Document Type : Technical note


1 Ph.D Student, Bioelectric Department, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

2 Assistant Professor, Bioelectric Department, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

3 Professor, CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran - Professor, School of Cognitive Science, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran - Image Analysis Laboratory, Henry Ford Health System, Detroit, Michigan, USA



Although the cognitive deficits due to age-related brain differences have been largely analyzed, the altered connectivity of task related functional networks in aging requires more studies. As the brain of old adults experience some alterations in task performance during cognitive challenges, the related effects on connectivity of functional networks are here evaluated using event-related functional Magnetic Resonance Imaging (fMRI). The fMRI data have been acquired for simple visual and motor tasks. For each subject, several Functional Connectivity (FC) networks including, motor, visual and the default mode networks are firstly calculated using a conventional voxel-wise correlation analysis with predefined region of interest. Then, the strength of functional connectivity is assessed and compared for different age groups. The current study has evaluated three hypotheses on FC of aging brain: the frontal regions involved with motor network try to compensate for declines in the posterior regions, default-mode network is less suppressed and, the posterior regions involved with visual network exhibit less connectivity. The first two hypotheses are accepted by analysis results but visual network behaves differently. Also, results show that the task related functional connectivity is considerably altered in old adults compared to young adults. Old adults demonstrate higher connectivity strength on average with a slightly smaller variance than young adults.


