Neuroimaging in Alzheimer disease: an evidence-based review☆
Section snippets
How accurate are the clinical criteria for the diagnosis of Alzheimer disease?
Clinical diagnosis of AD in a living person is labeled either possible or probable AD. Definitive diagnosis of AD requires tissue examination, through biopsy or autopsy of the brain. Histopathologic hallmarks of the disease are neurofibrillary tangles and senile plaques, which show marked heterogeneity in the pathologic progression of AD and are also encountered to a lesser extent in elderly individuals with normal cognition [7], [8], [9], [10], [11]. Thus, the boundary between the
Structural neuroimaging
The traditional use of structural neuroimaging to differentiate potentially reversible or modifiable causes of dementia, such as brain tumor, subdural hematoma, normal pressure hydrocephalus, and vascular dementia from AD, is widely accepted [27]. In addition to the potential causes of dementia mentioned previously however, structural neuroimaging can also identify anatomic changes that occur from the pathologic involvement in AD [28]. Neurofibrillary pathology, which correlates with neuron
Can neuroimaging identify individuals at elevated risk and predict future development of Alzheimer disease?
Risk groups for AD are composed of individuals identified either through clinical examination or family history and genetic testing who have a greater probability of developing AD than members of the general population, and in whom the relevant exposures are absent. The rationale for identifying imaging criteria for those at elevated risk comes from recent advances on disease-modifying therapies. Individuals with elevated probability of developing AD are the primary targets of these treatment
Can neuroimaging measure disease progression in Alzheimer disease?
Recent advances in treatments aimed at inhibiting the pathologic process of AD have created a need for biologic markers that can accurately measure the effectiveness of therapeutic interventions. Neuropsychologic measures of memory and cognitive function can monitor the symptomatic progression in patients with AD. Yet, monitoring biologic progression is only possible with markers closely related to the neurodegenerative pathology.
Is neuroimaging cost-effective for clinical evaluation of Alzheimer disease?
Cost-effectiveness analysis calculates the costs and benefits of a diagnostic modality or therapeutic intervention by quality-adjusted life-year, or QALY. Current treatment options for AD may reduce the social and economic costs of the disease by slowing the rate of cognitive decline, improving quality of life, and delaying nursing home placement. Neuroimaging may contribute to identification of individuals with early AD who may benefit from such therapies. Only one cost-effectiveness analysis
Summary
Current clinical criteria (DSM-IIIR and NINCDS-ADRDA) for the diagnosis of dementia and AD are reliable; however, these criteria remain to be validated by clinicians of different levels of expertise at different clinical settings. Structural neuroimaging has an important role in initial evaluation of dementia for ruling out potentially treatable causes. Although CT is the appropriate choice when brain tumors, subdural hematoma, or normal pressure hydrocephalus is suspected, MR imaging is more
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Brain MRI findings related to Alzheimer's disease in older African American adults
2019, Progress in Molecular Biology and Translational ScienceCitation Excerpt :There are two reasons to focus on this topic. First is that the large and steadily increasing public health burden of Alzheimer's disease in the United States is well known,1 and brain MRI has played a prominent role in the effort to identify valid in vivo markers of AD risk, progression, and treatment response.2,3 Second is that although substantial progress has been made in establishing brain MRI based markers of AD by collecting brain MRI scans from large numbers of adults, the vast majority of those scans were collected from non-Hispanic white American individuals.
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This article was supported by the National Institute on Aging grants no. AG11378, AG16574, and AG06786.