20.9.2 Brain, Cortex, Alzheimer's Disease, Dementia

Chapter Contents (Back)
Includes other mental problems. Brain. Cortex. Alzheimer's Disease. See also Brain, Cortex, Registration, Alignment, MRI, Other. See also Functional Magnetic Resonance, fMRI.

Freeborough, P.A., Fox, N.C.,
MR Image Texture Analysis Applied to the Diagnosis and Tracking of Alzheimers-Disease,
MedImg(17), No. 3, June 1998, pp. 475-479.
IEEE Top Reference. 9809
BibRef

Freeborough, P.A.[Peter A.],
A Comparison of Fractal Texture Descriptors,
BMVC97(xx-yy).
HTML Version. 0209
BibRef

Bhattacharya, M., Majumder, D.D.,
Registration of CT and MR images of Alzheimer's patient: A Shape Theoretic Approach,
PRL(21), No. 6-7, June 2000, pp. 531-548. 0006
BibRef

Studholme, C., Cardenas, V., Song, E., Ezekiel, F., Maudsley, A.A., Weiner, M.,
Accurate Template-Based Correction of Brain MRI Intensity Distortion With Application to Dementia and Aging,
MedImg(23), No. 1, January 2004, pp. 99-110.
IEEE Abstract. 0403
BibRef

Wang, L.[Lei], Beg, F.[Faisal], Ratnanather, J.T.[J. Tilak], Ceritoglu, C.[Can], Younes, L.[Laurent], Morris, J.C.[John C.], Csernansky, J.G.[John G.], Miller, M.I.[Michael I.],
Large Deformation Diffeomorphism and Momentum Based Hippocampal Shape Discrimination in Dementia of the Alzheimer type,
MedImg(26), No. 4, April 2007, pp. 462-470.
IEEE DOI 0704
See also Diffeomorphic Active Contours. BibRef

Tward, D.[Daniel], Miller, M., Trouvé, A.[Alain], Younes, L.[Laurent],
Parametric Surface Diffeomorphometry for Low Dimensional Embeddings of Dense Segmentations and Imagery,
PAMI(39), No. 6, June 2017, pp. 1195-1208.
IEEE DOI 1705
Biological system modeling, Diseases, Hippocampus, Image segmentation, Magnetic resonance imaging, Measurement, Shape, Computational anatomy, diffeomorphometry, medical imaging, neuroimaging, shape, analysis BibRef

Tward, D.[Daniel], Jovicich, J.[Jorge], Soricelli, A.[Andrea], Frisoni, G.[Giovanni], Trouvé, A.[Alain], Younes, L.[Laurent], Miller, M.[Michael],
Improved Reproducibility of Neuroanatomical Definitions through Diffeomorphometry and Complexity Reduction,
MLMI14(223-230).
Springer DOI 1410
BibRef

Tu, Z.W.[Zhuo-Wen], Zheng, S.F.[Song-Feng], Yuille, A.L.[Alan L.], Reiss, A.L.[Allan L.], Dutton, R.A.[Rebecca A.], Lee, A.D.[Agatha D.], Galaburda, A.M.[Albert M.], Dinov, I.D.[Ivo D.], Thompson, P.M.[Paul M.], Toga, A.W.[Arthur W.],
Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach,
MedImg(26), No. 4, April 2007, pp. 541-552.
IEEE DOI 0704
BibRef

Shi, Y.G., Tu, Z., Reiss, A.L., Dutton, R.A., Lee, A.D., Galaburda, A.M., Dinov, I.D., Thompson, P.M., Toga, A.W.,
Joint Sulcal Detection on Cortical Surfaces With Graphical Models and Boosted Priors,
MedImg(28), No. 3, March 2009, pp. 361-373.
IEEE DOI 0903
BibRef

Liu, X.Y.[Xin-Yang], Liu, X.W.[Xiu-Wen], Shi, Y.G.[Yong-Gang], Thompson, P.M.[Paul M.], Mio, W.[Washington],
A Model of Volumetric Shape for the Analysis of Longitudinal Alzheimer's Disease Data,
ECCV10(III: 594-606).
Springer DOI 1009
BibRef

Alvarez Illan, I., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Lopez, M., Segovia, F., Padilla, P., Puntonet, C.G.,
Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease,
PRL(31), No. 11, 1 August 2010, pp. 1342-1347.
Elsevier DOI 1008
Alzheimer's disease; Independent Component Analysis; Computer aided diagnosis; Support vector machine; Supervised learning BibRef

Padilla, P., Lopez, M., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Alvarez, I.,
NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease,
MedImg(31), No. 2, February 2012, pp. 207-216.
IEEE DOI 1202
BibRef

Salas-Gonzalez, D., Gorriz, J.M., Ramirez, J., Alvarez, I., Lopez, M., Segovia, F., Gomez-Rio, M.,
Skewness as feature for the diagnosis of Alzheimer's disease using SPECT images,
ICIP09(837-840).
IEEE DOI 0911
BibRef

Ye, J.P.[Jie-Ping], Wu, T.[Teresa], Li, J.[Jing], Chen, K.W.[Ke-Wei],
Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease,
Computer(44), No. 4, April 2011, pp. 99-101.
IEEE DOI 1104
BibRef

