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1. Full Text Hierarchical anatomical brain networks for MCI prediction: Revisiting volumetric measures
by Zhou, Luping and Wang, Yaping and Li, Yang and Yap, Pew-Thian and Shen, Dinggang and Alzheimer's Disease Neuroimaging Initiative (ADNI), Disease Neuroimaging Initiative (ADNI) and Alzheimer's Dis Neuroimaging Initi and Alzheimer's Disease Neuroimaging Initiative (ADNI) and and the Alzheimer's Disease Neuroimaging Initiative (ADNI)
PLoS ONE, ISSN 1932-6203, 2011, Volume 6, Issue 7, p. e21935
Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of... 
DIAGNOSIS | PATTERN-CLASSIFICATION | MRI | ATROPHY | MULTIDISCIPLINARY SCIENCES | SEGMENTATION | PRODROMAL ALZHEIMERS-DISEASE | MILD COGNITIVE IMPAIRMENT | Magnetic Resonance Imaging | Humans | Brain - pathology | Female | Male | Aged | Cognitive Dysfunction - diagnosis | Cognitive Dysfunction - pathology | Models, Neurological | Nerve Net - pathology | Medicine, Experimental | Medical research | Advertising executives | Alzheimer's disease | Medical imaging equipment | Neuroimaging | Brain | Nuclear magnetic resonance--NMR | Image processing | Noise | Cognitive ability | Embedding | Cerebrospinal fluid | Substantia grisea | Noise sensitivity | Registration | Older people | Spots | Classification | Medical imaging | Neurodegenerative diseases | Data processing | Substantia alba | Studies | Pathology | Neurology | Magnetic resonance imaging | Correlation analysis | Feature extraction | Alzheimers disease | Psychiatry | Nuclear magnetic resonance | NMR
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2. Full Text Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network
by Huang, YC and Xu, JH and Zhou, YC and Tong, T and Zhuang, XH and Alzheimers Dis Neuroimaging Initia and Alzheimer’s Disease Neuroimaging Initiative (ADNI)
FRONTIERS IN NEUROSCIENCE, ISSN 1662-453X, 05/2019, Volume 13, p. 509
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to... 
deep learning | CNN | hippocampal | image classification | BIOMARKER | CLINICAL-TRIALS | multi-modality | CLASSIFICATION | MILD COGNITIVE IMPAIRMENT | Alzheimer's disease | NEUROSCIENCES | Advertising executives | Algorithms | Image processing | Neural networks | Machine learning | Diagnostic imaging | Data mining | Artificial intelligence | Cable television broadcasting industry
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3. Identification of Novel Quantitative Traits-Associated Susceptibility Loci for APOE epsilon 4 Non-Carriers of Alzheimer's Disease
by Jiang, S and Yang, WL and Qiu, Y and Chen, HZ and ADNI
CURRENT ALZHEIMER RESEARCH, ISSN 1567-2050, 2015, Volume 12, Issue 3, pp. 218 - 227
APOE epsilon 4 allele is a major risk factor in Late-Onset Alzheimer's Disease (AD). Distinct phenotypes that depend on the APOE epsilon 4 status have been... 
