Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software

Abstract

Koung Mi Kang,1 Chul-Ho Sohn,2 Min Soo Byun,3 Jun Ho Lee,4 Dahyun Yi,3 Younghwa Lee,4 Jun-Young Lee,5 Yu Kyeong Kim,6 Bo Kyung Sohn,7 Roh-Eul Yoo,1 Tae Jin Yun,1 Seung Hong Choi,2 Ji-hoon Kim,1 Dong Young Lee8 On behalf of the KBASE Research Group1Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea; 2Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea; 3Medical Research Center Seoul National University, Institute of Human Behavioral Medicine, Seoul, Republic of Korea; 4Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; 5Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea; 6Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea; 7Department of Psychiatry, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Republic of Korea; 8Department of Neuropsychiatry, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of KoreaCorrespondence: Chul-Ho SohnDepartment of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of KoreaTel [phone]Fax [phone]Email [email protected] Young LeeDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of KoreaTel [phone]Fax [phone]Email [email protected]: To assess the predictive ability of regional volume information provided by fully automated brain segmentation software for cerebral amyloid positivity in mild cognitive impairment (MCI).Methods: This study included 130 subjects with amnestic MCI who participated in the Korean brain aging study of early diagnosis and prediction of Alzheimer’s disease, an ongoing prospective cohort. All participants underwent comprehensive clinical assessment as well as 11C-labeled Pittsburgh compound PET/MRI scans. The predictive ability of volumetric results provided by automated brain segmentation software was evaluated using binary logistic regression and receiver operating characteristic curve analysis.Results: Subjects were divided into two groups: one with Aβ deposition (58 subjects) and one without Aβ deposition (72 subjects). Among the varied volumetric information provided, the hippocampal volume percentage of intracranial volume (%HC/ICV), normative percentiles of hippocampal volume (HCnorm), and gray matter volume were associated with amyloid-β (Aβ) positivity (all P < 0.01). Multivariate analyses revealed that both %HC/ICV and HCnorm were independent significant predictors of Aβ positivity (all P < 0.001). In addition, prediction scores derived from %HC/ICV with age and HCnorm showed moderate accuracy in predicting Aβ positivity in MCI subjects (the areas under the curve: 0.739 and 0.723, respectively).Conclusion: Relative hippocampal volume measures provided by automated brain segmentation software can be useful for screening cerebral Aβ positivity in clinical practice for patients with amnestic MCI. The information may also help clinicians interpret structural MRI to predict outcomes and determine early intervention for delaying the progression to Alzheimer’s disease dementia.Keywords: amyloid, brain segmentation, magnetic resonance imaging, cognitive impairment

Keywords

• amyloid
• brain segmentation
• magnetic resonance imaging
• cognitive impairment