Filipovych, R.[Roman], Wang, Y.[Ying], Davatzikos, C.[Christos],
Pattern analysis in neuroimaging: Beyond two-class categorization,
IJIST(21), No. 2, June 2011, pp. 173-178.
DOI Link 1101
clustering; MRI; aging; MCI; Alzheimer's disease BibRef

Pachauri, D., Hinrichs, C., Chung, M.K., Johnson, S.C., Singh, V.,
Topology-Based Kernels With Application to Inference Problems in Alzheimer's Disease,
MedImg(30), No. 10, October 2011, pp. 1760-1770.
IEEE DOI 1110
BibRef

Chaves, R., Ramírez, J., Górriz, J.M., Illán, I.A.,
Functional brain image classification using association rules defined over discriminant regions,
PRL(33), No. 12, 1 September 2012, pp. 1666-1672.
Elsevier DOI 1208
Functional brain imaging; Alzheimer's Disease; Fisher Discriminant Ratio; Association rules for the Alzheimer's Disease Neuroimaging Initiative, BibRef

Mesrob, L., Magnin, B., Colliot, O., Sarazin, M., Hahn-Barma, V., Dubois, B., Gallinari, P., Lehericy, S., Kinkingnehun, S., Benali, H.,
Identification of atrophy patterns in Alzheimer's disease based on SVM feature selection and anatomical parcellation,
BMVA(2009), No. 7, 2009, pp. 1-9.
PDF File. 1209
BibRef

Janousova, E.[Eva], Vounou, M.[Maria], Wolz, R.[Robin], Gray, K.R.[Katherine R.], Rueckert, D.[Daniel], Montana, G.[Giovanni],
Biomarker discovery for sparse classification of brain images in Alzheimer's disease,
BMVA(2012), No. 2, 2012, pp. 1-11.
PDF File. 1209
BibRef

Cuingnet, R.[Rémi], Glaunès, J.A.[Joan Alexis], Chupin, M.[Marie], Benali, H.[Habib], Colliot, O.[Olivier],
Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data,
PAMI(35), No. 3, March 2013, pp. 682-696.
IEEE DOI 1303
BibRef
Earlier:
Anatomical Regularization on Statistical Manifolds for the Classification of Patients with Alzheimer's Disease,
MLMI11(201-208).
Springer DOI 1109
BibRef

Zhao, M.B.[Ming-Bo], Chan, R.H.M., Tang, P.[Peng], Chow, T.W.S., Wong, S.W.H.,
Trace Ratio Linear Discriminant Analysis for Medical Diagnosis: A Case Study of Dementia,
SPLetters(20), No. 5, May 2013, pp. 431-434.
IEEE DOI 1304
BibRef

Zhao, M.B.[Ming-Bo], Chan, R.H.M., Chow, T.W.S., Tang, P.,
Compact Graph based Semi-Supervised Learning for Medical Diagnosis in Alzheimer's Disease,
SPLetters(21), No. 10, October 2014, pp. 1192-1196.
IEEE DOI 1407
Classification algorithms BibRef

Huang, S.[Shuai], Li, J.[Jing], Ye, J.P.[Jie-Ping], Fleisher, A.[Adam], Chen, K.W.[Ke-Wei], Wu, T.[Teresa], Reiman, E.[Eric],
A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data,
PAMI(35), No. 6, June 2013, pp. 1328-1342.
IEEE DOI 1305
the Alzheimer's Disease Neuroimaging Initiative. Apply to brain connectivity modeling. BibRef

Komlagan, M.[Mawulawoé], Ta, V.T.[Vinh-Thong], Pan, X.Y.[Xing-Yu], Domenger, J.P.[Jean-Philippe], Coupé, D.L.C.P.[D. Louis Collins Pierrick],
Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer's Disease,
MLMI14(141-148).
Springer DOI 1410
the Alzheimer's Disease Neuroimaging Initiative BibRef

Kodewitz, A.[Andreas], Lelandais, S.[Sylvie], Montagne, C.[Christophe], Vigneron, V.[Vincent],
Alzheimer's disease early detection from sparse data using brain importance maps,
ELCVIA(12), No. 1, 2013, pp. xx-yy.
DOI Link 1307
BibRef

Xie, J.[Jing], Fletcher, E.[Evan], Singh, B.[Baljeet], Carmichael, O.[Owen],
Robust measurement of individual localized changes to the aging hippocampus,
CVIU(117), No. 9, 2013, pp. 1128-1137.
Elsevier DOI 1307
Hippocampal shape change BibRef

Morabito, F.C.[Francesco Carlo],
The compressibility of an electroencephalography signal may indicate Alzheimer's disease,
SPIE(Newsroom), June 3, 2013
DOI Link 1307
By analyzing the content of electrical activity at the surface of the brain, researchers can distinguish between patients who are healthy and those with different types of cognitive impairment. BibRef

Ortiz, A.[Andrés], Górriz, J.M.[Juan M.], Ramírez, J.[Javier], Martínez-Murcia, F.J.,
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease,
PRL(34), No. 14, 2013, pp. 1725-1733.
Elsevier DOI 1308
Alzheimer's disease BibRef

