Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease

Evaluation of MRI post-processing methods combined with PET in detecting focal cortical dysplasia lesions for patients with MRI-negative epilepsy

OBJECTIVE

Accurate detection of focal cortical dysplasia (FCD) through magnetic resonance imaging (MRI) plays a pivotal role in the preoperative assessment of epilepsy. The integration of multimodal imaging has demonstrated substantial value in both diagnosing FCD and devising effective surgical strategies. This study aimed to enhance MRI post-processing by incorporating positron emission tomography (PET) analysis. We sought to compare the diagnostic efficacy of diverse image post-processing methodologies in patients presenting MRI-negative FCD.

METHODS

In this retrospective investigation, we assembled a cohort of patients with negative preoperative MRI results. T1-weighted volumetric sequences were subjected to morphometric analysis program (MAP) and composite parametric map (CPM) post-processing techniques. We independently co-registered images derived from various methods with PET scans. The alignment was subsequently evaluated, and its correlation was correlated with postoperative seizure outcomes.

RESULTS

A total of 41 patients were enrolled in the study. In the PET-MAP(p = 0.0189) and PET-CPM(p = 0.00041) groups, compared with the non-overlap group, the overlap group significantly associated with better postoperative outcomes. In PET(p = 0.234), CPM(p = 0.686) and MAP(p = 0.672), there is no statistical significance between overlap and seizure-free outcomes. The sensitivity of using the CPM alone outperformed the MAP (0.65 vs 0.46). The use of PET-CPM demonstrated superior sensitivity (0.96), positive predictive value (0.83), and negative predictive value (0.91), whereas the MAP displayed superior specificity (0.71).

CONCLUSIONS

Our findings suggested a superiority in sensitivity of CPM in detecting potential FCD lesions compared to MAP, especially when it is used in combination with PET for diagnosis of MRI-negative epilepsy patients. Moreover, we confirmed the superiority of synergizing metabolic imaging (PET) with quantitative maps derived from structural imaging (MAP or CPM) to enhance the identification of subtle epileptogenic zones (EZs). This study serves to illuminate the potential of integrated multimodal techniques in advancing our capability to pinpoint elusive pathological features in epilepsy cases.

The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease

BACKGROUND

Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD.

METHODS

This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index.

RESULTS

QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA.

CONCLUSIONS

QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.

Machine Learning-Derived MRI-Based Neurodegeneration Biomarker for Alzheimer's Disease: A Multi-Database Validation Study

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.

Plasma cell-free RNA profiling of Vietnamese Alzheimer's patients reveals a linkage with chronic inflammation and apoptosis: a pilot study

Introduction

Circulating cell-free RNA (cfRNA) is a potential hallmark for early diagnosis of Alzheimer's Disease (AD) as it construes the genetic expression level, giving insights into the pathological progress from the outset. Profiles of cfRNA in Caucasian AD patients have been investigated thoroughly, yet there was no report exploring cfRNAs in the ASEAN groups. This study examined the gap, expecting to support the development of point-of-care AD diagnosis.

Methods

cfRNA profiles were characterized from 20 Vietnamese plasma samples (10 probable AD and 10 age-matched controls). RNA reads were subjected to differential expression (DE) analysis. Weighted gene correlation network analysis (WGCNA) was performed to identify gene modules that were significantly co-expressed. These modules' expression profiles were then correlated with AD status to identify relevant modules. Genes with the highest intramodular connectivity (module membership) were selected as hub genes. Transcript counts of differentially expressed genes were correlated with key AD measures-MMSE and MTA scores-to identify potential biomarkers.

Results

136 genes were identified as significant AD hallmarks (p < 0.05), with 52 downregulated and 84 upregulated in the AD cohort. 45.6% of these genes are highly expressed in the hippocampus, cerebellum, and cerebral cortex. Notably, all markers related to chronic inflammation were upregulated, and there was a significant shift in all apoptotic markers. Three co-expressed modules were found to be significantly correlated with Alzheimer's status (p < 0.05; R2> 0.5). Functional enrichment analysis on these modules reveals an association with focal adhesion, nucleocytoplasmic transport, and metal ion response leading to apoptosis, suggesting the potential participation of these pathways in AD pathology. 47 significant hub genes were found to be differentially expressed genes with the highest connectivity. Six significant hub genes (CREB1, YTHDC1, IL1RL1, PHACTR2, ANKRD36B, RNF213) were found to be significantly correlated with MTA and MMSE scores. Other significant transcripts (XRN1, UBB, CHP1, THBS1, S100A9) were found to be involved in inflammation and neuronal death. Overall, we have identified candidate transcripts in plasma cf-RNA that are differentially expressed and are implicated in inflammation and apoptosis, which can jumpstart further investigations into applying cf-RNA as an AD biomarker in Vietnam and ASEAN countries.