Abstract
In recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.
References
Adeli, H. and Hung, S.L. (1995). Machine Learning – Neural Networks, Genetic Algorithms, and Fuzzy Systems (New York: John Wiley and Sons).Search in Google Scholar
Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2008). A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Neurosci. Lett. 444, 190–194.10.1201/9781439815328-c12Search in Google Scholar
Ahmadlou, M. and Adeli, H. (2010a). Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD. Clin. EEG Neurosci. 41, 1–10.10.1177/155005941004100103Search in Google Scholar PubMed
Ahmadlou, M. and Adeli, H. (2010b). Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr. Comput. Aided Eng. 17, 197–210.10.3233/ICA-2010-0345Search in Google Scholar
Ahmadlou, M., Adeli, H., and Adeli, A., (2010a). New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J. Neural Transm. 117, 1099–1109.10.1007/s00702-010-0450-3Search in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2010b). Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J. Clin. Neurophysiol. 27, 328–333.10.1097/WNP.0b013e3181f40dc8Search in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2012a). Fractality analysis of frontal brain in major depressive disorder. Int. J. Psychophysiol. 85, 206–211.10.1016/j.ijpsycho.2012.05.001Search in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2012b). Improved visibility graph fractality with application for diagnosis of autism spectrum disorder. Phys. A Stat. Mech. Appl. 391, 4720–4726.10.1016/j.physa.2012.04.025Search in Google Scholar
Almeida, S.R.M., Vicentini, J., Bonilha, L., Campos, B.M.D., Casseb, R.F., and Min, L.L. (2016). Brain connectivity and functional recovery in patients with ischemic stroke. J. NeuroImaging doi: 10.1111/jon.12362.Search in Google Scholar PubMed
Alsady, M., Blessing, E.M., and Beissner, F. (2016). MICA-A toolbox for masked independent component analysis of fMRI Data. Hum. Brain Mapp. 37, 3544–3556.10.1002/hbm.23258Search in Google Scholar PubMed PubMed Central
Arbel, T. and Nigris, D. (2015). Fast and efficient image registration based on gradient orientations of minimal uncertainty. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, pp. 1163–1166.10.1109/ISBI.2015.7164079Search in Google Scholar
Ashby, F.G. (2011). Preprocessing. Statistical Analysis of MRI Data (Cambridge, MA: MIT Press).10.7551/mitpress/8764.001.0001Search in Google Scholar
Bannister, L.C., Crewther, S.G., Gavrilescu, M., and Carey, L.M. (2015). Improvement in touch sensation after stroke is associated with resting functional connectivity changes. Front. Neurol. 6, 156.10.3389/fneur.2015.00165Search in Google Scholar
Bartes-Serrallong, M., Serra-Grabulosa, J.M., Adan, A., Falcon, C., Bargalló, N., and Solé-Casals, J. (2015) Smoothing FMRI data using an adaptive wiener filter. Comput. Intell. 557, 321–332.10.1007/978-3-319-11271-8_21Search in Google Scholar
Basser, P.J., Mattiello, J., and LeBihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267.10.1016/S0006-3495(94)80775-1Search in Google Scholar
Beckmann, C. and Smith, S.M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23, 137– 152.10.1109/TMI.2003.822821Search in Google Scholar PubMed
Beissner, F., Schumann, A., Brunn, F., Eisenträger, D., and Bär, K.J. (2014). Advances in functional magnetic resonance imaging of the human brainstem. NeuroImage, 86, 91–98.10.1016/j.neuroimage.2013.07.081Search in Google Scholar PubMed
Bell, A.J. and Sejnowski T.J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159.10.1162/neco.1995.7.6.1129Search in Google Scholar PubMed