Zeng, W.[Wei], Shi, R.[Rui], Wang, Y.L.[Ya-Lin], Yau, S.T.[Shing-Tung], Gu, X.F.[Xian-Feng],
Teichmüller Shape Descriptor and Its Application to Alzheimer's Disease Study,
IJCV(105), No. 2, November 2013, pp. 155-170.
Springer DOI 1309
BibRef

Dai, D.[Dai], He, H.G.[Hui-Guang], Vogelstein, J.T.[Joshua T.], Hou, Z.G.[Zeng-Guang],
Accurate prediction of AD patients using cortical thickness networks,
MVA(24), No. 7, October 2013, pp. 1445-1457.
Springer DOI 1309
BibRef
Earlier:
Network-Based Classification Using Cortical Thickness of AD Patients,
MLMI11(193-200).
Springer DOI 1109
Alzheimers BibRef

Osadebey, M.[Michael], Bouguila, N.[Nizar], Arnold, D.[Douglas], And: The Alzheimer's Disease Neuroimaging Initiative,
The clique potential of Markov random field in a random experiment for estimation of noise levels in 2D brain MRI,
IJIST(23), No. 4, 2013, pp. 304-313.
DOI Link 1312
magnetic resonance imaging BibRef

Osadebey, M.[Michael], Bouguila, N.[Nizar], Arnold, D.[Douglas], And: The Alzheimer's Disease Neuroimaging Initiative.
Four-neighborhood clique kernel: A general framework for Bayesian and variational techniques of noise reduction in magnetic resonance images of the brain,
IJIST(24), No. 3, 2014, pp. 224-238.
DOI Link 1408
magnetic resonance imaging BibRef

Zhao, X.J.[Xiao-Jie], Wen, X.T.[Xiao-Tong], Shen, J.H.[Jia-Hui], Hong, H.[Hao], Yao, L.[Li],
An improved fast marching method and its application in Alzheimer's disease,
IJIST(23), No. 4, 2013, pp. 346-352.
DOI Link 1312
fast marching method BibRef

Rueda, A., Gonzalez, F.A., Romero, E.,
Extracting Salient Brain Patterns for Imaging-Based Classification of Neurodegenerative Diseases,
MedImg(33), No. 6, June 2014, pp. 1262-1274.
IEEE DOI 1407
Brain modeling BibRef

Wan, J., Zhang, Z., Rao, B.D., Fang, S., Yan, J., Saykin, A.J., Shen, L.,
Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning,
MedImg(33), No. 7, July 2014, pp. 1475-1487.
IEEE DOI 1407
Alzheimer's disease BibRef

Yang, W.[Wenji], Huang, W.[Wei], Chen, S.X.[Shan-Xue],
Partial Volume Correction on ASL-MRI and Its Application on Alzheimer's Disease Diagnosis,
IEICE(E97-D), No. 11, November 2014, pp. 2912-2918.
WWW Link. 1412
BibRef

Poynton, C.B., Jenkinson, M., Adalsteinsson, E., Sullivan, E.V., Pfefferbaum, A., Wells, W.,
Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging,
MedImg(34), No. 1, January 2015, pp. 339-353.
IEEE DOI 1502
Fourier analysis BibRef

Liu, X.W.[Xin-Wang], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Zhang, J.[Jian], Yin, J.P.[Jian-Ping], Shen, D.G.[Ding-Gang],
An efficient radius-incorporated MKL algorithm for Alzheimer's disease prediction,
PR(48), No. 7, 2015, pp. 2141-2150.
Elsevier DOI 1504
Multiple kernel learning BibRef

Aggarwal, N.[Namita], Rana, B.[Bharti], Agrawal, R.K.,
3d discrete wavelet transform for computer aided diagnosis of Alzheimer's disease using t1-weighted brain MRI,
IJIST(25), No. 2, 2015, pp. 179-190.
DOI Link 1506
BibRef
Earlier:
Computer Aided Diagnosis of Alzheimer's Disease from MRI Brain Images,
ICIAR12(II: 259-267).
Springer DOI 1206
Alzheimer's disease BibRef

Li, Y., Pan, J., Long, J., Yu, T., Wang, F., Yu, Z., Wu, W.,
Multimodal BCIs: Target Detection, Multidimensional Control, and Awareness Evaluation in Patients With Disorder of Consciousness,
PIEEE(104), No. 2, February 2016, pp. 332-352.
IEEE DOI 1601
Biomedical signal processing BibRef

Warren, D.J., Kellis, S., Nieveen, J.G., Wendelken, S.M., Dantas, H., Davis, T.S., Hutchinson, D.T., Normann, R.A., Clark, G.A., Mathews, V.J.,
Recording and Decoding for Neural Prostheses,
PIEEE(104), No. 2, February 2016, pp. 374-391.
IEEE DOI 1601
Biomedical signal processing BibRef

Seo, D.H.[Do-Hyung], Ho, J.[Jeffrey], Vemuri, B.C.[Baba C.],
Covariant Image Representation with Applications to Classification Problems in Medical Imaging,
IJCV(116), No. 2, January 2016, pp. 190-209.
Springer DOI 1602
Apply to MR for Alzheimers and MR detection of seizures. BibRef

