of AD and of ApoE4 genotype on ventricular expansion. AD subjects when compared to age-matched controls demonstrated posterior and anterior ventricular horn expansion (left panel). When the (more ...)
Figure 7
Figure 7
Using a single surface template and an optical flow registration approach for ventricular segmentation, Carmichael et al. (102) compared the ventricular shape differences between MCI, AD and cognitively normal subjects. The figure shows the average thickness, (more ...)
Sporadic AD typically starts after age 65 (and is otherwise known as late-onset AD, or LOAD). Occasionally though, it presents earlier in life and in such cases the term early onset AD (EOAD) is generally used. The literature supports a more aggressive course and higher pathological burden in EOAD vs. LOAD subjects. Post mortem, EOAD subjects have greater NP and NFT burden relative to LOAD subjects throughout the brain. (103) Such phenomena can also be captured pre-mortem with neuroimaging. Using the classical VBM technique, Ishii et al. reported greater cortical atrophy in the parietal, posterior cingulate and precuneal regions in EOAD vs. LOAD subjects. (104) In agreement with pathologic observations, one recent cortical pattern matching study demonstrated widespread pancerebral gray matter atrophy with relative sparing of the primary sensorimotor and visual cortices in EOAD subjects when compared to age-matched controls. (105) The LOAD subjects in the same study showed a much more restricted atrophy pattern mainly centered on the lateral and mesial temporal and mesial parietal and occipital lobes relative to an age-matched control group. This study implies that at the same level of cognitive impairment, younger subjects typically exhibit much heavier pathologic burden, relative to older subjects - perhaps as a result of higher cognitive reserve.
AD is a neurodegenerative disorder presenting with progressive decline in all cognitive domains. Researchers have repeatedly looked into the cognitive correlates of the structural brain changes observed in AD. The hippocampus is the primary brain region where memory encoding, consolidation and retrieval occurs. Several neuroimaging studies have implicated the hippocampus in memory retention.(106, 107) Verbal memory is thought to lateralize to the left and spatial memory to the right hippocampus. (108, 109) Both memory and executive functions showed an association with hippocampal atrophy at baseline in a cohort comprised of normal, mildly impaired and demented subjects.(110) Verbal memory performance has also been associated with gray matter density in the parahippocampal gyrus in one VBM study (86) and in the bilateral precuneus, entorhinal/parahippocampal, inferior temporal and the left temporo-occipital cortices in a computational anatomy study. (95)Some recent advances in the structure-function memory correlations have been reviewed in detail elsewhere in this issue. (29)
Using the cortical pattern matching technique, one longitudinal (18) and one cross-sectional(111) study showed strong correlation between poorer MMSE performance and cortical atrophy in the entorhinal, parahippocampal, posterior cingulate, precuneal, lateral temporal, lateral parietal and subgenual cingulate association cortices with strong left-sided predilection. The longitudinal study demonstrated strong associations in the frontal association areas after 1.5-year follow-up period. (18) Associations between worsening MMSE score and hippocampal atrophy have likewise been reported in several ROI studies. (13, 112)
Performance on a measure of semantic fluency (animal fluency) and a picture-naming test (Boston Naming Test) was strongly correlated with the structural integrity of posterior lateral temporal and temporo- and parieto-occipital cortices on the left. (113) Declining performance on both measures associated with left more than right frontal atrophy, but the effect size for the semantic fluency instrument was stronger. The opposite observation was noted for the visual association cortices where the visually driven Boston Naming Test elicited stronger interactions (Figure 8). Two other studies reported language-related brain associations. The study by Pantel et al. (114) found an association between the Aachen aphasia battery and the left temporal lobe volume, while the magnetization transfer study by van der Flier et al. reported a link between the Boston Naming and animal fluency tests and the temporal and frontal lobe volumes (33).
Figure 8
Figure 8
Using the cortical pattern matching technique Apostolova et al. (113) studied the association between the performance on Boston Naming Test (BNT) and the Animal Fluency test and cortical atrophy in 19 subjects with AD and 5 MCI who later converted to (more ...)
One recent study (115) included 14 controls, 32 MCI, and 14 AD subjects and investigated the structural correlates of one verbal memory (California Verbal Learning test) and several executive function tests (Wechsler Adult Intelligence Scale – III Trail Making test and Digit span). The authors reported correlations between verbal memory and lateral frontal and medial temporal gray matter volume. Executive function correlated with the frontal lobe gray matter volume.
