Event Abstract

Back to Event

Task Related Modulation of Functional Connectivity Networks of Alzheimer’s Disease and Mild Cognitive Impairment Patients

Duygu Şahin1*, Adil D. Duru2, Ali Bayram3, Başar Bilgiç4, Tamer Demiralp3 and Ahmet Ademoğlu1
  • 1 Bogazici University, Institute of Biomedical Engineering, Türkiye
  • 2 Marmara University, Faculty of Sports Sciences, Türkiye
  • 3 Istanbul University School of Medicine, Department of Physiology, Türkiye
  • 4 İstanbul University School of Medicine, Department of Neurology, Türkiye

In our era, as the possible extension of the life span becomes longer, the neurodegenerative diseases, such as Alzheimer's disease (AD), pose a great threat upon the quality of life. Currently, one of the urgent goals of neuroscientists is to detect AD in its early stages. Since recent treatments and prevention techniques aim at early and presymptomatic stages, studies carried out to present possible biomarkers especially with non-invasive approaches are of importance. For about a decade, the methods investigating non-invasive techniques have the focus on resting state functional connectivity and effective connectivity of neurological disorders. The findings suggest that the distruptions in the networks have a pattern and sequence [1]. Therefore, in this study, the objective is to carry a pilot study to find a distinctive agent for distinguishing AD patients, mild cognitive impairment (MCI) patients and controls from each other by comparing their functional connectivity networks. In this context, fMRI data of five AD, six MCI patients and five controls (with the mean age of 68) are taken while they are doing an optimized auditory oddball task. The data collection is done with T2* weighted GRE-EPI sequence (1,5T, TR 2400 ms, TE 50 ms, 26 axial slices, 4 mm slice thick, without inter slice gap, matrix 64 × 64, FOV 230 mm) with 275 dynamics (plus two additional dynamics for steady state tissue magnetization). For the high resolution anatomical scan, T1 weighted MPRAGE sequence (voxel size 1.25 × 1.25 × 1.2 mm; 130 slices; FOV 240 mm) is used. In the event related auditory oddball task, the optimization is done for the inter-stimulus inval (mean 2.2 secs and min 1.2 secs). In this study, the preprocessing steps include skull stripping (by FreeSurfer v5.3.0 [2] and BrainSuite v.13a4 [3]), slice timing correction, motion correction, coregistration, normalization and smoothing respectively. All of the steps except skul stripping are done via SPM12b [4] and DARTEL algorithm is chosen for normalization step. In order to achieve the objective, functional connectivity networks are obtained from fMRI data via a group independent component (ICA) approach using temporal concatenation of the subject data by GIFT Toolbox from MIALab [5,6]. For the first step, the initial component number is chosen as thirty by minimum description length criteria and visual inspection. Inside the toolbox, ICASSO algorithm with Extended Infomax is chosen for more stable components whereas GICA3 backreconstruction algorithm is chosen to get the subject related components. As a result, eight different network groups, namely; attentional network, auditory network, cerebellum, default mode network (DMN), frontal network, sensory-motor network, visual network and an unclassifed network are determined by visual inspection of their spatial relatedness with the networks in the literature [6-10]. Then, spatial maps of the components and modulation of the timecourses by the task are compared between the groups statistically. In group comparisons, significant differences among spatial maps of MCI and AD patients are found for small number of voxels ( < 5 voxels) in precuneus region (with FDR correction, p<0.05) favoring the MCI patients. This may suggest that functional connectivity in precuneus region is stronger in MCI patients relative to AD patients and it may be due to tissue physiology and athropy related with the stages of the disease [1]. However, since the number of voxels as well as subjects are scarce, this study should be done on a larger group with a stronger (i.e. 3T) machine in order to get more reliable results. For sensory-motor network and auditory network, MCI group independent components showed stronger modulation for target sounds when compared to standart sounds (p<0.009) relative to control group. Moreover, when compared to AD group, MCI group showed stronger modulation for standart sounds in their attentional network component (p< 0.003). As for the cerebellum component, the control group showed stronger modulation for the target sounds when compared to MCI and AD patients (p< 0.008). Additionally, when the target sounds and their time derivatives are concerned, MCI patients showed stronger modulation relative to AD patients. Currently, there are no modulation by auditory oddball task studies regarding the AD and MCI patients in the literature to our knowledge. But these results have a pattern which can be adressed by the tissue athrophy and compensation for the tissue athrophy mechanisms as stated in [1,11]. In order to get statistically stronger results, the subject number should be increased and a longitudinal study should be carried to confirm the progression of the disease in future studies.