Bhat, S., Acharya, U.R., Adeli, H., Muralidhar Bairy, G.M., and Adeli, A. (2014a). Automated diagnosis of autism: in search of mathematical markers. Rev. Neurosci. 25, 851–861.10.1515/revneuro-2014-0036Search in Google Scholar PubMed
Bhat, S., Acharya, U.R., Adeli, H., Muralidhar Bairy, G.M., and Adeli, A. (2014b). Autism: cause factors, early diagnosis and therapies. Rev. Neurosci. 25, 841–850.10.1515/revneuro-2014-0056Search in Google Scholar PubMed
Biswal, B., Yetkin, F.Z., Haughton, V.M., and Hyde, J.S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541.10.1002/mrm.1910340409Search in Google Scholar PubMed
Borstad, A.L., Choi, S., Schmalbrock, P., and Nichols-Larsen, D.S. (2016). Frontoparietal white matter integrity predicts haptic performance in chronic stroke. Neuroimage Clin. 10, 129–139.10.1016/j.nicl.2015.11.007Search in Google Scholar
Bressler, S.L. and Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14, 277–290.10.1016/j.tics.2010.04.004Search in Google Scholar
Bullmore, E.D. and Sporns, O. (2007). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. 10, 186–198.10.1038/nrn2575Search in Google Scholar
Burel, G. (1992). Blind separation of sources: a non-linear neural algorithm. Neural Net. 5, 937–947.10.1016/S0893-6080(05)80090-5Search in Google Scholar
Calhoun, V.D., Golay, X., and Pearlson, G. (2000). Improved fMRI Slice Timing Correction: Interpolation Errors and Wrap around Effects. International Society for Magnetic Resonance in Medicine; Denver, Colorado.Search in Google Scholar
Calhoun, V.D., Adali, T., Hansen, L.K., Larsen, J., Pekar, J.J. (2003). ICA of Functional MRI Data: An Overview”; 4th International Symposium on ICA and Blind Separation; Nara, Japan, pp. 281–288.Search in Google Scholar
Carrillo-Reid, L., Lopez-Huerta, V.G., Garcia-Munoz, M., Theiss, S., and Arbuthnott, G.W. (2015). Cell assembly signatures defined by short-term synaptic plasticity in cortical networks. Int. J. Neural. Syst. 25, 1550026.10.1142/S0129065715500264Search in Google Scholar PubMed
Carter, A.R., Astafiev, S.V., Lang, C.E., Connor, L.T., Rengachary, J., Strube, M.J., Pope, D.L., Shulman, G.L., and Corbetta, M. (2010). Resting inter-hemispheric fMRI connectivity predicts performance after stroke. Ann. Neurol. 67, 365–375.10.1002/ana.21905Search in Google Scholar PubMed PubMed Central
Carter, A.R., Shulman, G.L., and Corbetta, M. (2012). Why use a connectivity-based approach to study stroke and recovery of function? Neuroimage 62, 2271–2280.10.1016/j.neuroimage.2012.02.070Search in Google Scholar PubMed PubMed Central
Chen, J.L. and Schlaug, G. (2016). Increased resting state connectivity between ipsilesional motor cortex and contralesional premotor cortex after transcranial direct current stimulation with physical therapy. Sci. Rep. 6, 23271.10.1038/srep23271Search in Google Scholar PubMed PubMed Central
Chen, Z., Ni, P., Zhang, J., Ye, Y., Xiao, H., Qian, G., Xu, S., Wang, J., Yang, X., Chen, J., et al. (2008). Evaluating ischemic stroke with diffusion tensor imaging. Neurol. Res. 30, 720–726.10.1179/174313208X297968Search in Google Scholar PubMed
Chen, Y., Mittelman, R., Kim, B., Meyer, C., and Hero, A. (2016). Particle filtering for slice-to-volume motion correction in EPI based functional MRI. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 679–683.10.1109/ICASSP.2016.7471761Search in Google Scholar
Chung, F. (1997) Spectral Graph Theory. Washington: Conference Board of the Mathematical Sciences.10.1090/cbms/092Search in Google Scholar
Chyzhyk, D., Graña, M., Ongur, D., and Shinn, A.K. (2015) Discrimination of schizophrenia auditory hallucinators from never hallucinators through machine learning of resting-state functional MRI. Int. J. Neural. Syst. 25, 1550007.10.1142/S0129065715500070Search in Google Scholar PubMed PubMed Central