The results of the imaging arm of the largest MCI therapeutic trial, to date, were recently published. (11) Aside of the main analyses concerned with the therapeutic effects of donepezil and vitamin E on hippocampal, entorhinal and global brain atrophy and ventricular expansion, the researchers scrutinized the associations between the cognitive measures used in the trial (MMSE, Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-Cog) and CDR and the observed brain atrophy rates. The methods utilized were the brain boundary shift integral(12) for whole brain and lateral ventricles and the ROI method for the hippocampus and entorhinal cortex. The hippocampus and entorhinal cortex showed uniformly strong correlations with all cognitive measures. Global brain atrophy showed strongest correlations with the ADAS-Cog 11 and ADAS-Cog 13 measures and ventricular expansion with CDR and ADAS-Cog 13.
The Alzheimer’s Disease Neuroimaging Initiative (ADNI; see Mueller et al., 2005 (116) http://www.loni.ucla.edu/ADNI and ADNI-info.org) is a large multi-site longitudinal MRI and FDG-PET study of 200 elderly controls, 400 mildly cognitively impaired subjects, and 200 Alzheimer’s disease subjects. One goal of this project is to develop improved imaging methods to measure longitudinal changes of the brain in normal aging, during the transition to early Alzheimer’s disease, and in Alzheimer’s disease patients. ADNI studies published to date have focused on determining the optimal scanning approaches for such a longitudinal study, and have examined which factors affect the longitudinal stability of image-derived measures, and the repeatability, reproducibility, and variability of changes detected in different scan types. In the image analysis community, there are also considerable efforts to improve scanner calibration, corrections for image distortion and inhomogeneity, identification of small artifactual differences in overall image scaling over time (with phantom-based or scalp-based image registration), as these are among the major sources of experimental error in examining the spatial profile of degenerative brain changes over short intervals. (117120)
Until recently, all AD clinical trials incorporated outcome measures of only two kinds – cognitive and functional scales. Unquestionably, any successful therapeutic intervention must unequivocally demonstrate clinical improvement (or, as it is more commonly observed, slowing of cognitive decline) in the active treatment relative to the placebo group. Even so, limiting ourselves to clinical outcomes alone as a measure of treatment effects has significant drawbacks. The cognitive instruments used in clinical trials are well established and have proven validity and reliability. Nevertheless, as with all other neuropsychologic measures, they suffer from test-retest fluctuations, learning/practice effects, and depend heavily on the patient’s attention, motivation, drug-induced side effects, psychological and physical states (e.g., anxiety, lack of sleep, etc.). Diagnostically, changes detected by cognitive tests also lag behind structural and pathologic changes in the brain. It has been hypothesized that disease-induced pathologic changes start accumulating many years and perhaps even a decade before disease detection and accurate diagnosis by clinical examination and neuropsychological measures is possible. In clinical trial settings, several months are needed for cognitive changes to become apparent between treatment and placebo groups. Measures of events earlier in the disease pathway, such as brain atrophy measures, may thus better capture early therapeutic effects and document disease modification. Now that the pre-dementia state of MCI has been described with its most common underlying pathology being AD, we are even more invested in detecting powerful surrogate markers of disease progression. Neuroimaging with its multitude of techniques is a promising alley for development of successful and sensitive biomarkers for clinical trials.
Thus far, four therapeutic studies with neuroimaging biomarkers have been published. A milameline AD trial (121) - which was terminated early because of lack of therapeutic efficacy - was the first one to include structural MRI imaging and hippocampal ROI analyses as a surrogate marker. The treatment assignment was discontinued, but the imaging portion of the study was fully conducted and the feasibility of hippocampal volumetry as a proxy clinical trial outcome measure was established. An A-beta immunotherapy trial also included structural MRI and VBM analyses of whole brain and ventricular changes in patients with AD.