Dementia affects nearly one tenth of all senior citizens, and nearly half of those older than 85 years. Alzheimer disease (AD) is the most common cause of dementia. Its incidence doubles every 5 years after age 60, afflicting about 1% of those aged 60 to 64 years and 30% to 40% of those aged 85 years and older. Many clinical and neuroimaging studies aim to identify presymptomatic candidates for preventive treatments before the extensive neuronal damage of AD develops. Brain imaging markers have already emerged as important tools in the differential diagnosis of dementia. Parameters derived from brain imaging are being intensively examined as potential predictors to identify persons with only mild cognitive losses who will imminently decline and in whom the full dementia syndrome of AD will develop. As novel, disease-modifying agents emerge, brain-imaging markers also may facilitate drug development1,2 and help monitor drug efficacy in clinical settings. Mild cognitive impairment (MCI) has a variety of definitions, all aimed at expressing an intermediate cognitive state between normal aging and dementia.3 In general, MCI patients have aberrant cognitive skills in 1 or more domains but function adequately in personal, social, and professional contexts. Scientific interest in MCI is rapidly increasing because the annual conversion rate from MCI to dementia is high.4,5 Many imaging studies have sought to identify which persons with MCI will convert to AD. These individuals are likely to benefit most from early intervention to prevent progression to global cognitive decline. Advanced 3-dimensional (3D) brain-imaging techniques, including MRI and positron emission tomography (PET), have greatly improved our understanding of aging and AD. Imaging techniques can be used to assess brain structure and function noninvasively in living persons (Figure 1), as well as to monitor disease progression, predict imminent cognitive decline, and quantify effects of therapy in large-scale clinical trials. These techniques are rapidly advancing. Structural MRI now can detect subtle, systematic brain volume changes at a rate of about 0.5% per year and can be used to map the dynamic trajectory of cortical atrophy as it spreads in the living brain. PET radioligands also are being developed for mapping the microstructure of the brain with respect to the distribution of such molecules as neurotransmitter receptors, tangles of agglomerated tau protein, and insoluble deposits of amyloid,6-8 in addition to mapping cerebral function.9 STRUCTURAL NEUROIMAGING MRI studies of MCI and AD Imaging studies of MCI have taken their lead from approaches that have successfully tracked AD pathology. AD pathology progresses in a known stereotypical sequence (Figure 2, Left), as documented in postmortem histologic studies of patients at different stages of the disease. Neurofibrillary pathology typically starts in the transentorhinal cortex and quickly spreads to the entorhinal cortex before involving the hippocampus.10-12 This temporal pathology persists for several years13 before spreading cortically to engulf the rest of the temporal, frontal, and parietal lobes.11,14-19 Longitudinal 3D MRI scans of groups of subjects can map this process in detail (Figure 2, Right). Time-lapse maps have been constructed from sequential brain MRI scans to reveal the anatomic sequence of cortical atrophy.19 Cortical regions that myelinate first are typically least vulnerable to AD pathology (eg, primary sensorimotor and visual cortices). In contrast, neurofibrillary tangles and neutrophil threads in dendrites accumulate early in the late-myelinating heteromodal association cortices, posterior cingulate, and phylogenetically older limbic areas that remain highly plastic throughout life.20 Quantitative methods to track brain changes with conventional MRI fall into 3 main categories: (1) volumetric measurement of specific structures, such as the hippocampus or entorhinal cortex; (2) image processing techniques that estimate rates of whole-brain atrophy as a percentage of volume loss per year13,21; and (3) map-based techniques that visualize the 3D profile of group differences in gray matter loss,22 atrophic rates,23,24 white matter integrity,25 or cortical gray matter thickness.26-28 Large-scale neuroimaging efforts (eg, the Alzheimer's Disease Neuroimaging Initiative, www.loni.ucla.edu/ADNI)29,30 are now comparing the ability of each imaging measure, together with biomarkers and other clinical functional measures, to differentiate MCI and AD from healthy aging, to predict future cognitive decline, and to predict conversion from MCI to AD. Even so, MCI is not readily distinguished from AD or from normal aging on MRI. Each of these categories overlaps substantially for all known MRI measures. Hippocampal volumes and maps Because