Liu, M.H.[Man-Hua], Zhang, D.Q.[Dao-Qiang], Shen, D.G.[Ding-Gang],
Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment,
MedImg(35), No. 6, June 2016, pp. 1463-1474.
IEEE DOI 1606
BibRef
Earlier:
Inherent Structure-Guided Multi-view Learning for Alzheimer's Disease and Mild Cognitive Impairment Classification,
MLMI15(296-303).
Springer DOI 1511
Alzheimer's disease BibRef

Jie, B.[Biao], Liu, M.X.[Ming-Xia], Zhang, D.Q.[Dao-Qiang], Shen, D.G.[Ding-Gang],
Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis,
IP(27), No. 5, May 2018, pp. 2340-2353.
IEEE DOI 1804
biomedical MRI, brain, cognition, diseases, graph theory, image classification, medical image processing, neurophysiology, mild cognitive impairment (MCI) BibRef

Cheng, B.[Bo], Liu, M.X.[Ming-Xia], Zhang, D.Q.[Dao-Qiang],
Multimodal Multi-label Transfer Learning for Early Diagnosis of Alzheimer's Disease,
MLMI15(238-245).
Springer DOI 1511
BibRef

Liu, M.X.[Ming-Xia], Zhang, D.Q.[Dao-Qiang], Adeli-Mosabbeb, E.[Ehsan], Shen, D.G.[Ding-Gang],
Relationship Induced Multi-atlas Learning for Alzheimer's Disease Diagnosis,
MCV15(24-33).
Springer DOI 1608
BibRef

Liu, M.H.[Man-Hua], Zhang, D.Q.[Dao-Qiang], Yap, P.T.[Pew-Thian], Shen, D.G.[Ding-Gang],
Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer's Disease,
MLMI12(27-35).
Springer DOI 1211
BibRef

Suk, H.I.[Heung-Il], Shen, D.G.[Ding-Gang],
Deep Ensemble Sparse Regression Network for Alzheimer's Disease Diagnosis,
MLMI16(113-121).
Springer DOI 1611
BibRef

Zhu, X.F.[Xiao-Feng], Suk, H.I.[Heung-Il], Zhu, Y.H.[Yong-Hua], Thung, K.H.[Kim-Han], Wu, G.R.[Guo-Rong], Shen, D.G.[Ding-Gang],
Multi-view Classification for Identification of Alzheimer's Disease,
MLMI15(255-262).
Springer DOI 1511
BibRef
Earlier: A1, A2, A6, Only:
Sparse Discriminative Feature Selection for Multi-class Alzheimer's Disease Classification,
MLMI14(157-164).
Springer DOI 1410
BibRef

Zhu, X.F.[Xiao-Feng], Suk, H.I.[Heung-Il], Thung, K.H.[Kim-Han], Zhu, Y.Y.[Ying-Ying], Wu, G.R.[Guo-Rong], Shen, D.G.[Ding-Gang],
Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis,
MLMI16(77-85).
Springer DOI 1611
BibRef

Zhu, X.F.[Xiao-Feng], Thung, K.H.[Kim-Han], Zhang, J.[Jun], Shen, D.G.[Ding-Gang],
Fast Neuroimaging-Based Retrieval for Alzheimer's Disease Analysis,
MLMI16(313-321).
Springer DOI 1611
BibRef

Zhang, D.Q.[Dao-Qiang], Shen, D.G.[Ding-Gang],
MultiCost: Multi-stage Cost-sensitive Classification of Alzheimer's Disease,
MLMI11(344-351).
Springer DOI 1109
BibRef

Jie, B.[Biao], Zhang, D.Q.[Dao-Qiang], Wee, C.Y.[Chong-Yaw], Shen, D.G.[Ding-Gang],
Structural Feature Selection for Connectivity Network-Based MCI Diagnosis,
MBIA12(175-184).
Springer DOI 1210
BibRef

Zhou, L.P.[Lu-Ping], Wang, Y.P.[Ya-Ping], Li, Y.[Yang], Yap, P.T.[Pew-Thian], Shen, D.G.[Ding-Gang], Adni,
Hierarchical anatomical brain networks for MCI prediction by partial least square analysis,
CVPR11(1073-1080).
IEEE DOI 1106
T1-weighted MRI for mild cognitive impairment. BibRef

Li, Z., Suk, H.I., Shen, D., Li, L.,
Sparse Multi-Response Tensor Regression for Alzheimer's Disease Study With Multivariate Clinical Assessments,
MedImg(35), No. 8, August 2016, pp. 1927-1936.
IEEE DOI 1608
Alzheimer's disease BibRef

Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Liu, L.Q.[Ling-Qiao], Ogunbona, P.O.[Philip O.], Shen, D.G.[Ding-Gang],
Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data,
PAMI(38), No. 11, November 2016, pp. 2269-2283.
IEEE DOI 1610
BibRef
Earlier:
Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network,
CVPR13(2243-2250)
IEEE DOI 1309
Bayes methods. Alzheimer's Disease BibRef

Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Ogunbona, P.O.[Philip O.],
Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification,
CVPR14(3097-3104)
IEEE DOI 1409
Graphical LASSO BibRef

Tong, T.[Tong], Gray, K.[Katherine], Gao, Q.[Qinquan], Chen, L.[Liang], Rueckert, D.[Daniel],
Multi-modal classification of Alzheimer's disease using nonlinear graph fusion,
PR(63), No. 1, 2017, pp. 171-181.
Elsevier DOI 1612
BibRef
Earlier:
Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer's Disease,
MLMI15(77-84).
Springer DOI 1511
Multiple modalities BibRef