(14) Following several reports of meningoencephalitis, the trial was terminated. Post-hoc imaging analyses revealed that subjects who responded favorably to the therapy by antibody production actually had greater ventricular enlargement and greater total brain volume loss relative to non-responders. These surprising findings did not correlate with cognitive decline. One explanation for this paradox was that any amyloid clearance from the brain may in theory result in a reduction of brain neuropil volume, but this was not anticipated in advance and requires further study. A third small study investigated the effect of donepezil on hippocampal atrophy rate and showed slowing down of atrophy in the treatment relative to the placebo group. (16) A three-year ADCS Donepezil/Vitamin E/placebo MCI trial included a baseline and a follow-up MRI. The study’s main objective was to establish if treatment with either donepezil or vitamin E would slow down the conversion of MCI to AD. (122) The MRI substudy of the trial (11) had several objectives: to ascertain whether or not there was a therapeutic effect of Donepezil and Vitamin E on structural MRI disease progression measures such as hippocampal, entorhinal, whole brain atrophy and ventricular expansion; to examine the correlations between clinical, cognitive and imaging measures and to evaluate and compare the annual atrophy rates in predefined study subgroups (e.g., converters vs. nonconverters and ApoE4 carriers vs. noncarriers). The imaging study reported no significant differences between subjects on donepezil, vitamin E or placebo. In the ApoE4 subanalyses a trend-level significant slowing down of hippocampal atrophy was observed in ApoE4 carriers vs. noncarriers in the donepezil treatment group. The latter finding relates well to the results of the main study where ApoE4 carriers randomized to donepezil were found to have the lowest conversion rates to AD. (122) As expected, the MRI substudy also reported higher atrophy rates in converters relative to nonconverters and in ApoE4 carriers relative to noncarriers. The cognitive correlations were all significant in the predicted direction (e.g., better performance in those with lower atrophy rates and vice versa).(11)
One natural history study from Japan (123) compared AD subjects who were taking up to 5 mg of donepezil (the maximal approved dose in Japan) from a research treatment cohort (N=54) and a historical control group (N=93) from before the drug was approved for clinical use in Japan. The authors reported that the patients treated with donepezil had significantly slower annual hippocampal atrophy rate relative to the historical control group (3.82 ±2.84% vs. 5.04±2.54%). The effect of donepezil remained significant after controlling for age, sex, disease duration, MRI interval, education, ApoE4 genotype and baseline cognition.
Taken together, this evidence suggests that hippocampal and brain atrophy may be advantageous surrogate markers for clinical trials, especially in cases where more sensitive techniques are required. Thus far, the only hippocampal analysis approach studied has been the ROI method. Newer, state-of-the-art methodologies allow for 3D visualization of the hippocampal and ventricular structures and for more powerful statistical comparisons of regional hippocampal and ventricular changes. These may well be the clinical trial biomarkers of the future. Computational anatomy techniques such as cortical pattern matching, automated ventricular segmentation, Freesurfer (89, 124) and TBM, could likewise provide improved sensitivity and the much-needed evidence for therapeutic effects of anti-AD therapy on the living brain.
AD is now one of the most important health concerns of the 21st century, due to several factors – the progressive aging of the population, the impending graying of the baby boomer generation and its corresponding social and economic impact. The AD research agenda is progressively moving back in time to the pre-dementia state of MCI and even further back in an attempt to identify cognitively normal elderly who already harbor the earliest AD–associated pathologic changes. These subjects would be the ideal therapeutic target for any disease modifying drug that may potentially abort or delay the onset of cognitive decline. In the past two decades, neuroimaging researchers have capitalized upon revolutionary technical advances. The rapid development of new techniques capable of reliable, sensitive and powerful detection of focal disease induced changes instills optimism that disease course and therapeutic response can be carefully monitored and appraised. With several promising disease-modifying agents in the pharmaceutical pipeline, most AD researchers anticipate imminent progress towards curing and preventing this devastating neurodegenerative disorder.
Acknowledgments
This work was generously supported by NIA K23 AG026803 (jointly sponsored by NIA, AFAR, The John A. Hartford Foundation, the Atlantic Philanthropies, the Starr Foundation and an anonymous donor; to LGA), NIA P50 AG16570 (to LGA and PMT); NIBIB EB01651, NLM LM05639, NCRR RR019771, NIH/NIMH R01 MH071940, NIH/NCRR P41 RR013642 and NIH U54 RR021813 (to PMT).
Footnotes
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1. Alzheimer Association. 2007 Alzheimer’s disease facts and figures. 2007. Available at: http://www.alz.org/news_and_events_rates_rise.asp.
2. Farrer LA, Cupples LA, Haines JL, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. Jama. 1997;278:1349–1356. [PubMed]
3. Graff-Radford NR, Green RC, Go RC, et al. Association between apolipoprotein E genotype and Alzheimer disease in African American subjects. Archives of Neurology. 2002;59:594–600. [PubMed]
4. Campion D, Dumanchin C, Hannequin D, et al. Early-onset autosomal dominant Alzheimer disease: prevalence, genetic heterogeneity, and mutation spectrum. Am J Hum Genet. 1999;65:664–670. [PubMed]
5. Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med. 2004;256:240–246. [PubMed]
