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000281527 041__ $$aEnglish
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000281527 1001_ $$0P:(DE-2719)9000532$$aDoering, Elena$$b0$$udzne
000281527 245__ $$aWhen Age Is More Than a Number: Acceleration of Brain Aging in Neurodegenerative Diseases.
000281527 260__ $$aNew York, NY$$bSoc.$$c2025
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000281527 520__ $$aAging of the brain is characterized by deleterious processes at various levels including cellular/molecular and structural/functional changes. Many of these processes can be assessed in vivo by means of modern neuroimaging procedures, allowing the quantification of brain age in different modalities. Brain age can be measured by suitable machine learning strategies. The deviation (in both directions) between a person's measured brain age and chronologic age is referred to as the brain age gap (BAG). Although brain age, as defined by these methods, generally is related to the chronologic age of a person, this relationship is not always parallel and can also vary significantly between individuals. Importantly, whereas neurodegenerative disorders are not equivalent to accelerated brain aging, they may induce brain changes that resemble those of older adults, which can be captured by brain age models. Inversely, healthy brain aging may involve a resistance or delay of the onset of neurodegenerative pathologies in the brain. This continuing education article elaborates how the BAG can be computed and explores how BAGs, derived from diverse neuroimaging modalities, offer unique insights into the phenotypes of age-related neurodegenerative diseases. Structural BAGs from T1-weighted MRI have shown promise as phenotypic biomarkers for monitoring neurodegenerative disease progression especially in Alzheimer disease. Additionally, metabolic and molecular BAGs from molecular imaging, functional BAGs from functional MRI, and microstructural BAGs from diffusion MRI, although researched considerably less, each may provide distinct perspectives on particular brain aging processes and their deviations from healthy aging. We suggest that BAG estimation, when based on the appropriate modality, could potentially be useful for disease monitoring and offer interesting insights concerning the impact of therapeutic interventions.
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000281527 650_7 $$2Other$$abrain age
000281527 650_7 $$2Other$$adementia
000281527 650_7 $$2Other$$amachine learning
000281527 650_7 $$2Other$$aneurodegeneration
000281527 650_7 $$2Other$$aneuroimaging
000281527 650_2 $$2MeSH$$aHumans
000281527 650_2 $$2MeSH$$aNeurodegenerative Diseases: diagnostic imaging
000281527 650_2 $$2MeSH$$aNeurodegenerative Diseases: physiopathology
000281527 650_2 $$2MeSH$$aNeurodegenerative Diseases: pathology
000281527 650_2 $$2MeSH$$aAging: pathology
000281527 650_2 $$2MeSH$$aBrain: diagnostic imaging
000281527 650_2 $$2MeSH$$aBrain: pathology
000281527 650_2 $$2MeSH$$aBrain: physiopathology
000281527 650_2 $$2MeSH$$aNeuroimaging: methods
000281527 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000281527 650_2 $$2MeSH$$aMachine Learning
000281527 7001_ $$aHoenig, Merle C$$b1
000281527 7001_ $$aCole, James H$$b2
000281527 7001_ $$0P:(DE-2719)2811239$$aDrzezga, Alexander$$b3$$eLast author$$udzne
000281527 773__ $$0PERI:(DE-600)2040222-3$$a10.2967/jnumed.125.270325$$gVol. 66, no. 10, p. 1516 - 1521$$n10$$p1516 - 1521$$tJournal of nuclear medicine$$v66$$x0097-9058$$y2025
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