Neuroimaging sharpens focus on mild cognitive impairment

Brain imaging markers have 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 face imminent decline and the full dementia syndrome of Alzheimer's disease. As novel disease modifying agents emerge, brain imaging markers also may facilitate drug development^1,2 and help monitor drug efficacy in clinical settings.

Mild cognitive impairment has a variety of definitions, all aimed at expressing an intermediate cognitive state between normal aging and dementia.³ 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.

Advanced 3D brain imaging techniques, including MRI and PET, have greatly improved our understanding of aging and AD. Imaging techniques can be used to assess brain structure and function noninvasively (Figure 1), monitor disease progression, predict imminent cognitive decline, and quantify effects of therapy in large-scale clinical trials. Structural MRI 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 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,^4-6 in addition to mapping cerebral function.⁷

STRUCTURAL IMAGING

Imaging studies of MCI have taken their lead from approaches that have successfully tracked AD pathology, which progresses in a known stereotypical sequence. Longitudinal 3D MR scans of groups of subjects can map this process in detail (Figure 2). Quantitative methods to track brain changes with conventional MRI fall into three main categories:

- volumetric measurement of specific structures, such as the hippocampus or entorhinal cortex;

- image processing techniques that estimate rates of whole-brain atrophy as a percentage of volume loss per year;^8,9 and

- map-based techniques that visualize the 3D profile of group differences in gray matter loss, atrophic rates, white matter integrity, or cortical gray matter thickness.^10-16

Large-scale neuroimaging efforts such as the Alzheimer's Disease Neuroimaging Initiative (www.loni. ucla.edu/ADNI),^17,18 are comparing the ability of each imaging measure, together with biomarkers and other clinical functional measures, to differentiate MCI and AD from healthy aging, predict future cognitive decline, and 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.

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 spaces (Figure 3).¹⁹

The hippocampal volume in patients with mild dementia in whom AD is clinically suspected is roughly 25% less than that in matched healthy controls,^20-22 whereas the hippocampal volume in patients with MCI shows a mean reduction of about 11%.²² Early studies²³ 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,^24-27 or they are similar to volumes found in patients in whom AD is suspected.^28,29

Advanced 3D modeling techniques can localize tissue atrophy or shape alterations³⁰ and can map the average pattern of reductions in hippocampal thickness in millimeters.³¹ An automated technique known as voxel-based morphometry^32,33 has shown significant gray matter loss in the hippocampus, inferior and middle temporal gyrus, posterior cingulate, and precuneus in patients whose MCI had converted to AD. All of these regions show severe deficits in mild AD. Cortical pattern-matching techniques³⁴ 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. Researchers have imaged white matter abnormalities associated with MCI and AD using diffusion tensor and diffusion MR imaging.^13,35-37

Recent studies 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 Status Examination scores are used and 320 patients if the AD Assessment Scale Cognitive Subscale is used.

Among the most complex of MRI analysis approaches, tensor mapping, or deformation morphometry,^11,18,38,39 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, such as effects of lithium on brain structure, over a period of less than a month.¹⁷

FUNCTIONAL NEUROIMAGING

Clinicians and researchers have substantial experience using 3D PET and SPECT for the differential diagnosis of dementia.⁴⁰ 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 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.

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. Some overlap of that pattern has been observed, however, in patients with Lewy body dementia and dementia of Parkinson's disease (see table). Neuronuclear imaging abnormalities correlate with severity and specific patterns of cognitive failure in AD and also correlate with regional densities of neurofibrillary tangles.⁴¹

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.⁴⁰

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.⁷ 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. Associated decreases in glucose metabolism in certain brain areas are readily detectable on FDG-PET images (Figure 4).

The diagnostic accuracy of PET has been 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 same time. Clinical diagnosis can be inaccurate, however, 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 analysis⁴² of three studies,^43-45 the sensitivity and specificity of PET for detecting histopathologically confirmed AD were 92% and 71%, respectively. The largest single institution study⁴⁶ 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.⁴⁷ PET had a sensitivity of 94% and a specificity of 73%. This study, which included more than three times as many patients as the four previous studies combined, included a stratified examination of a subset of patients with documented early or mild disease. 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.⁴⁸ That study reported sensitivities ranging from 90% to 96% and specificities of 67% to 97%. A recent review of published PET studies showing that diagnostic accuracies of PET ranged from 86% to 100%⁴⁹ concluded that "PET scanning appears to have promise for use as an adjunct to clinical diagnosis [of AD]."

In stratified analyses of the database within our own institution,⁵⁰ specificity tends to be higher for scans performed on the newer generation of PET scanners than for those performed on older scanners (87%; 95% confidence interval [CI], 73% to 100%, versus 76%; 95% CI, 63% to 90%).

Regional cerebral metabolic changes associated with early AD can be detected with PET even before symptoms of the disease are evident.^51-53 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.

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.^52,54 The magnitude of decline over a two-year period correlates with the initial degree of hypometabolism in the inferior parietal, superior temporal, and posterior cingulate cortical regions.⁵³ As cognitive impairment caused by a neurodegenerative disease progresses, so do the regions of hypometabolism.

