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A new approach to charging energy-dense electric vehicle batteries, using temperature modulation with a dual-salt electrolyte, promises a range in excess of 500,000 miles using only rapid (under 12 minute) charges.

This study finds that sST2 is a disease-causing factor for Alzheimer’s disease. Higher sST2 levels impair microglial Aβ clearance in APOE4⁺ female individuals. A genetic variant, rs1921622, is associated with a reduction in sST2 level and protects against AD in APOE4⁺ female individuals.

Alzheimer’s disease has been associated with increased structural brain aging. Here the authors describe a model that predicts brain aging from resting state functional connectivity data, and demonstrate this is accelerated in individuals with pre-clinical familial Alzheimer’s disease.

Whether Alzheimer’s disease originates in basal forebrain or entorhinal cortex remains highly debated. Here the authors use structural magnetic resonance data from a longitudinal sample of participants stratified by cerebrospinal biomarker and clinical diagnosis to show that tissue volume changes appear earlier in the basal forebrain than in the entorhinal cortex.

Late-onset Alzheimer's disease (LOAD) is a complex multi-factorial disorder. Here, the authors perform a data-driven analysis of LOAD progression, including multimodal brain imaging, plasma and CSF biomarkers, and find vascular dysfunction is among the earliest and strongest altered events.

Brain-iron elevation is implicated in Alzheimer’s disease (AD), but the impact of the metal on disease outcomes has not been analysed in a longitudinal study. Here, the authors examine the association between the levels of ferritin, an iron storage protein, in the cerebrospinal fluid (CSF) of AD patients and show that CSF ferritin levels predict AD outcomes.

Nabil Benzina et al. use a reversal learning task to examine behavioral flexibility in human and mouse models of obsessive compulsive disorder (OCD). They report that only subsets of human patients or OCD-like mice show deficits in behavioral flexibility, highlighting the diverse presentation of cognitive deficits in compulsive disorders.

The interplay between amyloid and tau pathology in Alzheimer’s disease is still not well understood. Here, the authors show that amyloid-related increased in soluble p-tau is related to subsequent accumulation of tau aggregates and cognitive decline in early stage of the disease.

In Alzheimer’s disease (AD) tau and neurodegeneration have complex regional relationships. Here, the authors show neuronal hypometabolism discordant with tau burden defines functional resilience or susceptibility to Alzheimer’s pathology via limbic/cortical axes. Susceptible groups have faster cognitive decline and evidence of non-Alzheimer’s pathologies.

Systematic characterization of longitudinal tau variability in human Alzheimer’s disease using an unbiased subtyping algorithm reveals four trajectories of tau deposition with distinct clinical features.

The KL-VS haplotype of the Klotho gene has been associated with reduced risk of Alzheimer’s disease and dementia. Here the authors show an association between the KL-VS haplotype and amyloid-dependent tau accumulation using PET data.

Several studies show that APOE-ε4 coding variants are associated with Alzheimer’s disease (AD) risk. Here, Zhou et al. perform fine-mapping of the APOE region and find AD risk haplotypes with non-coding variants in the PVRL2 and APOC1 regions that are associated with relevant endophenotypes.

The tau protein is theorized to spread transneuronally in Alzheimers disease, though this theory remains unproven in humans. Our simulations of epidemic-like protein spreading across human brain networks support this theory, and suggest the spreading dynamics are modified by β-amyloid

The BIN1 SNP rs744373 is associated with higher CSF tau and phosphorylated tau levels. Here the authors show, using PET imaging, that this SNP is associated with tau accumulation in the brain as well as impaired memory in older individuals without dementia.

Alzheimer’s disease is heterogeneous in its neuroimaging and clinical phenotypes. Here the authors present a semi-supervised deep learning method, Smile-GAN, to show four neurodegenerative patterns and two progression pathways providing prognostic and clinical information.

In this study the authors identify a possible link between the gene FAM222A and brain atrophy. The protein it encodes is found to accumulate in plaques seen in Alzheimer’s disease, and functional analysis suggests it interacts with amyloid-beta.

Tau accumulation is associated with disease progression in Alzheimer’s disease. Here the authors use resting state fMRI and tau-PET to demonstrate that baseline connectivity in Alzheimer's disease is associated with tau spreading.

Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.

Sun et al. report human lifespan changes in the brain’s functional connectome in 33,250 individuals, which highlights critical growth milestones and distinct maturation patterns and offers a normative reference for development, aging and diseases.

Inglese et al. develop a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted magnetic resonance imaging scans. Their model reliably discriminates people with Alzheimer’s disease-related pathologies from those without.

Azevedo et al. conduct a machine learning-based analysis of structural MRI data from multiple cohorts. The authors identify Alzheimer’s disease-like neuroimaging phenotypes associated with poorer cognitive performance in a healthy UK Biobank cohort, potentially highlighting a population at risk of Alzheimer’s disease.

Zhou et al. utilise deep learning to improve polygenic risk analysis for Alzheimer’s disease. Their computational approach outperforms existing statistical methods and helps to identify potential biological mechanisms of Alzheimer’s disease risk.