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http://dx.doi.org/10.29220/CSAM.2020.27.6.603

Penalized logistic regression using functional connectivity as covariates with an application to mild cognitive impairment  

Jung, Jae-Hwan (Department of Information and Statistics, Chungnam National University)
Ji, Seong-Jin (Department of Information and Statistics, Chungnam National University)
Zhu, Hongtu (Department of Biostatistics, University of North Carolina at Chapel Hill)
Ibrahim, Joseph G. (Department of Biostatistics, University of North Carolina at Chapel Hill)
Fan, Yong (Department of Radiology, University of Pennsylvania)
Lee, Eunjee (Department of Information and Statistics, Chungnam National University)
Publication Information
Communications for Statistical Applications and Methods / v.27, no.6, 2020 , pp. 603-624 More about this Journal
Abstract
There is an emerging interest in brain functional connectivity (FC) based on functional Magnetic Resonance Imaging in Alzheimer's disease (AD) studies. The complex and high-dimensional structure of FC makes it challenging to explore the association between altered connectivity and AD susceptibility. We develop a pipeline to refine FC as proper covariates in a penalized logistic regression model and classify normal and AD susceptible groups. Three different quantification methods are proposed for FC refinement. One of the methods is dimension reduction based on common component analysis (CCA), which is employed to address the limitations of the other methods. We applied the proposed pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and deduced pathogenic FC biomarkers associated with AD susceptibility. The refined FC biomarkers were related to brain regions for cognition, stimuli processing, and sensorimotor skills. We also demonstrated that a model using CCA performed better than others in terms of classification performance and goodness-of-fit.
Keywords
resting-state functional magnetic resonance imaging; penalized logistic regression; common component analysis; Alzheimers disease; mild cognitive impairment;
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1 Koenigs M, Barbey AK, Postle BR, and Grafman J (2009). Superior parietal cortex is critical for the manipulation of information in working memory, Journal of Neuroscience, 29, 14980-14986.   DOI
2 Kryscio RJ, Schmitt FA, Salazar JC, Mendiondo MS, and Markesbery WR (2006). Risk factors for transitions from normal to mild cognitive impairment and dementia, Neurology, 66, 828-832.   DOI
3 Li C, Wang J, Gui L, Zheng J, Liu C, and Du H (2011). Alterations of whole-brain cortical area and thickness in mild cognitive impairment and Alzheimer's disease, Journal of Alzheimer's Disease, 27, 281-290.   DOI
4 Li W, Qin W, Liu H, Fan L, Wang J, Jiang T, and Yu C (2013). Subregions of the human superior frontal gyrus and their connections, Neuroimage, 78, 46-58.   DOI
5 Liu Z, Zhang Y, Yan H, et al. (2012). Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer's disease: a resting-state fMRI study, Psychiatry Research: Neuroimaging, 202, 118-125.   DOI
6 Martinez-Murcia FJ, Gorriz JM, Ramirez J, Puntonet CG, Illan IA, and Alzheimer's Disease Neuroimaging Initiative (2013). Functional activity maps based on significance measures and independent component analysis, Computer Methods and Programs in Biomedicine, 111, 255-268.   DOI
7 Mechelli A, Humphreys GW, Mayall K, Olson A, and Price CJ (2000). Differential effects of word length and visual contrast in the fusiform and lingual gyri during. In Proceedings of the Royal Society of London. Series B: Biological Sciences, 267, 1909-1913.   DOI
8 Menini A (2009). The Neurobiology of Olfaction, CRC Press.
9 Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J, and Alzheimer's Disease Neuroimaging Initiative (2015). Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects, Neuroimage, 104, 398-412.   DOI
10 Mohs RC (1983). The Alzheimer's disease assessment scale: an instrument for assessing treatment efficacy, Psychopharmacol Bull, 19, 448-450.
11 Morris JC (2005). Early-stage and preclinical Alzheimer disease, Alzheimer Disease and Associated Disorders, 19, 163-165.   DOI
12 Orrison WW (2008). Atlas of Brain Function (2nd ed), Thieme, New York.
13 Petrella JR, Sheldon FC, Prince SE, Calhoun VD, and Doraiswamy PM (2011). Default mode network connectivity in stable vs progressive mild cognitive impairment, Neurology, 76, 511-517.   DOI
14 Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, and Kokmen E (1999). Mild cognitive impairment: clinical characterization and outcome, Archives of Neurology, 56, 303-308.   DOI
15 Shankle WR, Romney AK, Hara J, Fortier D, Dick MB, Chen JM, Chan T, and Sun X (2005). Methods to improve the detection of mild cognitive impairment. In Proceedings of the National Academy of Sciences, 102, 4919-4924.   DOI
16 Rathore S, Habes M, Iftikhar MA, Shacklett A, and Davatzikos C (2017). A review on neuroimagingbased classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages, NeuroImage, 155, 530-548.   DOI
17 Renier LA, Anurova I, De Volder AG, Carlson S, VanMeter J, and Rauschecker JP (2010). Preserved functional specialization for spatial processing in the middle occipital gyrus of the early blind, Neuron, 68, 138-148.   DOI
18 Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, and Bullmore ED (2005). Neurophysiological architecture of functional magnetic resonance images of human brain, Cerebral Cortex, 15, 1332-1342.   DOI
19 Small GW, Chen ST, Komo S, et al. (1999). Memory self-appraisal in middle-aged and older adults with the apolipoprotein E-4 allele, American Journal of Psychiatry, 156, 1035-1038.
