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Background: Pilot study showed that Alzheimer's disease resemblance atrophy index (AD-RAI), a machine learning-derived MRI-based neurodegeneration biomarker of AD, achieved excellent diagnostic performance in diagnosing AD with moderate to severe dementia. Objective: The primary objective was to validate and compare the performance of AD-RAI with conventional volumetric hippocampal measures in diagnosing AD with mild dementia. The secondary objectives were 1) to investigate the association between imaging biomarkers with age and gender among cognitively unimpaired (CU) participants; 2) to analyze whether the performance of differentiating AD with mild dementia from CU will improve after adjustment for age/gender. Methods: AD with mild dementia (n = 218) and CU (n = 1,060) participants from 4 databases were included. We investigated the area under curve (AUC), sensitivity, specificity, and balanced accuracy of AD-RAI, hippocampal volume (HV), and hippocampal fraction (HF) in differentiating between AD and CU participants. Among amyloid-negative CU participants, we further analyzed correlation between the biomarkers with age/gender. We also investigated whether adjustment for age/gender will affect performance. Results: The AUC of AD-RAI (0.93) was significantly higher than that of HV (0.89) and HF (0.89). Subgroup analysis among A + AD and A- CU showed that AUC of AD-RAI (0.97) was also higher than HV (0.94) and HF (0.93). Diagnostic performance of AD-RAI and HF was not affected by age/gender while that of HV improved after age adjustment. Conclusions: AD-RAI achieves excellent clinical validity and outperforms conventional volumetric hippocampal measures in aiding the diagnosis of AD mild dementia without the need for age adjustment. [ABSTRACT FROM AUTHOR]

Introduction: We compared the machine learning‐derived, MRI‐based Alzheimer's disease (AD) resemblance atrophy index (AD‐RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects. Methods: We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p‐tau181, NfL), cerebrospinal fluid biomarkers (CSF) (Aβ42, p‐tau181), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aβ42 (A+) and p‐tau181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years. Results: Of 589 participants, 96 (16.3%) were A+T+C+. AD‐RAI performed better than plasma NfL when added on top of clinical features, plasma p‐tau181, and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+. Discussion: AD‐RAI outperformed plasma NfL in predicting syndromal conversion of early AD. Highlights: AD‐RAI outperformed plasma NfL in predicting syndromal conversion among early AD.AD‐RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion.Combining clinical features, plasma p‐tau181 and apolipoprotein E (APOE) with AD‐RAI is the best model for predicting syndromal conversion. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) was recently defined as a biological construct to reflect neuropathologic status, and both abnormal amyloid and tau are required for a diagnosis of AD. We aimed to determine the proton MR spectroscopic (1H-MRS) patterns of the posterior cingulate in biologically defined AD. A total of 68 participants were included in this study, comprising 37 controls, 16 early AD, and 15 late AD, who were classified according to their amyloid and tau status and presence of hippocampal atrophy. Compared with controls, early AD showed lower N-acetylaspartate (NAA)/creatine (Cr) (p = 0.003), whereas late AD showed lower NAA/Cr and higher myoInositol (mI)/Cr (all with p < 0.05). Lower NAA/Cr correlated with a greater global amyloid load (r = −0.47, p < 0.001) and tau load (r = −0.51, p < 0.001) and allowed a discrimination of early AD from controls (p < 0.001). Subgroup analysis showed that NAA/Cr also allowed a differentiation of early AD from controls in the cognitively unimpaired subjects, with an area under the receiver operating characteristics curve, sensitivity, and specificity of 0.96, 100%, and 83.8%, respectively. Lower posterior cingulate NAA levels may help to inform underlying neuropathologic changes in the early stage of AD. [ABSTRACT FROM AUTHOR]