ALLELE | haplotype | single nucleotide polymorphism | brain atrophy | NEUROIMAGING INITIATIVE ADNI | RISK | POLYMORPHISM | CEREBROSPINAL-FLUID | NEUROSCIENCES | CLINICAL NEUROLOGY | apolipoprotein E epsilon 4 | APOLIPOPROTEIN-E | GENE | AMYLOID-BETA | EARLY-ONSET | WHOLE-GENOME ASSOCIATION | Alzheimer's disease | diagnostic biomarker
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4. Automatic classification method of Alzheimer's disease by voxel-based morphometry on MR images
by Guo, Shengwen and Chi, Minyue and Cen, Guiyin and Kuang, Cuili and Niu, Chuanxiao and Lai, Chunren and Wu, Xiaoming and The Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimer's Disease Neuroimaging Initiative (ADNI)
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition), ISSN 1001-0505, 03/2015, Volume 45, Issue 2, pp. 260 - 265
Mild cognitive impairment | Voxel-based morphometry | Support vector machine | Recursive feature elimination | Alzheimer's disease
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5. Full Text Genomic copy number analysis in Alzheimer's disease and mild cognitive impairment: An ADNI study
by Saykin, Andrew J and Swaminathan, Shanker and Kim, Sungeun and Shen, Li and Risacher, Shannon L and Foroud, Tatiana and Pankratz, Nathan and Potkin, Steven G and Huentelman, Matthew J and Craig, David W and Weiner, Michael W and The Alzheimer's Disease Neuroimaging Initiative (ADNI)
International Journal of Alzheimer's Disease, ISSN 2090-0252, 2011, Volume 2011, pp. 729478 - 10
Copy number variants (CNVs) are DNA sequence alterations, resulting in gains (duplications) and losses (deletions) of genomic segments. They often overlap... 
Gene mutations | Genetic aspects | Diagnosis | Research | Cognition disorders | Alzheimer's disease | Health aspects
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6. Full Text Associations between APOE genotype and cerebral small-vessel disease: A longitudinal study
by Luo, Xiao and Jiaerken, Yerfan and Yu, Xinfeng and Huang, Peiyu and Qiu, Tiantian and Jia, Yunlu and Li, Kaicheng and Xu, Xiaojun and Shen, Zhujing and Guan, Xiaojun and Zhou, Jiong and Zhang, Minming and ADNI
Oncotarget, ISSN 1949-2553, 2017, Volume 8, Issue 27, pp. 44477 - 44489
Objective: It remains unclear if and how the interactions between APOE genotypes and cerebral small-vessel diseases (CSVD) lead to cognitive decline in the... 
White matter hyperintensities (WMH) | Apolipoprotein E (APOE) | Cerebral small-vascular disease (CSVD) | Cognition | Dilated perivascular space (dPVS) | white matter hyperintensities (WMH) | cognition | ALZHEIMERS-DISEASE | ENLARGED PERIVASCULAR SPACES | VULNERABILITY | STROKE PATIENTS | MILD COGNITIVE IMPAIRMENT | cerebral small-vascular disease (CSVD) | APOLIPOPROTEIN-E | CELL BIOLOGY | apolipoprotein E (APOE) | dilated perivascular space (dPVS) | MRI MARKERS | WHITE-MATTER HYPERINTENSITIES | VIRCHOW-ROBIN SPACES | BRAIN | Neuroimaging | Humans | Middle Aged | Cognitive Dysfunction | tau Proteins - metabolism | Male | Cerebrovascular Disorders - metabolism | Positron-Emission Tomography | Amyloid beta-Peptides - metabolism | Aged, 80 and over | Female | Genetic Predisposition to Disease | Genetic Association Studies | Comorbidity | Risk Factors | Genotype | Cerebrovascular Disorders - etiology | Disease Progression | Neuropsychological Tests | Magnetic Resonance Imaging | Cerebrovascular Disorders - diagnosis | Apolipoproteins E - genetics | Alleles | Biomarkers | Aged | Cerebrovascular Disorders - genetics | Longitudinal Studies
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7. Full Text Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer’s disease progression
by Fiot, Jean-Baptiste and Raguet, Hugo and Risser, Laurent and Cohen, Laurent D and Fripp, Jurgen and Vialard, F. -X and For The Alzheimer'S Disease Neuroimaging Initiative, (ADNI)
Neuroimage-Clinical, ISSN 2213-1582, 05/2014, Issue 4, pp. 718 - 729
Computer Science | Computer Vision and Pattern Recognition
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8. Full Text A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression
by Llano, DA and Bundela, S and Mudar, RA and Devanarayan, V and Alzheimer's Dis Neuroimaging Init and Alzheimer’s Disease Neuroimaging Initiative (ADNI) and for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
PLOS ONE, ISSN 1932-6203, 08/2017, Volume 12, Issue 8, p. e0182098
To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer's Disease (AD) and normal aged controls... 
