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Estimation of Intrinsic Connectivity Networks by Multivariate Empirical Mode Decomposition

Year 2019, Volume: 7 Issue: 1, 155 - 161, 15.01.2019
https://doi.org/10.21541/apjes.457360

Abstract

Functional brain mapping is based on electrical and
haemodynamic changes occured in the brain. Blood oxeygenated level dependency
(BOLD) signal can be non-invasively collected through the use of functional
Magnetic Resonance Imaging (fMRI). Brain functional activity can also be
observed in the absence of a given task. These activation patterns are named as
brain resting state networks. The aim of this study is, to perform functional
brain mapping using the coherence metrics between the decomposed BOLD time series
insted of using the raw BOLD time series. Multivariate Emprical mode
decomposition procedure is applied for the BOLD series decomposition. Limited
number of anatomical locations are selected for node positions using anatomical
templates. Further each subseries are used to compute the correlations in
frequency domain as coherence values between the node points. By this way,
spectral properties of subseries are investigated without imposing any a priori
information. FMRI data were collected from 19 volunteers and the preprocessing
steps are applied prior to analysis of spectral properties. Four subcomponents
whose spectral peaks are determined at 0.007 Hz, 0.014 Hz, 0.03 Hz and 0.064 Hz
were determined. In the first component, superior temporal gyrus and occipital
lobe connections were exhibited which contribute to the functionality of the auditory
and visual networks. Posterior and anterior cingulate areas that are the major
parts of the default mode network were found to be present in the second component.
In the third component, nodes of the attention network were observed with a
center frequency of 0.03 Hz to 0.06 Hz. Additionally, connections of superior
temporal gyrus were observed in the fourth component. 

References

  • Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.J. 1993. Functional connectivity: the principal component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5–14.
  • S. Ogawa, T. M. Lee, A. R. Kay ve D. W. Tank, 1990. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. NatI. Acad. Sci., 87(1990), 9868-9872.
  • Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541,
  • H. Littow, A. A. Elseoud, M. Haapea, M. Isohanni, I. Moilanen, K. Mankinen, J. Nikkinen, J. Rahko, H. Rantala, J. Remes, T. Starck, O. Tervonen, J. Veijola, C. Beckmann ve Vesa J. Kiviniemi, 2010. Agerelated differences in functional nodes of the brain cortex – a high model order group ICA study. Frontiers in Systems Neuroscience 4(32)
  • Jeanne Langan, Scott J. Peltier, Jin Bo, Brett W. Fling, Robert C. Welsh ve Rachael D. Seidler, 2010. Functional implications of age differences in motor system connectivity. Frontiers in Systems Neuroscience, 4(17)June 2010
  • Li, S. J., Li, Z., Wu, G., Zhang, M. J., Franczak, M., ve Antuono, P. G. 2002. Alzheimer disease: evaluation of a functional MR imaging index as a marker. Radiology 225, 253–259.
  • Mohammadi, B., Kollewe, K., Samii, A., Krampfl, K., Dengler, R., and Munte,T. F. (2009). Changes of resting state brain networks in amyotrophic lateral sclerosis. Exp. Neurol. 217, 147–153.
  • X. Di, S. Zhu, H. Kin, P. Wang, Z. Ye, K. Zhou, Y. Zhuo and H. Rao Altered Resting Brain Function and Structure in Professional Badminton Players, 2(4), Brain Connectivity, 2012
  • S. Whitfield-Gabrieli ve A. Nieto-Castanon,Conn: A Functional Connectivity Toolbox for Correlatedand Anticorrelated Brain Networks”, Brain Connectivity, Vol 2, No 3, 2012, DOI: 10.1089/brain.2012.0073, 2012.
  • M.J. McKeown, S. Makeig, G.G. Brown, T.P. Jung, S.S. Kindermann, A.J. Bell, T.J. Sejnowski ,1998. Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapp. 1998; 6(3):160-88.
  • T. Zhang, P. Xu, L. Guo, R Chen, R. Zhang, H. He, Q. Xie, T. Liu, C. Luo ve D. Yao,2015. Multivariate empirical mode decomposition based sub-frequency bands analysis of the default mode network: a restingstate fMRI data study. Applied Informatics 2015 2(2) DOI: 10.1186/s40535-014-0005-z,
  • K.J. Friston, J. Ashburner, C.D. Frith, J.B. Poline, J. D. Heather, R.S. Frackowiak. 1995. Spatial registration and normalization of images. Human Brain Mapping. 1995:3:165-18,
  • Blair, R.C. & Karniski, W. (1993) An alternative method for significance testing of waveform difference potentials. Psychophysiology
  • S. R. Gohel ve Bharat B. Biswal, 2015. Functional Integration Between Brain Regions at Rest Occurs in Multiple-Frequency Bands. BRAIN CONNECTIVITY, 5(2015), 23-34.
  • M. N. Baliki, Baliki, A. R. Mansour, A. T. Baria, A. V. Apkarian ,1998. Functional Reorganization of the Default Mode Network across Chronic Pain Conditions. PLoS ONE 9(9): 1061 33.doi:10.1371/journal.pone.0106133 (2014)

