A data-driven study of Alzheimer's disease related amyloid and tau pathology progression

Very low levels of ABCA7 in the cerebrum and Alzheimer's disease onset between the ages of 60 and 80 independently of APOE

This cross-sectional study addressed the ABCA7-Alzheimer's disease (AD) association. ABCA7 protein levels were quantified in 3 cerebral regions of brain donors with Braak neurofibrillary tangle (NFT) stages 0-V. Ordinal regression models were implemented to estimate the effect of ABCA7 on stopping in an earlier Braak NFT stage versus progressing to the later stages in 2 prespecified age segments. In the final model, high ABCA7 levels in the parietal cortex increased the odds of remaining cognitively healthy (ie, in stages 0/I) versus experiencing AD onset (ie, progressing to stages II-V) in the 61-80 age segment (OR = 2.87, adj 95% CI = 1.41-7.86, adj P = .007, n = 109), after controlling for APOE and other covariates. No ABCA7-AD association was found in the 81-98 age segment (n = 113). Parietal ABCA7 levels in 61-80-year-old with stages II-V were very low, even significantly lower than in 81-98-year-old with stages II-V. ABCA7 levels in the prefrontal cortex and hippocampus predicted AD onset in the 61-80 age segment after adjustment for APOE. ABCA7 levels were also the lowest in 61-80-year-old with frequent neuritic plaques. Thus, very low ABCA7 levels in the cerebrum are associated with AD onset in the 7th-8th decade of life.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

NeuroSynth: MRI-Derived Neuroanatomical Generative Models and Associated Dataset of 18,000 Samples

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data are necessary for model building and optimization. To help overcome such limitations in the context of brain MRI, we present NeuroSynth: a collection of generative models of normative regional volumetric features derived from structural brain imaging. NeuroSynth models are trained on real brain imaging regional volumetric measures from the iSTAGING consortium, which encompasses over 40,000 MRI scans across 13 studies, incorporating covariates such as age, sex, and race. Leveraging NeuroSynth, we produce and offer 18,000 synthetic samples spanning the adult lifespan (ages 22-90 years), alongside the model's capability to generate unlimited data. Experimental results indicate that samples generated from NeuroSynth agree with the distributions obtained from real data. Most importantly, the generated normative data significantly enhance the accuracy of downstream machine learning models on tasks such as disease classification. Data and models are available at: https://huggingface.co/spaces/rongguangw/neuro-synth.

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers

INTRODUCTION

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers.

METHODS

We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN.

RESULTS

Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression.

DISCUSSION

Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

PAD2 dysregulation and aberrant protein citrullination feature prominently in reactive astrogliosis and myelin protein aggregation in sporadic ALS

Alteration in protein citrullination (PC), a common posttranslational modification (PTM), contributes to pathogenesis in various inflammatory disorders. We previously reported that PC and protein arginine deiminase 2 (PAD2), the predominant enzyme isoform that catalyzes this PTM in the central nervous system (CNS), are altered in mouse models of amyotrophic lateral sclerosis (ALS). We now demonstrate that PAD2 expression and PC are altered in human postmortem ALS spinal cord and motor cortex compared to controls, increasing in astrocytes while trending lower in neurons. Furthermore, PC is enriched in protein aggregates that contain the myelin proteins PLP and MBP in ALS. These results confirm our findings in ALS mouse models and suggest that altered PAD2 and PC contribute to neurodegeneration in ALS.