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Background: As a tool of automated brain volumetry, AccuBrain has been widely used to identify brain structural changes in different neurological diseases with the older version (IV1.2). As AccuBrain underwent a substantial revision update (IV2.0) for segmentation algorithms recently, a comparison of the two versions would be necessary to identify and characterize any differences that could affect the interpretation and clinical translation of the older research findings. Method: We included 200 Alzheimer's disease (AD) patients (75.3±7.6 years old, 44.0% women) and 200 normal control (NC) subjects (75.2±5.6 years old, 41.5% women) with 3D T1‐weighted scans processed with AccuBrain IV1.2 and IV2.0. To measure the volumetric difference or agreement of the two versions, we calculated the mean relative absolute error (MRAE, i.e., the mean of |(V2‐V1)/V1| for each volumetric measure) and intraclass correlation coefficient (ICC) between two versions regarding the volumes of typical brain structures, lobar atrophy indices and the quantitative medial temporal lobe atrophy (QMTA) score. In addition, we investigated the difference of the two versions regarding volumetric associations with age using ANCOVA, with the volumetric measures as the dependent variable, the version as fixed variable and age as the covariate. An interaction term between version and age was additionally included in the model to determine whether these two versions were statistically different in this respect. Result: The ICC between the volumetric measures of the two versions were >0.85 for absolute volumes of typical brain structures (e.g., hippocampus) and >0.60 for lobar atrophy indices and QMTA (Table 1), indicating moderate to good agreement between versions. Furthermore, the MRAE indicated substantial difference between versions with >5% for half of the measures, and >10% for QMTA and most lobar atrophy indices, where the MRAE was slightly larger in AD than NC. There were no significant differences between versions regarding their volumetric associations with age (Table 2) for either NC or AD group (regression lines shown for NC in Figure 1∼2). Conclusion: The two versions of AccuBrain are not suggested for a mixed use. Previous research findings with older version may be repeatable with the new version of AccuBrain. [ABSTRACT FROM AUTHOR]

Background: ATN biomarkers (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) have been known for their predictive value of Alzheimer's disease (AD). Here, we aimed to investigate their independent contributions to predicting progression of amnestic mild cognitive impairment (aMCI) to AD on top of neuropsychological tests. Method: We included 496 aMCI patients with neuropsychological tests and ATN biomarkers from cerebrospinal fluid (CSF) examinations, MRI and FDG‐PET at baseline. The involved ATN biomarkers included CSF‐Aβ, CSF‐Ptau, CSF‐Tau, FDG‐PET and an MRI‐based Alzheimer's disease resemblance atrophy index (AD‐RAI) calculated by AccuBrain IV2.0. All the demographic and neuropsychological features (Table 1) were entered into the cox model with backward elimination to predict time to AD (subscores of FAQ and ADAS‐cog also entered as candidate predictors) using training cohort (n = 330, 34.8% developed AD within 3.60±2.46 years). The resulting model was treated as the basic model. The enriched models with individual and multiple ATN biomarkers were further compared with the basic model regarding the improvement in C‐index (measuring prediction performance of time to AD) and time‐dependent AUC (t‐AUC, measuring prediction performance of time‐specific progression) in validation cohort (n = 166, 35.5% developed AD within 3.66±2.52 years). Result: The features that survived backward elimination for the basic model were shown in Table 2. Binary form of A/T/N variables were preferred for Aβ/Tau, while continuous form preferred for Ptau/FDG‐PET/AD‐RAI (Table 3). Compared with the basic model, all the enriched models with individual A/T/N biomarkers (except for Basic+Tau) presented higher C‐index in the training cohort (p<0.05), while only Basic+(AD‐RAI) and Basic+(FDG‐PET) presented higher C‐index in the validation cohort (Table 4). Basic+(FDG‐PET) presented highest t‐AUC (0.861∼0.924) among the models with individual A/T/N biomarkers (Figure 1A). The enriched model with multiple ATN biomarkers (Aβ/Ptau/FDG‐PET/AD‐RAI) presented highest C‐index in both training and validation cohort, which also showed higher t‐AUCs (generally ≥0.90 through year 3∼7) than the models with individual A/T/N biomarkers in trend (Figure 1B). These findings indicated that multiple ATN biomarkers should be used for the optimal model (Figure 2). Conclusion: ATN biomarkers, when combined in use, presented most contribution to predicting aMCI‐to‐AD progression independent of demographic characteristics and neuropsychological tests. [ABSTRACT FROM AUTHOR]

