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Brain atrophy in Alzheimer’s Disease and aging
Abstract
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
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Brain atrophy is a validated marker for identifying the risk of developing AD.
“Brain atrophy is a validated marker for identifying the risk of developing AD [16–19], assisting clinicians in making decisions regarding diagnosis and prognosis [20].”
From Association of APOC1 with cortical atrophy during conversion to Alzheimer’s disease (2025) · cites this paper as [16]
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Bachli, M. B., Sedeño, L., Ochab, J. K., Piguet, O., Kumfor, F., Reyes, P., Torralva, T., Roca, M., Cardona, J. F., Campo, C. G., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116456 · Cited by 57
Iaccarino, L., Sala, A., Caminiti, S. P., Presotto, L., Perani, D., & Initiative, F. T. A. D. N. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-190954 · Cited by 40
Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases
Legaz, A., Abrevaya, S., Dottori, M., González Campo, C., Birba, A., Martorell Caro, M., Aguirre, J., Slachevsky, A., Aranguiz, R., Serrano, C., et al. (2022) · Brain · doi:10.1093/brain/awab345 · Cited by 34
Teipel, S. J., Cavedo, E., Weschke, S., Grothe, M. J., Rojkova, K., Fontaine, G., Dauphinot, L., Gonzalez-Escamilla, G., Potier, M.-C., Bertin, H., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.05.016 · Cited by 22
Self-citationWhittaker, H. T., Zhu, S., Di Curzio, D. L., Buist, R., Li, X.-M., Noy, S., Wiseman, F. K., Thiessen, J. D., & Martin, M. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.03.010 · Cited by 12
Association of APOC1 with cortical atrophy during conversion to Alzheimer’s disease
Oh, S., Kim, S., Kim, J. P., Seo, S. W., Park, B.-Y., & Park, H. (2025) · GeroScience · doi:10.1007/s11357-025-01695-6 · Cited by 4
Li, Y., Tang, Z., Yao, M., Ran, Y., & Qiu, Z. (2026) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-25-00006
Metadata
- Authors
- Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., Galluzzi, S., Marizzoni, M., & Frisoni, G. B.
- Venue
- Ageing Research Reviews
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 30
- Pages
- 25-48
- ISSN
- 1568-1637
- Cited by
- 836
- References
- 45
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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