6. Petersen RC. Mild cognitive impairment. Continuum Lifelong Learning Neurol. 2007;13:13–36.
7. Petersen RC, Doody R, Kurz A, et al. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58:1985–1992. [PubMed]
8. Haroutunian V, Perl DP, Purohit DP, et al. Regional distribution of neuritic plaques in the nondemented elderly and subjects with very mild Alzheimer disease. Arch Neurol. 1998;55:1185–1191. [PubMed]
9. Jicha GA, Parisi JE, Dickson DW, et al. Neuropathologic Outcome of Mild Cognitive Impairment Following Progression to Clinical Dementia. Arch Neurol. 2006;63:674–681. [PubMed]
10. Price JL, Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol. 1999;45:358–368. [PubMed]
11. Jack CR, Jr, Petersen RC, Grundman M, et al. Longitudinal MRI findings from the vitamin E and donepezil treatment study for MCI. Neurobiol Aging. 2007 doi: 10.1016/j.neurobiolaging.2007.1003.1004. in press:Online access. [PMC free article] [PubMed] [Cross Ref]
12. Fox NC, Cousens S, Scahill R, Harvey RJ, Rossor MN. Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects. Arch Neurol. 2000;57:339–344. [PubMed]
13. Jack CR, Jr, Shiung MM, Gunter JL, et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology. 2004;62:591–600. [PMC free article] [PubMed]
14. Fox NC, Black RS, Gilman S, et al. Effects of Abeta immunization (AN1792) on MRI measures of cerebral volume in Alzheimer disease. Neurology. 2005;64:1563–1572. [PubMed]
15. Grundman M, Sencakova D, Jack CR, Jr, et al. Brain MRI hippocampal volume and prediction of clinical status in a mild cognitive impairment trial. Journal of Molecular Neuroscience. 2002;19:23–27. [PubMed]
16. Krishnan KR, Charles HC, Doraiswamy PM, et al. Randomized, placebo-controlled trial of the effects of donepezil on neuronal markers and hippocampal volumes in Alzheimer’s disease. American Journal of Psychiatry. 2003;160:2003–2011. [PubMed]
17. Chou Y, Lepore N, Zubiricay GI, et al. Automated 3D mapping and shape analysis of the lateral ventricles via fluid registration of multiple surface-based atlases. ISBI. 2007 in press.
18. Thompson PM, Hayashi KM, de Zubicaray G, et al. Dynamics of gray matter loss in Alzheimer’s disease. Journal of Neuroscience. 2003;23:994–1005. [PubMed]
19. Thompson PM, Hayashi KM, De Zubicaray GI, et al. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage. 2004;22:1754–1766. [PubMed]
20. Csernansky JG, Wang L, Miller JP, Galvin JE, Morris JC. Neuroanatomical predictors of response to donepezil therapy in patients with dementia. Arch Neurol. 2005;62:1718–1722. [PubMed]
21. Braskie MN, Klunder AD, Hayashi KM, et al. Dynamic trajectory of cortical plaque and tangle load correlates with cognitive impairment in normal aging and Alzheimer’s disease. Journal of Neuroscience. 2007 submitted (unpublished data)
22. Small GW, Kepe V, Ercoli LM, et al. PET of brain amyloid and tau in mild cognitive impairment. N Engl J Med. 2006;355:2652–2663. [PubMed]
23. Protas HD, Huang SC, Kepe V, et al. Hemispheric cortical surface map for assessing the rate of changes of regional cortical FDDNP distribution versus MMSE score. 54th Annual Meeting of the Society for Nuclear Medicine; Washington, DC. 2007. unpublished data.
24. Salmon ea. Functional imaging of cognition in Alzheimer’s disease using positron emission tomography. Neuropsychologia. 2007 Current issue.
25. Mosconi L. Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. FDG-PET studies in MCI and AD. Eur J Nucl Med Mol Imaging. 2005;32:486–510. [PubMed]
26. Kepe V, Huang SC, Small GW, Satyamurthy N, Barrio JR. Visualizing pathology deposits in the living brain of patients with Alzheimer’s disease. Methods Enzymol. 2006;412:144–160. [PubMed]
27. Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55:306–319. [PubMed]
28. Nordberg A. Amyloid imaging, multi-tracer functional studies and cognitive processes in Alzheimer’s disease. Neuropsychologia. 2007 Current issue.
29. Dickerson B, Sperling R. Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer’s disease: insights from functional MRI studies. Neuropsychologia. 2007 Current issue.