Shi, B.[Bibo], Chen, Y.[Yani], Zhang, P.[Pin], Smith, C.D.[Charles D.], Liu, J.D.[Jun-Dong],
Nonlinear Feature Transformation and Deep Fusion for Alzheimer's Disease Staging Analysis,
PR(63), No. 1, 2017, pp. 487-498.
Elsevier DOI 1612
BibRef
And: Erratum: PR(66), No. 1, 2017, pp. 447-.
Elsevier DOI 1704
BibRef
Earlier: A2, A1, A4, A5, Only: MLMI15(304-312).
Springer DOI 1511
Metric learning BibRef

Shi, B.[Bibo], Chen, Y.[Yani], Hobbs, K.[Kevin], Smith, C.D.[Charles D.], Liu, J.D.[Jun-Dong],
Nonlinear Metric Learning for Alzheimer’s Disease Diagnosis with Integration of Longitudinal Neuroimaging Features,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Guerrero, R., Ledig, C., Schmidt-Richberg, A., Rueckert, D.,
Group-constrained manifold learning: Application to AD risk assessment,
PR(63), No. 1, 2017, pp. 570-582.
Elsevier DOI 1612
Alzheimer's disease BibRef

Zhang, J., Gao, Y., Gao, Y., Munsell, B.C., Shen, D.,
Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis,
MedImg(35), No. 12, December 2016, pp. 2524-2533.
IEEE DOI 1612
Feature extraction BibRef

Nanni, L.[Loris], Salvatore, C.[Christian], Cerasa, A.[Antonio], Castiglioni, I.[Isabella],
Combining multiple approaches for the early diagnosis of Alzheimer's Disease,
PRL(84), No. 1, 2016, pp. 259-266.
Elsevier DOI 1612
Alzheimer's Disease BibRef

Lei, B., Yang, P., Wang, T., Chen, S., Ni, D.,
Relational-Regularized Discriminative Sparse Learning for Alzheimer's Disease Diagnosis,
Cyber(47), No. 4, April 2017, pp. 1102-1113.
IEEE DOI 1704
Cybernetics BibRef

Alam, S.[Saruar], Kwon, G.R.[Goo-Rak], Initiative, T.A.D.N.[The Alzheimer's Disease Neuroimaging],
Alzheimer disease classification using KPCA, LDA, and multi-kernel learning SVM,
IJIST(27), No. 2, 2017, pp. 133-143.
DOI Link 1706
FreeSurfer, CIVET, KPCA, PCA, LDA, MK-SVM BibRef

Dadar, M., Pascoal, T.A., Manitsirikul, S., Misquitta, K., Fonov, V.S., Tartaglia, M.C., Breitner, J., Rosa-Neto, P., Carmichael, O.T., Decarli, C., Collins, D.L.,
Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer's Disease,
MedImg(36), No. 8, August 2017, pp. 1758-1768.
IEEE DOI 1708
Alzheimer's disease, Image segmentation, Lesions, Magnetic resonance imaging, Robustness, Alzheimer's disease, White matter hyperintensities, aging, segmentation BibRef

Cao, P.[Peng], Shan, X.F.[Xuan-Feng], Zhao, D.[Dazhe], Huang, M.[Min], Zaiane, O.[Osmar],
Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer's disease,
PR(72), No. 1, 2017, pp. 219-235.
Elsevier DOI 1708
Alzheimer's, disease BibRef

Cao, P.[Peng], Liu, X.[Xiaoli], Yang, J.[Jinzhu], Zhao, D.[Dazhe], Huang, M.[Min], Zaiane, O.[Osmar],
L2,1-l1 regularized nonlinear multi-task representation learning based cognitive performance prediction of Alzheimer's disease,
PR(79), 2018, pp. 195-215.
Elsevier DOI 1804
Alzheimer's disease, Regression, Sparse learning, Multi-task learning, Kernel method BibRef

Abdullah, S., Choudhury, T.,
Sensing Technologies for Monitoring Serious Mental Illnesses,
MultMedMag(25), No. 1, January 2018, pp. 61-75.
IEEE DOI 1804
Biomedical monitoring, Biosensors, Global Positioning System, Mental disorders, Multimedia communication, Sensors, sensing BibRef

Alvarez, F., Popa, M., Solachidis, V., Hernández-Peñaloza, G., Belmonte-Hernández, A., Asteriadis, S., Vretos, N., Quintana, M., Theodoridis, T., Dotti, D., Daras, P.,
Behavior Analysis through Multimodal Sensing for Care of Parkinson's and Alzheimer's Patients,
MultMedMag(25), No. 1, January 2018, pp. 14-25.
IEEE DOI 1804
Alzheimer's disease, Behavioral sciences, Biomedical imaging, Calibration, Feature extraction, Patient monitoring, Sensors, wireless sensor networks BibRef

Alvarez, F., Popa, M., Vretos, N., Belmonte-Hernández, A., Asteriadis, S., Solachidis, V., Mariscal, T., Dotti, D., Daras, P.,
Multimodal monitoring of Parkinson's and Alzheimer's patients using the ICT4LIFE platform,
AVSS17(1-6)
IEEE DOI 1806
Internet of Things, diseases, feature extraction, medical computing, patient monitoring, sensor fusion, Wireless sensor networks BibRef