Çokdeğişkenli Ampirik Mod Ayrıştırımı ile İçsel Bağlantı Ağları Kestirimi

Year 2019, Volume: 7 Issue: 1, 155 - 161, 15.01.2019
https://doi.org/10.21541/apjes.457360

Abstract

Beyin fonksiyonlarının
haritalanması, elektriksel aktivite ve hemodinamik bilgiler ışığında
gerçekleştirilebilmektedir. Kanın oksijenlenmesine bağıl (BOLD) sinyali  girişimsel olmayacak şekilde fonksiyonel
manyetik rezonans görüntülemesi (fMRG) ile elde edilebilmektedir. Herhangi bir
mental görev gerçekleştirilmediği esnada bile beyin bölgelerinde aktivasyonlar
izlenebilmektedir. Bu aktivasyon izgelerine dinlenim durumu beyin ağları  adı verilmektedir. Bu çalışmanın amacı, BOLD
zaman serilerinin doğrudan kendilerini kullanmak yerine, dekompoze edimesi ile
elde edilen alt zaman serilerinin birbirleri arasındaki koherans bilgisine
dayanarak haritalama işlemi yapmaktır. Ayrıştırma işlemi için çokdeğişkenli
ampirik mod dekompozisyonu kullanılmıştır (MEMD). Beyin dokusunda sınırlı
sayıda düğüm bölgesi anatomik şablonlar yardımı ile belirlenmiştir. Belirlenen
anatomik bölgelere ait zaman serileri girdi olarak kullanılmıştır. Her düğüm
alt zaman serisinin diğer düğümlerin alt zaman serileri arasındaki koherans
bilgisi hesaplanarak, frekans alanında korelasyonlar belirlenmiştir.  Böylece, herhangi bir önbilgi empoze
edilmeden BOLD zaman serisinin alt bileşenlerinin spektral özelliklerinin
incelenmesi sağlanmıştır.19 gönüllüden alınan dinlenim durumu fMRG verisi  önişleme tekniklerinin uygulanmasından sonra
analiz edilerek spektral özellikleri incelenmiştir. Elde edilen dört farklı
bileşenin zaman serilerinin spektral özellikleri 0.007, 0.014, 0.03 ve 0.064
Hz frekanslarında tepe değerler almıştır. Birinci bileşende işitsel
fonksiyonların  ve görsel işlevlerin yürütülmesinde
rol oynayan süperiyor temporal gyrus ve oksipital bağlantılar, ikinci bileşende
varsayılan kip ağının önemli bileşenleri olan posteriyor ve anteriyor singulat
izlenmişir. Üçüncü bileşende 0.03 Hz ile 0.06 Hz civarında dikkat ağının
düğümleri gözlenmiştir. Dördüncü bileşende ise superiyor temporal girus
bağlantıları baskın olarak izlenmiştir.