pathology emerges first in the entorhinal cortex and hippocampus, most volumetric MRI studies of MCI patients have focused on medial temporal lobe structures. Neuronal atrophy, decreased synaptic density, and overt neuronal loss are evident on MRI as progressive cortical gray matter loss, reduced subcortical gray and white matter volumes, and expanding ventricular and sulcal cerebrospinal fluid (CSF) spaces.31 The hippocampal volume in patients with mild dementia in whom AD is clinically suspected is roughly 25% less than that in matched healthy controls,32-34 whereas the hippocampal volume in patients with MCI shows a mean reduction of about 11%.34 Early studies35 found that the hippocampal volumes of 97.2% of patients in whom mild AD was suspected (Clinical Dementia Rating, 0.5) were below normal. In mildly impaired patients, mean hippocampal volumes either fall roughly halfway between those of demented patients with suspected AD and normal patients,36-39 or are similar to volumes found in patients in whom AD is suspected.40,41 One study42 found that entorhinal cortex and hippocampal volume measures provided roughly equal intergroup discrimination ability in 30 control, 30 MCI, and 30 suspected-AD patients; although the entorhinal cortex typically atrophies earlier, it is harder to delineate reliably on MRI. Many groups also have assessed the value of premorbid hippocampal atrophy, rated visually,43 or using volumetry,41-44 for predicting crossover to AD. One study37 found that hippocampal atrophy at baseline was associated with conversion from MCI to suspected AD at 33-month follow-up (relative risk, 0.69; P = .01 for a group of 80 MCI patients of whom 27 [34%] were demented at follow-up). Advanced 3D modeling techniques have localized specific regions of atrophy within the hippocampus in MCI. For example, one group reported45 use of 3D surface reconstruction techniques to model the shape of the hippocampus, creating average shape models for cohorts of subjects with amnestic MCI, nonamnestic MCI, and AD, and for healthy controls. These techniques can localize tissue atrophy or shape alterations46 and can map the average pattern of reductions in hippocampal thickness in millimeters.19 Amnestic MCI patients showed diffuse hippocampal atrophy,45 as well as reduced volumes in the mesial temporal lobe including the hippocampus, entorhinal cortex, and amygdala.47 Nonamnestic MCI patients, who show cognitive impairments in single or multiple domains other than memory, typically have greater atrophy outside the hippocampus, specifically in multimodal association cortices. Automated mapping of brain structure Conventional region-of-interest approaches, which use manual tracing to determine the volume of the structures, are ubiquitous but are gradually being complemented or replaced by more automated techniques for rapid large-scale processing of scans.30,48,49 Automated image registration approaches can align groups of images into a common space, and intergroup differences can be assessed with voxel-by-voxel statistics. 3D statistical maps of group differences in brain structure can be visualized (see Figure 3), identifying regions where atrophy correlates with diagnosis, as well as clinical, therapeutic, genetic, and functional measures. Researchers51 used an automated technique known as voxel-based morphometry51 to map gray matter changes in 18 amnestic MCI patients. In a follow-up scan at 18 months, patients whose MCI had converted to AD showed significantly greater gray matter loss in the hippocampus, inferior and middle temporal gyrus, posterior cingulate, and precuneus compared with nonconverters. All of these regions show severe deficits in mild AD (Figure 2). Cortical pattern-matching techniques52 also can map the profile of gray matter thickness across the cortical mantle, providing better localization and statistical power by matching data from corresponding gyri across subjects. Composite maps of cortical thickness, based on MRI, reveal a complex shifting pattern of cortical atrophy over the human life span, which is thought to reflect neuronal shrinkage rather than overt neuronal loss.27,53 These cortical mapping techniques show great promise but have yet to be applied to MCI. Imaging white matter with DTI White matter changes in MCI also are of interest. Conventional MRI has insufficient contrast to discriminate fiber tract organization within the white matter, but diffusion tensor imaging (DTI) MRI is sensitive to myelin breakdown, as well as to fiber integrity and orientation.54-56 One team of researchers25 found that groups of patients with MCI and suspected AD showed abnormal reductions in fractional anisotropy, a DTI-based measure of fiber integrity, in multiple posterior white matter regions. Other measures of water diffusion, such as the apparent diffusion