Main Subjects

[1]           D. C. Park, P. Reuter-Lorenz, “The adaptive brain: aging and neurocognitive scaffolding,” Annu. Rev. Psychol., vol. 60, pp. 173-96, 2009.
[2]           N. Raz, K. M. Rodrigue, E. M. Haacke, “Brain aging and its modifiers: insights from in vivo neuromorphometry and susceptibility weighted imaging,” Ann. N. Y. Acad. Sci., vol. 1097, pp. 84-93, 2007.
[3]           T. Hedden, J. D. E. Gabrieli, “Insights into the ageing mind: A view from cognitive neuroscience,” Nat. Rev. Neurosci., vol. 5, no. 2, pp. 87-96, 2004.
[4]           A. S. Dekaban, “Changes in brain weights during the span of human life: relation of brain weights to body heights and body weights,” Ann. Neurol., vol. 4, no. 4, pp. 345-56, 1987.
[5]           J. Langan, S. J. Peltier, J. Bo, B.W. Fling, R. C. Welsh, R. D. Seidler, “Functional implications of age differences in motor system connectivity,” Front. Syst. Neurosci., pp. 4-17, 2010.
[6]           D. Meunier, S. Achard, A. Morcom, E. Bullmore, “Age-related changes in modular organization of human brain functional networks,” Neuroimage, vol. 44, no. 3, pp. 715-23, 2009.
[7]           L. Wang, Y. Li, P. Metzak, Y. He, T. S. Woodward, “Age-related changes in topological patterns of large-scale brain functional networks during memory encoding and recognition,” Neuroimage, vol. 50, no. 3, pp. 862-72, 2010.
[8]           Z. J. Chen, Y. He, P. Rosa-Neto, G. Gong, A. C. Evans, “Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI,” Neuroimage, vol. 56, no. 1, pp. 235-45, 2011.
[9]           G. Gong, P. Rosa-Neto, F. Carbonell, Z. J. Chen, Y. He, A. C. Evans, “Age- and gender-related differences in the cortical anatomical network,” J. Neurosci., vol. 29, no. 50, pp. 15684-93, 2009.
[10]         D. J. Madden, M. C. Costello, N. A. Dennis, S. W. Davis, A. M. Shepler, J. Spaniol, B. Bucur, R. Cabeza, “Adult age differences in functional connectivity during executive control,” Neuroimage, vol. 52, no. 2, pp. 643-57, 2010.
[11]         R. D. Seidler, J.A. Bernard, T. B. Burutolu, T. B. Fling, M. T. Gordon, J. T. Gwin, Y. Kwak, D. B. Lipps, “Motor Control and Aging: Links to Age-Related Brain Structural, Functional, and Biochemical Effects,” Neurosci. Biobehav. Rev., vol. 34, no. 5, pp. 721–733, 2010.
[12]         G. Tononi, O. Sporns, G. M. Edelman, “A measure for brain complexity: relating functional segregation and integration in the nervous system,” Proc. Natl. Acad. Sci. U. S. A, vol. 91, no. 11, pp. 5033-7, 1998.
[13]         D. H. Salat, D. S. Tuch, D. N. Greve, A. J. van der Kouwe, N. D. Hevelone, A. K, Zaleta, B. R. Rosen, B. Fischl, S. Corkin, H. D. Rosas, A. M. Dale, “Age-related alterations in white matter microstructure measured by diffusion tensor imaging,” Neurobiol. Aging, vol. 26, no. 8, pp. 1215-27, 2005.
[14]         J. Sun, Sh. Tong, G. Y. Yang, “Reorganization of Brain Networks in Aging and Age-related Diseases,” Aging Dis., vol. 3, no. 2, pp. 181-193, 2012.
[15]         F. Varela, J. P. Lachaux, E. Rodriguez, J. Martinerie,” The brainweb: phase synchronization and large-scale integration,” Nat. Rev. Neurosci., vol.  2, no. 4, pp. 229-39, 2001.
[16]         M. D. Greicius, G. Srivastava, A. L. Reiss, V. Menon, “Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI,” Proc. Natl. Acad. Sci. U. S. A., vol. 101, no.13, pp. 4637-42, 2004.
[17]         C. Sorg, V. Riedl, M. Muhlau, V. D. Calhoun, T. Eichele, L. Laer, A. Drzezga, H. Forstl, A. Kurz, C. Zimmer, A. M. Wohlschlager, “Selective changes of resting-state networks in individuals at risk for Alzheimer's disease,” Proc. Natl.Acad. Sci. U. S. A., vol. 104, no. 47, pp. 18760-5, 2007.
[18]         E. Bullmore, O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nat. Rev. Neurosci., vol. 10, no.3, pp. 186-98, 2009.
[19]         D. J. Madden, I. J. Bennett, A. W. Song, “Cerebral white matter integrity and cognitive aging: contributions from diffusion tensor imaging,” Neuropsychol. Rev., vol. 19, no. 4, pp. 415-435, 2009.
[20]         C. L. Grady, A. B. Protzner, N. Kovacevic, S. C. Strother, B. Afshin-Pour, M. Wojtowicz, J. A. E. Anderson, N. Churchill, A. R. McIntosh, “A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains,” Cereb. Cortex, vol. 20, no. 6, pp. 1432-1447, 2010.
[21]         J. O. S. Goh, “Functional Dedifferentiation and AlteredConnectivity in Older Adults: Neural Accounts of Cognitive Aging,” Aging Dis., vol. 2, no. 1, pp. 30-48, 2011.
[22]         J. S. Damoiseaux, S. F. Beckmann, E. J. S. Arigita, F. Barkhof, P. Scheltens, C. J. Stam, S. M. Smith, S. Rombouts, “Reduced resting-state brain activity in the "default network" in normal aging,” Cereb. Cortex, vol. 18, no. 8, pp. 1856-1864, 2008.
[23]         N. Chen, Y. Chou, A. W. Song, D. J. Madden, “Measurement of spontaneous signal fluctuations in fMRI: adult age differences in intrinsic functional connectivity,” Brain Struct. Funct., vol. 213, no. 6, pp. 571-585, 2009.
[24]         M. E. Raichle, A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, G. L. Shulman, “A default mde of brain function,” Proc. Natl. Acad. Sci. U. S. A., vol.  98, no. 2, pp. 676-682, 2001.
[25]         D. C. Park, T. A. Polk, A. C. Hebrank, L. J. Jenkins, “Age differences in default mode activity on easy and difficult spatial judgment tasks,” Front. Hum. Neurosci., pp. 3-75, 2010.
[26]         M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. VanEssen, M. E. Raichle, “The human brain isintrinsically organized into dynamic, anticorrelated functional networks,” Proc. Natl. Acad. Sci. U. S. A., vol. 102, pp. 9673–9678, 2005.
[27]         T. Jiang, Y. He, Y. Zang, X. Weng, “Modulation of functional connectivity duringthe resting state and the motor task,” Hum. Brain Mapp., vol. 22, pp. 63–71, 2004.
[28]         M. J. Lowe, B. J. Mock, J. A. Sorenson, “Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations,” Neuroimag, vol.  7, pp. 119–132, 1998.
[29]         H. J. Aizenstein, K. A. Clark, M. A. Butters, J. Cochran, V. A. Stenger, C. C. Meltzer, Ch. F. Reynolds, C. S. Carter, “The BOLD hemodynamic response in healthy aging,”  J. Cogn. Neurosci., vol. 16, no. 5, pp. 786–793, 2004.
[30]         M. Jenkinson, P. Bannister, M. Brady, S. Smith, “Improved optimisation for the robust and accurate linear registration and motion correction of brain images,” Neuroimage, vol. 17, no. 2, pp. 825-841, 2002.
[31]         M. Jenkinson, S. M. Smith, “A Global OptimisationMethod for Robust Affine Registration of Brain Images,” Med. Image Anal., vol. 5, no. 2, pp. 143-156, 2001.
[32]         S. Smith, “Fast Robust Automated Brain Extraction,” Human Brain Mapping, vol. 17, no. 3, pp. 143-155, 2002.