Coletta, L.F.S., Hruschka, E.R., Acharya, A., and Ghosh, J. (2015). Using metaheuristics to optimize the combination of classifier and cluster ensembles. Integr. Comput. Aided Eng. 22, 229–242.10.3233/ICA-150485Search in Google Scholar
Collins, D.L., Neelin, P., Peters, T.M., and Evans, A.C. (1994). Automatic 3D intersubject registration of MR columetric data in standardized talairach space. J. Comput. Assist. Tomogr. 18, 192–205.10.1097/00004728-199403000-00005Search in Google Scholar
CONN toolbox, available at https://www.nitrc.org/projects/conn, Access Date: August 2016.Search in Google Scholar
Corbetta, M., Kincade, M.J., Lewis, C., Snyder, A.Z., and Sapir, A. (2005). Neural basis and recovery of spatial attention deficits in spatial neglect. Nat. Neurosci. 8, 1603–1610.10.1038/nn1574Search in Google Scholar PubMed
Cordes, D., Haughton, V.M., Arfanakis, K., Wendt, G.J., Turski, P. A., Moritz, C.H., Quigley, M.A., and Meyerand, M.E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. Am. J. Neuroradiol. 21, 1636–1644.Search in Google Scholar
Correa, N., Adali, T., Li, Y.O., and Calhoun, V.D. (2005). Comparison of blind source separation algorithms for fMRI using a new Matlab toolbox: GIFT. IEEE International Conference on Acoustics, Speech, and Signal (ICASSP’05), 5, 401–404.10.1109/ICASSP.2005.1416325Search in Google Scholar
Correas, A., Rodriguez Holguín, S., Cuesta, P., López-Caneda, E., García-Moreno, L.M., Cadaveira, F., and Maestú, F. (2015). Exploratory analysis of power spectra and functional connectivity during resting state in young binge drinkers: a magnetoencephalography study. Int. J. Neural. Syst. 25, 1550008.10.1142/S0129065715500082Search in Google Scholar PubMed
Craddoc, R.C., James, G.A., Holtzheimer, P.E., Hu, X.P., and Mayberg, H. (2012). A whole brain FMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914–1928.10.1002/hbm.21333Search in Google Scholar PubMed PubMed Central
Crinion, J. and Price, C.J. (2005). Right anterior superior temporal activation predicts auditory sentence comprehension following aphasic stroke. Brain 128, 2858–2871.10.1093/brain/awh659Search in Google Scholar PubMed
Crofts, J.J. and Higham, D.J. (2009). A weighted communicability measure applied to complex brain networks. J. R. Soc. Interface 6, 411–414.10.1098/rsif.2008.0484Search in Google Scholar PubMed PubMed Central
Crofts, J.J., Higham, D.J., Bosnell, R., Jbabdi, S., Matthews, P.M., Behrens, T.E. and Johansen-Berg, H. (2011). Network analysis detects changes in the contralesional hemisphere following stroke. Neuroimage 45, 161–169.10.1016/j.neuroimage.2010.08.032Search in Google Scholar PubMed PubMed Central
Dacosta-Aguayo, R., Grana, M., Savio, A., Fernandez-Andujar, M., Millán, M., López-Cancio, E., Cáceres, C., Bargalló, N., Garrido, C., Barrios, M., Clemente, I.C., et al. (2014). Prognostic value of changes in resting-state functional connectivity patterns in cognitive recovery after stroke: a 3T fMRI pilot study. Hum. Brain Mapp. 35, 3819–3831.10.1002/hbm.22439Search in Google Scholar PubMed PubMed Central
Dacosta-Aguayo, R., Grana, M., Iturria-Medina, Y., Fernández-Andújar, M., López-Cancio, E., Cáceres, C., Bargalló, N., Barrios, M., Clemente, I., Toran, P., et al. (2015). Impairment of functional integration of the default mode network correlates with cognitive outcome at three months after stroke. Hum. Brain Mapp. 36, 577–590.10.1002/hbm.22648Search in Google Scholar PubMed PubMed Central
Dai, H., Wang, W., Zhang, H. (2015). A multiwavelet neural network-based response surface method for structural reliability analysis. Comput.-Aided Civil Infrastruct. Eng. 30, 151–162.10.1111/mice.12086Search in Google Scholar
