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J. Neurosci."},{"issue":"5","key":"10.1016\/j.media.2026.103952_bib0057","doi-asserted-by":"crossref","first-page":"2732","DOI":"10.3390\/ijms22052732","article-title":"Prediction of functional consequences of missense mutations in ano4 gene","volume":"22","author":"Reichhart","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.media.2026.103952_bib0058","unstructured":"Romero, A., Carrier, P. L., Erraqabi, A., Sylvain, T., Auvolat, A., Dejoie, E., Legault, M.-A., Dub\u00e9, M.-P., Hussin, J. G., Bengio, Y., 2016. 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Immunol."},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0063","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.neuroimage.2006.12.036","article-title":"Hippocampal shape analysis in alzheimer\u2019s disease: a population-based study","volume":"36","author":"Scher","year":"2007","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0064","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jalz.2013.01.008","article-title":"Genome-wide association study of the rate of cognitive decline in alzheimer\u2019s disease","volume":"10","author":"Sherva","year":"2014","journal-title":"Alzheimer\u2019s & Dementia"},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0065","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/42.668698","article-title":"A nonparametric method for automatic correction of intensity nonuniformity in MRI data","volume":"17","author":"Sled","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0066","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuron.2021.12.004","article-title":"The developmental roots of neurodegeneration","volume":"110","author":"Stephens","year":"2022","journal-title":"Neuron"},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0067","doi-asserted-by":"crossref","first-page":"7816","DOI":"10.1038\/s41598-023-34787-4","article-title":"Transcriptomic and glycomic analyses highlight pathway-specific glycosylation alterations unique to alzheimer\u2019s disease","volume":"13","author":"Tang","year":"2023","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.media.2026.103952_bib0068","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1016\/j.neuroimage.2004.03.040","article-title":"Mapping hippocampal and ventricular change in alzheimer disease","volume":"22","author":"Thompson","year":"2004","journal-title":"Neuroimage"},{"issue":"24","key":"10.1016\/j.media.2026.103952_bib0069","doi-asserted-by":"crossref","first-page":"8986","DOI":"10.1523\/JNEUROSCI.4029-14.2015","article-title":"ANKS1B gene product AIDA-1 controls hippocampal synaptic transmission by regulating GluN2B subunit localization","volume":"35","author":"Tindi","year":"2015","journal-title":"J. Neurosci."},{"issue":"4","key":"10.1016\/j.media.2026.103952_bib0070","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.3233\/JAD-161088","article-title":"Oxidative stress, synaptic dysfunction, and alzheimer\u2019s disease","volume":"57","author":"T\u00f6nnies","year":"2017","journal-title":"J. Alzheimers Dis."},{"key":"10.1016\/j.media.2026.103952_bib0071","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.neuroimage.2014.05.044","article-title":"Large-scale evaluation of ANTs and freesurfer cortical thickness measurements","volume":"99","author":"Tustison","year":"2014","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2026.103952_bib0072","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102842","article-title":"Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases","volume":"88","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103952_bib0073","series-title":"2022 IEEE 19Th International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma","author":"Wei","year":"2022"},{"issue":"8","key":"10.1016\/j.media.2026.103952_bib0074","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1038\/s41419-018-0886-1","article-title":"Oxidation of KCNB1 channels in the human brain and in mouse model of alzheimer\u2019s disease","volume":"9","author":"Wei","year":"2018","journal-title":"Cell. Death Disease"},{"key":"10.1016\/j.media.2026.103952_bib0075","doi-asserted-by":"crossref","first-page":"2831","DOI":"10.1007\/s00429-015-1075-y","article-title":"Impaired and preserved aspects of feedback learning in aMCI: contributions of structural connectivity","volume":"221","author":"Wessa","year":"2016","journal-title":"Brain Struct. Function"},{"key":"10.1016\/j.media.2026.103952_bib0076","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jns.2019.01.020","article-title":"Cortical thinning across parkinson\u2019s disease stages and clinical correlates","volume":"398","author":"Wilson","year":"2019","journal-title":"J. Neurol. Sci."},{"issue":"Preprint","key":"10.1016\/j.media.2026.103952_bib0077","first-page":"1","article-title":"Osteoclasts link dysregulated peripheral degradation processes and accelerated progression in alzheimer\u2019s disease","author":"Wu","year":"2024","journal-title":"J. Alzheimers Dis."},{"key":"10.1016\/j.media.2026.103952_bib0078","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"1555","article-title":"Hippocampus morphometry study on pathology-confirmed alzheimer\u2019s disease patients with surface multivariate morphometry statistics","author":"Wu","year":"2018"},{"key":"10.1016\/j.media.2026.103952_bib0079","article-title":"Role of hippocampal subfields in neurodegenerative disease progression analyzed with a multi-scale attention-based network","volume":"38","author":"Xu","year":"2023","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.media.2026.103952_bib0080","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neuroimage.2017.07.036","article-title":"Imaging-wide association study: integrating imaging endophenotypes in GWAS","volume":"159","author":"Xu","year":"2017","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2026.103952_bib0081","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s11682-018-0003-1","article-title":"Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients","volume":"14","author":"Yao","year":"2020","journal-title":"Brain. Imaging Behav."},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0082","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1038\/s41467-018-05892-0","article-title":"Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference","volume":"9","author":"Young","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.media.2026.103952_bib0083","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.bbr.2016.09.038","article-title":"Roles of hippocampal subfields in verbal and visual episodic memory","volume":"317","author":"Zammit","year":"2017","journal-title":"Behav. Brain Res."},{"key":"10.1016\/j.media.2026.103952_bib0084","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"27456","article-title":"Multimodal representation learning by alternating unimodal adaptation","author":"Zhang","year":"2024"},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0085","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.imed.2022.06.002","article-title":"Deep learning in cortical surface-based neuroimage analysis: a systematic review","volume":"3","author":"Zhao","year":"2023","journal-title":"Intell. Medic."},{"issue":"9","key":"10.1016\/j.media.2026.103952_bib0086","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1111\/cns.14189","article-title":"Classification of alzheimer\u2019s disease based on hippocampal multivariate morphometry statistics","volume":"29","author":"Zheng","year":"2023","journal-title":"CNS Neurosci. Therapeut."},{"key":"10.1016\/j.media.2026.103952_bib0087","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"Zhou","year":"2016"},{"issue":"1","key":"10.1016\/j.media.2026.103952_bib0088","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TBME.2018.2824725","article-title":"Brain-wide genome-wide association study for alzheimer\u2019s disease via joint projection learning and sparse regression model","volume":"66","author":"Zhou","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"10.1016\/j.media.2026.103952_bib0089","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1002\/hbm.24428","article-title":"Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis","volume":"40","author":"Zhou","year":"2019","journal-title":"Hum. Brain. 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