{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T17:29:42Z","timestamp":1787592582596,"version":"build-2736575974"},"reference-count":76,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,2,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer\u2019s 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<\/jats:p>","DOI":"10.1162\/imag_a_00089","type":"journal-article","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T15:10:45Z","timestamp":1705677045000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":10,"title":["Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer\u2019s disease"],"prefix":"10.1162","volume":"2","author":[{"given":"Elinor","family":"Thompson","sequence":"first","affiliation":[{"name":"UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Schroder","sequence":"additional","affiliation":[{"name":"UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"He","sequence":"additional","affiliation":[{"name":"UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cameron","family":"Shand","sequence":"additional","affiliation":[{"name":"UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonja","family":"Soskic","sequence":"additional","affiliation":[{"name":"UCL Centre for Medical Image 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