ArticleImaging neuroscience (Cambridge, Mass.)2024
Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer's disease.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-bioRxiv : the preprint server for biology · 2026Article
- Rise-and-fall dynamics reveal a molecular and cellular vulnerability axis in prion-likebioRxiv : the preprint server for biology · 2026Article
- Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer's disease.Research square · 2026Article
- The relation between neuroinflammation, amyloid-β load, grey matter loss and brain activity in visual object recognition regions in Alzheimer's Disease.Neuroimage. Reports · 2026Article
- Mapping individual molecular connectomes in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Neuroimaging Epicenters as Vulnerable Nodes in Plasma p-tau217/Aβ42-Positive Alzheimer's Disease.Research (Washington, D.C.) · 2026Article
- Article
- Network Diffusion-Constrained Variational Generative Models for Investigating the Molecular Dynamics of Brain Connectomes Under Neurodegeneration.International journal of molecular sciences · 2025Article
- Fully individualized models for cross-sectional and longitudinal network-based tau spread.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Characterizing Amyloid Pathogenic Spread in Alzheimer's Disease Through A Network Diffusion Model.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Experimental quality control induces changes in Allen mouse brain connectomes.Imaging neuroscience (Cambridge, Mass.)Article
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9 authors.
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Abstract
Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity.
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