An Attempt to Correlate the Activation of Resting State Network with Behavioral Data during Virtual Object Transfer Task Performance
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1
National Center for Geriatrics and Gerontology, Neuroimaging & Informatics, Japan
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2
Nanyang Technological University, Division of Psychology, School of Humanities and Social Sciences, Singapore
Introduction
Resting state networks (RSN) are detected as functional connectivity of spontaneous low frequency fluctuations (< 0.1 Hz) in the BOLD signal during ‘rest status’, i.e. the subjects are not performing any task but they are awake. Several RSNs, which are supposed to be analogues of task related networks, have been identified, such as default mode network (DMN), motor network, somatosensory network, auditory network, language network, and so on [1]. Several studies reported dependency of activation in resting state networks on aging [2 - 4]. Given that a population of older adults will provide more variability and heterogeneity due to differences in physical and cognitive status as the results of their backgrounds and histories, it will be pertinent to examine if we can obtain a more sensitive index with specific RSN map corresponding to the neurological target of interest. One approach to extract age-related change of RSN will be classification of the subjects by using some behavioral data. In this study, we evaluated the effect of different phases of behavioral performance data included in one complex processing on the RSN activity detection.
Material and Methods
Neurologically healthy 24 older (61 - 75, 12 females) and 23 young (20 - 36, 12) adult volunteers who gave written informed consent participated in this study. The older volunteers were recruited from a community club. As the reference behavioral data, a virtual bean transfer task using turnkeys [5], which simulates one of the physical batteries for older adults was used. This task consisted of three serial operations; 1) a small, round object (bean) on the left (Lt) side of the visual field appears and the subjects hold them with two sticks, 2) the object is moved with the sticks toward a red round target (a pot) on the right (Rt) side, 3) finally, it is dropped into the target by releasing the sticks. The subjects repeated this operation for 6 minutes. For resting state fMRI, the subjects kept their eyes open and fixed their eyes to the cross hair displayed on the LCD monitor for 7 minutes.
The functional magnetic resonance imaging (fMRI) data were obtained using a GRE-EPI sequence (3T, TR 3000 ms, TE 30 ms, 39 axial slices, 3 mm slice thick, 0.75 mm inter slice gap, matrix 64x64, FOV 192 mm). T1 weighted 3D images were obtained for anatomical reference. The functional images were pre-processed (slice-time adjusted, realigned, normalized and smoothed) with SPM8 (Wellcome Trust Centre for Neuroimaging, UCL, London). For RSN analysis, 25 independent components (ICs) were obtained using GIFT toolbox. Behavioral data were obtained from the turnkey log during the steps described above. The success rate of holding the object and transferring it throughout the session was obtained as an index representing difficulty of task performance for each subject. The resultant T-statistics maps of the ICS for each subject were processed using SPM8 for 2nd level analysis (p<0.001, uncorrected).
Results
The correlation between the success rate of holding and that of transfer were r = 0.56 in the older adults and r = 0.71 in the young adults group. The following differences of RSN were detected by employing the success rates of these two operations as covariates in the 2nd level analysis of IC maps.
1) Dorsal default mode network (DMN): Lt caudate head (T = 5.5) by holding covariate (HC); Lt BA7 (T = 4.8) by transfer covariate (TC) in the contrast of older – younger (E - Y). Lt caudate body (4.4) by TC, but no significant differences by HC for the contrast of young vs elderly (Y > E).
2) Ventral DMN: Lt BA40 (T = 6.1) and Lt Putamen (T = 4.7) by HC; Lt caudate body (T = 5.2) and Rt caudate tail (T = 4.5) in E > Y. Rt BA7 (T = 4.4) by HC; Lt BA7 (T = 4.7) by TC in Y > E.
3) Sensorimotor network (SMN): Rt caudate head (T = 4.6) and Rt BA6 (T = 4.1) by HC; Rt BA31 (T = 4.8) and Rt BA33 (T = 4.5) by TC in Y > E. Rt BA39 (T = 4.5) and Rt Putamen (T = 4.4) by HC; Lt BA3 (T = 5.7) and Rt BA4 (T = 4.9) by TC in Y > E.
4) Posterior DMN: No significant differences.
Conclusion
In this analysis, we focused on the RSNs of DMN and SMN. It was suggested that the activity of these RSNs may partially correlate with segmented behavioral data. Although the functional structure of DMN is considered to be heterogeneous and its role is still controversial, its relationship with attention and executive function has been suggested [6]. Since the trials were randomly started after a short rest, which was modulated by jittering, this task demands both attention for starting the operations and serial switching among the 3 operations. Therefore, we hypothesized that age-related decline of these functions may be reflected to the analogue RSN activity. The age-related decline observed in the DMN and increase in SMN was compatible with previous reports [3]. By introducing covariates representing performance level of different cognitive steps but continuously performed towards one goal, age-related change of RSN activity could be differentially characterized in several nodes, although such difference was not detected in the posterior node of DMN. Although this study was cross-sectional in nature, it potentially suggested that classification of RSN activity in the older adults may reflect the performance level.
Acknowledgements
This study was supported by the JSPS-NTU Research and Development grant, under the Japan-Singapore Research Corporative Program, FY 2014-2015 and by Grant-in-Aid for Scientific Research (KAKENHI) #25560383.
References
[1] Shirer WR et al., Cerebral Cortex, 158-, 2012
[2] Andrews-Hanna JR et al., Neuron 56, 924-, 2007
[3] Chen ASH et al., ISMICT 2013, 218-, 2013, doi: 10.1109/ISMICT.2013.6521732
[4] Vidal-Pineiro D et al., Front Aing Neurosci 6, 256, 2014, doi: 10.3389/fnagi.2014.00256
[5] Nakai T et al., Neuro Informatics 2014, Leiden, doi: 10.3389/conf.fninf.2014.18.00012
[6] Fornito A et al., ProNAS 109, 12788-, 2012
Keywords:
resting state networks,
fMRI,
motor control,
Aging,
cognitive decline
Conference:
Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015.
Presentation Type:
Poster, to be considered for oral presentation
Topic:
Neuroimaging
Citation:
Nakai
T,
Kunimi
M,
Kiyama
S,
Tanaka
A and
Chen
S
(2015). An Attempt to Correlate the Activation of Resting State Network with Behavioral Data during Virtual Object Transfer Task Performance.
Front. Neurosci.
Conference Abstract:
Neuroinformatics 2015.
doi: 10.3389/conf.fnins.2015.91.00066
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Received:
06 Apr 2015;
Published Online:
05 Aug 2015.
*
Correspondence:
Prof. Toshiharu Nakai, National Center for Geriatrics and Gerontology, Neuroimaging & Informatics, Ohbu, Aichi, 474-8511, Japan, nakai.iniinf@gmail.com