{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:16:25Z","timestamp":1780341385926,"version":"3.54.1"},"posted":{"date-parts":[[2024,8,5]]},"group-title":"Neuroscience","reference-count":77,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,8,6]]},"abstract":"<jats:p>\n                  Alzheimer\u2019s disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (A\n                  <jats:italic>\u03b2<\/jats:italic>\n                  ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain\u2019s neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations\n                  <jats:italic>in vivo<\/jats:italic>\n                  . In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer\u2019s Disease Neuroimaging Initiative; age range: 55-95 years, 349M\/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain\u2019s neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer\u2019s disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE\n                  <jats:italic>\u025b<\/jats:italic>\n                  3\/\n                  <jats:italic>\u025b<\/jats:italic>\n                  3, carriers of the AD-risk conferring APOE\n                  <jats:italic>\u025b<\/jats:italic>\n                  4 genotype show microstructural abnormalities, while carriers of the protective\n                  <jats:italic>\u025b<\/jats:italic>\n                  2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.\n                <\/jats:p>","DOI":"10.1101\/2024.08.05.606560","type":"posted-content","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T03:35:12Z","timestamp":1722915312000},"source":"Crossref","is-referenced-by-count":6,"title":["Amyloid, Tau, and APOE in Alzheimer\u2019s Disease: Impact on White Matter Tracts"],"prefix":"10.64898","author":[{"given":"Bramsh Qamar","family":"Chandio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julio E.","family":"Villalon-Reina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talia M.","family":"Nir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0046-4070","authenticated-orcid":false,"given":"Sophia I.","family":"Thomopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixue","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Benavidez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neda","family":"Jahanshad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaroslaw","family":"Harezlak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleftherios","family":"Garyfallidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4720-8867","authenticated-orcid":false,"given":"Paul M.","family":"Thompson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2024080804100876000_2024.08.05.606560v2.1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1750-3639.1991.tb00671.x"},{"key":"2024080804100876000_2024.08.05.606560v2.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0206-4"},{"key":"2024080804100876000_2024.08.05.606560v2.3","doi-asserted-by":"crossref","unstructured":"V. L. Villemagne , V. Dor\u00e9 , S. C. Burnham , C. L. Masters and C. C. Rowe , Imaging tau and amyloid-\u03b2 proteinopathies in alzheimer disease and other conditions, Nature Reviews Neurology 14, 225 (2018).","DOI":"10.1038\/nrneurol.2018.9"},{"key":"2024080804100876000_2024.08.05.606560v2.4","doi-asserted-by":"crossref","unstructured":"C. R. Jack Jr , D. A. Bennett , K. Blennow , M. C. Carrillo , B. Dunn , S. B. Haeberlein , D. M. Holtzman , W. Jagust , F. Jessen , J. Karlawish et al., Nia-aa research framework: toward a biological definition of alzheimer\u2019s disease, Alzheimer\u2019s & dementia 14, 535 (2018).","DOI":"10.1016\/j.jalz.2018.02.018"},{"key":"2024080804100876000_2024.08.05.606560v2.5","doi-asserted-by":"crossref","unstructured":"C. Haass and D. J. Selkoe , Soluble protein oligomers in neurodegeneration: lessons from the alzheimer\u2019s amyloid \u03b2-peptide, Nature reviews Molecular cell biology 8, 101 (2007).","DOI":"10.1038\/nrm2101"},{"key":"2024080804100876000_2024.08.05.606560v2.6","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awz162"},{"key":"2024080804100876000_2024.08.05.606560v2.