Computer-Aided Diagnosis of Mild Cognitive Impairment Based on SVM
451
5 Ethics and Permissions
Ethical statements and the informed consent from participants are present in the
Alzheimer’s Disease Neuroimaging Initiative, and we confirmed that all experiments were performed in accordance with relevant guidelines and regulations
of access. And we confirmed that all experimental protocols were approved by
the College of Medicine and Biological Information Engineering, Northeastern
University, China.
Acknowledgements. This work was supported by the Fundamental Research Funds
for the Central Universities (N182410001) and National Training Program of Innovation
and Entrepreneurship for Undergraduates (201910145101).
References
1. Petersen RC, Negash S (2008) Mild cognitive impairment: an overview. CNS Spectr
13(1):45–53
2. Patterson C (2018) World Alzheimer Report 2018–the state of the art of dementia
research: new frontiers. Alzheimer’s Disease International (ADI), London, UK
3. Van Den Heuvel MP, Pol HEH (2010) Exploring the brain network: a review on
resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534
4. Khazaee A, Ebrahimzadeh A, Babajani-Feremi A, Initiative ADN et al (2017)
Classification of patients with MCI and AD from healthy controls using directed
graph measures of resting-state fMRI. Behav Brain Res 322:339–350
5. Ye J, Farnum M, Yang E, Verbeeck R, Lobanov V, Raghavan N, Novak G,
DiBernardo A, Narayan VA (2012) Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC Neurol 12(1):46
6. Yan CG, Wang XD, Zuo XN, Zang YF (2016) DPABI: data processing & analysis
for (resting-state) brain imaging. Neuroinformatics 14(3):339–351
7. Statistical parametric mapping. https://www.fil.ion.ucl.ac.uk/spm/
8. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix
N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in
SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject
brain. Neuroimage 15(1):273–289
9. Kintali S (2008) Betweenness centrality: algorithms and lower bounds. arXiv
preprint arXiv:0809.1906
10. Fagiolo G (2007) Clustering in complex directed networks. Phys Rev E
76(2):026107
11. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys
Rev Lett 87(19):198701
12. Boldi P, Santini M, Vigna S (2009) PageRank: functional dependencies. ACM
Trans Inf Syst 27(4):19-1
13. Newman ME (2002) Assortative mixing in networks. Phys Rev Lett 89(20):208701
14. Liu J, Yuan L, Ye J (2011) SLEP: sparse learning with efficient projections. Technical report, Computer Science Center, Arizona State University
15. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM
Trans Intell Syst Technol (TIST) 2(3):27
16. Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R, Thomas RG, Walter S,
Trojanowski JQ, Shaw LM, Beckett LA et al (2010) Clinical core of the Alzheimer’s
disease neuroimaging initiative: progress and plans. Alzheimer’s Dement 6(3):239–
246
451
5 Ethics and Permissions
Ethical statements and the informed consent from participants are present in the
Alzheimer’s Disease Neuroimaging Initiative, and we confirmed that all experiments were performed in accordance with relevant guidelines and regulations
of access. And we confirmed that all experimental protocols were approved by
the College of Medicine and Biological Information Engineering, Northeastern
University, China.
Acknowledgements. This work was supported by the Fundamental Research Funds
for the Central Universities (N182410001) and National Training Program of Innovation
and Entrepreneurship for Undergraduates (201910145101).
References
1. Petersen RC, Negash S (2008) Mild cognitive impairment: an overview. CNS Spectr
13(1):45–53
2. Patterson C (2018) World Alzheimer Report 2018–the state of the art of dementia
research: new frontiers. Alzheimer’s Disease International (ADI), London, UK
3. Van Den Heuvel MP, Pol HEH (2010) Exploring the brain network: a review on
resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534
4. Khazaee A, Ebrahimzadeh A, Babajani-Feremi A, Initiative ADN et al (2017)
Classification of patients with MCI and AD from healthy controls using directed
graph measures of resting-state fMRI. Behav Brain Res 322:339–350
5. Ye J, Farnum M, Yang E, Verbeeck R, Lobanov V, Raghavan N, Novak G,
DiBernardo A, Narayan VA (2012) Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC Neurol 12(1):46
6. Yan CG, Wang XD, Zuo XN, Zang YF (2016) DPABI: data processing & analysis
for (resting-state) brain imaging. Neuroinformatics 14(3):339–351
7. Statistical parametric mapping. https://www.fil.ion.ucl.ac.uk/spm/
8. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix
N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in
SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject
brain. Neuroimage 15(1):273–289
9. Kintali S (2008) Betweenness centrality: algorithms and lower bounds. arXiv
preprint arXiv:0809.1906
10. Fagiolo G (2007) Clustering in complex directed networks. Phys Rev E
76(2):026107
11. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys
Rev Lett 87(19):198701
12. Boldi P, Santini M, Vigna S (2009) PageRank: functional dependencies. ACM
Trans Inf Syst 27(4):19-1
13. Newman ME (2002) Assortative mixing in networks. Phys Rev Lett 89(20):208701
14. Liu J, Yuan L, Ye J (2011) SLEP: sparse learning with efficient projections. Technical report, Computer Science Center, Arizona State University
15. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM
Trans Intell Syst Technol (TIST) 2(3):27
16. Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R, Thomas RG, Walter S,
Trojanowski JQ, Shaw LM, Beckett LA et al (2010) Clinical core of the Alzheimer’s
disease neuroimaging initiative: progress and plans. Alzheimer’s Dement 6(3):239–
246