30. Du AT, Jahng GH, Hayasaka S, et al. Hypoperfusion in frontotemporal dementia and Alzheimer disease by arterial spin labeling MRI. Neurology. 2006;67:1215–1220. [PMC free article] [PubMed]
31. House MJ, St Pierre TG, Foster JK, Martins RN, Clarnette R. Quantitative MR imaging R2 relaxometry in elderly participants reporting memory loss. AJNR Am J Neuroradiol. 2006;27:430–439. [PubMed]
32. Kantarci K. Magnetic resonance markers for early diagnosis and progression of Alzheimer’s disease. Expert Rev Neurother. 2005;5:663–670. [PubMed]
33. van der Flier WM, van den Heuvel DM, Weverling-Rijnsburger AW, et al. Magnetization transfer imaging in normal aging, mild cognitive impairment, and Alzheimer’s disease. Annals of Neurology. 2002;52:62–67. [PubMed]
34. Hogan RE, Mark KE, Wang L, Joshi S, Miller MI, Bucholz RD. Mesial temporal sclerosis and temporal lobe epilepsy: MR imaging deformation-based segmentation of the hippocampus in five patients. Radiology. 2000;216:291–297. [PubMed]
35. Pitiot A, Delingette H, Thompson PM, Ayache N. Expert knowledge-guided segmentation system for brain MRI. Neuroimage. 2004;23(Suppl 1):S85–96. [PubMed]
36. Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31:1116–1128. [PubMed]
37. Chou Y, Lepore N, de Zubiricaray GI, et al. Automated ventricular mappping with multi-atlas fluid image allignment reveals genetic effects in Alzheimer’s disease. Neuroimage. 2007 in press.
38. Morra JH, Tu Z, Apostolova LG, et al. Comparison of AdaBoost and Support Vector Machines for detecting Alzheimer’s disease through automated hippocampal segmentation. IEEE Trans Med Imaging. 2007 submitted (unpublished data)
39. Good CD, Johnsrude I, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage. 2001;14:685–700. [PubMed]
40. Bookstein FL. Voxel-based morphometry” should not be used with imperfectly registered images. Neuroimage. 2001;14:1454–1462. [PubMed]
41. Good CD, Ashburner J, Frackowiak RS. Computational neuroanatomy: new perspectives for neuroradiology. Rev Neurol (Paris) 2001;157:797–806. [PubMed]
42. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14:21–36. [PubMed]
43. Davatzikos C, Genc A, Xu D, Resnick SM. Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy. Neuroimage. 2001;14:1361–1369. [PubMed]
44. Thompson PM, Woods RP, Mega MS, Toga AW. Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain. Hum Brain Mapp. 2000;9:81–92. [PubMed]
45. Zeineh MM, Engel SA, Thompson PM, Bookheimer SY. Dynamics of the hippocampus during encoding and retrieval of face-name pairs. Science. 2003;299:577–580. [PubMed]
46. Rasser PE, Johnston P, Lagopoulos J, et al. Functional MRI BOLD response to Tower of London performance of first-episode schizophrenia patients using cortical pattern matching. Neuroimage. 2005;26:941–951. [PubMed]
47. Shi Y, Thompson PM, de Zubicaray GI, et al. Direct mapping of hippocampal surfaces with intrinsic shape context. Neuroimage. 2007;37:792–807. [PMC free article] [PubMed]
48. Shi Y, Thompson PM, Dinov I, Osher S, Toga AW. Direct cortical mapping via solving partial differential equations on implicit surfaces. Med Image Anal. 2007;11:207–223. [PMC free article] [PubMed]
49. Gee JC, Thompson PM. Guest editorial: Special issue on computational neuroanatomy. IEEE Trans Med Imaging. 2007 in press.
50. Csernansky JG, Wang L, Joshi S, et al. Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Dementia of the Alzheimer type. Neurology. 2000;55:1636–1643. [PubMed]
51. Apostolova LG, Steiner CA, Akopyan GG, Toga AW, Cummings JL, Thompson PM. 3D gray matter atrophy mapping in mild cognitive impairment and mild Alzheimer’s disease. Arch Neurol. 2007;64:1489–1495. [PMC free article] [PubMed]
52. Lerch JP, Pruessner JC, Zijdenbos A, Hampel H, Teipel SJ, Evans AC. Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy. Cereb Cortex. 2005;15:995–1001. [PubMed]
53. Singh V, Chertkow H, Lerch JP, Evans AC, Dorr AE, Kabani NJ. Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease. Brain. 2006;129:2885–2893. [PubMed]
54. Thompson PM, Dutton RA, Hayashi KM, et al. Thinning of the cerebral cortex visualized in HIV/AIDS reflects CD4+ T lymphocyte decline. Proc Natl Acad Sci U S A. 2005;102:15647–15652. [PubMed]
55. Hayashi KM, Thompson PM, Mega MS, Zoumalan CI. Medial hemispheric surface gyral pattern delineation in 3D: surface curve protocol. Available at: http://www.loni.ucla.edu/~khayashi/Public/Medial_suface/MedialLinesProtocol [online]. Available.
