Systems biology and brain activity in neuronal pathways by smart device and advanced signal processing

Frontiers in Genetics

Volumn 5, Issue JUL, 2014, Pages

  Castellani, Gastone ^a   Intrator, Nathan ^b   Remondini, Daniel ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BRAIN FUNCTION; DEMENTIA; ELECTROENCEPHALOGRAPHY; FATIGUE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENE EXPRESSION; GENE ONTOLOGY; HUMAN; INSOMNIA; METAGENOME; METAGENOMICS; MICROARRAY ANALYSIS; NONHUMAN; SIGNAL PROCESSING; SLEEP; SOCIETY; SYSTEMS BIOLOGY;

EID: 84906256010     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2014.00253     Document Type: Article

References (73)

1. Cooper, Leon N., ed. Theory of cortical plasticity. World Scientific, 2004.
2. Lynch G, Kessler M, Arai A, Larson J. The nature and causes of hippocampal long-term potentiation. Prog Brain Res. 1990;83:233-50.
3. Kandel, Eric R., et al. Neuroscience thinks big (and collaboratively). Nature Reviews Neuroscience 14.9 (2013): 659-664.
4. Kurzweil, Ray. The singularity is near: When humans transcend biology. Penguin, 2005.
5. Edelman, Shimon. Representation and recognition in vision. MIT press, 1999.
6. Biederman, Irving. Recognition-by-components: a theory of human image understanding. Psychological review 94.2 (1987): 115.
7. Cabeza R, Nyberg L. Imaging cognition II: An empirical review of 275 PET and fMRI studies. J Cogn Neurosci. 2000 Jan;12(1):1-47.
8. Haas, L. F. Hans Berger (1873-1941), Richard Caton (1842-1926), and electroencephalography. Journal of Neurology, Neurosurgery & Psychiatry 74.1 (2003): 9-9.
9. Niedermeyer, E., and Lopes da Silva F. H., eds. Electroencephalography: basic principles, clinical applications, and related fields. Wolters Kluwer Health, 2005.
10. Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain research reviews 29.2 (1999): 169-195.
11. Luck, S. J. An introduction to the event-related potential technique (cognitive neuroscience). (2005).
12. Fisher, R. S., et al. Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46.4 (2005): 470-472.
13. Duncan, D., Talmon, R., Zaveri, H. P., & Coifman, R. R. (2013). IDENTIFYING PRESEIZURE STATE IN INTRACRANIAL EEG DATA USING DIFFUSION KERNELS. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 10(3), 579-590.
14. Intrator, N. "Brain Activity Features: A Continuous Window Into the Mind." Preprint, 2013.
15. Remondini D, O'Connell B, Intrator N, Sedivy JM, Neretti N, Castellani GC, Cooper LN. Targeting c-Myc-activated genes with a correlation method: detection of global changes in large gene expression network dynamics. Proc Natl Acad Sci US A. 2005 May 10;102(19):6902-6.
16. Milanesi L, Romano P, Castellani G, Remondini D, Liò P. Trends in modeling Biomedical Complex Systems. BMC Bioinformatics. 2009 Oct 15;10 Suppl 12:I1.
17. Francesconi M, Remondini D, Neretti N, Sedivy JM, Cooper LN, Verondini E, Milanesi L, Castellani G. Reconstructing networks of pathways via significance analysis of their intersections. BMC Bioinformatics. 2008 Apr 25;9 Suppl 4:S9
18. Swartz BE. The advantages of digital over analog recording techniques. Electroencephalogr Clin Neurophysiol. 1998 Feb;106(2):113-7.
19. Buzsáki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004 Jun 25;304(5679):1926-9.
20. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004 Mar 15;134(1):9-21.
21. Yang, H., Long, X.Y., Yang, Y., Yan, H., Zhu, C.Z., Zhou, X.P., Zang, Y.F., Gong, Q.Y., 2007. Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage 36, 144-152.
22. Davidson, R. J. (1988). EEG measures of cerebral asymmetry: Conceptual and methodological issues. International Journal of Neuroscience, 39, 71-89.
23. Henriques, J. B., & Davidson, R. J. (1990). Regional brain electrical asymmetries discriminate between previously depressed and healthy control subjects. Journal of Abnormal Psychology, 99, 22-31.
24. van Kampen Stochastic processes in physics and chemistry. Elsevier; 2007.
25. Friedman N, Cai L, Xie XS. Linking Stochastic Dynamics to Population Distribution: An Analytical Framework of Gene Expression. Phys Rev Lett. 2006;97:168302.
26. Cai L, Friedman N, Xie XS. Stochastic Protein Expression in Individual Cells at the Single Molecule Level. Nature. 2006;440:358.
27. Holme P, Newman Nonequilibrium phase transition in the coevolution of networks and opinions.ME.Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Nov;74(5 Pt 2):056108.
28. Gross T, Blasius B.Adaptive coevolutionary networks: a review.J R Soc Interface. 2008 Mar 6;5(20):259-71.
29. Grady et al., Nat. Commun. 3(864) 2012
30. Pauls & Remondini, Phys. Rev. E 85, 066127 2012
31. Singleton, Science 343, 497 (2014)
32. Boorman, Scott A. and Harrison C. White, 'Social Structure from Multiple Networks. II. Role Structures.'American Journal of Sociology, 81 (6), 1384-1446. 1976)
33. Latour, B. (1987). Science in Action: How to Follow Scientists and Engineers Through Society.Milton Keynes: Open University Press.
