Thickness network features for prognostic applications in dementia

Neurobiology of Aging

Volumn 36, Issue S1, 2015, Pages S91-S102

  Raamana, Pradeep Reddy ^a   Weiner, Michael W ^b   Wang, Lei ^c   Beg, Mirza Faisal ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Alzheimer; Cortical thickness; Early detection; Fusion; Mild cognitive impairment; Multiple kernel learning; Network properties

Indexed keywords

ALZHEIMER DISEASE; AREA UNDER THE CURVE; ARTICLE; COGNITIVE DEFECT; CONTROLLED STUDY; CORTICAL THICKNESS (BRAIN); DEMENTIA; DISEASE CLASSIFICATION; DISEASE MARKER; DISEASE SEVERITY; EARLY DIAGNOSIS; HUMAN; IMAGE ANALYSIS; KERNEL METHOD; LEARNING; MAJOR CLINICAL STUDY; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PROGNOSIS; SENSITIVITY AND SPECIFICITY; CEREBRAL CORTEX; DIAGNOSIS, DIFFERENTIAL; HUMANS; MAGNETIC RESONANCE IMAGING; METHODS; MILD COGNITIVE IMPAIRMENT; PATHOLOGY;

BIOLOGICAL MARKERS;

ALZHEIMER DISEASE; BIOLOGICAL MARKERS; CEREBRAL CORTEX; DEMENTIA; DIAGNOSIS, DIFFERENTIAL; EARLY DIAGNOSIS; HUMANS; MAGNETIC RESONANCE IMAGING; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; PROGNOSIS; SENSITIVITY AND SPECIFICITY;

EID: 84916624596     PISSN: 01974580     EISSN: 15581497     Source Type: Journal    
DOI: 10.1016/j.neurobiolaging.2014.05.040     Document Type: Article

References (57)

