Baxter, Leslie A.

AbstractINTRODUCTIONWe previously demonstrated that single‐domain amnestic mild cognitive impairment (SDAm‐MCI) detected through longitudinal neuropsychological testing in our research cohort is less severe than in clinically incident symptomatic individuals. This study aims to replicate and expand this work using a National Alzheimer's Coordinating Center dataset.METHODSParticipants were classified into two groups: those enrolled with normal cognition (Normal Enrollees) and those enrolled with SDAm‐MCI (Amnestic Enrollees). Groups were compared at the time of diagnosis and on rates of cognitive decline post‐diagnosis.RESULTSAt SDAm‐MCI diagnosis, Normal Enrollees were 17% to 39% less impaired on memory tasks, after which the slope of change over time steepened compared to their pre‐symptomatic change and was steeper than in Amnestic Enrollees.DISCUSSIONIncident MCI detected through longitudinal neuropsychological testing is initially milder but may progress more steeply than in clinically established MCI. Sensitive assessment of cognitive decline is essential for capturing early changes that are more likely to be amenable to therapeutic treatment.Highlights Longitudinal cognitive evaluation detects symptomatic Alzheimer's disease earlier than mental status exams. At the time of diagnosis, converters were less impaired compared to initially symptomatic patients. Converters’ cognition declined more steeply than initially symptomatic patients. Sensitive cognitive tests are essential to capture early changes, given new disease‐modifying therapies.

Abstract Characterizing intra- and inter-tumoral heterogeneity of glioblastoma has historically relied on discrete classifications of malignant cell populations leaving immune and other cell populations, known to exist admixed with the malignant tumor cells, relatively neglected. Manifold learning algorithms can manage deconvolving multiple cell populations and are often used to track cell state transitions in single cell transcriptomics. We applied a manifold learning approach to TCGA microarray data (Nf525) and bulk transcriptomics of 134 image localized biopsies across 30 patients with primary and 9 with recurrent glioblastoma to further elucidate how to organize biopsies across a spectrum of possible tissue states. The algorithm revealed a low-dimensional manifold graph for which each biopsy lives across 3 polarizing tissue states - one that is associated with diffusely invaded brain, one that is enriched in mesenchymal genes, and one that is enriched in classical proliferative tumor signatures. We deconvolved the bulk transcriptomics of the image-localized biopsies to reveal the relative abundance of 18 malignant, immune, and other cell subpopulations in each biopsy. Overlaying the cellular decomposition onto the manifold graph visualizing the tissue state distributions revealed that transitions between states correlate with changes in cellular cohabitation composition. The tumor cellular cohabitation ecologies have the lowest diversity, as inferred by ecological measures such as Shannon entropy and evenness, at the distal poles of the graph when compared to the transitional arms. Further, we found that the relationship between imaging appearance of contrast enhancement on T1-weighted MRI and the biopsy cellular composition varies with sex and primary vs recurrent biopsy status. The limited spectrum of possible tissue states revealed by the manifold learning is suggestive of a limited continuum along which tumor and non-tumoral cell populations can cohabitate. Such a limited low-dimensional biological space may constrain the dynamics of tumor biology in a predictable manner.