Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

Heuristic scoring method utilizing FDG-PET statistical parametric mapping in the evaluation of suspected Alzheimer disease and frontotemporal lobar degeneration

Distinguishing frontotemporal lobar degeneration (FTLD) and Alzheimer Disease (AD) on FDG-PET based on qualitative review alone can pose a diagnostic challenge. SPM has been shown to improve diagnostic performance in research settings, but translation to clinical practice has been lacking. Our purpose was to create a heuristic scoring method based on statistical parametric mapping z-scores. We aimed to compare the performance of the scoring method to the initial qualitative read and a machine learning (ML)-based method as benchmarks. FDG-PET/CT or PET/MRI of 65 patients with suspected dementia were processed using SPM software, yielding z-scores from either whole brain (W) or cerebellar (C) normalization relative to a healthy cohort. A non-ML, heuristic scoring system was applied using region counts below a preset z-score cutoff. W z-scores, C z-scores, or WC z-scores (z-scores from both W and C normalization) served as features to build random forest models. The neurological diagnosis was used as the gold standard. The sensitivity of the non-ML scoring system and the random forest models to detect AD was higher than the initial qualitative read of the standard FDG-PET [0.89-1.00 vs. 0.22 (95% CI, 0-0.33)]. A categorical random forest model to distinguish AD, FTLD, and normal cases had similar accuracy than the non-ML scoring model (0.63 vs. 0.61). Our non-ML-based scoring system of SPM z-scores approximated the diagnostic performance of a ML-based method and demonstrated higher sensitivity in the detection of AD compared to qualitative reads. This approach may improve the diagnostic performance.

Meta-Analysis of Smooth Muscle Lineage Transcriptomes in Atherosclerosis and Their Relationships to In Vitro Models

BACKGROUND

Vascular smooth muscle cells (VSMC) exhibit phenotypic plasticity in atherosclerotic plaques, and among other approaches, has been modeled in vitro by cholesterol loading.

METHODS

Meta-analysis of scRNA-seq data from VSMC lineage traced cells across five experiments of murine atherosclerosis was performed. In vivo expression profiles were compared to three in vitro datasets of VSMCs loaded with cholesterol and three datasets of polarized macrophages.

RESULTS

We identified 24 cell clusters in the meta-analysis of single cells from mouse atherosclerotic lesions with notable heterogeneity across studies, especially for macrophage populations. Trajectory analysis of VSMC lineage positive cells revealed several possible paths of state transitions with one traversing from contractile VSMC to macrophages by way of a proliferative cell cluster. Transcriptome comparisons between in vivo and in vitro states underscored that data from three in vitro cholesterol-treated VSMC experiments did not mirror cell state transitions observed in vivo. However, all in vitro macrophage profiles analyzed (M1, M2, and oxLDL) were more similar to in vivo profiles of macrophages than in vitro VSMCs were to in vivo profiles of VSMCs. oxLDL loaded macrophages showed the most similarity to in vivo states. In contrast to the in vitro data, comparison between mouse and human in vivo data showed many similarities.

CONCLUSIONS

Identification of the sources of variation across single cell datasets in atherosclerosis will be an important step towards understanding VSMC fate transitions in vivo. Also, we conclude that cholesterol-loading in vitro is insufficient to model the VSMC cell state transitions observed in vivo, which underscores the need to develop better cell models. Mouse models, however, appear to reproduce a number of the features of VSMCs in human plaques.

Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases

Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.