34. John Law and John Hassard (eds) (1999). Actor Network Theory and After (Oxford and Keele: Blackwell and the Sociological Review).
35. R. Albert and A.-L. Barabasi, Rev. Mod. Phys. 74, 47 (2002).
36. M. E. J. Newman, SIAM Rev. 45, 167 (2003).
37. S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, Phys. Rep. 424, 175 (2006).
38. S. N. Dorogovtsev, A. Goltsev, and J. F. F. Mendes, Rev. Mod. Phys. 80, 1275 (2008).
39. Wasserman S, Faust K (1994) Social network analysis: Methods and applications. (Cambridge Univ Press, Cambridge, UK), pp 37
40. Bianconi G. Statistical mechanics of multiplex networks: entropy and overlap. Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jun;87(6):062806.
41. M. Szell, R. Lambiotte, and S. Thurner, Proc. Natl. Acad. Sci.USA 107, 13636 (2010).
42. Hekstra D, Taussig AR, Magnasco M, Naef F: Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide arrays.Nucleic Acids Res 2003, 31:1962-1968.
43. Affymetrix Inc: Technical note: guide to probe logarithmic intensity error (PLIER) estimation
44. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data.Biostatistics 2003, 4:249-264.
45. Fortunel NO, Otu HH, Ng HH, Chen J, Mu X, Chevassut T, Li X, Joseph M, Bailey C, Hatzfeld JA, Hatzfeld A, Usta F, Vega VB, Long PM, Libermann TA, Lim B: Comment on " 'Stemness': transcriptional profiling of embryonic and adult stem cells" and "a stem cell molecular signature.Science 2003, 302(5644):393.
46. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci USA 2005, 102:15545-15550.
47. Manoli T, Gretz N, Gröne HJ, Kenzelmann M, Eils R, Brors B: Group testing for pathway analysis improves comparability of different microarray datasets.Bioinformatics 2006, 22:2500-2506.
48. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat Genet 2000, 25:25-29.
49. Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes.Nucleic Acids Res 2000, 28:27-30.
50. Beissbarth T, Speed T.P.: GOstat: find statistically overrepresented Gene Ontologies within a group of genes.Bioinformatics 2004, 20:1464-1465.
51. Pandey R, Guru RK, Mount DW: Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data.Bioinformatics 2004, 20:2156-2158.
52. Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC: A global test for groups of genes: testing association with a clinical outcome.Bioinformatics 2004, 20:93-99.
53. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.Nat Genet 2003, 34:267-273.
54. Granger, CWJ. Causality, cointegration, and control. Journal of Economic Dynamics and Control 12.2 (1988): 551-559.
55. Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R. (2009). Classification of patterns of EEG synchronization for seizure prediction. Clinical neurophysiology, 120(11), 1927-1940.
56. Yin J, Li H. Model Selection and Estimation in the Matrix Normal Graphical Model. J Multivar Anal. 2012 May 1;107:119-140
57. Butte AJ, Kohane IS: Unsupervised knowledge discovery in medical databases using relevance networks.Proc AMIA Symp 1999, 711-715.
58. Schreiber T et al Physica D 142 (2000) 346-382
59. Camacho D, de la Fuente A, Mendes P: The origin of correlations in metabolomics data. Metabolomics 2005, 1(1):53-63.
60. Martins AM, Camacho D, Shuman J, Sha W, Mendes P, Shulaev V: A Systems Biology Study of Two Distinct Growth Phases of Saccharomyces cerevisiae Cultures.Current Genomics 2004, 5:649-663.
61. Kishino H, Waddell PJ: Correspondence analysis of genes and tissue types and finding genetic links from microarray data. Genome Inform Ser Workshop Genome Inform 2000, 11:83-95.
62. Toh H, Horimoto K: Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics 2002, 18:287-297.
63. Waddell PJ, Kishino H: Cluster inference methods and graphical models evaluated on NCI60 microarray gene expression data.Genome Inform Ser Workshop Genome Inform 2000, 11:129-140.
64. de la Fuente A, Bing N, Hoeschele I, Mendes P Discovery of meaningful associations in genomic data using partial correlation coefficients.Bioinformatics 2004, 20(18):3565-3574.
65. Magwene PM, Kim J Estimating genomic coexpression networks using first-order conditional independence.Genome Biol 2004, 5(12):R100.
66. Wille A, Zimmermann P, Vranova E, Furholz A, Laule O, Bleuler S, Hennig L, Prelic A, von Rohr P, Thiele L, et al. Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. Genome Biol 2004, 5(11):R92.
67. Dobra A, Hans C, Jones B, Nevins JR, West M Sparse graphical models for exploring gene expression data. J Multiv Analysis 2004, 90:196-212.
68. Schafer J, Strimmer K An empirical Bayes approach to inferring large-scale gene association networks.Bioinformatics 2005, 21(6):754-764.
69. Kishino H, Waddell PJ Correspondence analysis of genes and tissue types and finding genetic links from microarray data. Genome Inform Ser Workshop Genome Inform 2000, 11:83-95.
70. Liu & Duyn, PNAS 2013 110(11):4392-7
71. Honey et al. PNAS 104(24) 2007, 10240-10245
72. Buzsaki & Draguhn, Science 304 2004
73. Haimovici et al., PRL 110, 178101 (2013)