1. Alexander-Bloch A., Giedd J.N., Bullmore E. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 2013, 14:322-336.
2. Alexander-Bloch A., Raznahan A., Bullmore E., Giedd J. The convergence of maturational change and structural covariance in human cortical networks. J.Neurosci. 2013, 33:2889-2899.
3. 2012 Alzheimer's disease facts and figures. Alzheimer's Dement. 2012, 8:131-168. Alzheimer's Association.
4. Amieva H., Le Goff M., Millet X., Orgogozo J.M., Pérès K., Barberger Gateau P., Jacqmin Gadda H., Dartigues J.F. Prodromal Alzheimer's disease: successive emergence of the clinical symptoms. Ann. Neurol. 2008, 64:492-498.
5. Bassett D.S., Bullmore E., Verchinski B.A., Mattay V.S., Weinberger D.R., Meyer-Lindenberg A. Hierarchical organization of human cortical networks in health and schizophrenia. J.Neurosci. 2008, 28:9239-9248.
6. Beg M.F., Raamana P.R., Barbieri S., Wang L. Comparison of four shape features for detecting hippocampal shape changes in early Alzheimer's. Stat. Methods Med. Res. 2013, 22:439-462.
7. Braak H., Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica 1991, 82:239-259.
8. Braak H., Del Tredici K. The pathological process underlying Alzheimer's disease in individuals under thirty. Acta Neuropathologica 2011, 121:171-181.
9. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009, 10:186-198.
10. Chen Z.J., He Y., Rosa-Neto P., Germann J., Evans A. Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb. Cortex 2008, 18:2374-2381.
11. Coupé P., Eskildsen S.F., Manjón J.V., Fonov V.S., Pruessner J.C., Allard M., Collins D.L. Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease. Neuroimage Clin. 2012, 1:141-152. Alzheimer's Disease Neuroimaging Initiative.
12. Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehericy S., Habert M.O., Chupin M., Benali H., Colliot O., Initiative A.D.N. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011, 56:766-781.
13. Damoulas T., Girolami M.A. Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection. Bioinformatics 2008, 24:1264-1270.
14. Desikan R.S., Cabral H.J., Hess C.P., Dillon W.P., Glastonbury C.M., Weiner M.W., Schmansky N.J., Greve D.N., Salat D.H., Buckner R.L., Fischl B. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease. Brain 2009, 132(Pt 8):2048-2057. Alzheimer's Disease Neuroimaging Initiative.
15. Dickerson B.C., Bakkour A., Salat D., Feczko E., Pacheco J., Greve D., Grodstein F., Wright C., Blacker D., Rosas H. The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb. Cortex 2009, 19:497-510.
16. Du A.T., Schuff N., Kramer J.H., Rosen H.J., Gorno-Tempini M.L., Rankin K., Miller B.L., Weiner M.W. Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. Brain 2007, 130:1159-1166.
17. Duchesne S., Bocti C., De Sousa K., Frisoni G., Chertkow H., Collins D.L. Amnestic MCI future clinical status prediction using baseline MRI features. Neurobiol. Aging 2010, 31:1606-1617.
18. Eskildsen S.F., Coupé P., García-Lorenzo D., Fonov V., Pruessner J.C., Collins D.L. Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 2013, 65:511-521.
19. Evans A.C. Networks of anatomical covariance. Neuroimage 2013, 80:489-504.
20. Fawcett T. An introduction to ROC analysis. Pattern Recognition Lett. 2006, 27:861-874.
21. Fischl B., Salat D.H., Busa E., Albert M., Dieterich M., Haselgrove C., van der Kouwe A., Killiany R., Kennedy D., Klaveness S., Montillo A., Makris N., Rosen B., Dale A.M. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002, 33:341-355.
22. Fitzpatrick M., Sonka M. Handbook of Medical Imaging Vol. 2: Medical Image Processing & Analysis (PM80) 2000, SPIE-International Society for Optical Engineering, Seattle, WA.
23. Gibson, E., Wang, L., Beg, M.F., 2009. Cortical thickness measurement using eulerian PDEs and surface-based global topological information. Org Human Brain Mapping, 15th Annual Meeting.
24. Gong G., He Y., Chen Z.J., Evans A.C. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. Neuroimage 2012, 59:1239-1248.
25. Hagmann P., Cammoun L., Gigandet X., Meuli R., Honey C.J., Wedeen V.J., Sporns O. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008, 6:e159.
26. He Y., Chen Z.J., Evans A.C. Small-world anatomical networks in the human brain revealed by cortical thickness from mri. Cereb. Cortex 2007, 17:2407-2419.
27. He Y., Chen Z., Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J.Neurosci. 2008, 28:4756-4766.
28. He Y., Chen Z., Gong G., Evans A. Neuronal networks in Alzheimer's disease. Neuroscientist 2009, 15:333-350.
29. He Y., Dagher A., Chen Z., Charil A., Zijdenbos A., Worsley K., Evans A. Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 2009, 132:3366-3379.
30. Iturria-Medina Y. Anatomical brain networks on the prediction of abnormal brain states. Brain Connect. 2013, 3:1-21.
31. Jack C.R., Knopman D.S., Jagust W.J., Shaw L.M., Aisen P.S., Weiner M.W., Petersen R.C., Trojanowski J.Q. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010, 9:119-128.