56. Chiang MC, Dutton RA, Hayashi KM, et al. 3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry. Neuroimage. 2007;34:44–60. [PMC free article] [PubMed]
57. Studholme C, Cardenas V, Schuff N, Rosen H, Miller B, Weiner MW. Detecting spatially consistent structural diferences in Alzheimer’s and Frontotemporal dementia using deformation morpjometry. MICCAI. 2001;2001:41–48.
58. Joshi AA, Shattuck DW, Thompson PM, Leahy RM. Simlultaneous surface and volumetric brain registration using harmonic mappings. IEEE Trans Med Imaging. 2007 in press.
59. Thompson PM, Toga AW. A surface-based rechnique for warping 3D images of the brain. IEEE Trans Med Imaging. 1996;15:1–16.
60. Toga AW, Thompson PM. What is where and why it is important. Neuroimage. 2007;37:1045–1049. [PMC free article] [PubMed]
61. De Leon MJ, George AE, Golomb J, et al. Frequency of hippocampal formation atrophy in normal aging and Alzheimer’s disease. Neurobiol Aging. 1997;18:1–11. [PubMed]
62. Bottino CM, Castro CC, Gomes RL, Buchpiguel CA, Marchetti RL, Neto MR. Volumetric MRI measurements can differentiate Alzheimer’s disease, mild cognitive impairment, and normal aging. Int Psychogeriatr. 2002;14:59–72. [PubMed]
63. Callen DJ, Black SE, Gao F, Caldwell CB, Szalai JP. Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD. Neurology. 2001;57:1669–1674. [PubMed]
64. Juottonen K, Laakso MP, Partanen K, Soininen H. Comparative MR Analysis of the Entorhinal Cortex and Hippocampus in Diagnosing Alzheimer Disease. AJNR Am J Neuroradiol. 1999;20:139–144. [PubMed]
65. Krasuski JS, Alexander GE, Horwitz B, et al. Volumes of medial temporal lobe structures in patients with Alzheimer’s disease and mild cognitive impairment (and in healthy controls) Biological Psychiatry. 1998;43:60–68. [PubMed]
66. Xu Y, Jack CR, Jr, O’Brien PC, et al. Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. [see comment] Neurology. 2000;54:1760–1767. [PubMed]
67. Bobinski M, de Leon MJ, Convit A, et al. MRI of entorhinal cortex in mild Alzheimer’s disease. Lancet. 1999;353:38–40. [PubMed]
68. Du AT, Schuff N, Amend D, et al. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2001;71:441–447. [PMC free article] [PubMed]
69. Pennanen C, Kivipelto M, Tuomainen S, et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiology of Aging. 2004;25:303–310. [PubMed]
70. Kantarci K, Xu Y, Shiung MM, et al. Comparative diagnostic utility of different MR modalities in mild cognitive impairment and Alzheimer’s disease. Dementia & Geriatric Cognitive Disorders. 2002;14:198–207. [PMC free article] [PubMed]
71. Laakso MP, Soininen H, Partanen K, et al. Volumes of hippocampus, amygdala and frontal lobes in the MRI-based diagnosis of early Alzheimer’s disease: correlation with memory functions. J Neural Transm Park Dis Dement Sect. 1995;9:73–86. [PubMed]
72. Laakso MP, Soininen H, Partanen K, et al. MRI of the hippocampus in Alzheimer’s disease: sensitivity, specificity, and analysis of the incorrectly classified subjects. Neurobiol Aging. 1998;19:23–31. [PubMed]
73. Du AT, Schuff N, Kramer JH, et al. Higher atrophy rate of entorhinal cortex than hippocampus in AD. Neurology. 2004;62:422–427. [PMC free article] [PubMed]
74. Jack CR, Jr, Petersen RC, Xu Y, et al. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology. 2000;55:484–489. [PMC free article] [PubMed]
75. Jack CR, Jr, Petersen RC, Xu Y, et al. Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. Neurology. 1998;51:993–999. [PMC free article] [PubMed]
76. Ridha BH, Barnes J, Bartlett JW, et al. Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. Lancet Neurol. 2006;5:828–834. [PubMed]
77. De Toledo-Morrell L, Goncharova I, Dickerson B, Wilson RS, Bennett DA. From healthy aging to early Alzheimer’s disease: in vivo detection of entorhinal cortex atrophy. Ann N Y Acad Sci. 2000;911:240–253. [PubMed]