coefficient, are abnormally elevated in the hippocampus in patients with MCI,57,58 and in broader areas, including the parietal white matter, in those with suspected AD.59 Abnormal white matter changes can thus be detected in MCI, before the development of dementia. MCI in therapeutic trials Recent studies also suggest that imaging can be useful as a biomarker for therapeutic efficacy in AD. Even if only conventional volumetric measures are used, investigators estimated1 that in each arm of a therapeutic trial, only 21 patients would be needed to detect a 50% reduction in the rate of decline if hippocampal volume were used as the outcome measure. This is comparable to including 241 patients if Mini-Mental State Examination scores are used and 320 patients if the AD Assessment Scale Cognitive Subscale is used. Automated measures of atrophy also are gaining acceptance in longitudinal studies. Researchers applied2 a powerful image-analysis approach, known as the Brain Boundary Shift Integral, to estimate the overall brain volume decrease in registered serial images from 288 AD patients in a phase 2a immunotherapy trial. Paradoxically, when assessed 11 months later, antibody responders (n = 45) had greater brain volume decreases (on average 3.1% vs 2.0%), and greater ventricular enlargement than patients given placebo (n = 57). Because this atrophy did not correlate with cognitive decline, the authors speculated that these volume changes may be attributed to amyloid removal and associated CSF shifts. The best MRI measures for monitoring disease progression in dementia also depend on the follow-up interval. At relatively short follow-up intervals (6 months), ventricular measures are 3 times more powerful than whole-brain atrophic rates for distinguishing patients with AD from controls; this advantage dissipates if the follow-up interval is extended to 1 year.60 Among the most complex of MRI analysis approaches, tensor mapping, or deformation morphometry,23,30,61,62 can visualize the 3D profile of atrophic rates of tissue growth throughout the brain. This approach may ultimately offer great power for clinical trials, since deformation morphometry can detect subtle medication-related changes over a period of less than a month, such as effects of lithium(Drug information on lithium) on brain structure.29 FUNCTIONAL NEUROIMAGING Over the past 3 decades, clinicians and researchers have obtained substantial experience in using the 3D PET imaging capabilities and single photon emission computed tomography (SPECT) for the differential diagnosis of dementia.63 Indeed, these imaging studies show a consistent pattern of focally decreased cerebral metabolism and perfusion. Disease especially involves the posterior cingulate and neocortical association cortex, but mostly spares basal ganglia and the thalamus, cerebellum, and primary sensorimotor cortex. Most studies report that the parietal lobe deficit is more sensitive to disease severity than the temporal lobe deficit. Frontal lobe hypoperfusion also is often reported, but not without parietotemporal abnormalities. A pattern of focal cortical inhomogeneities, all accounted for by areas of infarction on MRI, implies dementia secondary to cerebrovascular disease, which often affects the cerebellum and subcortical structures. A pattern of focal cortical inhomogeneities unmatched by MRI findings is consistent with a primary neurodegenerative disorder (eg, AD, Pick disease, other frontotemporal dementia, Lewy body dementia, dementia of Parkinson disease, Huntington disease, progressive subcortical gliosis). The pattern of bilateral parietotemporal hypoperfusion or hypometabolism generally provides good discrimination of AD patients, not only from age-matched normal controls but also from patients with vascular dementia or frontal lobe dementia. However, some overlap of that pattern has been observed in patients with Lewy body dementia and dementia of Parkinson disease (Table). Neuronuclear imaging abnormalities correlate with severity and specific patterns of cognitive failure in AD; they also correlate with regional densities of neurofibrillary tangles.64 Diagnostic and prognostic accuracy of PET for cognitive dysfunction Numerous studies suggest that significant alterations in brain function caused by many neurodegenerative diseases are detectable with SPECT and PET even if structural images appear normal on CT or MRI.63 Thousands of patients with clinically diagnosed-and in some cases histopathologically confirmed-cognitive disorders from many independent laboratories have been studied using PET measures of cerebral blood flow, glucose metabolism, or oxygen use.9 The best-studied application of this type for evaluation of AD is fluorodeoxyglucose (FDG)-PET. The typically high sensitivity of FDG-PET, even in patients with mild