20 Shenhav A, Botvinick MM, and Cohen JD (2013). The expected value of control: an integrative theory of anterior cingulate cortex function, Neuron, 79, 217-240.   DOI
21 Smith SM, Vidaurre D, Beckmann CF, et al. (2013). Functional connectomics from resting-state fMRI, Trends in Cognitive Sciences, 17, 666-682.   DOI
22 Sperling RA, Aisen PS, Beckett LA, et al. (2011). Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease, Alzheimer's & Dementia, 7, 280-292.   DOI
23 Tibshirani R (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological), 58, 267-288.   DOI
24 Tijms BM, Wink AM, de Haan W, van der Flier WM, Stam CJ, Scheltens P, and Barkhof F (2013). Alzheimer's disease: connecting findings from graph theoretical studies of brain networks, Neurobiology of Aging, 34, 2023-2036.   DOI
25 Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, and Joliot M (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage, 15, 273-289.   DOI
26 Wang H, Banerjee A, and Boley D (2011). Common component analysis for multiple covariance matrices. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 956-964.
27 Warrier C, Wong P, Penhune V, Zatorre R, Parrish T, Abrams D, and Kraus N (2009). Relating structure to function: Heschl's gyrus and acoustic processing, Journal of Neuroscience, 29, 61-69.   DOI
28 Wang J, Zuo X, Dai Z, et al. (2013). Disrupted functional brain connectome in individuals at risk for Alzheimer's disease, Biological Psychiatry, 73, 472-481.   DOI
29 Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, and Jiang T (2007). Altered functional connectivity in early Alzheimer's disease: A resting-state fMRI study, Human Brain Mapping, 28, 967-978.   DOI
30 Wang XN, Zeng Y, Chen GQ, et al. (2016). Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment, Oncotarget, 7, 48953-48962.   DOI
31 Wee CY, Yap PT, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, and Shen D (2012). Resting-state multi-spectrum functional connectivity networks for identification of MCI patients, PLoS One, 7, e37828.   DOI
32 Wee CY, Yap PT, Shen D, and Alzheimer's Disease Neuroimaging Initiative (2013). Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns, Human Brain Mapping, 34, 3411-3425.   DOI
33 Bischkopf J, Busse A, and Angermeyer MC (2002). Mild cognitive impairment 1-a review of prevalence, incidence and outcome according to current approaches, Acta Psychiatrica Scandinavica, 106, 403-414.   DOI
34 Allen G, Barnard H, and McColl R, et al. (2007). Reduced hippocampal functional connectivity in Alzheimer disease, Archives of Neurology, 64, 1482-1487.   DOI
35 Ardila A, Bernal B, and Rosselli M (2014). The elusive role of the left temporal pole (BA38) in language: a preliminary meta-analytic connectivity study, International Journal of Brain Science, 2014, 946039.
36 Ardila A, Bernal B, and Rosselli M (2017). Should Broca's area include Brodmann area 47?, Psicothema, 29, 73-77.
37 Bai F, Zhang Z, Watson DR, Yu H, Shi Y, Yuan Y, Zang Y, Zhu C, and Qian Y (2009). Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment, Biological Psychiatry, 65, 951-958.   DOI
38 Barrat A, Barthelemy M, Pastor-Satorras R, and Vespignani A (2004). The architecture of complex weighted networks. In Proceedings of the National Academy of Sciences, 101, 3747-3752.   DOI
39 Beckmann CF, DeLuca M, Devlin JT, and Smith SM (2005). Investigations into resting-state connectivity using independent component analysis, Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 1001-1013.   DOI
40 Ben-Shabat E, Matyas TA, Pell GS, Brodtmann A, and Carey LM (2015). The right supramarginal gyrus is important for proprioception in healthy and stroke-affected participants: a functional MRI study, Frontiers in Neurology, 6, 248.   DOI
41 Blefari ML, Martuzzi R, Salomon R, Bello-Ruiz J, Herbelin B, Serino A, and Blanke O (2017). Bilateral Rolandic operculum processing underlying heartbeat awareness reflects changes in bodily self-consciousness, European Journal of Neuroscience, 45, 1300-1312.   DOI
42 Alzheimer's Association (2019). 2019 Alzheimer's disease facts and figures, Alzheimer's & Dementia, 15, 321-387.   DOI
43 Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Quigley MA, and Meyerand ME (2001). Frequencies contributing to functional connectivity in the cerebral cortex in restingstate data, American Journal of Neuroradiology, 22, 1326-1333.
