Application of modern neuroimaging technology in the diagnosis and study of Alzheimer's disease.

Abstract:

:Neurological abnormalities identified via neuroimaging are common in patients with Alzheimer's disease. However, it is not yet possible to easily detect these abnormalities using head computed tomography in the early stages of the disease. In this review, we evaluated the ways in which modern imaging techniques such as positron emission computed tomography, single photon emission tomography, magnetic resonance spectrum imaging, structural magnetic resonance imaging, magnetic resonance diffusion tensor imaging, magnetic resonance perfusion weighted imaging, magnetic resonance sensitive weighted imaging, and functional magnetic resonance imaging have revealed specific changes not only in brain structure, but also in brain function in Alzheimer's disease patients. The reviewed literature indicated that decreased fluorodeoxyglucose metabolism in the temporal and parietal lobes of Alzheimer's disease patients is frequently observed via positron emission computed tomography. Furthermore, patients with Alzheimer's disease often show a decreased N-acetylaspartic acid/creatine ratio and an increased myoinositol/creatine ratio revealed via magnetic resonance imaging. Atrophy of the entorhinal cortex, hippocampus, and posterior cingulate gyrus can be detected early using structural magnetic resonance imaging. Magnetic resonance sensitive weighted imaging can show small bleeds and abnormal iron metabolism. Task-related functional magnetic resonance imaging can display brain function activity through cerebral blood oxygenation. Resting functional magnetic resonance imaging can display the functional connection between brain neural networks. These are helpful for the differential diagnosis and experimental study of Alzheimer's disease, and are valuable for exploring the pathogenesis of Alzheimer's disease.

journal_name

Neural Regen Res

authors

Zeng HM,Han HB,Zhang QF,Bai H

doi

10.4103/1673-5374.286957

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

73-79

issue

1

eissn

1673-5374

issn

1876-7958

pii

NeuralRegenRes_2021_16_1_73_286957

journal_volume

16

pub_type

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