FATTY-ACID-BINDING | PROTEIN | DEMENTIA | CEREBROSPINAL-FLUID BIOMARKERS | MULTIDISCIPLINARY SCIENCES | BRAIN-TYPE | SERUM | MILD COGNITIVE IMPAIRMENT | MASS-SPECTROMETRY | DISCOVERY | PROTEOMICS | Models, Theoretical | Severity of Illness Index | Amino Acid Sequence | Multivariate Analysis | Peptide Fragments - cerebrospinal fluid | Area Under Curve | Humans | Male | tau Proteins - cerebrospinal fluid | Alzheimer Disease - cerebrospinal fluid | Case-Control Studies | Disease Progression | Alzheimer Disease - pathology | Algorithms | Apolipoproteins E - cerebrospinal fluid | Aged, 80 and over | Amyloid beta-Peptides - cerebrospinal fluid | Female | ROC Curve | Aged | Databases, Factual | Cerebrospinal fluid | Research | Peptides | Biological markers | Alzheimer's disease | Analysis | Neuroimaging | Nuclear magnetic resonance--NMR | Cognitive ability | Identification | Multivariate analysis | Proteins | Ethics | Apolipoprotein E | Classification | Signatures | Pentraxins | Medical imaging | Neurodegenerative diseases | Review boards | Metabolism | Fatty acids | Disease control | Heart fatty acid-binding protein | Medicine | Studies | Human subjects | Neurology | Hypotheses | Brain research | Hospitals | Tau protein | Fatty acid-binding protein | Proteomics | Biomarkers | β-Amyloid | Scientific imaging | Alzheimers disease | Mass spectrometry | Dementia | Nuclear magnetic resonance | NMR
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9. Full Text Selection for depression-specific dementia cases with replication in two cohorts
by Royall, DR and Palmer, RF and Pavlik, V and Massman, P and Darby, E and Rodriguear, M and Ansari, AK and DeToledo, JC and Reddy, H and Wilms, H and Johnson, K and Perez, V and Fairchild, T and Knebl, J and O'Bryant, SE and Hall, JR and Johnson, L and Barber, RC and Mains, D and Alvarez, L and Cullum, M and Rosenberg, R and Williams, B and Quiceno, M and Reisch, J and Hynan, LS and Huebinger, R and Smith, J and Nguyen, T and Royall, D and Palmer, R and Polk, M and Stevens, A and Ory, M and Paydarfar, D and Bertelson, J and Woon, M and Ayres, G and Aguirre, A and Wilhelmsen, KC and Tilson, JL and TARCC and ADNI and for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Texas Alzheimer’s Research and Care Consortium (TARCC)
PLOS ONE, ISSN 1932-6203, 05/2019, Volume 14, Issue 5, p. e0216413
The latent variable "delta" (for "dementia") provides an etiologically "agnostic" omnibus dementia severity metric capable of recognizing the dementing... 
MILD | DIAGNOSIS | INSTRUMENTAL ACTIVITIES | ALZHEIMERS-DISEASE | MULTIDISCIPLINARY SCIENCES | MISSING DATA | VALIDATION | PHENOTYPE | SCALES | STATE | ASSOCIATION | Neuroimaging | Signs and symptoms | Medical imaging | Activities of daily living | Cognitive ability | Systematic review | Mental depression | Risk analysis | Apolipoproteins | Risk factors | Consortia | Proteins | Neurology | Algorithms | Cognition & reasoning | Etiology | Dementia disorders | Biomarkers | Bioindicators | Dementia
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10. Full Text Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network
by Huang, Yechong and Xu, Jiahang and Zhou, Yuncheng and Tong, Tong and Zhuang, Xiahai and the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Frontiers in Neuroscience, ISSN 1662-453X, 05/2019, Volume 13
Abstract Alzheimer’s Disease (AD) is one of the most concerned neurodegenerative diseases. In the last decade, studies on AD diagnosis attached great... 