Damoiseaux, J.S., Rombouts, S.A.R.B., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M., and Beckmann, C.F. (2006). Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. USA 103, 13848–13853.10.1073/pnas.0601417103Search in Google Scholar PubMed PubMed Central
Donnarumma, F., Prevete, R., Chersi, F., and Pezzulo, G. (2015). A Programmer-interpreter neural network architecture for prefrontal cognitive control. Int. J. Neural. Syst. 25, 1550017.10.1142/S0129065715500173Search in Google Scholar PubMed
Estrada, E. and Hatano, N. (2008). Communicability in complex networks. Phys. Rev. E 77, 036 111.10.1103/PhysRevE.77.036111Search in Google Scholar PubMed
Evans, A.C., Collins, D.L., Mills, S.R., Brown, E.D., Kelly, R.L., and Peters, T.M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. Proceedings of IEEE-Nuclear Science Symposium and Medical Imaging Conference, San Francisco, 1813–1817.10.1109/NSSMIC.1993.373602Search in Google Scholar
Ferdowsi, S., Sanei, S., and Abolghasemi, V. (2015). A predictive modeling to analyze data in EEG-fMRI experiments. Int. J. Neural. Syst. 25, 1440008.10.1142/S0129065714400085Search in Google Scholar PubMed
Ferrazzi, G., Nunes, R.G., Arichi, T., Gaspar, A.S., Barone, G., Allievi, A., Vasylechko, S., Abaei, M., Hughes, E., Rueckert D., et al. (2016). An exploration of task based fMRI in neonates using echo-shifting to allow acquisition at longer TE without loss of temporal efficiency. Neuroimage, 127, 298–306.10.1016/j.neuroimage.2015.12.025Search in Google Scholar PubMed
Fowlkes, C, Belongie, S., Chung, F., and Malik, J. (2004). Spectral grouping using the Nystrom method. IEEE Trans. Pattern Anal. Mach. Intell. 26, 214–225.10.1109/TPAMI.2004.1262185Search in Google Scholar PubMed
Fox, M.D., Corbetta, M., Snyder, A.Z., Vincent, J.L., and Raichie, M.E. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl. Acad. Sci. USA 103, 10046–10051.10.1073/pnas.0604187103Search in Google Scholar PubMed PubMed Central
Fransson, P., Skiöld, B., Horsch, S., Nordell, A., Blennow, M., Lagercrantz, H., and Aden, U. (2007). Resting-state networks in infant brain. Proc. Natl. Acad. Sci. USA 104, 15531–15536.10.1073/pnas.0704380104Search in Google Scholar PubMed PubMed Central
Fransson, P., Aden, U., Blennow, M., and Lagercrantz, H. (2011). The functional architecture of the infant brain as revealed by resting-state fMRI. Cereb. Cortex 21, 145–154.10.1093/cercor/bhq071Search in Google Scholar PubMed
Friston, K.J. (2011). Functional and effective connectivity: a review. Brain Connect. 1, 13–36.10.1089/brain.2011.0008Search in Google Scholar PubMed
Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., and Frackowiak, R.S.J. (1995). Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210.10.1002/hbm.460020402Search in Google Scholar
Gauthier, L.V., Taub, E., Perkins, C., Ortmann, M., Mark, V.W., and Uswatte, G. (2008). Remodeling the brain plastic structural brain changes produced by different motor therapies after stroke. Stroke 39, 1520–1525.10.1161/STROKEAHA.107.502229Search in Google Scholar PubMed PubMed Central
Georgiev, P. and Cichocki, A. (2001). Blind Source separation via symmetric Eigenvalue decomposition. Sixth International, Symposium on Signal Processing and its Applications, 1, 17–20.10.1109/ISSPA.2001.949764Search in Google Scholar
Ghodrati Amiri, G., Abdolahi Rad, A., and Khanmohamadi Hazaveh, N. (2014). Wavelet based method for generating non-stationary artificial pulse-like near-fault ground motions. Comput.-Aided Civil Infrastruct. Eng. 29, 758–770.10.1111/mice.12110Search in Google Scholar
Ghosh-Dastidar, S., Adeli, H., and Dadmehr, N. (2006). Voxel-based morphometry in Alzheimer’s patients. J. Alzheimers Dis. 10, 445–447.10.3233/JAD-2006-10414Search in Google Scholar PubMed
