title: 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 (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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA; Veterans Administration Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA.
authors.forename: Duygu
authors.initials: D
authors.lastname: Tosun
authors.forename: Sarang
authors.initials: S
authors.lastname: Joshi
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016229:Amyloid beta-Peptides / Q000134:cerebrospinal fluid; D000686:Amyloidosis / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000473:pathology; D001057:Apolipoproteins E / Q000235:genetics; D015415:Biomarkers; D001923:Brain Chemistry / Q000502:physiology*; D060825:Cognitive Dysfunction / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000473:pathology; D005260:Female; D006801:Humans; D008297:Male; D059906:Neuroimaging / Q000379:methods*; D010446:Peptide Fragments / Q000134:cerebrospinal fluid; D011237:Predictive Value of Tests; D015203:Reproducibility of Results
chemical_list: D016229:Amyloid beta-Peptides; D001057:Apolipoproteins E; D015415:Biomarkers; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42)