Brain | Medical imaging | Segmentation | Image processing | Neurodegenerative diseases | Cognitive ability | Metabolism | Deep learning | Learning algorithms | Teaching methods | Algorithms | Magnetic resonance imaging | Neural networks | Classification | Biomarkers | Diagnosis | Artificial intelligence | Alzheimer's disease | Positron emission tomography | Hippocampus | Dementia | deep learning | multi-modality | CNN | hippocampal | Alzheimer’s disease | image classification
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11. Full Text Influence of Genetic Variation on Plasma Protein Levels in Older Adults Using a Multi-Analyte Panel
by Kim, Sungeun and Swaminathan, Shanker and Inlow, Mark and Risacher, Shannon L and Nho, Kwangsik and Shen, Li and Foroud, Tatiana M and Petersen, Ronald C and Aisen, Paul S and Soares, Holly and Toledo, Jon B and Shaw, Leslie M and Trojanowski, John Q and Weiner, Michael W and McDonald, Brenna C and Farlow, Martin R and Ghetti, Bernardino and Saykin, Andrew J and ADNI and Alzheimer’s Disease Neuroimaging Initiative (ADNI) and for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
PLoS ONE, ISSN 1932-6203, 07/2013, Volume 8, Issue 7, p. e70269
Proteins, widely studied as potential biomarkers, play important roles in numerous physiological functions and diseases. Genetic variation may modulate... 
QUANTITATIVE TRAIT LOCI | ANGIOTENSINOGEN GENE | ALZHEIMERS-DISEASE | MULTIDISCIPLINARY SCIENCES | NEUROIMAGING INITIATIVE ADNI | WHOLE-GENOME ASSOCIATION | FACTOR-H | FUNCTIONAL ANNOTATION | MILD COGNITIVE IMPAIRMENT | THYROXINE-BINDING GLOBULIN | APOLIPOPROTEIN-E | Genome-Wide Association Study | Humans | Middle Aged | Complement C3b Inactivator Proteins - genetics | Linear Models | Male | Complement C3b Inactivator Proteins - analysis | Apolipoproteins E - blood | Apolipoproteins E - genetics | Aged, 80 and over | Female | Aged | Polymorphism, Single Nucleotide | Alzheimer Disease - genetics | Alzheimer Disease - blood | Medical research | Genomics | Medicine, Experimental | Genetic aspects | Genomes | Single nucleotide polymorphisms | Alzheimer's disease | Chromosomes | Blood proteins | Multiplexing | Neuroimaging | Haplotypes | Elderly people | Plasma | Disease | Laboratories | Physicians | Linkage disequilibrium | Genes | Interleukin | Biology | Single-nucleotide polymorphism | Interleukin 6 | Proteins | Complement factor H | Peptidyl-dipeptidase A | Older people | Angiotensinogen | Aging | Genetics | Blood pressure | Bioindicators | Bioinformatics | Hypertension | Medical imaging | Data processing | Genetic diversity | Regression analysis | Disease control | Medicine | Studies | Neurology | Pathology | Proteomics | Angiotensin | Biomarkers | Adults | Variation
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12. Full Text Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer’s disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database
by Ben Bouallègue, Fayçal and Mariano-Goulart, Denis and Payoux, Pierre and the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Alzheimer's Research & Therapy, ISSN 1758-9193, 12/2017, Volume 9, Issue 1, pp. 1 - 13
Abstract Background The relative performance of semi-quantitative amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) markers in... 
ADNI | MCI | CSF markers | Alzheimer’s disease | Amyloid PET
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13. Full Text Dependency criterion based brain pathological age estimation of Alzheimer’s disease patients with MR scans
by Li, Yongming and Liu, Yuchuan and Wang, Pin and Wang, Jie and Xu, Sha and Qiu, Mingguo and For the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
BioMedical Engineering OnLine, ISSN 1475-925X, 12/2017, Volume 16, Issue 1, pp. 1 - 20
Abstract Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore... 
