ArticleBrain : a journal of neurology2020
Morphometric network differences in ageing versus Alzheimer's disease dementia.
Article in Brain : a journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 61 citations in OpenAlex.
- Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes.Journal of neurology, neurosurgery, and psychiatry · 2021Trial
- Network-level structural alterations distinguish persistent from remitted post-traumatic stress disorder: a morphometric covariance approach.European journal of psychotraumatology · 2026Article
- Variations of global brain asymmetry are associated with aging and related diseases.Science advances · 2026Article
- Piezo1 in microglial cells: Implications for neuroinflammation and tumorigenesis.Channels (Austin, Tex.) · 2025Review
- Aging as an active player in Alzheimer's disease classification: Insights from feature selection in BrainAge models.NeuroImage · 2025Article
- Aging as an active player in Alzheimer's Disease Classification: Insights from feature selection in BrainAge Models.medRxiv : the preprint server for health sciences · 2025Article
- BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.Brain topography · 2025Article
- Age and Alzheimer's disease affect functional connectivity along separate axes of functional brain organization.bioRxiv : the preprint server for biology · 2025Article
- Uncovering atrophy progression pattern and mechanisms in individuals at risk of Alzheimer's disease.Brain communications · 2025Article
- Pharmacological effects, molecular mechanisms and strategies to improve bioavailability of curcumin in the treatment of neurodegenerative diseases.Frontiers in pharmacology · 2025Review
- Cognitive subgroups of affective and non-affective psychosis show differences in medication and cortico-subcortical brain networks.Scientific reports · 2024Article
- Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population.Human brain mapping · 2024Article
- Alterations in Brain Morphometric Networks and Their Relationship with Memory Dysfunction in Patients with Type 2 Diabetes Mellitus.Experimental neurobiology · 2024Article
- Alzheimer's disease, aging, and cannabidiol treatment: a promising path to promote brain health and delay aging.Molecular biology reports · 2024Review
- A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study.EClinicalMedicine · 2023Article
- Distinct patterns of neurodegeneration after TBI and in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Altered structural covariance of locus coeruleus in individuals with significant memory concern and patients with mild cognitive impairment.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis.NPJ Parkinson's disease · 2023Article
- Article
- Functional connectome fingerprinting across the lifespan.Network neuroscience (Cambridge, Mass.) · 2023Article
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
Age being the main risk factor for Alzheimer's disease, it is particularly challenging to disentangle structural changes related to normal brain ageing from those specific to Alzheimer's disease. Most studies aiming to make this distinction focused on older adults only and on a priori anatomical regions. Drawing on a large, multi-cohort dataset ranging from young adults (n = 468; age range 18-35 years), to older adults with intact cognition (n = 431; age range 55-90 years) and with Alzheimer's disease (n = 50 with late mild cognitive impairment and 71 with Alzheimer's dementia, age range 56-88 years), we investigated grey matter organization and volume differences in ageing and Alzheimer's disease. Using independent component analysis on all participants' structural MRI, we first derived morphometric networks and extracted grey matter volume in each network. We also derived a measure of whole-brain grey matter pattern organization by correlating grey matter volume in all networks across all participants from the same cohort. We used logistic regressions and receiver operating characteristic analyses to evaluate how well grey matter volume in each network and whole-brain pattern could discriminate between ageing and Alzheimer's disease. Because increased heterogeneity is often reported as one of the main features characterizing brain ageing, we also evaluated interindividual heterogeneity within morphometric networks and across the whole-brain organization in ageing and Alzheimer's disease. Finally, to investigate the clinical validity of the different grey matter features, we evaluated whether grey matter volume or whole-brain pattern was related to clinical progression in cognitively normal older adults. Ageing and Alzheimer's disease contributed additive effects on grey matter volume in nearly all networks, except frontal lobe networks, where differences in grey matter were more specific to ageing. While no networks specifically discriminated Alzheimer's disease from ageing, heterogeneity in grey matter volumes across morphometric networks and in the whole-brain grey matter pattern characterized individuals with cognitive impairments. Preservation of the whole-brain grey matter pattern was also related to lower risk of developing cognitive impairment, more so than grey matter volume. These results suggest both ageing and Alzheimer's disease involve widespread atrophy, but that the clinical expression of Alzheimer's disease is uniquely associated with disruption of morphometric organization.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.