Ghosh-Dastidar, S., Adeli, H., and Dadmehr, N. (2008). Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Bio Med. Eng. 55, 512–518.10.1109/TBME.2007.905490Search in Google Scholar PubMed
Grefkes, C. and Fink, G.R. (2011). Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain 134, 1264–1276.10.1093/brain/awr033Search in Google Scholar PubMed PubMed Central
Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 21, 424–430.10.1097/WCO.0b013e328306f2c5Search in Google Scholar PubMed
Greicius, M.D., Supekar, K., Menon, V., Dougherty, and R.F. (2009). Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 16, 72–78.10.1093/cercor/bhn059Search in Google Scholar PubMed PubMed Central
Guo, Y. (2008). Group independent component analysis of multi-subject fMRI data: connections and distinctions between two methods. 2008 International Conference on BioMedical Engineering and Informatics, Sanya, 748–752.10.1109/BMEI.2008.191Search in Google Scholar
Gur, R.E. and Gur, R.C. (2010). Functional magnetic resonance imaging in schizophrenia. Dialogues Clin. Neurosci. 12, 333–343.10.31887/DCNS.2010.12.3/rgurSearch in Google Scholar
Halai, A.D., Woollams, A.M., and Lambon Ralph, M.A. (2016). Using principal component analysis to capture individual differences within a unified neuropsychological model of chronic post-stroke aphasia: revealing the unique neural correlates of speech fluency, phonology and semantics. Cortex, in press.10.1016/j.cortex.2016.04.016Search in Google Scholar PubMed PubMed Central
Henson, R., Buechel, C., Josephs, O., and Friston, K. (1999). The slice-timing problem in event-related fMRI. Neuroimage 9, 125.Search in Google Scholar
Heuvel, M.P.V.D. and Pol, H.E.H. (2010). Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20, 519–534.10.1016/j.euroneuro.2010.03.008Search in Google Scholar PubMed
Hill, D.L.G., Batchelor, P.H., Holden, M., and Hawkes, D.J. (2001). Medical image registration. Phys. Med. Biol. 46, R1–R45.10.1088/0031-9155/46/3/201Search in Google Scholar PubMed
Horowitz-Kraus, T., DiFrancesco, M., Kay, B., Wang, Y., and Holland, S.K. (2015). Increased resting-state functional connectivity of visual- and cognitive-control brain networks after training in children with reading difficulties. Neuroimage Clin. 8, 619–630.10.1016/j.nicl.2015.06.010Search in Google Scholar PubMed PubMed Central
Hsu, W.Y. (2015). Assembling a multi-feature EEG classifier for left-right motor data using wavelet-based fuzzy approximate entropy for improved accuracy. Int. J. Neural. Syst. 25, 1550037.10.1142/S0129065715500379Search in Google Scholar
Hung, S.L. and Adeli, H. (1993). Parallel backpropagation learning algorithms on Cray Y-MP8/864 supercomputer. Neurocomputing 5, 287–302.10.1016/0925-2312(93)90042-2Search in Google Scholar
Jiang, D., Du, Y., Cheng, H., Jiang, T., and Fan, Y. (2013a). Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns. Neuroimage 82, 355–372.10.1016/j.neuroimage.2013.05.093Search in Google Scholar
Jiang, Q., Zhang, Z.G., and Xhopp, M. (2013b). MRI of stroke recovery. Stroke 41, 410–414.10.1161/STROKEAHA.109.568048Search in Google Scholar
Jo, H.J., Saad, Z.S., Simmons, W.K., Milbury, L.A., and Cox, R.W. (2010). Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage 52, 571–582.10.1016/j.neuroimage.2010.04.246Search in Google Scholar
Jo, H.J., Gotts, S.J., Reynolds, R.C., Bandettini, P.A., Martin, A., Cox, R.W., and Saad, Z.S. (2013). Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. J. Appl. Math. 2013, 9.10.1155/2013/935154Search in Google Scholar
Joel, E.S., Caffo, B.S., Zijl, P.C.V., and Pekar, J. (2011). On the relationship between seed-based and ICA-based measures of functional connectivity. Magn. Reson. Med. 66, 644–657.10.1002/mrm.22818Search in Google Scholar