impairment, suggests that cortical metabolic function is already substantially altered by the time a patient presents with symptoms of a neurodegenerative dementia. The associated decreases in glucose metabolism in certain brain areas are readily detectable on FDG-PET images (Figure 4). Until recently, the diagnostic accuracy of PET was difficult to assess because of the paucity of studies involving long-term clinical follow-up or subsequent histopathologic confirmation of the diagnosis. Most clinical studies simply compared PET findings to clinical assessments performed near the time of PET. The latter approach is less able to assess diagnostic accuracy because clinical diagnosis can be inaccurate, particularly during the earliest stages of disease when the opportunity for providing effective therapy is greatest. Studies comparing neuropathologic examination with imaging are thus most informative in assessing the diagnostic value of PET. In a pooled analysis65 of 3 studies,66-68 the sensitivity and specificity of PET for detecting histopathologically confirmed AD were 92% and 71%, respectively. The largest single-institution study69 found that the sensitivity and specificity of PET for diagnosis of AD ranged from 88% to 93% and 63% to 67%, respectively. A subsequent multicenter study collected data from an international consortium of clinical facilities that had acquired both brain FDG-PET and histopathologic data for patients evaluated for dementia.70 PET had a sensitivity of 94% and a specificity of 73%. This study, which included more than 3 times as many patients as the 4 previous studies combined, included a stratified examination of a subset of patients with documented early or mild disease. The sensitivity (95%), specificity (71%), and overall accuracy (89%) of PET were unaltered. These values accord with the ranges found in a broader review of the literature on PET that included studies lacking neuropathologic confirmation of diagnoses.71 That study reported sensitivities ranging from 90% to 96% and specificities ranging from 67% to 97%. A recent review of the PET literature72 concluded that "PET scanning appears to have promise for use as an adjunct to clinical diagnosis [of AD]." This was based on a review of published studies showing that diagnostic accuracies of PET ranged from 86% to 100%. Over the past decade, there have been changes in scanner type (eg, brain PET conducted on older-generation scanners displaying 15 planes, compared with newer-generation scanners displaying at least 3 times that number of planes). In stratified analyses of the database within our own institution,73 specificity tends to be higher for scans performed on the newer generation of scanners than for those performed on the older-generation scanners (87%; 95% confidence interval [CI], 73% to 100%, vs 76%; 95% CI, 63% to 90%). Regional cerebral metabolic changes associated with early AD can be detected with PET even before the symptoms of the disease are evident.74-76 How accurate is FDG-PET in the evaluation of nondemented patients who are in the earliest stages of cognitive impairment? Overall, the rate of accuracy for FDG-PET in diagnosing MCI is almost as high as it is for diagnosing dementia: it generally exceeds 80%, ranging from 75% to 100% in recent studies. PET may be especially valuable in this clinical setting, considering the difficulty in distinguishing between patients with incipient AD and those with mild memory loss attributed to normal aging and other causes. FDG metabolism of the associative cortex can be used to predict whether cognitive decline will occur at a faster rate than would be expected for normal aging.75,77 Moreover, the magnitude of decline over a 2-year period correlates with the initial degree of hypometabolism in the inferior parietal, superior temporal, and posterior cingulate cortical regions.76 As cognitive impairment caused by a neurodegenerative disease progresses, so do the regions of hypometabolism. In examining the additive value of PET beyond conventional clinical assessment among patients who have working diagnoses presuming nonprogressive etiologies for their cognitive complaints, patients whose PET patterns were nevertheless indicative of progressive dementia were more than 18 times more likely to experience progressive decline than patients with nonprogressive PET pat-terns.77 When neurologists diagnosed their patients as having progressive dementia, they were correct in 84% of those cases. Adding a positive diagnosis from a PET scan boosted the accuracy of that prediction to 94%, and a negative PET scan made it 12 times more likely that the patient would remain cognitively stable. Comparisons of SPECT and PET Historically, SPECT has been the most widely available functional brain imaging modality. It became the most commonly used