References

[1] M. R. Brier, J. B. Thomas, and B. M. Ances, “Network dysfunction in Alzheimer’s disease: refining the disconnection hypothesis.,” Brain Connect., vol. 4, no. 5, pp. 299–311, Jun. 2014.
[2] Free Surfer, ver. v5.3.0, [Online]. Available: http://surfer.nmr.mgh.harvard.edu/.
[3] Brain Suite, ver. v.13a4, [Online]. Available: http://brainsuite.org/.
[4] Statistical Parametric Mapping, ver. SPM12b, [Online]. Available: http://www.fil.ion.ucl.ac.uk/spm/software/spm12b/.
[5] Group ICA, ver. GroupICATv3.0a, [Online]. Available: http://mialab.mrn.org/software/\#gica.
[6] E. A. Allen, E. B. Erhardt, E. Damaraju, W. Gruner, J. M. Segall, R. F. Silva, M. Havlicek, S. Rachakonda, J. Fries, R. Kalyanam, A. M. Michael, A. Caprihan, J. A. Turner, T. Eichele, S. Adelsheim, A. D. Bryan, J. Bustillo, V. P. Clark, S. W. Feldstein Ewing, F. Filbey, C. C. Ford, K. Hutchison, R. E. Jung, K. A. Kiehl, P. Kodituwakku, Y. M. Komesu, A. R. Mayer, G. D. Pearlson, J. P. Phillips, J. R. Sadek, M. Stevens, U. Teuscher, R. J. Thoma, and V. D. Calhoun, “A baseline for the multivariate comparison of resting-state networks.,” Front. Syst. Neurosci., vol. 5, p. 2, Jan. 2011.
[7] S. M. Smith, P. T. Fox, K. L. Miller, D. C. Glahn, P. M. Fox, C. E. Mackay, N. Filippini, K. E. Watkins, R. Toro, A. R. Laird, and C. F. Beckmann, “Correspondence of the brain’s functional architecture during activation and rest.,” Proc. Natl. Acad. Sci. U. S. A., vol. 106, no. 31, pp. 13040–5, Aug. 2009.
[8] C. F. Beckmann, M. DeLuca, J. T. Devlin, and S. M. Smith, “Investigations into resting-state connectivity using independent component analysis.,” Philos. Trans. R. Soc. Lond. B. Biol. Sci., vol. 360, no. 1457, pp. 1001–13, May 2005.
[9] A. R. Laird, P. M. Fox, S. B. Eickhoff, J. A. Turner, K. L. Ray, D. R. McKay, D. C. Glahn, C. F. Beckmann, S. M. Smith, and P. T. Fox, “Behavioral interpretations of intrinsic connectivity networks.,” J. Cogn. Neurosci., vol. 23, no. 12, pp. 4022–37, Dec. 2011.
[10] J. S. Damoiseaux, S. A. R. B. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam, S. M. Smith, and C. F. Beckmann, “Consistent resting-state networks across healthy subjects.,” Proc. Natl. Acad. Sci. U. S. A., vol. 103, no. 37, pp. 13848–53, Sep. 2006.
[11] K. A. Celone, V. D. Calhoun, B. C. Dickerson, A. Atri, E. F. Chua, S. L. Miller, K. DePeau, D. M. Rentz, D. J. Selkoe, D. Blacker, M. S. Albert, and R. A. Sperling, “Alterations in memory networks in mild cognitive impairment and Alzheimer’s disease: an independent component analysis.,” J. Neurosci., vol. 26, no. 40, pp. 10222–31, Oct. 2006.

Keywords: Alzheimer's disease, Mild Cognitive Impairment, oddball paradigm, fMRI, Independent Component Analysis

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Brain disorders

Citation: Şahin D, Duru AD, Bayram A, Bilgiç B, Demiralp T and Ademoğlu A (2016). Task Related Modulation of Functional Connectivity Networks of Alzheimer’s Disease and Mild Cognitive Impairment Patients. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00087

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 31 May 2016; Published Online: 18 Jul 2016.

* Correspondence: Miss. Duygu Şahin, Bogazici University, Institute of Biomedical Engineering, Istanbul, Türkiye, sahin.duygu.sahin@gmail.com