Strickland, E.,
The digital fingerprints of brain disorders,
Spectrum(55), No. 5, May 2018, pp. 12-13.
IEEE DOI 1805
[News] BibRef

Asim, Y.[Yousra], Raza, B.[Basit], Malik, A.K.[Ahmad Kamran], Rathore, S.[Saima], Hussain, L.[Lal], Iftikhar, M.A.[Mohammad Aksam],
A multi-modal, multi-atlas-based approach for Alzheimer detection via machine learning,
IJIST(28), No. 2, 2018, pp. 113-123.
WWW Link. 1806
BibRef

Kahindo, C., El-Yacoubi, M.A., Garcia-Salicetti, S., Rigaud, A., Cristancho-Lacroix, V.,
Characterizing Early-Stage Alzheimer Through Spatiotemporal Dynamics of Handwriting,
SPLetters(25), No. 8, August 2018, pp. 1136-1140.
IEEE DOI 1808
Bayes methods, diseases, feature extraction, handwritten character recognition, neurophysiology, probabilistic modeling BibRef

El-Yacoubi, M.A.[Mounîm A.], Garcia-Salicetti, S.[Sonia], Kahindo, C.[Christian], Rigaud, A.S.[Anne-Sophie], Cristancho-Lacroix, V.[Victoria],
From aging to early-stage Alzheimer's: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning,
PR(86), 2019, pp. 112-133.
Elsevier DOI 1811
Online handwriting, Mild Cognitive Impairment, Aging, Unsupervised & semi-supervised learning, Temporal representation learning BibRef

Mishra, S.[Shiwangi], Beheshti, I.[Iman], Khanna, P.[Pritee],
A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease,
IJIST(28), No. 4, December 2018, pp. 302-314.
WWW Link. 1811
Alzheimer's Disease Neuroimaging Initiative BibRef

Baumgartner, C.F., Koch, L.M., Tezcan, K.C., Ang, J.X.,
Visual Feature Attribution Using Wasserstein GANs,
CVPR18(8309-8319)
IEEE DOI 1812
Visualization, Biomedical imaging, Alzheimer's disease, Neural networks, Neuroimaging BibRef

Islam, J., Zhang, Y.,
Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study with Deep Learning Architectures,
WiCV18(1962-19622)
IEEE DOI 1812
Alzheimer's disease, Magnetic resonance imaging, Brain modeling, Training, Medical diagnosis BibRef

Zhang, Y.[Yu], Zhang, H.[Han], Chen, X.B.[Xiao-Bo], Liu, M.X.[Ming-Xia], Zhu, X.F.[Xiao-Feng], Lee, S.W.[Seong-Whan], Shen, D.G.[Ding-Gang],
Strength and similarity guided group-level brain functional network construction for MCI diagnosis,
PR(88), 2019, pp. 421-430.
Elsevier DOI 1901
Alzheimers disease, Mild cognitive impairment, Resting-state functional magnetic resonance imaging (rs-fMRI), Diagnosis BibRef

Peng, J.[Jialin], Zhu, X.F.[Xiao-Feng], Wang, Y.[Ye], An, L.[Le], Shen, D.G.[Ding-Gang],
Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis,
PR(88), 2019, pp. 370-382.
Elsevier DOI 1901
Structured sparsity, Multimodal features, Multiple kernel learning, Feature selection, Alzheimer's disease diagnosis BibRef

Zhu, Y., Zhu, X., Kim, M., Yan, J., Kaufer, D., Wu, G.,
Dynamic Hyper-Graph Inference Framework for Computer-Assisted Diagnosis of Neurodegenerative Diseases,
MedImg(38), No. 2, February 2019, pp. 608-616.
IEEE DOI 1902
Imaging, Training, Testing, Diseases, Data models, Training data, Neuroimaging, Hyper-graph learning, computer assisted diagnosis, neurodegenerative disease BibRef

Singh, S.[Sneha], Anand, R.S.,
Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain,
IJIST(29), No. 1, March 2019, pp. 50-64.
WWW Link. 1902
BibRef

Karami, V.[Vania], Nittari, G.[Giulio], Amenta, F.[Francesco],
Neuroimaging computer-aided diagnosis systems for Alzheimer's disease,
IJIST(29), No. 1, March 2019, pp. 83-94.
WWW Link. 1902
BibRef

He, M., Baker, S.L., Shah, V.D., Lockhart, S.N., Jagust, W.J.,
Scan-Time Corrections for 80-100-min Standardizetd Uptake Volume Ratios to Measure the 18F-AV-1451 Tracer for Tau Imaging,
MedImg(38), No. 3, March 2019, pp. 697-709.
IEEE DOI 1903
Dementia, Image reconstruction, Interpolation, Training, Magnetic resonance imaging, Tau, PET Imaging, SUVR, scan-time error, Alzheimer's disease BibRef

Yu, R.P.[Ren-Ping], Qiao, L.S.[Li-Shan], Chen, M.M.[Ming-Ming], Lee, S.W.[Seong-Whan], Fei, X.[Xuan], Shen, D.G.[Ding-Gang],
Weighted graph regularized sparse brain network construction for MCI identification,
PR(90), 2019, pp. 220-231.
Elsevier DOI 1903
Graph Laplacian regularization, Sparse representation, Brain functional network, Mild cognitive impairment (MCI) BibRef