Jutten, C. and Herault, J. (1988). Une Solution Neuromimétique au Problème de Séparation de Sources. Traitement du Signal 5, 389–403.Search in Google Scholar
Jutten, C. and Herault, J. (1991). Blind separation of sources, part I: an adaptive algorithm based on a neuromimetic architecture. Signal Processing 24, 1–10.10.1016/0165-1684(91)90079-XSearch in Google Scholar
Kahan, J., Urner, M., Moran, R., Flandin, G., Marreiros, A., Mancini, L., White, M., Thornton, J., Yousry, T., Zrinzo, L., et al. (2014). Resting state functional MRI in Parkinson’s disease: the impact of deep brain stimulation on ‘effective’ connectivity. Brain 137, 1130–1144.10.1093/brain/awu027Search in Google Scholar PubMed PubMed Central
Kana, R.K., Uddin, L.Q., Kenet, T., Chugani, D., and Muller, R.A. (2014). Brain connectivity in autism. Front. Hum. Neurosci. 8, 349.10.3389/978-2-88919-282-3Search in Google Scholar
Kantak, S.S., Zahedi, N., and McGrath, R.L. (2016). Task-dependent bimanual coordination after stroke: relationship with sensorimotor impairments. Arch. Phys. Med. Rehabil. 97, 798–806.10.1016/j.apmr.2016.01.020Search in Google Scholar PubMed
Kasiri, K., Clausi, D.A., and Fieguth, P. (2014). Multi-Modal image registration using structural features. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5550–5553.10.1109/EMBC.2014.6944884Search in Google Scholar PubMed
Khazaee, A., Ebrahimzadeh, A., and Babajani-Feremi, A. (2015). Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin. Neurophysiol. 126, 2132–2141.10.1016/j.clinph.2015.02.060Search in Google Scholar PubMed
Khullar, S., Michael, A.M., Cahill, N.D., Kiehl, K.A., Pearlson G., Baum, S.A. and Calhoun, V.D. (2011). ICA-fNORM: spatial normalization of fMRI data using intrinsic group-ICA networks. Front. Syst. Neurosci. 5, 1–18.10.3389/fnsys.2011.00093Search in Google Scholar PubMed PubMed Central
Kiebel, S.J., Kloppel, S., Weiskopf, N., and Friston, K.J. (2007). Dynamic causal modeling: a generative model of slice timing in fMRI. Neuroimage 34, 1487–1496.10.1016/j.neuroimage.2006.10.026Search in Google Scholar PubMed
Kielar, A., Deschamps, T., Chu, R.K.O., Jokel, R., Khatamian, Y.B., Chen, J.J., and Meltzer, J.A. (2016). Identifying dysfunctional cortex: dissociable effects of stroke and aging on resting state dynamics in MEG and fMRI. Front. Aging Neurosci. 8, 40.10.3389/fnagi.2016.00040Search in Google Scholar PubMed PubMed Central
Koldovsky, Z., Tichavsky, P., and Oja, E. (2006). Efficient variant of algorithm FastICA for independent component analysis attaining the Cramér-Rao lower bound. IEEE T Neural. Networ. 17, 1265–1277.10.1109/SSP.2005.1628758Search in Google Scholar
Koldewyn, K., Yendiki, A., Weigelt, S., Gweon, H., Julian, J., Richardson, H., , Malloy, C., Saxe, R., Fischl, B., Kanwisher, N., et al. (2014). Differences in the right inferior longitudinal fasciculus but no general disruption of white matter tracts in children with autism spectrum disorder. Proc Natl Acad Sci USA, 111, 1981–1986.10.1073/pnas.1324037111Search in Google Scholar PubMed PubMed Central
Laney, J., Adali, T., Waller, S.M., and Westlake, K.P. (2015). Quantifying motor recovery after stroke using independent vector analysis and graph-theoretical analysis. Neuroimage Clin. 8, 298–304.10.1016/j.nicl.2015.04.014Search in Google Scholar PubMed PubMed Central
Langs, G., Golland, P., Tie, Y., Rigolo, L., and Golby, A. (2010). Functional geometry alignment and localization of brain areas. Adv. Neural. Inf. Process. Syst. 1, 1225–1233.Search in Google Scholar
Lee, M.H., Smyser, C.D., and Shimony, J.S. (2013). Resting-state fMRI: a review of methods and clinical applications. Am. J. Neuroradiol. 34, 1866–1872.10.3174/ajnr.A3263Search in Google Scholar PubMed PubMed Central