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2012.01.016"},{"key":"2024080804100876000_2024.08.05.606560v2.8","doi-asserted-by":"crossref","unstructured":"C. Bancher , C. Brunner , H. Lassmann , H. Budka , K. Jellinger , G. Wiche , F. Seitelberger , I. Grundke-Iqbal , K. Iqbal and H. Wisniewski , Accumulation of abnormally phosphorylated \u03c4 precedes the formation of neurofibrillary tangles in alzheimer\u2019s disease, Brain research 477, 90 (1989).","DOI":"10.1016\/0006-8993(89)91396-6"},{"key":"2024080804100876000_2024.08.05.606560v2.9","doi-asserted-by":"crossref","unstructured":"P. V. Arriagada , J. H. Growdon , E. T. Hedley-Whyte and B. T. Hyman , Neurofibrillary tangles but not senile plaques parallel duration and severity of alzheimer\u2019s disease, Neurology 42, 631 (1992).","DOI":"10.1212\/WNL.42.3.631"},{"key":"2024080804100876000_2024.08.05.606560v2.10","doi-asserted-by":"crossref","unstructured":"K. Kantarci , M. E. Murray , C. G. Schwarz , R. I. Reid , S. A. Przybelski , T. Lesnick , S. M. Zuk , M. R. Raman , M. L. Senjem , J. L. Gunter et al., White-matter integrity on dti and the pathologic staging of alzheimer\u2019s disease, Neurobiology of aging 56, 172 (2017).","DOI":"10.1016\/j.neurobiolaging.2017.04.024"},{"key":"2024080804100876000_2024.08.05.606560v2.11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13024-020-00413-4","article-title":"Apoe2: protective mechanism and therapeutic implications for alzheimer\u2019s disease","volume":"15","year":"2020","journal-title":"Molecular neurodegeneration"},{"key":"2024080804100876000_2024.08.05.606560v2.12","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1212\/WNL.0000000000003585","article-title":"Multimodal characterization of older apoe2 carriers reveals selective reduction of amyloid load","volume":"88","year":"2017","journal-title":"Neurology"},{"key":"2024080804100876000_2024.08.05.606560v2.13","doi-asserted-by":"publisher","DOI":"10.1126\/science.8346443"},{"key":"2024080804100876000_2024.08.05.606560v2.14","doi-asserted-by":"crossref","first-page":"35514","DOI":"10.1038\/srep35514","article-title":"Colocalization of cerebral iron with amyloid beta in mild cognitive impairment","volume":"6","year":"2016","journal-title":"Scientific reports"},{"key":"2024080804100876000_2024.08.05.606560v2.15","doi-asserted-by":"crossref","unstructured":"P. J. Basser , J. Mattiello and D. LeBihan , MR diffusion tensor spectroscopy and imaging, Biophysical Journal 66, 259 (1994).","DOI":"10.1016\/S0006-3495(94)80775-1"},{"key":"2024080804100876000_2024.08.05.606560v2.16","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1002\/jmri.1076","article-title":"Diffusion tensor imaging: concepts and applications","volume":"13","year":"2001","journal-title":"Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine"},{"key":"2024080804100876000_2024.08.05.606560v2.17","doi-asserted-by":"publisher","DOI":"10.1016\/j.nurt.2007.05.011"},{"key":"2024080804100876000_2024.08.05.606560v2.18","doi-asserted-by":"publisher","DOI":"10.3171\/2013.2.JNS121294"},{"key":"2024080804100876000_2024.08.05.606560v2.19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2008.05.004"},{"key":"2024080804100876000_2024.08.05.606560v2.20","doi-asserted-by":"crossref","unstructured":"G. Gong , Y. He , L. Concha , C. Lebel , D. W. Gross , A. C. Evans and C. Beaulieu , Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography, Cerebral cortex 19, 524 (2008).","DOI":"10.1093\/cercor\/bhn102"},{"key":"2024080804100876000_2024.08.05.606560v2.21","first-page":"23","article-title":"Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy","volume":"5","year":"2011","journal-title":"Frontiers in neuroinformatics"},{"key":"2024080804100876000_2024.08.05.606560v2.22","doi-asserted-by":"crossref","unstructured":"M. Cousineau , P.-M. Jodoin , E. Garyfallidis , M.-A. C\u00f4t\u00e9 , F. C. Morency , V. Rozanski , M. Grand\u2019Maison , B. J. Bedell and M. Descoteaux , A test-retest study on parkinson\u2019s ppmi dataset yields statistically significant white matter fascicles, NeuroImage: Clinical 16, 222 (2017).","DOI":"10.1016\/j.nicl.2017.07.020"},{"key":"2024080804100876000_2024.08.05.606560v2.23","doi-asserted-by":"crossref","first-page":"17149","DOI":"10.1038\/s41598-020-74054-4","article-title":"Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations","volume":"10","year":"2020","journal-title":"Scientific Reports"},{"key":"2024080804100876000_2024.08.05.606560v2.24","doi-asserted-by":"crossref","unstructured":"B. Q. Chandio , T. Chattopadhyay , C. Owens-Walton , J. E. V. Reina , L. Nabulsi , S. I. Thomopoulos , E. Garyfallidis and P. M. Thompson , Fiberneat: Unsupervised white matter tract filtering, in 