32. Khundrakpam B.S., Reid A., Brauer J., Carbonell F., Lewis J., Ameis S., Karama S., Lee J., Chen Z., Das S., Evans A.C., Ball W.S., Byars A.W., Schapiro M., Bommer W., Carr A., German A., Dunn S., Rivkin M.J., Waber D., Mulkern R., Vajapeyam S., Chiverton A., Davis P., Koo J., Marmor J., Mrakotsky C., Robertson R., McAnulty G., Brandt M.E., Fletcher J.M., Kramer L.A., Yang G., McCormack C., Hebert K.M., Volero H., Botteron K., McKinstry R.C., Warren W., Nishino T., Robert Almli C., Todd R., Constantino J., McCracken J.T., Levitt J., Alger J., O'Neil J., Toga A., Asarnow R., Fadale D., Heinichen L., Ireland C., Wang D.J., Moss E., Zimmerman R.A., Bintliff B., Bradford R., Newman J., Evans A.C., Arnaoutelis R., Bruce Pike G., Louis Collins D., Leonard G., Paus T., Zijdenbos A., Das S., Fonov V., Fu L., Harlap J., Leppert I., Milovan D., Vins D., Zeffiro T., Van Meter J., Lange N., Froimowitz M.P., Botteron K., Robert Almli C., Rainey C., Henderson S., Nishino T., Warren W., Edwards J.L., Dubois D., Smith K., Singer T., Wilber A.A., Pierpaoli C., Basser P.J., Chang L.C., Koay C.G., Walker L., Freund L., Rumsey J., Baskir L., Stanford L., Sirocco K., Gwinn-Hardy K., Spinella G., McCracken J.T., Alger J.R., Levitt J., O'Neill J. Developmental changes in organization of structural brain networks. Cereb. Cortex 2013, 23:2072-2085. Brain Development Cooperative Group.
33. Koikkalainen J., Lötjönen J., Thurfjell L., Rueckert D., Waldemar G., Soininen H. Multi-template tensor-based morphometry: application to analysis of Alzheimer's disease. Neuroimage 2011, 56:1134-1144.
34. La Joie R., Perrotin A., Barré L., Hommet C., Mézenge F., Ibazizene M., Camus V., Abbas A., Landeau B., Guilloteau D., de La Sayette V., Eustache F., Desgranges B., Chételat G. Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (aβ) load in Alzheimer's disease dementia. J.Neurosci. 2012, 32:16265-16273.
35. Lee J.K., Lee J.M., Kim J.S., Kim I.Y., Evans A.C., Kim S.I. Anovel quantitative cross-validation of different cortical surface reconstruction algorithms using MRI phantom. Neuroimage 2006, 31:572-584.
36. Lerch J.P., Evans A.C. Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage 2005, 24:163-173.
37. Lerch J.P., Worsley K., Shaw W.P., Greenstein D.K., Lenroot R.K., Giedd J., Evans A.C. Mapping anatomical correlations across cerebral cortex (macacc) using cortical thickness from MRI. Neuroimage 2006, 31:993-1003.
38. McClish D.K. Analyzing a portion of the ROC curve. Med. Decis. Making 1989, 9:190-195.
39. Mcevoy L.K., Holland D., Hagler D.J., Fennema-Notestine C., Brewer J.B., Dale A.M. Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis. Radiology 2011, 259:834-843. Alzheimer's DiseaseNeuroimaging Initiative.
40. Mechelli A., Friston K.J., Frackowiak R.S., Price C.J. Structural covariance in the human cortex. J.Neurosci. 2005, 25:8303-8310.
41. Misra C., Fan Y., Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 2009, 44:1415-1422.
42. Raamana P., Wang L., Rosen H., Miller B., Weiner M., Beg M.F. Differential diagnosis among Alzheimer's disease, frontotemporal disease and healthy aging: Comparative study using subcortical features. Alzheimer's Dement. 2012, 8:P163-P164.
43. Raj A., Mueller S.G., Young K., Laxer K.D., Weiner M. Network-level analysis of cortical thickness of the epileptic brain. Neuroimage 2010, 52:1302-1313.
44. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010, 52:1059-1069.
45. Seeley W.W., Crawford R.K., Zhou J., Miller B.L., Greicius M.D. Neurodegenerative diseases target large-scale human brain networks. Neuron 2009, 62:42-52.
46. Tijms B.M., Seriès P., Willshaw D.J., Lawrie S.M. Similarity-based extraction of individual networks from gray matter MRI scans. Cereb. Cortex 2012, 22:1530-1541.
47. Tosun D., Schuff N., Mathis C., Jagust W., Weiner M.W. Spatial patterns of brain amyloid-β burden and atrophy rate associations in mild cognitive impairment. Brain 2011, 134:1077-1088.
48. Villain N., Chételat G., Grassiot B., Bourgeat P., Jones G., Ellis K.A., Ames D., Martins R.N., Eustache F., Salvado O., Masters C.L., Rowe C.C., Villemagne V.L. Regional dynamics of amyloid- deposition in healthy elderly, mild cognitive impairment and Alzheimer's disease: a voxelwise PiB-PET longitudinal study. Brain 2012, 135:2126-2139. AIBL Research Group.
49. Walhovd K., Fjell A., Brewer J., McEvoy L., Fennema-Notestine C., Hagler D., Jennings R., Karow D., Dale A. Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. AJNR Am. J. Neuroradiol. 2010, 31:347-354. Alzheimer's Disease Neuroimaging Initiative.
50. Wang L., Beg M.F., Ratnanather J.T., Ceritoglu C., Younes L., Morris J.C., Csernansky J.G., Miller M.I. Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the Alzheimer type. IEEE Trans. Med. Imaging 2007, 26:462-470.
51. Wee C.Y., Yap P.T., Zhang D., Denny K., Browndyke J.N., Potter G.G., Welsh-Bohmer K.A., Wang L., Shen D. Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 2012, 59:2045-2056.
52. Wen W., He Y., Sachdev P. Structural brain networks and neuropsychiatric disorders. Curr. Opin. Psychiatry 2011, 24:219-225.
53. Westman E., Muehlboeck J.S., Simmons A. Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. Neuroimage 2012, 62:229-238.
54. Wolz R., Julkunen V., Koikkalainen J., Niskanen E., Zhang D.P., Rueckert D., Soininen H., Lötjönen J. Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PLoS One 2011, 6:e25446. Alzheimer's Disease Neuroimaging Initiative.
55. Woodward M., Mackenzie I.R.A., Hsiung G.Y.R., Jacova C., Feldman H. Multiple brain pathologies in dementia are common. Eur. Geriatr. Medecine 2010, 1:259-265.
56. Yao Z., Zhang Y., Lin L., Zhou Y., Xu C., Jiang T. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Comput. Biol. 2010, 6:e1001006. Alzheimer's DiseaseNeuroimaging Initiative.
57. Yezzi A.J., Prince J.L. An eulerian PDE approach for computing tissue thickness. IEEE Trans. Med. Imaging 2003, 22:1332-1339.