Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (8): 14-21.doi: 10.6040/j.issn.1671-7554.0.2019.1503
• Special Topic on Brain Science and Brain Like Intelligence • Previous Articles Next Articles
Yilong YIN1,*(),Xiaoming XI2,Xianjing MENG3
CLC Number:
1 | Thies W , Bleiler L . 2012 Alzheimer's disease facts and figures Alzheimer's Association[J]. Alzheimers & Dement, 2012, 8 (2): 131- 168. |
2 | The Global Impact of Dementia: An analysis of prevalence, incidence, cost and trends: world Alzheimer report[R]. London: Alzheimer's Disease International, 2015. |
3 | 滕玉环, 周红, 张志珺. 阿尔茨海默病早期诊断生物学指标研究进展[J]. 中华脑血管病杂志(电子版), 2010, 4 (6): 24- 27. |
TENG Yuhuan , ZHOU Hong , ZHANG Zhijun . Biomakers of early diagnosis of Alzheimer's disease[J]. Chinese Journal of Cerebrovascular Disease(Electronic Version), 2010, 4 (6): 24- 27. | |
4 |
Jagust W . Vulnerable neural systems and the borderland of brain aging and neurodegeneration[J]. Neuron, 2013, 77 (2): 219- 234.
doi: 10.1016/j.neuron.2013.01.002 |
5 |
Rathore S , Habes M , Iftikhar MA , et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages[J]. Neuroimage, 2017, 155: 530- 548.
doi: 10.1016/j.neuroimage.2017.03.057 |
6 | Frisoni GB , Fox NC , Jack Jr CR , et al. The clinical use of structural MRI in Alzheimer disease[J]. Nat Rev Neurol, 2010, 6 (2): 67- 77. |
7 |
Petrella JR , Coleman RE , Doraiswamy PM . Neuroimaging and early diagnosis of Alzheimer disease: a look to the future[J]. Radiology, 2003, 226 (2): 315- 336.
doi: 10.1148/radiol.2262011600 |
8 |
Rosen WG , Mohs RC , Davis KL , et al. A new rating scale for Alzheimer's disease[J]. Am J Psychiat, 1984, 141 (11): 1356- 1364.
doi: 10.1176/ajp.141.11.1356 |
9 |
Lian C , Liu M , Zhang J , et al. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI[J]. IEEE Trans Pattern Anal Mach Intell, 2020, 42 (4): 880- 893.
doi: 10.1109/TPAMI.2018.2889096 |
10 |
Mueller SG , Weiner MW , Thal LJ , et al. The Alzheimer's disease neuroimaging initiative[J]. Neuroimag Clin N Am, 2005, 15 (4): 869- 877.
doi: 10.1016/j.nic.2005.09.008 |
11 |
Jack Jr CR , Bernstein MA , Fox NC , et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods[J]. J Magn Reson Imaging, 2008, 27 (4): 685- 691.
doi: 10.1002/jmri.21049 |
12 |
Marcus DS , Fotenos AF , Csernansky JG , et al. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults[J]. J Cognitive Neurosci, 2010, 22 (12): 2677- 2684.
doi: 10.1162/jocn.2009.21407 |
13 |
Malone IB , Cash D , Ridgway GR , et al. MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset[J]. Neuroimage, 2013, 70: 33- 36.
doi: 10.1016/j.neuroimage.2012.12.044 |
14 |
Ellis KA , Rowe CC , Villemagne VL , et al. Addressing population aging and Alzheimer's disease through the Australian imaging biomarkers and lifestyle study: collaboration with the Alzheimer's Disease Neuroimaging Initiative[J]. Alzheimers Dement, 2010, 6 (3): 291- 296.
doi: 10.1016/j.jalz.2010.03.009 |
15 |
Glahn DC , Thompson PM , Blangero J . Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function[J]. Human Brain Mapping, 2007, 28 (6): 488- 501.
doi: 10.1002/hbm.20401 |
16 |
Buckner RL . Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate[J]. Neuron, 2004, 44 (1): 195- 208.
doi: 10.1016/j.neuron.2004.09.006 |
17 |
Klöppel S , Stonnington CM , Chu C , et al. Automatic classification of MR scans in Alzheimer's disease[J]. Brain, 2008, 131 (3): 681- 689.
doi: 10.1093/brain/awm319 |
18 |
Li S , Yuan X , Pu F , et al. Abnormal changes of multidimensional surface features using multivariate pattern classification in amnestic mild cognitive impairment patients[J]. J Neurosci, 2014, 34 (32): 10541- 10553.
doi: 10.1523/JNEUROSCI.4356-13.2014 |
19 | Möller C , Pijnenburg YAL , van der Flier WM , et al. Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis[J]. Radiology, 2015, 279 (3): 838- 848. |
20 |
Liu X , Tosun D , Weiner MW , et al. Locally linear embedding (LLE) for MRI based Alzheimer's disease classification[J]. Neuroimage, 2013, 83: 148- 157.