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022.","DOI":"10.1109\/EMBC48229.2022.9870877"},{"key":"2024080804100876000_2024.08.05.606560v2.25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0049790"},{"key":"2024080804100876000_2024.08.05.606560v2.26","doi-asserted-by":"crossref","first-page":"e065339","DOI":"10.1002\/alz.065339","article-title":"Microstructural changes in the white matter tracts of the brain due to mild cognitive impairment","volume":"18","year":"2022","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"2024080804100876000_2024.08.05.606560v2.27","doi-asserted-by":"crossref","unstructured":"A. H. Zhu , T. M. Nir , S. Javid , J. E. Villalon-Reina , A. L. Rodrigue , L. T. Strike , G. I. de Zubicaray , K. L. McMahon , M. J. Wright , S. E. Medland et al., Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion mri, bioRxiv (2024).","DOI":"10.1101\/2024.02.22.581646"},{"key":"2024080804100876000_2024.08.05.606560v2.28","doi-asserted-by":"crossref","unstructured":"J. E. Villal\u00f3n-Reina , A. H. Zhu , T. M. Nir , S. I. Thomopoulos , E. Laltoo , L. Kushan , C. E. Bearden , N. Jahanshad and P. M. Thompson , Large-scale normative modeling of brain microstructure, in 2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM), 2023.","DOI":"10.1109\/SIPAIM56729.2023.10373451"},{"key":"2024080804100876000_2024.08.05.606560v2.29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.02.024"},{"key":"2024080804100876000_2024.08.05.606560v2.30","doi-asserted-by":"crossref","unstructured":"S. I. Thomopoulos , T. M. Nir , J. E. Villalon-Reina , A. Zavaliangos-Petropulu , P. Maiti , H. Zheng , E. Nourollahimoghadam , N. Jahanshad and P. M. Thompson , Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches, in 17th International Symposium on Medical Information Processing and Analysis, (SPIE, Campinas, Brazil, December 2021).","DOI":"10.1117\/12.2606337"},{"key":"2024080804100876000_2024.08.05.606560v2.31","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3389\/fninf.2019.00002","article-title":"Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multiprotocol Approach in ADNI3","volume":"13","year":"2019","journal-title":"Frontiers in Neuroinformatics"},{"key":"2024080804100876000_2024.08.05.606560v2.32","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.nicl.2014.02.001","article-title":"Associations between white matter microstructure and amyloid burden in preclinical alzheimer\u2019s disease: a multimodal imaging investigation","volume":"4","year":"2014","journal-title":"NeuroImage: Clinical"},{"key":"2024080804100876000_2024.08.05.606560v2.33","doi-asserted-by":"crossref","first-page":"17149","DOI":"10.1038\/s41598-020-74054-4","article-title":"Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations","volume":"10","year":"2020","journal-title":"Scientific Reports"},{"key":"2024080804100876000_2024.08.05.606560v2.34","doi-asserted-by":"crossref","unstructured":"I. B. Gari , A. Ramesh , S. Javid , S. P. Gadewar , E. Nourollahimoghadam , S. I. Thomopoulos , P. M. Thompson , T. M. Nir , N. Jahanshad , A. D. N. Initiative et al., Medial tractography analysis (meta) for white matter population analyses across datasets, in 2023 11th International IEEE\/EMBS Conference on Neural Engineering (NER), 2023.","DOI":"10.1109\/NER52421.2023.10123727"},{"key":"2024080804100876000_2024.08.05.606560v2.35","unstructured":"B. Q. Chandio , Advancing white matter tractometry of the brain using diffusion MRI and machine learning, PhD thesis, Indiana University 2022."},{"key":"2024080804100876000_2024.08.05.606560v2.36","doi-asserted-by":"crossref","first-page":"2612","DOI":"10.1038\/s41467-020-15701-2","article-title":"Spread of pathological tau proteins through communicating neurons in human alzheimer\u2019s disease","volume":"11","year":"2020","journal-title":"Nature communications"},{"key":"2024080804100876000_2024.08.05.606560v2.37","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.112.109009"},{"key":"2024080804100876000_2024.08.05.606560v2.38","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-014-2753-3"},{"key":"2024080804100876000_2024.08.05.606560v2.39","doi-asserted-by":"publisher","DOI":"10.1002\/ana.23650"},{"key":"2024080804100876000_2024.08.05.606560v2.40","doi-asserted-by":"publisher","DOI":"10.1002\/ana.23908"},{"key":"2024080804100876000_2024.08.05.606560v2.41","doi-asserted-by":"crossref","unstructured":"N. Jahanshad , P. V. Kochunov , E. Sprooten , R. C. Mandl , T. E. Nichols , L. Almasy , J. Blangero , R. M. Brouwer , J. E. Curran , G. I. de Zubicaray et al., Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA\u2013DTI working group, Neuroimage 