Lee, J., Lee, M., and Kim, Y.H. (2015). Functional reorganization and prediction of motor recovery after a stroke: a graph theoretical analysis of functional networks. Restor. Neurol. Neurosci. 33, No. 6, 785–793.10.3233/RNN-140467Search in Google Scholar PubMed
Leff, A, Crinion, J., Scott, S., Turkheimer, F., Howard, D., and Wise, R. (2002). A physiological change in the homotopic cortex following left posterior temporal lobe infarction. Ann. Neurol. 51, 553–558.10.1002/ana.10181Search in Google Scholar PubMed
Li, H. and Fan, Y. (2014). Spatial alignment of human cortex by matching hierarchical patterns of functional connectivity. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, 392–332.10.1109/ISBI.2014.6867875Search in Google Scholar
Li, K., Guo, L., Faraco, C., Zhu, D., Chen, H., Yuan, Y., Lv, J., Deng, F., Jiang, X., Zhang, T., et al. (2012). Visual analytics of brain networks. Neuroimage 61, 82–97.10.1016/j.neuroimage.2012.02.075Search in Google Scholar PubMed PubMed Central
Li, X., Yao, L., Ye, Q., and Zhao, X. (2014). Online spatial normalization for real-time fMRI. PLoS One 9, e103302.10.1371/journal.pone.0103302Search in Google Scholar PubMed PubMed Central
Li, Y., Wang, D., Zhang, H., Wang, Y., Wu, P., Zhang, H., Yang, Y., and Huang, W. (2016). Changes of brain connectivity in the primary motor cortex after subcortical stroke. Medicine 95, e2579.10.1097/MD.0000000000002579Search in Google Scholar PubMed PubMed Central
Lindquist, M.A. and Wager, T.D. (2008). Spatial smoothing in fMRI using prolate spheroidal wave functions. Hum. Brain Mapp. 29, 1276–1287.10.1002/hbm.20475Search in Google Scholar PubMed PubMed Central
Logothetis, N.K. (2002). The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Philos Trans R Soc Lond B Biol Sci. 357, 1003–1037.10.1098/rstb.2002.1114Search in Google Scholar PubMed PubMed Central
Luxburg, U.V. (2007). A tutorial on spectral clustering. Stat. Comput. 17, 395–416.10.1007/s11222-007-9033-zSearch in Google Scholar
Ma, S., Phlypo, R., Calhoun, V., and Adalı, T. (2013). Capturing group variability using IVA: a simulation study and graph-theoretical analysis. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, 3128–3132.10.1109/ICASSP.2013.6638234Search in Google Scholar
Maclaren, J., Herbst, M., Speck, O., and Zaitsev, M. (2013). Prospective motion correction in brain imaging: a review. Magn Reson Med. 69, 621–636.10.1002/mrm.24314Search in Google Scholar
Maier, O. and Handels, H. (2015). Local problem forests: classifier training for locally limited sub-problems using spectral clustering. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, 806–809.10.1109/ISBI.2015.7163994Search in Google Scholar
Maintz, J.B.A. and Viergever, M.A. (1998). A survey of medical image registration. Med. Image Anal. 2, 1–36.10.1016/S1361-8415(01)80026-8Search in Google Scholar
Maudoux, A., Lefebvre, P.H., Cabay, J.E., Demertzi, A., Vanhaudenhuyse, A., Laureys, S., and Soddu, A. (2012). Connectivity graph analysis of the auditory resting state network in tinnitus. Brain Res. 1485, 10–21.10.1016/j.brainres.2012.05.006Search in Google Scholar PubMed
Meyer-Lindenberg, A., Poline, J.B., Kohn, P.D., Holt, J.L., Egan, M.F., Weinberger, D.R., and Berman, K.F. (2001). Evidence for abnormal cortical functional connectivity during working memory in schizophrenia. Am. J. Psychiatry 158, 1809–1817.10.1176/appi.ajp.158.11.1809Search in Google Scholar PubMed
Michael, A., Anderson, M., Miller, R., Adalı, T., and Calhoun, V.D. (2014). Preserving subject variability in group fMRI analysis: performance evaluation of GICA versus IVA. Front. Syst. Neurosci. 8, 106.10.3389/fnsys.2014.00106Search in Google Scholar PubMed PubMed Central