doi: 10.1016/j.neuroimage.2013.06.033 |
21 |
Salvatore C , Cerasa A , Battista P , et al. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach[J]. Front Neurosci, 2015, 9: 307.
doi: 10.3389/fnins.2015.00307 |
22 | Magnin B , Mesrob L , Kinkingnéhun S , et al. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI[J]. Neuroradiology, 2009, 51 (2): 73- 83. |
23 |
Zhu X , Suk HI , Wang L , et al. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis[J]. Medical Image Anal, 2017, 38: 205- 214.
doi: 10.1016/j.media.2015.10.008 |
24 |
Long Z , Huang J , Li B , et al. A comparative atlas-based recognition of mild cognitive impairment with voxel-based morphometry[J]. Front Neurosci, 2018, 12: 916.
doi: 10.3389/fnins.2018.00916 |
25 |
Liu M , Zhang D , Shen D , et al. Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis[J]. Human Brain Mapping, 2014, 35 (4): 1305- 1319.
doi: 10.1002/hbm.22254 |
26 |
Tong T , Wolz R , Gao Q , et al. Multiple instance learning for classification of dementia in brain MRI[J]. Med Image Anal, 2014, 18 (5): 808- 818.
doi: 10.1016/j.media.2014.04.006 |
27 |
Zhang J , Gao Y , Gao Y , et al. Detecting anatomical landmarks for fast Alzheimer's disease diagnosis[J]. IEEE T Med Imaging, 2016, 35 (12): 2524- 2533.
doi: 10.1109/TMI.2016.2582386 |
28 | Tanveer M , Richhariya B , Khan RU , et al. Machine learning techniques for the diagnosis of Alzheimer's disease: a review[J]. ACM T Multim Comput, 2019, 16 (1): 35. |
29 |
Sanzarigita EJ , Schoonheim MM , Damoiseaux JS , et al. Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity[J]. PLoS One, 2010, 5 (11): e13788.
doi: 10.1371/journal.pone.0013788 |
30 |
Karwowski W , Vasheghani Farahani F , Lighthall N . Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review[J]. Front Neurosci, 2019, 13: 585.
doi: 10.3389/fnins.2019.00585 |
31 | Chen G , Ward BD , Xie C , et al. Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging[J]. Radiology, 2011, 259 (1): 213- 221. |
32 |
Wang Z , Zheng Y , Zhu DC , et al. Classification of Alzheimer's disease, mild cognitive impairment and normal control subjects using resting-state fMRI Based network Connectivity analysis[J]. IEEE J Transl Eng Health Med, 2018, 6: 1801009.
doi: 10.1109/JTEHM.2018.2874887 |
33 | Jie B , Zhang D , Gao W , et al. Integration of network topological and connectivity properties for neuroimaging classification[J]. IEEE Trans Biomed Eng, 2013, 61 (2): 576- 589. |
34 | Tong T , Gray KR , Gao Q , et al. Multi-modal classification of Alzheimer's disease using nonlinear graph fusion[J]. Pattern Recognit, 2017, 171- 181. |
35 |
Lian C , Liu M , Zhang J , et al. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI[J]. IEEE T Pattern Anal, 2020, 42 (4): 880- 893.
doi: 10.1109/TPAMI.2018.2889096 |
36 | Shi Y , Suk H , Gao Y , et al. Leveraging coupled interaction for multimodal Alzheimer's disease diagnosis[J]. IEEE T Neural Network, 2020, 31 (1): 186- 200. |
37 |
Kohannim O , Hua X , Hibar DP , et al. Boosting power for clinical trials using classifiers based on multiple biomarkers[J]. Neurobiol Aging, 2010, 31 (8): 1429- 1442.
doi: 10.1016/j.neurobiolaging.2010.04.022 |
38 |
Walhovd KB , Fjell AM , Brewer JB , et al. Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease[J]. Am J Neuroradiol, 2010, 31 (2): 347- 354.
doi: 10.3174/ajnr.A1809 |
39 |
Westman E , Muehlboeck J , Simmons A , et al. Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion[J]. Neuroimage, 2012, 62 (1): 229- 238.
doi: 10.1016/j.neuroimage.2012.04.056 |
40 |
Zhang D , Wang Y , Zhou L , et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment[J]. Neuroimage, 2011, 55 (3): 856- 867.
doi: 10.1016/j.neuroimage.2011.01.008 |
41 |
Suk H , Shen D . Subclass-based multi-task learning for Alzheimer's disease diagnosis[J]. Front Aging Neurosci, 2014, 6: 168- 168.