81, 455 (2013).","DOI":"10.1016\/j.neuroimage.2013.04.061"},{"key":"2024080804100876000_2024.08.05.606560v2.42","doi-asserted-by":"crossref","first-page":"e046654","DOI":"10.1002\/alz.046654","article-title":"Diffusion MRI metrics of brain microstructure in alzheimer\u2019s disease: Boosting disease sensitivity with multishell imaging and advanced pre-processing: Neuroimaging\/new imaging methods","volume":"16","year":"2020","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"2024080804100876000_2024.08.05.606560v2.43","doi-asserted-by":"crossref","unstructured":"J. V. Manj\u00f3n , P. Coup\u00e9 , L. Concha , A. Buades , D. L. Collins and M. Robles , Diffusion weighted image denoising using overcomplete local PCA, PLoS ONE 8 (2013).","DOI":"10.1371\/journal.pone.0073021"},{"key":"2024080804100876000_2024.08.05.606560v2.44","first-page":"8","article-title":"Dipy, a library for the analysis of diffusion MRI data","volume":"8","year":"2014","journal-title":"Frontiers in Neuroinformatics"},{"key":"2024080804100876000_2024.08.05.606560v2.45","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26054"},{"key":"2024080804100876000_2024.08.05.606560v2.46","doi-asserted-by":"publisher","DOI":"10.1016\/J.NEUROIMAGE.2019.116137"},{"key":"2024080804100876000_2024.08.05.606560v2.47","doi-asserted-by":"crossref","unstructured":"S. M. Smith , Fast robust automated brain extraction, Human Brain Mapping 17, 143 (2002).","DOI":"10.1002\/hbm.10062"},{"key":"2024080804100876000_2024.08.05.606560v2.48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"key":"2024080804100876000_2024.08.05.606560v2.49","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.10.019"},{"key":"2024080804100876000_2024.08.05.606560v2.50","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2046908"},{"key":"2024080804100876000_2024.08.05.606560v2.51","doi-asserted-by":"crossref","first-page":"e0138910","DOI":"10.1371\/journal.pone.0138910","article-title":"Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization","volume":"10","year":"2015","journal-title":"PLoS ONE"},{"key":"2024080804100876000_2024.08.05.606560v2.52","doi-asserted-by":"crossref","unstructured":"G. Girard , K. Whittingstall , R. Deriche and M. Descoteaux , Towards quantitative connectivity analysis: reducing tractography biases, Neuroimage 98, 266 (2014).","DOI":"10.1016\/j.neuroimage.2014.04.074"},{"key":"2024080804100876000_2024.08.05.606560v2.53","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.neuroimage.2017.07.015","article-title":"Recognition of white matter bundles using local and global streamline-based registration and clustering","volume":"170","year":"2018","journal-title":"NeuroImage"},{"key":"2024080804100876000_2024.08.05.606560v2.54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.05.027"},{"key":"2024080804100876000_2024.08.05.606560v2.55","doi-asserted-by":"crossref","unstructured":"B. Q. Chandio , E. Olivetti , D. Romero-Bascones , J. Harezlak and E. Garyfallidis , Bundlewarp, streamline-based nonlinear registration of white matter tracts, bioRxiv (2023).","DOI":"10.1101\/2023.01.04.522802"},{"key":"2024080804100876000_2024.08.05.606560v2.56","first-page":"175","article-title":"Quickbundles, a method for tractography simplification","volume":"6","year":"2012","journal-title":"Frontiers in Neuroscience"},{"key":"2024080804100876000_2024.08.05.606560v2.57","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxj037"},{"key":"2024080804100876000_2024.08.05.606560v2.58","doi-asserted-by":"crossref","unstructured":"J.-P. Fortin , N. Cullen , Y. I. Sheline , W. D. Taylor , I. Aselcioglu , P. A. Cook , P. Adams , C. Cooper , M. Fava , P. J. McGrath et al., Harmonization of cortical thickness measurements across scanners and sites, Neuroimage 167, 104 (2018).","DOI":"10.1016\/j.neuroimage.2017.11.024"},{"key":"2024080804100876000_2024.08.05.606560v2.59","doi-asserted-by":"crossref","unstructured":"B. Q. Chandio , J. E. Villalon-Reina , T. M. Nir , S. I. Thomopoulos , Y. Feng , S. Benavidez , N. Jahanshad , J. Harezlak , E. Garyfallidis and P. M. Thompson , Bundle analytics based data harmonization for multi-site diffusion mri tractometry, bioRxiv, 2024 (2024).","DOI":"10.1101\/2024.02.03.578764"},{"key":"2024080804100876000_2024.08.05.606560v2.60","doi-asserted-by":"publisher","DOI":"10.1037\/\/0033-2909.93.2.388"},{"key":"2024080804100876000_2024.08.05.606560v2.61","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.21105\/joss.03384","article-title":"Fury: advanced