ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
A data-driven, multi-domain brain gray matter signature as a powerful biomarker associated with several clinical outcomes.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Oxygen extraction fraction mapping between normal cognition and mild cognitive impairment in older adults using quantitative BOLD.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- A data-driven, multi-domain brain gray matter signature as a powerful biomarker associated with several clinical outcomes.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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9 authors.
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Abstract
introductionCharacterizing pathological changes in the brain that underlie cognitive impairment, including Alzheimer's disease and related disorders, is central to clinical concerns of prevention, diagnosis, and treatment.
methodsWe describe the properties of a brain gray matter region ("Union Signature") that is derived from four behavior-specific, data-driven signatures in a discovery cohort.
resultsIn a separate validation set, the Union Signature demonstrates clinically relevant properties. Its associations with episodic memory, executive function, and Clinical Dementia Rating Sum of Boxes are stronger than those of several standardly accepted brain measures (e.g., hippocampal volume, cortical gray matter) and other previously developed brain signatures. The ability of the Union Signature to classify clinical syndromes among normal, mild cognitive impairment, and dementia exceeds that of the other measures. DISCUSSION: The Union Signature is a powerful, multipurpose correlate of clinically relevant outcomes and a strong classifier of clinical syndromes. Highlights: Data-driven brain signatures are potentially valuable in models of cognitive aging.In previous work, we outlined rigorous validation of signatures for memory.This work demonstrates a signature predicting multiple clinical measures.This could be useful in models of interventions for brain support of cognition.
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