78. deToledo-Morrell L, Stoub TR, Bulgakova M, et al. MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiol Aging. 2004;25:1197–1203. [PubMed]
79. Killiany RJ, Hyman BT, Gomez-Isla T, et al. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology. 2002;58:1188–1196. [PubMed]
80. Baron JC, Chetelat G, Desgranges B, et al. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease. Neuroimage. 2001;14:298–309. [PubMed]
81. Frisoni GB, Testa C, Zorzan A, et al. Detection of grey matter loss in mild Alzheimer’s disease with voxel based morphometry. Journal of Neurology, Neurosurgery & Psychiatry. 2002;73:657–664. [PMC free article] [PubMed]
82. Chetelat G, Desgranges B, De La Sayette V, Viader F, Eustache F, Baron JC. Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport. 2002;13:1939–1943. [PubMed]
83. Busatto GF, Garrido GE, Almeida OP, et al. A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease. Neurobiology of Aging. 2003;24:221–231. [PubMed]
84. Karas GB, Scheltens P, Rombouts SA, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage. 2004;23:708–716. [PubMed]
85. Scahill RI, Schott JM, Stevens JM, Rossor MN, Fox NC. Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. [see comment] Proc Natl Acad Sci U S A. 2002;99:4703–4707. [PubMed]
86. Bozzali M, Filippi M, Magnani G, et al. The contribution of voxel-based morphometry in staging patients with mild cognitive impairment. Neurology. 2006;67:453–460. [PubMed]
87. Chetelat G, Landeau B, Eustache F, et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage. 2005;27:934–946. [PubMed]
88. Smith CD, Chebrolu H, Wekstein DR, et al. Brain structural alterations before mild cognitive impairment. Neurology. 2007;68:1268–1273. [PubMed]
89. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000;97:11050–11055. [PubMed]
90. Lohmann G, Preul C, Hund-Georgiadis M. Morphology-based cortical thickness estimation. Inf Process Med Imaging. 2003;18:89–100. [PubMed]
91. Ratnanather JT, Barta PE, Honeycutt NA, et al. Dynamic programming generation of boundaries of local coordinatized submanifolds in the neocortex: application to the planum temporale. Neuroimage. 2003;20:359–377. [PubMed]
92. Ratnanather JT, Botteron KN, Nishino T, et al. Validating cortical surface analysis of medial prefrontal cortex. Neuroimage. 2001;14:1058–1069. [PubMed]
93. Schonheit B, Zarski R, Ohm TG. Spatial and temporal relationships between plaques and tangles in Alzheimer-pathology. Neurobiol Aging. 2004;25:697–711. [PubMed]
94. Apostolova LG, Dinov ID, Dutton RA, et al. 3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and Alzheimer’s disease. Brain. 2006;129:2867–2873. [PubMed]
95. Apostolova LG, Dutton RA, Dinov ID, et al. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol. 2006;63:693–699. [PubMed]
96. Wang L, Swank JS, Glick IE, et al. Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging. Neuroimage. 2003;20:667–682. [PubMed]
97. Frisoni GB, Sabattoli F, Lee AD, Dutton RA, Toga AW, Thompson PM. In vivo neuropathology of the hippocampal formation in AD: a radial mapping MR-based study. Neuroimage. 2006;32:104–110. [PubMed]
98. Becker JT, Davis SW, Hayashi KM, et al. Three-dimensional patterns of hippocampal atrophy in mild cognitive impairment. Arch Neurol. 2006;63:97–101. [PubMed]
99. Wang L, Miller JP, Gado MH, et al. Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type. Neuroimage. 2006;30:52–60. [PMC free article] [PubMed]
100. Csernansky JG, Wang L, Swank J, et al. Preclinical detection of Alzheimer’s disease: hippocampal shape and volume predict dementia onset in the elderly. Neuroimage. 2005;25:783–792. [PubMed]
101. Ringman JM, Dutton RA, Lai J, et al. Cortical thinning detected and visualized in the pre-clinical phase of familial Alzheimer’s disease. 59th Annual Meeting of the American Academy of Neurology; Boston, MA. 2007. unpublished data.