imaging resource for the evaluation of dementia. Most clinical and research studies of SPECT for the diagnosis of dementia are perfusion-based. Although the specific radiopharmaceuticals and instrumentation differ from those used in PET, the principles of interpretation, as well as the neurobiologic processes underlying its use, are similar. The primary practical differences are that PET scans provide better spatial resolution than SPECT images and that SPECT cannot detect the generally parallel relationship between cortical metabolism (usually measured with PET) and perfusion in the presence of certain cerebrovascular disorders. In addition, the magnitude of hypometabolism seen with FDG-PET is generally greater than the amplitude of hypoperfusion seen with SPECT.78-80 As might be expected, studies of AD using SPECT have yielded results similar to those using PET, but typically with less sensitivity and decreased overall accuracy. The higher diagnostic accuracy achieved with PET than with SPECT has been proved in side-by-side comparisons. These include studies of AD patients with mild symptoms81 and studies of "high-resolution" SPECT scanners.82,83 The findings: PET is approximately 15% to 20% more accurate than SPECT. Another study84 compared the ability of PET and SPECT to identify abnormalities in patients with suspected AD using statistical parametric mapping to assess the number of abnormal voxels relative to an age-matched control group for each technique. The best correspondence was in parietotemporal and posterior cingulate cortices (ratio = 0.90). However, tracer uptake reductions were significantly more pronounced with PET than with SPECT. Researchers also measured the correlation between clinical severity of impairment and the number of abnormal voxels, which was somewhat better for PET than for SPECT. The higher sensitivity of PET is especially relevant for identifying disease in its earliest stages to target patients for therapy while neurodegeneration is minimal. CONCLUSIONS Overall, structural MRI scanning has yielded several neuroimaging measures that help to differentiate mildly impaired patients from controls, to predict who will imminently convert to suspected AD, and to gauge how well interventions resist atrophic brain changes, if at all, in clinical trials. Because therapeutic trials require sensitive biomarkers that track the disease process in detail, serial MRI scanning is often combined with powerful, automated analysis methods that compute maps of brain changes, and volumetric measures for brain regions, such as the hippocampus and entorhinal cortex, that change early in AD. Other innovations in MRI include ongoing developments in diffusion tensor imaging, relaxometry, and high-field MRI scanning, which aim to increase the repertoire of signals available for assessing gray and white matter integrity in early neurodegenerative disease. Functional imaging techniques also are developing rapidly and are in widespread clinical use. The high sensitivity of PET is especially useful in identifying progressive dementia among those for whom suspicion of a progressive dementing illness is otherwise low; this may delay decline by leading to earlier therapeutic intervention. The best time to obtain functional neuroimaging, therefore, is early in the clinical workup.85 Although a cure for AD does not exist, symptomatic treatment has proven effective, especially in earlier stages of the disease. Emerging therapies targeting etiology-based treatments are in development. Future cures that target the etiology would be most effective during the milder phases of the disease, making it especially important to have an early clinical diagnosis of AD, when treatments can alter the progression of symptoms. Progress in the understanding of the etiology of AD will lead to new and more effective methods of treatment. Studies examining combination treatment options are limited. Combining current treatment options, such as cholinesterase inhibitors, with other types of interventions may further improve the management of AD. For example, the proven benefits of psychosocial intervention86 might be improved by combining such intervention with cholinesterase inhibitors. Studies combining cholinesterase inhibitors with other agents such as memantine(Drug information on memantine) (Axura, Merz) and/or anti-inflammatory/antioxidant agents also are being conducted. As more studies take place that combine currently available treatments, better use of multimodal treatment plans will emerge. As our understanding of the biology of AD moves forward, it will be possible to tailor treatments for patients in different stages of disease. Currently, categorizations such as "mild cognitive impairment," "cognitive impairment nondemented," and "age-associated memory impairment" are not uniformly applied across investigative groups or clinical centers, but the opportunity to establish categories that are directly tied to the underlying biology of a disease process, using diagnostic approaches involving pertinent neuroimaging methods such as those examined here, is at hand. Daniel H. S. Silverman, MD, PhD, is associate clinical professor, Ahmanson Biological Imaging Division, Department of Molecular and Medical Pharmacology, and Paul M. Thompson, PhD, is associate professor, Laboratory of Neuro Imaging, Department of Neurology, both at David Geffen School of Medicine, University of California, Los Angeles. For references, please go to www.appneurology.com. REFERENCES 1. 