Background: The fimbria is a small white matter bundle that connects the hippocampus to the rest of the brain. Damage to the hippocampal gray matter is established in Alzheimer's disease (AD), but the hippocampal fimbrial status in the pathogenesis of AD is unclear. AD‐related demyelination and iron deposition alter the diamagnetic and paramagnetic composition of tissues, which can be measured by quantitative susceptibility mapping (QSM). Hypothesis: AD is associated with microstructural changes in the fimbria that might be detected by QSM. Study type: Retrospective cross‐sectional study. Subjects: In all, 53 adults comprised of controls (n = 30), subjects with early stage AD (n = 13), and late stage AD (n = 10) who were classified according to their amyloid and tau status and presence of hippocampal atrophy. Field Strength / Sequence: 3T; 3D fast‐field echo sequence for QSM analysis and 3D T1‐weighted MP‐RAGE sequence for anatomical analysis. Assessment: Segmentation of the left hippocampal fimbria subfield was performed on T1‐weighted images and was applied to the coregistered QSM map for extraction of the mean, median, minimum, and maximum values of QSM. Statistical tests: Group comparison of QSM values using analysis of variance (ANOVA) with post‐hoc Tukey's test, accuracy of binary differentiation using receiver operating characteristic (ROC), and individual classification using discriminant analysis. Results: QSMmean and QSMmedian values were significantly different among the three groups (P < 0.05) and showed a shifting from negative in the control group to positive in the AD group. The control and early AD subjects, who have normal hippocampal volumes, were differentiated by the QSMmean value (area under the curve [AUC] 0.744, P < 0.05) and the QSMmedian value (AUC 0.782, P < 0.05). Up to 76% of subjects (inclusive of 26 controls and six with early AD) were correctly classified using a model incorporating clinical and radiologic data. Data Conclusion: The fimbria showed higher magnetic susceptibility in AD compared with controls. Level of Evidence: 2 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]

Background: One significant barrier to incorporate Alzheimer's disease (AD) imaging biomarkers into diagnostic criteria is the lack of standardized methods for biomarker quantification. The European Alzheimer's Disease Consortium-Alzheimer's Disease Neuroimaging Initiative (EADC-ADNI) Harmonization Protocol project provides the most authoritative guideline for hippocampal definition and has produced a manually segmented reference dataset for validation of automated methods.Purpose: To validate automated hippocampal volumetry using AccuBrain™, against the EADC-ADNI dataset, and assess its diagnostic performance for differentiating AD and normal aging in an independent cohort.Material and Methods: The EADC-ADNI reference dataset comprise of manually segmented hippocampal labels from 135 volumetric T1-weighted scans from various scanners. Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and Pearson's r were obtained for AccuBrain™ and FreeSurfer. The magnetic resonance imaging (MRI) of a separate cohort of 299 individuals (150 normal controls, 149 with AD) were obtained from the ADNI database and processed with AccuBrain™ to assess its diagnostic accuracy. Area under the curve (AUC) for total hippocampal volumes (HV) and hippocampal fraction (HF) were determined.Results: Compared with EADC-ADNI dataset ground truths, AccuBrain™ had a mean DSC of 0.89/0.89/0.89, ICC of 0.94/0.96/0.95, and r of 0.95/0.96/0.95 for right/left/total HV. AccuBrain™ HV and HF had AUC of 0.76 and 0.80, respectively. Thresholds of ≤ 5.71 mL and ≤ 0.38% afforded 80% sensitivity for AD detection.Conclusion: AccuBrain™ provides accurate automated hippocampus segmentation in accordance with the EADC-ADNI standard, with great potential value in assisting clinical diagnosis of AD. [ABSTRACT FROM AUTHOR]

The article focuses on independent stroke inpatient sample to find the reliability of a reported Mini‐Mental State Examination (MMSE)‐ Montreal Cognitive Assessment (MoCA) score conversion. Topics discussed include study conducted on people with mild cognitive impairment, Alzheimer's, and Parkinson's disease, use of integrated mood and neurocognitive screening scheme to study acute stroke inpatients, and conversion of MMSE scores to MoCA scores using equipercentile method.