Brain age estimation | Alzheimer’s disease | Magnetic resonance imaging | Brain pathological age | Classification | Correlation criterion
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14. Full Text Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
by Young, Alexandra L and Marinescu, Razvan V and Oxtoby, Neil P and Bocchetta, Martina and Yong, Keir and Firth, Nicholas C and Cash, David M and Thomas, David L and Dick, Katrina M and Cardoso, Jorge and van Swieten, John and Borroni, Barbara and Galimberti, Daniela and Masellis, Mario and Tartaglia, Maria Carmela and Rowe, James B and Graff, Caroline and Tagliavini, Fabrizio and Frisoni, Giovanni B and Laforce, Robert and Finger, Elizabeth and de Mendonça, Alexandre and Sorbi, Sandro and Warren, Jason D and Crutch, Sebastian and Fox, Nick C and Ourselin, Sebastien and Schott, Jonathan M and Rohrer, Jonathan D and Alexander, Daniel C and Andersson, Christin and Archetti, Silvana and Arighi, Andrea and Benussi, Luisa and Binetti, Giuliano and Black, Sandra and Cosseddu, Maura and Fallström, Marie and Ferreira, Carlos and Fenoglio, Chiara and Freedman, Morris and Fumagalli, Giorgio G and Gazzina, Stefano and Ghidoni, Roberta and Grisoli, Marina and Jelic, Vesna and Jiskoot, Lize and Keren, Ron and Lombardi, Gemma and Maruta, Carolina and Meeter, Lieke and Mead, Simon and van Minkelen, Rick and Nacmias, Benedetta and Öijerstedt, Linn and Padovani, Alessandro and Panman, Jessica and Pievani, Michela and Polito, Cristina and Premi, Enrico and Prioni, Sara and Rademakers, Rosa and Redaelli, Veronica and Rogaeva, Ekaterina and Rossi, Giacomina and Rossor, Martin and Scarpini, Elio and Tang-Wai, David and Thonberg, Hakan and Tiraboschi, Pietro and Verdelho, Ana and Weiner, Michael W and Aisen, Paul and Petersen, Ronald and Jack, Clifford R and Jagust, William and Trojanowki, John Q and Toga, Arthur W and Beckett, Laurel and Green, Robert C and Saykin, Andrew J and Morris, John and Shaw, Leslie M and Khachaturian, Zaven and Sorensen, Greg and Kuller, Lew and Raichle, Marc and Paul, Steven and Davies, Peter and Fillit, Howard and Hefti, Franz and Holtzman, Davie and Mesulam, M Marcel and Potter, William and Snyder, Peter and Schwartz, Adam and Montine, Tom and Thomas, Ronald G and Donohue, Michael and Walter, Sarah and ... and Genetic FTD Initiative GENFI and Alzheimer's Dis Neuroimaging Initi and Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Genetic FTD Initiative (GENFI) and The Genetic FTD Initiative (GENFI) and The Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Nature Communications, ISSN 2041-1723, 12/2018, Volume 9, Issue 1, pp. 4273 - 16
The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include... 
COGNITIVE DECLINE | REPEAT EXPANSIONS | DEMENTIA | DEFINED SUBTYPES | MULTIDISCIPLINARY SCIENCES | SPORADIC ALZHEIMERS-DISEASE | ATROPHY PATTERNS | BIOMARKER CHANGES | MODEL | FRONTOTEMPORAL LOBAR DEGENERATION | HUNTINGTONS-DISEASE | Frontotemporal Dementia - genetics | Phenotype | Reproducibility of Results | Neurodegenerative Diseases - pathology | Time Factors | Humans | Genotype | Models, Neurological | Alzheimer Disease - genetics | Alzheimer Disease - pathology | Frontotemporal Dementia - pathology | Neurodegenerative Diseases - classification | Phenotypes | Medical imaging | Disease | Neurodegenerative diseases | Medical treatment | Inference | Trajectories | Patients | Subgroups | Complexity | Diseases | Neurological diseases | Heterogeneity | Learning algorithms | Neurodegeneration | Machine learning | Dementia disorders | Diagnostic systems | Frontotemporal dementia | Genotypes
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