ArticleBrain imaging and behavior2012
Cortical signatures of cognition and their relationship to Alzheimer's disease.
Article in Brain imaging and behavior, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Association of APOC1 with cortical atrophy during conversion to Alzheimer's disease.GeroScience · 2025Article
- Rethinking neuropsychological test validity in dementia assessment: a critical review in the age of neuroimaging and digital markers.Frontiers in human neuroscience · 2025Review
- Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.Frontiers in immunology · 2025Article
- Toward a statistical validation of brain signatures as robust measures of behavioral substrates.Human brain mapping · 2023Article
- Higher motor cortical excitability linked to greater cognitive dysfunction in Alzheimer's disease: results from two independent cohorts.Neurobiology of aging · 2021Article
- A robust brain signature region approach for episodic memory performance in older adults.Brain : a journal of neurology · 2021Article
- Tau PET imaging predicts cognition in atypical variants of Alzheimer's disease.Human brain mapping · 2018Article
- Thiamine Deficiency and Neurodegeneration: the Interplay Among Oxidative Stress, Endoplasmic Reticulum Stress, and Autophagy.Molecular neurobiology · 2017Review
- Independent value added by diffusion MRI for prediction of cognitive function in older adults.NeuroImage. Clinical · 2017Article
- Suspected non-AD pathology in mild cognitive impairment.Neurobiology of aging · 2015Article
- 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2015Review
- Multimodality imaging of Alzheimer disease and other neurodegenerative dementias.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2014Review
- A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative.Biological psychiatry · 2014Review
- CERAD Neuropsychological Total Scores Reflect Cortical Thinning in Prodromal Alzheimer's Disease.Dementia and geriatric cognitive disorders extra · 2013Article
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Authors and funding
10 authors.
Funding
Abstract
Recent changes in diagnostic criteria for Alzheimer's disease (AD) state that biomarkers can enhance certainty in a diagnosis of AD. In the present study, we combined cognitive function and brain morphology, a potential imaging biomarker, to predict conversion from mild cognitive impairment to AD. We identified four biomarkers, or cortical signatures of cognition (CSC), from regressions of cortical thickness on neuropsychological factors representing memory, executive function/processing speed, language, and visuospatial function among participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Neuropsychological factor scores were created from a previously validated multidimensional factor structure of the neuropsychological battery in ADNI. Mean thickness of each CSC at the baseline study visit was used to evaluate risk of conversion to clinical AD among participants with mild cognitive impairment (MCI) and rate of decline on the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score. Of 307 MCI participants, 119 converted to AD. For all domain-specific CSC, a one standard deviation thinner cortical thickness was associated with an approximately 50% higher hazard of conversion and an increase of approximately 0.30 points annually on the CDR-SB. In combined models with a domain-specific CSC and neuropsychological factor score, both CSC and factor scores predicted conversion to AD and increasing clinical severity. The present study indicated that factor scores and CSCs for memory and language both significantly predicted risk of conversion to AD and accelerated deterioration in dementia severity. We conclude that predictive models are best when they utilize both neuropsychological measures and imaging biomarkers.
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