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. 2. 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. 3. Petersen RC. Mild Cognitive Impairment: Aging to Alzheimer's Disease. New York: Oxford University Press; 2003. 4. Petersen RC, Smith GE, Waring SC, et al. Mild cognitive impairment: clinical characterization and outcome [erratum appears in Arch Neurol. 1999;56:760]. Arch Neurol. 1999;56:303-308. 5. Petersen RC. Aging, mild cognitive impairment, and Alzheimer's disease. Neurol Clin. 2000;18:789-806. 6. Klunk WE, Lopresti BJ, Ikonomovic MD, et al. Binding of the positron emission tomography tracer Pittsburgh compound-B reflects the amount of amyloid-beta in Alzheimer's disease brain but not in transgenic mouse brain. J Neurosci. 2005;25:10598-10606. 7. Barrio JR, Huang SC, Cole GM, et al. PET imaging of tangles and plaques in Alzheimer disease. J Nucl Med. 1999;40(suppl):70P-71P. 8. Kepe V, Barrio JR, Huang SC, et al. Serotonin 1A receptors in the living brain of Alzheimer's disease patients. Proc Natl Acad Sci U S A. 2006;103:702-707. 9. Silverman DH, Alavi A. PET imaging in the assessment of normal and impaired cognitive function. Radiol Clin North Am. 2005;43:67-77. 10. Braak H, Braak E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol (Berl). 1991;82:239-259. 11. Braak H, Braak E. Staging of Alzheimer-related cortical destruction. Int Psychogeriatr. 1997;9(suppl 1):257-261, 269-272. 12. Gomez-Isla T, Price JL, McKeel DW, et al. Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer's disease. J Neurosci. 1996;16:4491-4500. 13. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002;17:479-489. 14. Frisoni GB, Laakso MP, Beltramello A, et al. Hippocampal and entorhinal cortex atrophy in frontotemporal dementia and Alzheimer's disease. Neurology. 1999;52:91-100. 15. Laakso MP, Partanen K, Riekkinen P, et al. Hippocampal volumes in Alzheimer's disease, Parkinson's disease with and without dementia, and in vascular dementia: an MRI study. Neurology. 1996;46:678-681. 16. 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. 17. Dickerson BC, Goncharova I, Sullivan MP, et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer's disease. Neurobiol Aging. 2001;22:747-754. 18. Thal LJ. How to define treatment success using cholinesterase inhibitors. Int J Geriatr Psychiatry. 2002;17:388-390. 19. Thompson PM, Hayashi KM, de Zubicaray GI, et al. Dynamics of gray matter loss in Alzheimer's disease. J Neuroscience. 2003;23:994-1005. 20. Mesulam MM. A plasticity-based theory of the pathogenesis of Alzheimer's disease. Ann N Y Acad Sci. 2000;924:42-52. 21. Fox NC, Cousens S, Scahill R, et al. 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. 22. 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. 23. Fox NC, Crum WR, Scahill RI, et al. Imaging of onset and progression of Alzheimer's disease with voxel-compression mapping of serial magnetic resonance images. Lancet. 2001;358:201-205. 24. Leow AD, Huang SC, Geng A, et al. Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties. Presented at: Information Processing in Medical Imaging (IPMI) 2005; July 11-15, 2005; Glenwood Springs, Colo. Available at: http://www2.wiau.man.ac.uk/ caws/Conferences/ 26/online_proceedings/ papers/paper-53-40.html. Accessed January 19, 2006. 25. Medina D, Detoledo-Morrell L, Urresta F, et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging. 2005 Jul 6; [Epub ahead of print]. 26. 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. 27. Salat DH, Buckner RL, Snyder AZ, et al. Thinning of the cerebral cortex in aging. Cereb Cortex. 2004;14:721-370. 28. 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. 29. Leow AD, Thompson PM, Hayashi KM, et al. Lithium effects on human brain structure mapped using longitudinal MRI. Presented at the 35th Annual Meeting of the Society for Neuroscience. Washington, DC, November 12-16, 2005. 30. Leow AD, Klunder AD, Jack CR, et al; ADNI Preparatory Phase Study. Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. 2006b. Neuroimage. In press. 31. Thompson PM, Hayashi KM, de Zubicaray GI, et al. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage. 2004;22:1754-1766. 