De Stefano, C.[Claudio], Fontanella, F.[Francesco], Impedovo, D.[Donato], Pirlo, G.[Giuseppe], di Freca, A.S.[Alessandra Scotto],
Handwriting analysis to support neurodegenerative diseases diagnosis: A review,
PRL(121), 2019, pp. 37-45.
Elsevier DOI 1904
BibRef

Cilia, N.D.[Nicole Dalia], De Stefano, C.[Claudio], Fontanella, F.[Francesco], Molinara, M.[Mario], di Freca, A.S.[Alessandra Scotto],
Handwriting Analysis to Support Alzheimer's Disease Diagnosis: A Preliminary Study,
CAIP19(II:143-151).
Springer DOI 1909
BibRef

Li, Y., Yang, H., Lei, B., Liu, J., Wee, C.,
Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification,
MedImg(38), No. 5, May 2019, pp. 1227-1239.
IEEE DOI 1905
Feature extraction, Time series analysis, Functional magnetic resonance imaging, Diseases, machine learning BibRef

Neffati, S.[Syrine], Ben Abdellafou, K.[Khaoula], Jaffel, I.[Ines], Taouali, O.[Okba], Bouzrara, K.[Kais],
An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification,
IJIST(29), No. 2, June 2019, pp. 121-131.
WWW Link. 1906
BibRef

Chen, J., Liu, J., Calhoun, V.D.,
Translational Potential of Neuroimaging Genomic Analyses to Diagnosis and Treatment in Mental Disorders,
PIEEE(107), No. 5, May 2019, pp. 912-927.
IEEE DOI 1906
Genomics, Bioinformatics, Biomedical imaging, Diseases, Neuroimaging, Mental disorders, Precision engineering, Predictive models, transdiagnostic BibRef

Wang, P., Liu, Y., Shen, D.,
Flexible Locally Weighted Penalized Regression With Applications on Prediction of Alzheimer's Disease Neuroimaging Initiative's Clinical Scores,
MedImg(38), No. 6, June 2019, pp. 1398-1408.
IEEE DOI 1906
Kernel, Diseases, Sociology, Statistics, Brain modeling, Forestry, Training, Heterogeneity, local models, ordinal classification, random forests BibRef

Khagi, B.[Bijen], Kwon, G.R.[Goo-Rak], Lama, R.[Ramesh],
Comparative analysis of Alzheimer's disease classification by CDR level using CNN, feature selection, and machine-learning techniques,
IJIST(29), No. 3, September 2019, pp. 297-310.
DOI Link 1908
BibRef

Im, J.J.[Jooyeon J.], Jeong, H.[Hyeonseok], Oh, J.K.[Jin Kyoung], Chung, Y.A.[Yong-An], Song, I.U.[In-Uk], Lee, K.S.[Kwang-Soo],
Effects of nicergoline treatment on regional cerebral blood flow in early Alzheimer's disease,
IJIST(29), No. 3, September 2019, pp. 323-328.
DOI Link 1908
BibRef

Ahmad, F.[Fayyaz], Zulifqar, H.[Hifza], Malik, T.[Tamoor],
Classification of Alzheimer disease among susceptible brain regions,
IJIST(29), No. 3, September 2019, pp. 222-233.
DOI Link 1908
BibRef

Latha, M.[Manohar], Kavitha, G.[Ganesan],
Assessment of severity in neuropsychiatric disorders based on radiomic features with prior shape level set and metaheuristic algorithms,
IJIST(29), No. 3, September 2019, pp. 210-221.
DOI Link 1908
BibRef

Wang, J.J.[Jian-Jia], Huo, J.Y.[Jia-Yu], Zhang, L.C.[Li-Chi],
Thermodynamic edge entropy in Alzheimer's disease,
PRL(125), 2019, pp. 570-575.
Elsevier DOI 1909
Alzheimer's disease, Maxwell-Boltzmann statistics, Network edge entropy BibRef

Zhou, T., Liu, M., Thung, K., Shen, D.,
Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data,
MedImg(38), No. 10, October 2019, pp. 2411-2422.
IEEE DOI 1910
Magnetic resonance imaging, Feature extraction, Genetics, Neuroimaging, Alzheimer's disease, Positron emission tomography, latent representation space BibRef

Huang, W., Luo, M., Liu, X., Zhang, P., Ding, H., Xue, W., Ni, D.,
Arterial Spin Labeling Images Synthesis From sMRI Using Unbalanced Deep Discriminant Learning,
MedImg(38), No. 10, October 2019, pp. 2338-2351.
IEEE DOI 1910
Deep learning, Dementia, Biomedical imaging, Medical diagnosis, Magnetic resonance imaging, Magnetic resonance imaging (MRI), machine learning BibRef

Li, J.[Jiaye], Wu, L.[Lin], Wen, G.Q.[Guo-Qiu], Li, Z.[Zhi],
Exclusive feature selection and multi-view learning for Alzheimer's Disease,
JVCIR(64), 2019, pp. 102605.
Elsevier DOI 1911
Alzheimer's Disease, Multi-view, Exclusive lasso learning, Feature selection, Sparse learning BibRef

