This site uses cookies to measure how you use the website so it can be updated and improved based on your needs and also uses cookies to help remember the notifications you’ve seen, like this one, so that we don’t show them to you again. If you could also tell us a little bit about yourself, this information will help us understand how we can support you better and make this site even easier for you to use and navigate.

Neuroimaging predictors of brain amyloidosis in mild cognitive impairment


Tosun, Duygu, Joshi, Sarang, Weiner, Michael W.


Annals Of Neurology, Volume: 74, No.: 2, Pages.: 188-198

Year of Publication



Objective: To identify a neuroimaging signature predictive of brain amyloidosis as a screening tool to identify individuals with mild cognitive impairment (MCI) that are most likely to have high levels of brain amyloidosis or to be amyloid-free.; Methods: The prediction model cohort included 62 MCI subjects screened with structural magnetic resonance imaging (MRI) and (11) C-labeled Pittsburgh compound B positron emission tomography (PET). We identified an anatomical shape variation-based neuroimaging predictor of brain amyloidosis and defined a structural MRI-based brain amyloidosis score (sMRI-BAS). Amyloid beta positivity (Aβ(+) ) predictive power of sMRI-BAS was validated on an independent cohort of 153 MCI patients with cerebrospinal fluid Aβ1-42 biomarker data but no amyloid PET scans. We compared the Aβ(+) predictive power of sMRI-BAS to those of apolipoprotein E (ApoE) genotype and hippocampal volume, the 2 most relevant candidate biomarkers for the prediction of brain amyloidosis.; Results: Anatomical shape variations predictive of brain amyloidosis in MCI embraced a characteristic spatial pattern known for high vulnerability to Alzheimer disease pathology, including the medial temporal lobe, temporal-parietal association cortices, posterior cingulate, precuneus, hippocampus, amygdala, caudate, and fornix/stria terminals. Aβ(+) prediction performance of sMRI-BAS and ApoE genotype jointly was significantly better than the performance of each predictor separately (area under the curve [AUC] = 0.88 vs AUC = 0.70 and AUC = 0.81, respectively) with >90% sensitivity and specificity at 20% false-positive rate and false-negative rate thresholds. Performance of hippocampal volume as an independent predictor of brain amyloidosis in MCI was only marginally better than random chance (AUC = 0.56).; Interpretation: As one of the first attempts to use an imaging technique that does not require amyloid-specific radioligands for identification of individuals with brain amyloidosis, our findings could lead to development of multidisciplinary/multimodality brain amyloidosis biomarkers that are reliable, minimally invasive, and widely available.; © 2013 American Neurological Association.

Bibtex Citation

@article{Tosun_2013, doi = {10.1002/ana.23921}, url = {}, year = 2013, month = {sep}, publisher = {Wiley-Blackwell}, pages = {n/a--n/a}, author = {Duygu Tosun and Sarang Joshi and Michael W. Weiner}, title = {Neuroimaging predictors of brain amyloidosis in mild cognitive impairment}, journal = {Annals of Neurology} }


aged, aged, 80 and over, amyloidosis, apolipoproteins e, biological markers, brain chemistry, cerebrospinal fluid, diagnosis, female, genetics, humans, male, methods, mild cognitive impairment, neuroimaging, pathology, peptide fragments, physiology, predictive value of tests, reproducibility of results

Countries of Study


Types of Dementia

Mild Cognitive Impairment (MCI)

Types of Study

Cohort Study

Type of Outcomes


Type of Interventions

Diagnostic Target Identification

Diagnostic Targets

Neuroimaging (e.g. MRI, PET, CAT etc.)