A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study

Abstract

Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems.

Methods: Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD.

Findings: IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p < 1 × 10^-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aβ (HR = 3.78 [95% CI: 2.63∼5.43], p = 2.13 × 10^-14) and CSF Tau (HR = 3.77 [95% CI: 2.64∼5.39], p = 9.53 × 10^-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta = -0.70, p < 1 × 10^-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aβ (beta = -0.26, p = 4.40 × 10^-9) and CSF Tau (beta = 0.12, p = 1.02 × 10^-5).

Interpretation: Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials.

Funding: Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.

Keywords: Alzheimer's disease; Longitudinal progression; Multisite; Neuroimaging biomarker; Specific.

Fig. 1

The pipeline of the present study. (A) Multi-scan neuroimaging features were utilised in this study, including grey matter volume (GMV), regional radiomics features (R2F), regional radiomics similarity network (R2SN), and R2SN mean connectivity (RMCS). (B) Training and testing strategy employed in this study, including detailed progression for constructing the IBRAIN model. (C) Validation of the IBRAIN model in cross-sectional analysis, including its ability to distinguish AD and NC, its relationship to clinical measures and gene expression, and its ability to distinguish AD and non-AD disorders. (D) Validation of the IBRAIN in longitudinal analysis, including its ability to reveal the distinct progression of MCI, its ability to quantify MCI converting to AD within a certain period, and examining individual IBRAIN progression in MCI converting to AD.

Fig. 2

The results for distinguishing AD and NC. (A) The IBRAIN model was developed using an inner cross-validation strategy applied to the ADNI dataset. The dataset was partitioned into inner training and testing sets, with the inner training set used for constructing the IBRAIN model using different parameter settings. The performance of the model was assessed on the inner testing set to determine the optimal configuration of the IBRAIN model for accurately distinguishing between individuals with AD and NC. The process of the training IBRAIN model. (B) The receiver operating character (ROC) curve and areas under the ROC curve (AUC) for all testing datasets and each independent dataset.

Fig. 3

The biological basis of the IBRAIN. (A) The most correlated terms and gene pathways associated with IBRAIN include immunity-related terms such as neutrophil degranulation, leukocyte activation, immune response-regulating signalling pathway, and innate immune response. Additionally, protein metabolism-related terms such as metabolism of lipids and protein phosphorylation are also significantly associated with IBRAIN. (B) The correlation between existing biomarkers and IBRAIN to cognitive ability in the ADNI1&GO dataset and ADNI2&3 dataset, respectively. Here, the biomarkers primarily comprise CSF Aβ, Tau181, Ptau181, and global FDG, while the cognitive ability is primarily assessed by MMSE, ADAS-Cog score, and AVLT score.

Fig. 4

The specific of the IBRAIN for AD. (A) The distribution of IBRAIN levels in patients with AD and patients with Lewy Body disease (LBD), Frontotemporal Dementia (FTD), Parkinson's dementia (PD), Vascular dementia (VaD), Amyotrophic Lateral Sclerosis (ALS), other neurologic, genetic or infectious conditions (ON), Depression (DEPR), and Cognitive impairment for other specified reasons, i.e., written-in values (COR). (B) The distribution of IBRAIN in AD and all non-AD disorders. (C). The ROC curve for differentiation AD and patients with non-AD. (D) The ROC curve for differentiation of AD and each non-AD disorder via a bootstrap framework.

Fig. 5

Longitudinal analysis of the IBRAIN in patients with MCI. (A) The conversion of the MCI participants under the stratified framework via IBRAIN, CSF Aβ, and CSF Tau, respectively. Here, the patients with MCI were subdivided into three subgroups with low- (first quartile), middle- (second and third quartile), and high-values (fourth quartile) via IBRAIN, CSF Aβ, and CSF total Tau. (B) The individual progression of the IBRAIN, CSF Aβ, and CSF Tau in the patients with MCI that have undergone conversion to AD.