Daniel H.  S. Silverman, MD, PhD

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

 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.

 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,

 in addition to mapping cerebral function.

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;

- 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.

Large-scale neuroimaging efforts such as the Alzheimer's Disease Neuroimaging Initiative (www.loni. ucla.edu/ADNI),

 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).

The hippocampal volume in patients with mild dementia in whom AD is clinically suspected is roughly 25% less than that in matched healthy controls,

 whereas the hippocampal volume in patients with MCI shows a mean reduction of about 11%.

 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,

 or they are similar to volumes found in patients in whom AD is suspected.

Advanced 3D modeling techniques can localize tissue atrophy or shape alterations

 and can map the average pattern of reductions in hippocampal thickness in millimeters.

 An automated technique known as voxel-based morphometry

 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 techniques

 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.

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 estimated

 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,

 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.

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

 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.

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.

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.

 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 analysis

 the sensitivity and specificity of PET for detecting histopathologically confirmed AD were 92% and 71%, respectively. The largest single institution study

 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.

 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.

 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%

 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,

 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.

 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.

 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.

 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.

 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.

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 symptoms

 and studies of "high-resolution" SPECT scanners.

 Findings show that PET is approximately 15% to 20% more accurate than SPECT.

 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.

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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 and help monitor drug efficacy in clinical settings.

Daniel H. S. Silverman, MD, PhD

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