Neuroimaging predictors of brain amyloidosis in mild cognitive impairment

Abstract

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 ¹¹C‐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 Aβ_1–42 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. Ann Neurol 2013;74:188–198