Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
Koung Mi Kang1, Chul-Ho Sohn2, Min Soo Byun3
1Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Objective:
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.
Insights
Automated brain segmentation software can predict cerebral amyloid positivity in mild cognitive impairment (MCI) using hippocampal volume. This aids in early diagnosis and intervention for Alzheimer's disease.
Area of Science:
- Neuroimaging
- Alzheimer's Disease Research
- Brain Anatomy
Background:
- Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD).
- Cerebral amyloid positivity is a key biomarker for AD.
- Early detection of amyloid deposition is crucial for timely intervention.
Purpose of the Study:
- To evaluate the predictive capability of automated brain segmentation software for cerebral amyloid positivity in amnestic MCI.
- To determine if regional brain volume metrics can identify individuals with amyloid-β (Aβ) deposition.
Main Methods:
- 130 amnestic MCI subjects from the Korean Brain Aging Study underwent clinical assessment and 11C-Pittsburgh compound PET/MRI scans.
- Automated brain segmentation software was used to extract volumetric data.
- Binary logistic regression and ROC curve analysis assessed the predictive performance of volumetric measures.
Main Results:
- Hippocampal volume percentage of intracranial volume (%HC/ICV), normative hippocampal volume percentiles (HCnorm), and gray matter volume were significantly associated with Aβ positivity.
- %HC/ICV and HCnorm independently predicted Aβ positivity in multivariate analyses (P < 0.001).
- Prediction models using %HC/ICV and HCnorm achieved moderate accuracy (AUCs: 0.739 and 0.723) in predicting Aβ positivity.
Conclusions:
- Relative hippocampal volume measures from automated segmentation are valuable for screening cerebral Aβ positivity in amnestic MCI.
- These findings support the use of structural MRI in clinical practice for predicting AD progression.
- Early identification of Aβ positivity can guide interventions to delay dementia onset.