Background: It is still not clear about the independent contribution of ATN biomarkers (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) on top of neuropsychological tests for risk estimation of amnestic mild cognitive impairment (aMCI) in subjects with normal cognition (NC). Method: We included 306 NC subjects who underwent neuropsychological tests and were available for ATN biomarkers with cerebrospinal fluid (CSF) examinations, MRI and FDG‐PET at baseline. The involved ATN biomarkers included CSF‐Aβ, CSF‐Ptau, CSF‐Tau, FDG‐PET and an MRI‐based Alzheimer's disease resemblance atrophy index (AD‐RAI) calculated by AccuBrain. The study cohort was divided into training cohort (n = 204, 19.6% developed aMCI within 4.73±2.83 years) and validation cohort (n = 102, 20.6% developed aMCI within 4.67±2.72 years). All the demographic features and neuropsychological tests (Table 1) were entered into the cox regression model with backward elimination to predict time to incident aMCI (the subscores of FAQ and ADAS‐cog were also entered as candidate predictors), and the resulting model based on the training cohort was treated as the basic model. Individual ATN biomarkers were added on top of the basic model for cox regressions, and the enriched models would be tested in the validation cohort using time‐dependent AUC (t‐AUC) if they presented higher Chi‐squares than the basic model in training cohort. Result: The features that survived backward elimination for the basic model were shown in Table 2. Only the enriched model with Ptau or AD‐RAI presented higher Chi‐square than the basic model in the training cohort (p<0.05, Table 3). In the validation cohort, the model Basic+(AD‐RAI) presented significantly higher t‐AUC (0.770∼0.859) than the basic model through year 2∼7 and Basic+Ptau through year 1∼4 (p<0.05, Figure 1). The model with Ptau only presented significantly higher t‐AUC than the basic model for year 7 (p = 0.02). Also, adding Ptau on top of Basic+(AD‐RAI) presented no significant increase of t‐AUC for any time point (Figure 1). These findings indicated that Basic+(AD‐RAI) may be used as the optimal model for predicting risk of aMCI (nomogram shown in Figure 2). Conclusion: AD‐RAI consistently presented independent contribution to predicting aMCI among NC subjects on top of demographic characteristics and neuropsychological tests. [ABSTRACT FROM AUTHOR]

Background: Lifestyle‐related risk factors (e.g., smoking) and common health problems (e.g., hypertension) have been found to be associated with brain atrophy. However, it is still not clear about their independent contribution to brain atrophy, especially among the elderly with normal cognition. Here, we aim to identify the independent risk factors of brain atrophy in cognitively normal elderly. Method: We included 419 elderly subjects with normal cognition (Table 1) from the ADNI database (ADNI1/ADNI2/ADNI‐GO) who had available data of lifestyle‐related risk factors (alcohol abuse, smoking) and common health problems (obesity, hypertension, high cholesterol, high triglycerides, sleep disturbance) and underwent 3D T1‐weighted MRI scans at baseline. The T1‐weighted images were processed with AccuBrain IV2.0 to quantify the brain volumetric measures as listed in Table 2. Univariate analyses were performed with ANCOVA to investigate the influence of the risk factors for each brain volumetric measure, while age, gender, education and APOE4 carrier were covaried out. The independent contribution of the risk factors were further identified by entering them together in a multivariate linear regression model as predictors of brain atrophy using backward elimination. Result: In the univariate analyses, obesity showed the most widely influence of brain atrophy throughout the brain, followed by hypertension which contributed to atrophy in cerebellum and part of cortical gray matter (GM), alcohol abuse that contributed to atrophy in part of basal ganglia structures, high triglycerides for atrophy of pons and cerebellum, and smoking for medial temporal lobe atrophy (Table 3). When other risk factors were also considered (Table 4), the contribution of obesity to the atrophy of most brain regions remained significant. Hypertension, alcohol abuse and smoking also showed independent contributions (p<0.05) to the atrophy of several brain regions, while sleep disturbance (for brain parenchyma) and high triglycerides (for cerebellum) only presented mild independent contribution (0.05