doi: 10.3389/fnagi.2014.00168 |
42 |
Suk H , Lee S , Shen D , et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis[J]. Neuroimage, 2014, 101: 569- 582.
doi: 10.1016/j.neuroimage.2014.06.077 |
43 |
Jie B , Liu M , Zhang D , et al. Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis[J]. IEEE Trans Image Process, 2018, 27 (5): 2340- 2353.
doi: 10.1109/TIP.2018.2799706 |
44 |
Liu J , Wang J , Tang Z , et al. Improving Alzheimer's disease classification by combining multiple measures[J]. IEEE/ACM Trans Comput Biol Bioinf, 2018, 15 (5): 1649- 1659.
doi: 10.1109/TCBB.2017.2731849 |
45 |
Hinrichs C , Singh V , Xu G , et al. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population[J]. Neuroimage, 2011, 55 (2): 574- 589.
doi: 10.1016/j.neuroimage.2010.10.081 |
46 |
Kim J , Lee B . Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine[J]. Hum Brain Mapp, 2018, 39 (9): 3728- 3741.
doi: 10.1002/hbm.24207 |
47 | Zhang W , Luo T , Qiu S , et al. Identifying genetic risk factors for Alzheimer's disease via shared tree-guided feature learning across multiple tasks[J]. IEEE Trans Knowl Data Eng, 2018, 30 (11): 2145- 2156. |
48 | Xu J, Deng C, Gao X, et al. Predicting Alzheimer's disease cognitive assessment via robust low-rank structured sparse model[C]. IJCAI(US), 2017: 3880-3886. |
49 |
Zhu X , Suk H , Lee S , et al. Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification[J]. IEEE Trans Biomed Eng, 2016, 63 (3): 607- 618.
doi: 10.1109/TBME.2015.2466616 |
50 |
Ye T , Zu C , Jie B , et al. Discriminative multi-task feature selection for multi-modality classification of Alzheimer's disease[J]. Brain Imaging Behav, 2016, 10 (3): 739- 749.
doi: 10.1007/s11682-015-9437-x |
51 |
Cao P , Liu X , Liu H , et al. Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease[J]. Comput Meth Prog Bio, 2018, 162: 19- 45.
doi: 10.1016/j.cmpb.2018.04.028 |
52 |
Zhou T , Thung K , Liu M , et al. Brain-Wide genome-wide association study for Alzheimer's disease via joint projection learning and sparse regression model[J]. IEEE Trans Biomed Eng, 2019, 66 (1): 165- 175.
doi: 10.1109/TBME.2018.2824725 |
53 |
Wang M , Zhang D , Shen D , et al. Multi-task exclusive relationship learning for Alzheimer's disease progression prediction with longitudinal data[J]. Med Image Anal, 2019, 53: 111- 122.
doi: 10.1016/j.media.2019.01.007 |
54 |
Liu M , Zhang J , Adeli E , et al. Joint classification and regression via deep multi-Task multi-Channel learning for Alzheimer's disease diagnosis[J]. IEEE Trans Biomedi Eng, 2019, 66 (5): 1195- 1206.
doi: 10.1109/TBME.2018.2869989 |
55 |
Liu M , Zhang D , Shen D , et al. Relationship induced multi-template learning for diagnosis of Alzheimer's disease and mild cognitive impairment[J]. IEEE Trans Medi Imaging, 2016, 35 (6): 1463- 1474.
doi: 10.1109/TMI.2016.2515021 |
56 | Li W , Zhang L , Qiao L , et al. Towards a better estimation of functional brain network for mild cognitive impairment identification: a transfer learning view[J]. IEEE J Biomed Health Inform, 2019, 24 (4): 1160- 1168. |
57 |
Cheng B , Liu M , Shen D , et al. Multi-domain transfer learning for early diagnosis of Alzheimer's disease[J]. Neuroinf, 2017, 15 (2): 115- 132.
doi: 10.1007/s12021-016-9318-5 |
58 |
Cheng B , Liu M , Suk H , et al. Multimodal manifold-regularized transfer learning for MCI conversion prediction[J]. Brain Imaging Behav, 2015, 9 (4): 913- 926.
doi: 10.1007/s11682-015-9356-x |
59 |
Cheng B , Liu M , Zhang D , et al. Domain transfer learning for MCI conversion prediction[J]. IEEE Trans Biomed Eng, 2015, 62 (7): 1805- 1817.
doi: 10.1109/TBME.2015.2404809 |
60 |
Filipovych R , Davatzikos C . Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI)[J]. Neuroimage, 2011, 55 (3): 1109- 1119.
doi: 10.1016/j.neuroimage.2010.12.066 |
[1] | YU Xinguang, ZHANG Yanyang. Advances on the treatment of Alzheimers disease with deep brain stimulation [J]. Journal of Shandong University (Health Sciences), 2020, 58(8): 22-27. |
|