scientific visualization","volume":"6","year":"2021","journal-title":"Journal of Open Source Software"},{"key":"2024080804100876000_2024.08.05.606560v2.62","doi-asserted-by":"crossref","unstructured":"J. Neyman and E. S. Pearson , On the use and interpretation of certain test criteria for purposes of statistical inference part i, Biometrika 20, 175 (1928).","DOI":"10.2307\/2331945"},{"key":"2024080804100876000_2024.08.05.606560v2.63","doi-asserted-by":"crossref","unstructured":"O. J. Dunn , Multiple comparisons among means, Journal of the American statistical association 56, 52 (1961).","DOI":"10.2307\/2282330"},{"key":"2024080804100876000_2024.08.05.606560v2.64","doi-asserted-by":"crossref","unstructured":"Y. Benjamini and Y. Hochberg , Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal statistical society: series B (Methodological) 57, 289 (1995).","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"2024080804100876000_2024.08.05.606560v2.65","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.nicl.2013.07.006","article-title":"Effectiveness of regional dti measures in distinguishing alzheimer\u2019s disease, mci, and normal aging","volume":"3","author":"A. D. N. I. (ADNI","year":"2013","journal-title":"NeuroImage: clinical"},{"key":"2024080804100876000_2024.08.05.606560v2.66","doi-asserted-by":"crossref","unstructured":"Y. Feng , B. Q. Chandio , J. E. Villalon-Reina , S. I. Thomopoulos , T. M. Nir , S. Benavidez , E. Laltoo , T. Chattopadhyay , H. Joshi , G. Venkatasubramanian et al., Microstructural mapping of neural pathways in alzheimer\u2019s disease using macrostructure-informed normative tractometry, bioRxiv (2024).","DOI":"10.1101\/2024.04.25.591183"},{"key":"2024080804100876000_2024.08.05.606560v2.67","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.69.4.528"},{"key":"2024080804100876000_2024.08.05.606560v2.68","first-page":"e12425","article-title":"White matter microstructural metrics are sensitively associated with clinical staging in alzheimer\u2019s disease","volume":"15","year":"2023","journal-title":"Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring"},{"key":"2024080804100876000_2024.08.05.606560v2.69","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1017\/S0317167100000846","article-title":"Callosal atrophy correlates with temporal lobe volume and mental status in alzheimer\u2019s disease","volume":"27","year":"2000","journal-title":"Canadian Journal of Neurological Sciences"},{"key":"2024080804100876000_2024.08.05.606560v2.70","doi-asserted-by":"publisher","DOI":"10.1002\/ana.20334"},{"key":"2024080804100876000_2024.08.05.606560v2.71","doi-asserted-by":"crossref","unstructured":"F. Agosta , R. G. Henry , R. Migliaccio , J. Neuhaus , B. L. Miller , N. F. Dronkers , S. M. Brambati , M. Filippi , J. M. Ogar , S. M. Wilson et al., Language networks in semantic dementia, Brain 133, 286 (2010).","DOI":"10.1093\/brain\/awp233"},{"key":"2024080804100876000_2024.08.05.606560v2.72","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhh186"},{"key":"2024080804100876000_2024.08.05.606560v2.73","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awn063"},{"key":"2024080804100876000_2024.08.05.606560v2.74","doi-asserted-by":"crossref","unstructured":"A. Craig , Pain mechanisms: labeled lines versus convergence in central processing, Annual review of neuroscience 26, 1 (2003).","DOI":"10.1146\/annurev.neuro.26.041002.131022"},{"key":"2024080804100876000_2024.08.05.606560v2.75","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.13.11365"},{"key":"2024080804100876000_2024.08.05.606560v2.76","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.03.072"},{"key":"2024080804100876000_2024.08.05.606560v2.77","doi-asserted-by":"crossref","unstructured":"T. M. Nir , J. E. Villal\u00f3n-Reina , L. E. Salminen , E. Haddad , H. Zheng , S. I. Thomopoulos , C. R. Jack Jr , M. W. Weiner , P. M. Thompson , N. Jahanshad et al., Cortical microstructural associations with csf amyloid and ptau, Molecular Psychiatry, 1 (2023).","DOI":"10.1101\/2023.04.10.23288366"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2024.08.05.606560","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:48:33Z","timestamp":1768459713000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2024.08.05.606560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,5]]},"references-count":77,"URL":"https:\/\/doi.org\/10.1101\/2024.08.05.606560","relation":{},"subject":[],"published":{"date-parts":[[2024,8,5]]},"subtype":"preprint"}}