Fang, X.S.[Xu-Sheng], Liu, Z.B.[Zhen-Bing], Xu, M.C.[Ming-Chang],
Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis,
IET-IPR(14), No. 2, February 2020, pp. 318-326.
DOI Link 2001
BibRef

Bharanidharan, N., Rajaguru, H.[Harikumar],
Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly-based hybrid algorithms,
IJIST(30), No. 1, 2020, pp. 57-74.
DOI Link 2002
DA-PSO, dementia, hybrid swarm intelligence, MRI, OASIS BibRef

Choi, J.Y., Lee, B.,
Combining of Multiple Deep Networks via Ensemble Generalization Loss, Based on MRI Images, for Alzheimer's Disease Classification,
SPLetters(27), 2020, pp. 206-210.
IEEE DOI 2002
Alzheimer's disease classification, ensemble deep learning, generalization loss BibRef

Kam, T., Zhang, H., Jiao, Z., Shen, D.,
Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection,
MedImg(39), No. 2, February 2020, pp. 478-487.
IEEE DOI 2002
Ions, Noise measurement, Manganese, Diagnosis, convolutional neural networks, brain network, functional MRI BibRef

Lei, B.Y.[Bai-Ying], Yang, M.Y.[Meng-Ya], Yang, P.[Peng], Zhou, F.[Feng], Hou, W.[Wen], Zou, W.B.[Wen-Bin], Li, X.[Xia], Wang, T.F.[Tian-Fu], Xiao, X.H.[Xiao-Hua], Wang, S.Q.[Shu-Qiang],
Deep and joint learning of longitudinal data for Alzheimer's disease prediction,
PR(102), 2020, pp. 107247.
Elsevier DOI 2003
Alzheimer's disease, Longitudinal scores prediction, Joint learning, Correntropy, Deep polynomial network BibRef

Lian, C.F.[Chun-Feng], Liu, M.X.[Ming-Xia], Zhang, J.[Jun], Shen, D.G.[Ding-Gang],
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI,
PAMI(42), No. 4, April 2020, pp. 880-893.
IEEE DOI 2003
Feature extraction, Solid modeling, Atrophy, Brain modeling, Alzheimer's disease, Medical diagnosis, Support vector machines, structural MRI BibRef

Zhang, J.[Jun], Liu, M.X.[Ming-Xia], An, L.[Le], Gao, Y.Z.[Yao-Zong], Shen, D.G.[Ding-Gang],
Landmark-Based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images,
MCV16(35-45).
Springer DOI 1711
BibRef

Zhao, Y., Zhao, Y., Durongbhan, P., Chen, L., Liu, J., Billings, S.A., Zis, P., Unwin, Z.C., De Marco, M., Venneri, A., Blackburn, D.J., Sarrigiannis, P.G.,
Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease,
MedImg(39), No. 5, May 2020, pp. 1571-1581.
IEEE DOI 2005
Alzheimer's disease, dementia, visualisation, system identification, machine learning BibRef

Zhang, Y.[Yingteng], Liu, S.[Shenquan], Yu, X.[Xiaoli],
Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment,
IJIST(30), No. 2, 2020, pp. 421-433.
DOI Link 2005
Alzheimer's disease, gray matter volume, longitudinal analysis, longitudinal classification, mild cognitive impairment BibRef

Allioui, H.[Hanane], Sadgal, M.[Mohamed], Elfazziki, A.[Aziz],
Utilization of a convolutional method for Alzheimer disease diagnosis,
MVA(31), No. 4, April 2020, pp. Article25.
Springer DOI 2005
BibRef

Brand, L., Nichols, K., Wang, H., Shen, L., Huang, H.,
Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer's Disease Prediction,
MedImg(39), No. 6, June 2020, pp. 1845-1855.
IEEE DOI 2006
Alzheimer's disease, biomarker identification, joint regression-classification, longitudinal, multi-modal, multi-task BibRef

Koh, J.E.W.[Joel En Wei], Jahmunah, V.[Vicnesh], Pham, T.H.[The-Hanh], Oh, S.L.[Shu Lih], Ciaccio, E.J.[Edward J], Acharya, U.R.[U Rajendra], Yeong, C.H.[Chai Hong], Fabell, M.K.M.[Mohd Kamil Mohd], Rahmat, K.[Kartini], Vijayananthan, A.[Anushya], Ramli, N.[Norlisah],
Automated detection of Alzheimer's disease using bi-directional empirical model decomposition,
PRL(135), 2020, pp. 106-113.
Elsevier DOI 2006
BibRef

Slapnicar, G., Dovgan, E., Cuk, P., Lustrek, M.,
Contact-Free Monitoring of Physiological Parameters in People With Profound Intellectual and Multiple Disabilities,
CVPM19(1664-1672)
IEEE DOI 2004
Videos, Skin, Biomedical monitoring, Heart rate, Databases, Blood, Physiology, physiological signals, PIMD, deep learning, LSTM, rPPG, video cameras BibRef

Zhang, Y.[Yanfu], Zhan, L.[Liang], Thompson, P.M.[Paul M.], Huang, H.[Heng],
Biological Knowledge Guided Deep Neural Network for Brain Genotype-phenotype Association Study,
MBIA19(84-92).
Springer DOI 1912
BibRef