32. De Santi S, de Leon MJ, Rusinek H, et al. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging. 2001;22:529-539. 33. Callen DJ, Black SE, Gao F, et al. Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD. Neurology. 2001;57:1669-1674. 34. 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. 35. Jack CR Jr, Petersen RC, Xu YC, et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease. Neurology. 1997;49:786-794. 36. Soininen HS, Partanen K, Pitkanen A, et al. Volumetric MRI analysis of the amygdala and the hippocampus in subjects with age-associated memory impairment: correlation to visual and verbal memory. Neurology. 1994;44:1660-1668. 37. Jack CR Jr, Petersen RC, Xu YC, et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999;52:1397-1403. 38. Visser PJ, Verhey FR, Hofman PA, et al. Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry. 2002;72:491-497. 39. Pennanen C, Kivipelto M, Tuomainen S, et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging. 2004;25:303-310. 40. Dickerson BC, Salat DH, Bates JF, et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol. 2004;56:27-35. 41. Killiany RJ, Gomez-Isla T, Moss M, et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer's disease. Ann Neurol. 2000;47:430-439. 42. Xu Y, Jack CR Jr, O'Brien PC, et al. Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology. 2000;54:1760-1767. 43. de Leon MJ, Golomb J, George AE, et al. The radiologic prediction of Alzheimer disease: the atrophic hippocampal formation. Am J Neuroradiol. 1993;14:897-906. 44. Visser PJ, Scheltens P, Verhey FR, et al. Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment. J Neurol. 1999;246:477-485. 45. Becker JT, Davis SW, Hayashi KM, et al. 3D patterns of hippocampal atrophy in mild cognitive impairment. Arch Neurol. 2006;63:97-101. 46. 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. 47. Bell-McGinty S, Lopez OL, Meltzer CC, et al. Differential cortical atrophy in subgroups of mild cognitive impairment. Arch Neurol. In press. 49. Good CD, Johnsrude IS, Ashburner J, et al. A voxel-based morphometric study of aging in 465 normal adult human brains. Neuroimage. 2001;14(pt 1):21-36. 49. Ashburner J, Csernansky JG, Davatzikos C, et al. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol. 2003;2:79-88. 50. Chetelat G, Desgranges B, De La Sayette V, et al. Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport. 2002;13:1939-1943. 51. Ashburner J, Friston KJ. Voxel-based morphometry-the methods. Neuroimage. 2000;11(6 pt 1):805-821. 52. Thompson PM, Hayashi KM, Sowell ER, et al. Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia. Neuroimage. 2004;23(suppl 1):S2-S18. 54. Sowell ER, Peterson BS, Thompson PM, et al. Mapping cortical change across the human life span. Nat Neurosci. 2003;6:309-315. 54. Le Bihan D, Breton E, Lallemand D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161:401-407. 55. Moseley ME, Cohen Y, Kucharczyk J, et al. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology. 1990;176:439-445. 56. Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson. 1994;103:247-254. 57. Kantarci K, Jack CR Jr, Xu YC, et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer's disease: a 1H MRS study. Neurology. 2000;55:210-217. 58. Kantarci K, Jack CR Jr, Xu YC, et al. Mild cognitive impairment and Alzheimer disease: regional diffusivity of water. Radiology. 2001;219:101-107. 59. Sandson TA, Felician O, Edelman RR, Warach S. Diffusion-weighted magnetic resonance imaging in Alzheimer's disease. Dement Geriatr Cogn Disord. 1999;10:166-171. 60. Schott JM, Price SL, Frost C, et al. Measuring atrophy in Alzheimer disease: a serial MRI study over 6 and 12 months. Neurology. 2005;65:119-124. 61. Thompson PM, Giedd JN, Woods RP, et al. Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature. 2000;404:190-193. 62. Janke AL, de Zubicaray GI, Rose SE, et al. 4D deformation modeling of cortical disease progression in Alzheimer's dementia. Magn Reson Med. 2001;46:661-666. 63. Silverman DH, Devous MD. PET and SPECT imaging in evaluating Alzheimer's disease and related dementia. In: Ell PJ, Gambhir SS, eds. Nuclear Medicine in Clinical Diagnosis and Treatment. 3rd ed. New York: Churchill Livingstone; 2004:1435-1473. 64. DeCarli CS, Atack JR, Ball MJ, et al. Post-mortem regional neurofibrillary tangle densities but not senile plaque densities are related to regional cerebral metabolic rates for glucose during life in AD patients. Neurodegeneration. 1992;1:113-121. 65. Silverman DH, Small GW, Phelps ME. Clinical value of neuroimaging in the diagnosis of dementia: sensitivity and specificity of regional cerebral metabolic and other parameters for early identification of Alzheimer's disease. Clin Positron Imaging. 1999;2:119-130. 66. Mielke R, Schroder R, Fink GR, et al. Regional cerebral glucose metabolism and postmortem pathology in AD. Acta Neuropathol (Berl). 