Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

Metabolic differentiation of brushtail possum populations resistant and susceptible to plant toxins revealed via differential gene expression - David Carmelet-Rescan; Mary Morgan-Richards; Steven A. Trewick (Journal of Comparative Physiology B, 2025)
The Australian brushtail possum ( Trichosurus vulpecula ) is adapted to a wide range of food plants across its range and is exposed to numerous physiological challenges. Populations that are resistant to the plant toxin sodium fluoroacetate are of particular interest as this compound has been used since the 1940s for vertebrate pest management around the world. Candidate gene identification is an important first step in understanding how spatial populations have responded to local selection resulting in local physiological divergence. We employ differential gene expression of liver samples from wild-caught brushtail possums from toxin-resistant and toxin-susceptible populations to identify candidate genes that might be involved in metabolic pathways associated with toxin-resistance. This allowed us to identify genetic pathways involved in resistance to the plant toxin sodium fluoroacetate in Western Australian possums but not those originally from south eastern Australia. We identified differentially expressed genes in the liver that are associated with cell signalling, encapsulating structure, cell mobility, and tricarboxylic acid cycle. The gene expression differences detected indicate which metabolic pathways are most likely to be associated with sodium fluoroacetate resistance in these marsupials and we provide a comprehensive list of candidate genes and pathways to focus on for future studies.