The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification
Open accessAbstract
MRI patterns were examined together with cerebrospinal fluid (CSF) biomarkers in serial scans of ADNI participants with mild cognitive impairment (MCI). The SPARE-AD score, summarizing brain atrophy patterns, was tested as predictor of short-term conversion to AD. MCI individuals that converted to AD (MCI-C) had mostly positive baseline SPARE-AD and atrophy in temporal lobe grey (GM) and white (WM) matter, posterior cingulate/precuneous, insula. MCI-C had mostly AD-like baseline CSF biomarkers. MCI non-converters (MCI-NC) had mixed baseline SPARE-AD and CSF values, suggesting that some MCI-NC subjects may later convert. Those MCI-NC with most negative baseline SPARE-AD scores (normal brain structure) had significantly higher baseline MMSE scores (28.67) than others, and relatively low annual rate of MMSE decrease (−0.25). MCI-NC with mid-level baseline SPARE-AD displayed faster annual rates of SPARE-AD increase (indicating progressing atrophy). SPARE-AD and CSF combination improved prediction over individual values. In summary, both SPARE-AD and CSF biomarkers showed high baseline sensitivity, however, many MCI-NC had abnormal baseline SPARE-AD and CSF biomarkers. Longer follow-up will elucidate the specificity of baseline measurements.
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Cites
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Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects
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Self-citationLongitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
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“…recent studies have suggested that early changes of the SPARE-AD scores and other morphological measurements, as well as CSF biomarkers, in cognitively normal elderly are good predictors of future progression to MCI (Davatzikos et al., 2008a, 2008b, 2009; Driscoll et al., 2009; Fagan et al., 2009).”
Cited by
Multimodal classification of Alzheimer's disease and mild cognitive impairment
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,314
Building better biomarkers: brain models in translational neuroimaging
Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017) · Nature Neuroscience · doi:10.1038/nn.4478 · Cited by 1,056
Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968
“ROI-based approach considers the structurally or functionally predefined brain regions and extracts representative features from each region (Cuingnet et al., 2011; Davatzikos et al., 2011; Kohannim et al., 2010; Nordberg et al., 2010; Suk and Shen, 2013; Walhovd et al., 2010; Zhang and Shen, 2012).”
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637
Metadata
- Authors
- Davatzikos, C., Bhatt, P., Shaw, L. M., Batmanghelich, K. N., & Trojanowski, J. Q.
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2011-12
- Volume
- 32
- Issue
- 12
- Pages
- 2322.e19-2322.e27
- ISSN
- 0197-4580 (ISSN-L)
- OpenAlex
- W2102508963
- Cited by
- 578
- References
- 10
- FWCI
- 19.34
- Type
- journal article
- Language
- en
- Open access
- green
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- Davatzikos, C., Bhatt, P., Shaw, L. M., Batmanghelich, K. N., & Trojanowski, J. Q. (2011). Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging. https://doi.org/10.1016/j.neurobiolaging.2010.05.023
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