In examinations of PET patients who have working diagnoses presuming nonprogressive etiologies for their cognitive complaints, those with PET patterns indicative of progressive dementia were more than 18 times more likely to experience progressive decline than patients with nonprogressive PET patterns.⁵⁴ 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.

SPECT has historically been the most widely available functional brain imaging modality and 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 specific radiopharmaceuticals and instrumentation differ from those used in PET, the principles of interpretation, as well as the underlying neurobiologic processes, are similar. PET scans provide better spatial resolution, however, and SPECT cannot detect the generally parallel relationship between cortical metabolism (usually measured with PET) and perfusion in the presence of certain cerebrovascular disorders. The magnitude of hypometabolism seen with FDG-PET is generally greater than the amplitude of hypoperfusion seen with SPECT.^55-57

As might be expected, studies of AD using SPECT have yielded results similar to those using PET but, typically, produce less sensitivity and decreased overall accuracy. The higher diagnostic accuracy achieved with PET has been proved in side-by-side comparisons, including studies of AD patients with mild symptoms⁵⁸ and studies of "high-resolution" SPECT scanners.^59,60 Findings show that PET is approximately 15% to 20% more accurate than SPECT.

One study⁶¹ 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), and 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 in order to target patients for therapy while neurodegeneration is minimal.

Although a cure for AD does not exist, symptomatic treatment has proven effective, especially in earlier stages. As our understanding of the biology of AD moves forward, it will become possible to tailor treatments for patients in different stages of disease. The opportunity to establish categories tied directly to the underlying biology of a disease process, using diagnostic approaches involving pertinent neuroimaging methods, is at hand.

Dr. Silverman is an associate professor in the Ahmanson biological imaging division of the molecular and medical pharmacology department, and Dr. Thompson is an associate professor in the laboratory of neuroimaging of the neurology department, both at David Geffen School of Medicine, University of California, Los Angeles.

Dr. Silverman is a consultant to PETNet, Cardinal Health, and GE Healthcare.

Note: A version of this article appeared in the February 2006 issue of Applied Neurology. It has been revised for Diagnostic Imaging.

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. 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.

5. 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.

6. 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.

7. Silverman DH, Alavi A. PET imaging in the assessment of normal and impaired cognitive function. Radiol Clin North Am 2005;43:67-77.

8. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17:479-489.

9. 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.

10. 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.

11. 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.

12. 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, CO. Available at http://www2.wiau.man.ac.uk/caws/Conferences/26/online_proceedings/papers/paper-53-40.html. Accessed Jan. 19, 2006.

13. 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].

14. 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.

15. Salat DH, Buckner RL, Snyder AZ, et al. Thinning of the cerebral cortex in aging. Cereb Cortex 2004;14:721-370.

16. 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.

17. 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, Nov. 12-16, 2005.

18. 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.

19. Thompson PM, Hayashi KM, de Zubicaray GI, et al. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage 2004;22:1754-1766.

20. 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.

21. Callen DJ, Black SE, Gao F, et al. Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD. Neurology 2001;57:1669-1674.

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27. 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.

28. Dickerson BC, Salat DH, Bates JF, et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol 2004;56:27-35.

29. 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.

30. 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.

31. Thompson PM, Hayashi KM, de Zubicaray GI, et al. Dynamics of gray matter loss in Alzheimer's disease. J Neuroscience 2003;23:994-1005.

32. 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.

33. Ashburner J, Friston KJ. Voxel-based morphometry-the methods. Neuroimage 2000;11(6 pt 1):805-821.

34. 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.

35. 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.

36. Kantarci K, Jack CR Jr, Xu YC, et al. Mild cognitive impairment and Alzheimer disease: regional diffusivity of water. Radiology 2001;219:101-107.

37. 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.

38. 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.

39. 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.

40. 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.

41. 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.

42. 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.

43. Mielke R, Schroder R, Fink GR, et al. Regional cerebral glucose metabolism and postmortem pathology in AD. Acta Neuropathol (Berl) 1996;91:174-179.

44. Salmon E, Sadzot B, Maquet P, et al. Differential diagnosis of Alzheimer's disease with PET. J Nucl Med 1994;35:391-398.

45. Tedeschi E, Hasselbalch SG, Waldemar G, et al. Heterogeneous cerebral glucose metabolism in normal pressure hydrocephalus. J Neurol Neurosurg Psychiatry 1995;59:608-615.

46. 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.

47. 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.

48. 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.

49. 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.

50. 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.

51. 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.

52. 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.

53. 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.

54. 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.

55. Hoffman JM, Hanson MW, Welsh KA, et al. Interpretation variability of 18FDG-positron emission tomography studies in dementia. Invest Radiol 1996;31:316-322.

56. 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.

57. 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.

58. Mielke R, Heiss WD. Positron emission tomography for diagnosis of Alzheimer's disease and vascular dementia. J Neural Transm Suppl 1998;53:237-250.

59. 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.

60. 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.

61. 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.

62. Braak H, Braak E. Staging of Alzheimer-related cortical destruction. Int Psychogeriatr 1997;9(suppl 1):257-261, 269-272.

63. 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.

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 AD, 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 AD, but less sparing of occipital and striatal activity.