References

  • Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.J. 1993. Functional connectivity: the principal component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5–14.
  • S. Ogawa, T. M. Lee, A. R. Kay ve D. W. Tank, 1990. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. NatI. Acad. Sci., 87(1990), 9868-9872.
  • Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541,
  • H. Littow, A. A. Elseoud, M. Haapea, M. Isohanni, I. Moilanen, K. Mankinen, J. Nikkinen, J. Rahko, H. Rantala, J. Remes, T. Starck, O. Tervonen, J. Veijola, C. Beckmann ve Vesa J. Kiviniemi, 2010. Agerelated differences in functional nodes of the brain cortex – a high model order group ICA study. Frontiers in Systems Neuroscience 4(32)
  • Jeanne Langan, Scott J. Peltier, Jin Bo, Brett W. Fling, Robert C. Welsh ve Rachael D. Seidler, 2010. Functional implications of age differences in motor system connectivity. Frontiers in Systems Neuroscience, 4(17)June 2010
  • Li, S. J., Li, Z., Wu, G., Zhang, M. J., Franczak, M., ve Antuono, P. G. 2002. Alzheimer disease: evaluation of a functional MR imaging index as a marker. Radiology 225, 253–259.
  • Mohammadi, B., Kollewe, K., Samii, A., Krampfl, K., Dengler, R., and Munte,T. F. (2009). Changes of resting state brain networks in amyotrophic lateral sclerosis. Exp. Neurol. 217, 147–153.
  • X. Di, S. Zhu, H. Kin, P. Wang, Z. Ye, K. Zhou, Y. Zhuo and H. Rao Altered Resting Brain Function and Structure in Professional Badminton Players, 2(4), Brain Connectivity, 2012
  • S. Whitfield-Gabrieli ve A. Nieto-Castanon,Conn: A Functional Connectivity Toolbox for Correlatedand Anticorrelated Brain Networks”, Brain Connectivity, Vol 2, No 3, 2012, DOI: 10.1089/brain.2012.0073, 2012.
  • M.J. McKeown, S. Makeig, G.G. Brown, T.P. Jung, S.S. Kindermann, A.J. Bell, T.J. Sejnowski ,1998. Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapp. 1998; 6(3):160-88.
  • T. Zhang, P. Xu, L. Guo, R Chen, R. Zhang, H. He, Q. Xie, T. Liu, C. Luo ve D. Yao,2015. Multivariate empirical mode decomposition based sub-frequency bands analysis of the default mode network: a restingstate fMRI data study. Applied Informatics 2015 2(2) DOI: 10.1186/s40535-014-0005-z,
  • K.J. Friston, J. Ashburner, C.D. Frith, J.B. Poline, J. D. Heather, R.S. Frackowiak. 1995. Spatial registration and normalization of images. Human Brain Mapping. 1995:3:165-18,
  • Blair, R.C. & Karniski, W. (1993) An alternative method for significance testing of waveform difference potentials. Psychophysiology
  • S. R. Gohel ve Bharat B. Biswal, 2015. Functional Integration Between Brain Regions at Rest Occurs in Multiple-Frequency Bands. BRAIN CONNECTIVITY, 5(2015), 23-34.
  • M. N. Baliki, Baliki, A. R. Mansour, A. T. Baria, A. V. Apkarian ,1998. Functional Reorganization of the Default Mode Network across Chronic Pain Conditions. PLoS ONE 9(9): 1061 33.doi:10.1371/journal.pone.0106133 (2014)
There are 15 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Adil Deniz Duru 0000-0003-3014-9626

Publication Date January 15, 2019
Submission Date September 4, 2018
Published in Issue Year 2019 Volume: 7 Issue: 1

Cite

IEEE A. D. Duru, “Çokdeğişkenli Ampirik Mod Ayrıştırımı ile İçsel Bağlantı Ağları Kestirimi”, APJES, vol. 7, no. 1, pp. 155–161, 2019, doi: 10.21541/apjes.457360.