Found: 31
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A machine learning examination of the utility of CSF, neuroimaging, and plasma biomarkers in late onset Alzheimer Disease.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2023, v. 19, p. 1, doi. 10.1002/alz.070918
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- Article
Biomarker clustering in autosomal dominant Alzheimer's disease.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2023, v. 19, n. 1, p. 274, doi. 10.1002/alz.12661
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- Article
CSF tau phosphorylation at sites 217 and 205 mediate the relationship between PET amyloid and downstream AD markers.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2022, v. 18, n. 5, p. 1, doi. 10.1002/alz.065718
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Modeling functional and structural brain‐predicted age in relation to Alzheimer disease and cognition.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2022, v. 18, n. 5, p. 1, doi. 10.1002/alz.060606
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CSF tau phosphorylation at sites 217 and 181 are associated with preclinical and biphasic alterations in resting‐state functional connectivity in Autosomal Dominant AD.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2022, v. 18, p. 1, doi. 10.1002/alz.065641
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Clustering biomarkers in dominantly inherited Alzheimer's disease.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2021, v. 17, p. 1, doi. 10.1002/alz.052149
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Modeling brain‐predicted age from functional connectivity in early‐stage and preclinical Alzheimer disease.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2021, v. 17, p. 1, doi. 10.1002/alz.056280
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Modeling autosomal dominant Alzheimer's disease with machine learning.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2021, v. 17, n. 6, p. 1005, doi. 10.1002/alz.12259
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- Article
Application of machine learning to predict amyloid, metabolic, and structural neuroimaging biomarkers in the progression of autosomal dominant Alzheimer disease: Development of new models and analysis methods: Novel imaging analysis.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2020, v. 16, n. 11, p. 1, doi. 10.1002/alz.040452
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P3‐075: MODELING FUNCTIONAL CONNECTIVITY CHANGES IN LATE ONSET ALZHEIMER'S DISEASE USING DEEP LEARNING.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2019, v. 15, p. P954, doi. 10.1016/j.jalz.2019.06.3102
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P2‐085: PREDICTING METABOLIC AND STRUCTURAL CHANGES IN DOMINANTLY INHERITED ALZHEIMER'S DISEASE.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2019, v. 15, p. P604, doi. 10.1016/j.jalz.2019.06.2492
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IC‐P‐084: PREDICTING METABOLIC AND STRUCTURAL CHANGES IN DOMINANTLY INHERITED ALZHEIMER'S DISEASE.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2019, v. 15, p. P74, doi. 10.1016/j.jalz.2019.06.4927
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- Article
IC‐P‐084: PREDICTING METABOLIC AND STRUCTURAL CHANGES IN DOMINANTLY INHERITED ALZHEIMER'S DISEASE.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2019, v. 15, p. P74, doi. 10.1016/j.jalz.2019.06.4927
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- Article
IC‐P‐029: MODELING FUNCTIONAL CONNECTIVITY CHANGES IN LATE ONSET ALZHEIMER'S DISEASE USING DEEP LEARNING.
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- Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2019, v. 15, p. P36, doi. 10.1016/j.jalz.2019.06.4191
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Complexity organization of resting‐state functional‐MRI networks.
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- Human Brain Mapping, 2024, v. 45, n. 12, p. 1, doi. 10.1002/hbm.26809
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- Article
Initiation and early development of the postcanine deciduous dentition in the dasyurid marsupial Dasyurus viverrinus.
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- Memoirs of Museum Victoria, 2021, v. 80, p. 43, doi. 10.24199/j.mmv.2021.80.03
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Microscopic analysis of the developing dentition in the pouch young of the extinct marsupial Thylacinus cynocephalus, with an assessment of other developmental stages and eruption.
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- Memoirs of Museum Victoria, 2019, v. 78, p. 1, doi. 10.24199/j.mmv.2019.78.01
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Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.
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- Journal of Neurosurgery, 2023, v. 139, n. 5, p. 1258, doi. 10.3171/2023.3.JNS2314
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Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy.
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- Epilepsia (Series 4), 2022, v. 63, n. 6, p. 1542, doi. 10.1111/epi.17233
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- Article
Resting state network mapping in individuals using deep learning.
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- Frontiers in Neurology, 2023, v. 13, p. 1, doi. 10.3389/fneur.2022.1055437
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- Article
Structural gray matter alterations in glioblastoma and high-grade glioma—A potential biomarker of survival.
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- Neuro-Oncology Advances, 2023, v. 5, n. 1, p. 1, doi. 10.1093/noajnl/vdad034
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Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease.
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- Brain Communications, 2024, v. 6, n. 2, p. 1, doi. 10.1093/braincomms/fcae081
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Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study.
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- eLife, 2023, p. 1, doi. 10.7554/eLife.81869
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A metabolomic endotype of bioenergetic dysfunction predicts mortality in critically ill patients with acute respiratory failure.
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- Scientific Reports, 2021, v. 11, n. 1, p. 1, doi. 10.1038/s41598-021-89716-0
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Mapping of the Language Network With Deep Learning.
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- Frontiers in Neurology, 2020, p. N.PAG, doi. 10.3389/fneur.2020.00819
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Neuroimaging the Neuropathogenesis of HIV.
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- Current HIV/AIDS Reports, 2021, v. 18, n. 3, p. 221, doi. 10.1007/s11904-021-00548-z
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Predicting continuous amyloid PET values with CSF and plasma Aβ42/Aβ40.
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- Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2023, v. 15, n. 1, p. 1, doi. 10.1002/dad2.12405
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- Article
Cognitive Phenotypes of HIV Defined Using a Novel Data-driven Approach.
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- Journal of NeuroImmune Pharmacology, 2022, v. 17, n. 3/4, p. 515, doi. 10.1007/s11481-021-10045-0
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- Article
Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.
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- Journal of Neuro-Oncology, 2024, v. 169, n. 1, p. 175, doi. 10.1007/s11060-024-04715-1
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Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.
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- Journal of Neuro-Oncology, 2023, v. 164, n. 2, p. 309, doi. 10.1007/s11060-023-04439-8
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Predicting state transitions in brain dynamics through spectral difference of phase-space graphs.
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- Journal of Computational Neuroscience, 2019, v. 46, n. 1, p. 91, doi. 10.1007/s10827-018-0700-1
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