1996;91:174-179. 67. Salmon E, Sadzot B, Maquet P, et al. Differential diagnosis of Alzheimer's disease with PET. J Nucl Med. 1994;35:391-398. 68. Tedeschi E, Hasselbalch SG, Waldemar G, et al. Heterogeneous cerebral glucose metabolism in normal pressure hydrocephalus. J Neurol Neurosurg Psychiatry. 1995;59:608-615. 69. Hoffman JM, Welsh-Bohmer KA, Hanson M, et al. FDG PET imaging in patients with pathologically verified dementia. J Nucl Med. 2000;41:1920-1928. 70. Silverman DH, Small GW, Chang CY, et al. Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA. 2001;268:2120-2127. 71. Gambhir SS, Czernin J, Schwimmer J, et al. A tabulated summary of the FDG PET literature. J Nucl Med. 2001;42(5 suppl):1S-93S. 72. Knopman DS, DeKosky ST, Cummings JL, et al. Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56:1143-1153. 73. Silverman DH. Brain 18F-FDG PET in the diagnosis of neurodegenerative dementias: comparison with perfusion SPECT and with clinical evaluations lacking nuclear imaging. J Nucl Med. 2004;45:594-607. 74. Minoshima S, Giordani B, Berent S, et al. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol. 1997;42:85-94. 75. Herholz K, Nordberg A, Salmon E, et al. Impairment of neocortical metabolism predicts progression in Alzheimer's disease. Dement Geriatr Cogn Disord. 1999;10:494-504. 76. Small GW, Ercoli LM, Silverman DH, et al. Cerebral metabolic and cognitive decline in persons at genetic risk for AD. Proc Nat Acad Sci U S A. 2000;97:6037-6042. 77. Silverman DH, Truong CT, Kim SK, et al. Prognostic value of regional cerebral metabolism in patients undergoing dementia evaluation: comparison to a quantifying parameter of subsequent cognitive performance and to prognostic assessment without PET. Mol Genet Metab. 2003;80:350-355. 78. Hoffman JM, Hanson MW, Welsh KA, et al. Interpretation variability of 18FDG-positron emission tomography studies in dementia. Invest Radiol. 1996;31:316-322. 79. Masterman DL, Mendez MF, Fairbanks LA, Cummings JL. Sensitivity, specificity, and positive predictive value of technetium 99-HMPAO SPECT in discriminating Alzheimer's disease from other dementias. J Geriatr Psychiatry Neurol. 1997;10:15-21. 80. Van Heertum RL, Tikofsky RS, Ruben AB. Dementia. In: Van Heertum RL, Tikofsky RS, eds. Functional Cerebral SPECT and PET Imaging. 3rd ed. New York: Lippincott Williams & Wilkins; 2000:127-188. 81. Mielke R, Heiss WD. Positron emission tomography for diagnosis of Alzheimer's disease and vascular dementia. J Neural Transm Suppl. 1998;53:237-250. 82. Messa C, Perani D, Lucignani G, et al. High-resolution technetium-99m-HMPAO SPECT in patients with probable Alzheimer's disease: comparison with fluorine-18-FDG PET. J Nucl Med. 1994;35:210-216. 83. Mielke R, Pietrzyk U, Jacobs A, et al. HMPAO SPECT and FDG PET in Alzheimer's disease and vascular dementia: comparison of perfusion and metabolic pattern. Eur J Nucl Med. 1994;21:1052-1060. 84. Herholz K, Nordberg A, Salmon E, et al. Impairment of neocortical metabolism predicts progression in Alzheimer's disease. Dement Geriatr Cogn Disord. 1999;10:494-504. 85. Silverman DH, Cummings JL, Small GW, et al. Added clinical benefit of incorporating 2-deoxy-2-[18F]fluoro-D-glucose with positron emission tomography into the clinical evaluation of patients with cognitive impairment. Mol Imaging Biol. 2002;4:283-293. 86. Mittelman MS, Ferris SH, Shulman E, et al. A family intervention to delay nursing home placement of patients with Alzheimer disease. A randomized controlled trial. JAMA. 1996;276:1725-1731. 87. 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. --- Table Imaging findings pertaining to differential diagnosis of dementias Etiology of Dementia: Regional Deficits Identified by Neuronuclear Imaging AD: Parietal, temporal and posterior cingulate cortices affected early; relative sparing of primary sensorimotor and primary visual cortex; sparing of striatum, thalamus, and cerebellum. In early stages, deficits are often asymmetric, but bilateral degeneration eventually occurs. VASCULAR DEMENTIA: Hypometabolism and hypoperfusion affecting cortical, subcortical, and cerebellar areas. FRONTOTEMPORAL DEMENTIA(eg, Pick disease): Frontal cortex, anterior temporal, and mesiotemporal areas affected earlier or with greater initial severity than parietal and lateral temporal cortex; relative sparing of primary sensorimotor and visual cortex. HUNTINGTON DISEASE: Caudate and lentiform nuclei affected early, with gradual development of diffuse cortical involvement. PARKINSON DEMENTIA: Similar to Alzheimer disease, but less sparing of visual cortex. In early, untreated Parkinson disease, basal ganglia may appear more prominent than normal. DEMENTIA WITH LEWY BODIES: Similar to Alzheimer disease, but less sparing of occipital and striatal activity.