44 Botvinick MM, Braver TS, Barch DM, Carter CS, and Cohen JD (2001). Conflict monitoring and cognitive control, Psychological Review, 108, 624-652.   DOI
45 Brin S and Page L (1998). The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, 30, 107-117.   DOI
46 Coffman KA, Dum RP, and Strick PL (2011). Cerebellar vermis is a target of projections from the motor areas in the cerebral cortex. In Proceedings of the National Academy of Sciences, 108, 16068-16073.   DOI
47 Chertkow H (2008). Diagnosis and treatment of dementia: introduction. Introducing a series based on the Third Canadian Consensus Conference on the Diagnosis and Treatment of Dementia, CMAJ, 178, 316-321.
48 Cox RW (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages, Computers and Biomedical Research, 29, 162-173.   DOI
49 Damoiseaux JS, Beckmann CF, Arigita ES, Barkhof F, Scheltens P, Stam CJ, Smith SM, and Rombouts S (2008). Reduced resting-state brain activity in the default network in normal aging, Cerebral Cortex, 18, 1856-1864.   DOI
50 Davatzikos C, Priyanka B, Shaw LM, Batmanghelich KN, and Trojanowski JQ (2011). Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification, Neurobiology of Aging, 32, 2322-e19.
51 DeMaris A (1992). Logit Modeling: Practical Applications, Sage, 86.
52 Ganguli M, Fu B, Snitz BE, Hughes TF, and Chang CCH (2013). Mild cognitive impairment: incidence and vascular risk factors in a population-based cohort, Neurology, 80, 2112-2120.   DOI
53 Drzezga A, Becker JA, and Van Dijk KR, (2011). Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden, Brain, 134, 1635-1646.   DOI
54 Farrer LA, Cupples LA, Haines JL, et al. (1997). Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease: a meta-analysis, JAMA, 278, 1349-1356.   DOI
55 Feldman HH and Jacova C (2005). Mild cognitive impairment, The American Journal of Geriatric Psychiatry, 13, 645-655.   DOI
56 Friedman J, Hastie T, and Tibshirani R (2010). Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, 33, 1-22.
57 Ganguli M, Dodge HH, Shen C, and DeKosky ST (2004). Mild cognitive impairment, amnestic type: an epidemiologic study, Neurology, 63, 115-121.   DOI
58 Gili T, Cercignani M, Serra L, Perri R, Giove F, Maraviglia B, Caltagirone C, and Bozzali M (2011). Regional brain atrophy and functional disconnection across Alzheimer's disease evolution, Journal of Neurology, Neurosurgery & Psychiatry, 82, 58-66.   DOI
59 Grand JH, Caspar S, and MacDonald SW (2011). Clinical features and multidisciplinary approaches to dementia care, Journal of Multidisciplinary Healthcare, 4, 125-147.   DOI
60 Greicius MD, Srivastava G, Reiss AL, and Menon V (2004). Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI, Proceedings of the National Academy of Sciences, 101, 4637-4642.   DOI
61 Dubois B and Albert ML (2004). Amnestic MCI or prodromal Alzheimer's disease?, The Lancet Neurology, 3, 246-248.   DOI
62 Hall JE (2010). Guyton and Hall Textbook of Medical Physiology e-Book, Elsevier Health Sciences.
63 Zhang D, Shen D, and Alzheimer's Disease Neuroimaging Initiative (2012). Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers, PLoS One, 7, e33182.   DOI
64 Wollman DE and Prohovnik I (2003). Sensitivity and specificity of neuroimaging for the diagnosis of Alzheimer's disease, Dialogues in Clinical Neuroscience, 5, 89-99.
65 Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T, and Alzheimer's Disease Neuroimaging Initiative (2010). Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease, PLoS Computational Biology, 6, e1001006.   DOI
66 Ye J (2005). Generalized low rank approximations of matrices, Machine Learning, 61, 167-191.   DOI
67 Zou H and Hastie T (2005). Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 301-320.   DOI
68 Weisstein EW (2003). Graph Diameter, Wolfram Research, Inc., https://mathworld.wolfram.com/
69 Han Y, Lui S, Kuang W, Lang Q, Zou L, and Jia J (2012). Anatomical and functional deficits in patients with amnestic mild cognitive impairment, PLoS One, 7, e28664.   DOI
70 Hanninen T, Hallikainen M, Tuomainen S, Vanhanen M, and Soininen H (2002). Prevalence of mild cognitive impairment: a population-based study in elderly subjects, Acta Neurologica Scandinavica, 106, 148-154.   DOI
71 Hoerl AE and Kennard RW (1970). Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 12, 55-67.   DOI
72 Hutchison RM, Womelsdorf T, Allen EA, et al. (2013). Dynamic functional connectivity: promise, issues, and interpretations, Neuroimage, 80, 360-378.   DOI
73 Johnson KA, Fox NC, Sperling RA, and Klunk WE (2012). Brain imaging in Alzheimer disease, Cold Spring Harbor Perspectives in Medicine, 2, a006213.   DOI
74 Johnson SC, Schmitz TW, Moritz CH, et al. (2006). Activation of brain regions vulnerable to Alzheimer's disease: the effect of mild cognitive impairment, Neurobiology of Aging, 27, 1604-1612.   DOI