102. Carmichael OT, Kuller LH, Lopez OL, et al. Ventricular volume and dementia progression in the Cardiovascular Health Study. Neurobiol Aging. 2007;28:389–397. [PMC free article] [PubMed]
103. Marshall GA, Fairbanks LA, Tekin S, Vinters HV, Cummings JL. Early-onset Alzheimer’s disease is associated with greater pathologic burden. J Geriatr Psychiatry Neurol. 2007;20:29–33. [PubMed]
104. Ishii K, Kawachi T, Sasaki H, et al. Voxel-based morphometric comparison between early- and late-onset mild Alzheimer’s disease and assessment of diagnostic performance of z score images. AJNR Am J Neuroradiol. 2005;26:333–340. [PubMed]
105. Frisoni GB, Pievani M, Testa C, et al. The topography of grey matter involvement in early and late onset Alzheimer’s disease. Brain. 2007;130:720–730. [PubMed]
106. Kramer JH, Schuff N, Reed BR, et al. Hippocampal volume and retention in Alzheimer’s disease. J Int Neuropsychol Soc. 2004;10:639–643. [PMC free article] [PubMed]
107. Grundman M, Jack CR, Jr, Petersen RC, et al. Hippocampal volume is associated with memory but not monmemory cognitive performance in patients with mild cognitive impairment. Journal of Molecular Neuroscience. 2003;20:241–248. [PubMed]
108. de Toledo-Morrell L, Dickerson B, Sullivan MP, Spanovic C, Wilson R, Bennett DA. Hemispheric differences in hippocampal volume predict verbal and spatial memory performance in patients with Alzheimer’s disease. Hippocampus. 2000;10:136–142. [PubMed]
109. Petersen RC, Jack CR, Jr, Xu YC, et al. Memory and MRI-based hippocampal volumes in aging and AD. Neurology. 2000;54:581–587. [PubMed]
110. Mungas D, Harvey D, Reed BR, et al. Longitudinal volumetric MRI change and rate of cognitive decline. Neurology. 2005;65:565–571. [PMC free article] [PubMed]
111. Apostolova LG, Lu PH, Rogers S, et al. 3D mapping of mini-mental state examination performance in clinical and preclinical Alzheimer disease. Alzheimer Dis Assoc Disord. 2006;20:224–231. [PubMed]
112. Jack CR, Jr, Dickson DW, Parisi JE, et al. Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology. 2002;58:750–757. [PMC free article] [PubMed]
113. Apostolova LG, Lu P, Rogers S, et al. 3D mapping of language networks in clinical and pre-clinical Alzheimer’s disease. Brain Lang. 2007. Online access: http://dx.doi.org/10.1016/j.bandl.2007.1003.1008.
114. Pantel J, Schonknecht P, Essig M, Schroder J. Neuropsychological correlates of cerebral atrophy in Alzheimer’s dementia. Fortschr Neurol Psychiatr. 2004;72:192–203. [PubMed]
115. Duarte A, Hayasaka S, Du A, et al. Volumetric correlates of memory and executive function in normal elderly, mild cognitive impairment and Alzheimer’s disease. Neurosci Lett. 2006;406:60–65. [PMC free article] [PubMed]
116. Mueller SG, Weiner MW, Thal LJ, et al. Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) Alzheimers Dement. 2005;1:55–66. [PMC free article] [PubMed]
117. Alexander GE, Chen K, Reiman EM, et al. Regional gray matter reductions in Alzheimer’s dementia and amnestic mild cognitive impairment: preliminary findings from the Alzheimer’s Disease Neuroimaging Initiative using voxel-based morphometry. Alzheimer’s Association International Conference on the Prevention of Dementia; Washington, D.C. 2007. unpublished data.
118. Boyes RG, Gunter JL, Frost C, et al. Quantitative analyses of N3 on 3Tesla scanners with multichannel phased array coils. Neuroimage. 2007 in press.
119. Leow AD, Klunder AD, Jack CR, Jr, et al. Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. Neuroimage. 2006;31:627–640. [PMC free article] [PubMed]
120. Jack CR, Jr, Bernstein MA, Fox NC, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): the MR Imaging protocol. Journal of MRI. 2007 in press.
121. Jack CR, Jr, Slomkowski M, Gracon S, et al. MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD. Neurology. 2003;60:253–260. [PMC free article] [PubMed]
122. Petersen RC, Thomas RG, Grundman M, et al. Vitamin E and Donepezil for the Treatment of Mild Cognitive Impairment. N Engl J Med. 2005 NEJMoa050151.
123. Hashimoto M, Kazui H, Matsumoto K, Nakano Y, Yasuda M, Mori E. Does donepezil treatment slow the progression of hippocampal atrophy in patients with Alzheimer’s disease? Am J Psychiatry. 2005;162:676–682. [PubMed]
124. Fischl B, Salat DH, van der Kouwe AJ, et al. Sequence-independent segmentation of magnetic resonance images. Neuroimage. 2004;23(Suppl 1):S69–84. [PubMed]