Background: Automated brain volumetry has been widely used to assess brain volumetric changes that may indicate clinical states and progression. Among the tools that implement automated brain volumetry, AccuBrain has been validated for its accuracy, reliability and clinical applications for the older version (IV1.2). Recently, AccuBrain underwent a substantial revision update (IV2.0) for segmentation algorithms. Here, we aim to compare the performance of the two versions of AccuBrain for future use from several aspects. Method: Public datasets with 3D T1‐weighted scans were included for version comparisons, each with Alzheimer's disease (AD) patients and normal control (NC) subjects that were matched in age and gender (Table 1). For the comparisons of the brain volumetric measures quantified from the same scans, we investigated the difference of hippocampal segmentation accuracy (using Dice similarity coefficient [DSC] as the major measurement). As AccuBrain generates a composite index (AD resemblance atrophy index, AD‐RAI) that indicates similarity with AD‐like brain atrophy pattern, we also compared the two versions for the diagnostic accuracy of AD vs. NC with AD‐RAI. Also, we examined the intra‐scanner reproducibility of the two versions for the scans acquired with short‐intervals using intraclass correlation coefficient (ICC). Result: Regarding hippocampal segmentation, AccuBrain IV2.0 presented significantly higher accuracy than IV1.2 with manual segmentation as the reference, both in the entire cohort (DSC: 0.91 vs. 0.89, p<0.001) and in the subgroups (all with p<0.001) defined by diagnosis, field strength or manufacturer (Table 2 and Figure 1). Also, AccuBrain IV2.0 showed better diagnostic accuracy of AD (AUC: 0.977 vs. 0.921, p<0.001) than IV1.2 (Figure 2). Regarding the analyses of intra‐scanner reproducibility (Table 3 and Table 4), the point estimates of ICC values were all >0.95 for absolute volumes and AD‐RAI, and generally >0.90 for relative volumetric measures. These two versions generally presented no significant differences for ICC values for most brain volumetric measures in NC or AD. Conclusion: AccuBrain IV2.0 presented better segmentation accuracy and diagnostic accuracy of AD, and similar intra‐scanner reproducibility compared with IV1.2. Both versions should be feasible for use due to the small magnitude of differences. [ABSTRACT FROM AUTHOR]

Background: We investigated the ability of MRI, plasma, and cognitive biomarkers in detecting preclinical or prodromal Alzheimer's Disease (AD) who will progress to the next syndrome stage of the cognitive continuum. Method: 589 subjects with longitudinal cognitive data were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which included 227 cognitive unimpaired (CU) and 362 mild cognitive impairment (MCI) subjects. Preclinical or prodromal AD was defined by a low amyloid beta (Aβ)42 (A+) and high phosphorylated ‐tau (p‐tau) (T+) on cerebrospinal fluid (CSF) assessment at baseline. The following baseline MRI biomarkers were derived by an automatic segmentation tool (AccuBrain®): AD‐resemblance atrophy index (AD‐RAI), quantitative medial temporal lobe atrophy (QMTA) and hippocampus volume (HV). Plasma biomarkers included p‐tau 181, neurofilament (NFL) and APOE ε4. Montreal Cognitive Assessment (MoCA) score was collected for all subjects at baseline. Conversion (C+) was defined as subjects progressed from CU to MCI and from MCI to dementia within 4 years. Results: Of the 589 subjects (mean [SD] age, 72.2 [6.9] years; 314 men [53.3%]), 96 (16.3%) were A+T+C+ and 180 (30.6%) were A+T+C‐. In the ROC analysis, AD‐RAI achieved the best detection ability than other individual biomarker with a sensitivity of 83.4%, a specificity of 69.4% and an AUC of 87.8%. A combination of AD‐RAI, plasma p‐tau 181, APOE ε4 and MoCA score achieved the best detection ability with AUC of 89.8%, sensitivity of 85.1% and specificity of 80.2%. In the subgroup analysis, the combination of AD‐RAI, plasma p‐tau 181, APOE ε4 and MoCA score also showed good accuracy in identifying A+T+C+ subjects in the CU group (AUC = 87.6%) and in the MCI group (AUC = 89.3%). Conclusion: A panel of MRI, plasma and cognitive biomarkers might help to detect progressive early AD subjects. [ABSTRACT FROM AUTHOR]