Michalopoulos, K. and Bourbakis, N. (2015). Combining EEG microstates with fMRI structural features for modeling brain activity. Int. J. Neural. Syst. 25, 1550041.10.1142/S0129065715500410Search in Google Scholar PubMed
Mikell, C.B., Banks, G.P., Frey, H.P., Youngerman, B.E., Nelp, T.B., Karas, P.J., Chan, A.K., Voss, H.U., Connolly, E.S., and Claassen, J. (2015). Frontal networks associated with command following after hemorrhagic stroke. Stroke 46, 49–57.10.1161/STROKEAHA.114.007645Search in Google Scholar PubMed
Mirzaei, G., Adeli, A., and Adeli, H. (2016). Imaging and machine learning techniques for diagnosis of Alzheimer disease. Rev. Neurosci. 27, 857–870.10.1515/revneuro-2016-0029Search in Google Scholar PubMed
Morabito, F.C., Campolo, M., Labate, D., Morabito, G., Bonanno, L., Bramanti, A., de Salvo, S., Marra, A., and Bramanti, P. (2015). A longitudinal EEG study of Alzheimer’s disease progression based on a complex network approach. Int. J. Neural. Syst. 25, 1550005.10.1142/S0129065715500057Search in Google Scholar PubMed
Morgan, V.L., Gore, J.C., Szaflarski, J.P. (2008). Temporal clustering analysis: what does it tell us about the resting state of the brain? Med. Sci. Monit. 14, CR345–CR352.Search in Google Scholar
Mori, S. and Zhang, J. (2006). Principles of diffusion tensor primer imaging and its applications to basic neuroscience research. Neuron 51, 527–539.10.1016/j.neuron.2006.08.012Search in Google Scholar PubMed
New, A.B., Robin, D.A., Parkinson, A.L., Duffy, J.R., McNeil, M.R., Piguet, O., Hornberger, M., Price, C.J., Eickhoff, S.B., Ballard, K.J. (2015). Altered resting-state network connectivity in stroke patients with and without apraxia of speech. Neuroimage 8, 429–439.10.1016/j.nicl.2015.03.013Search in Google Scholar PubMed PubMed Central
Nomura, E.M., Gratton, C., Visser, R.M., Kayser, A., Perez, F., and D’Esposito, M. (2010). Double dissociation of two cognitive control networks in patients with focal brain lesions. Proc. Natl. Acad. Sci. USA 107, 12017–12022.10.1073/pnas.1002431107Search in Google Scholar PubMed PubMed Central
Ogawa, S., Lee, T.M., Nayak, A.S., and Glynn, P. (1990). Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn. Reson. Med. 14, 68–78.10.1002/mrm.1910140108Search in Google Scholar PubMed
Padula, M.C., Schaer, E., Scariati, E., and Schneider, M. (2015). Structural and functional connectivity in the default mode network in 22q11.2 deletion syndrome. J. Neurodev. Disord. 7, 23.10.1186/s11689-015-9120-ySearch in Google Scholar PubMed PubMed Central
Patel, A.X., Kundu, P., Rubinov, M., Jones, P.S., Vertes, P.E., Ersche, K.D., Suckling, J., and Bullmore, E.T. (2014). A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. Neuroimage 95, 287–304.10.1016/j.neuroimage.2014.03.012Search in Google Scholar PubMed PubMed Central
Patriat, R., Reynolds, R.C., and Birn, R.M. (2016). An improved model of motion-related signal changes in fMRI. Neuroimage. In press.10.1016/j.neuroimage.2016.08.051Search in Google Scholar PubMed PubMed Central
Pavithra, R., Ramya, R., and Alaiyarasi, G. (2015). Wavelet based non local means algorithm for efficient denoising of MRI images. IJARCEE 4, 388–392.10.17148/IJARCCE.2015.4288Search in Google Scholar
Perez, G., Conci, A., Moreno, A.B., and Hernandez-Tamames, J.A. (2014). Rician noise attenuation in the wavelet packet transformed domain for brain MRI. Integr. Comput. Aided Eng. 21, 163–175.10.3233/ICA-130457Search in Google Scholar
Piaggi, P., Menicucci, D., Gentili, C., Handjaras, G., Gemignani, A., and Landi, A. (2014). Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state FMRI Data. Int. J. Neural. Syst. 24, 1450010.10.1142/S0129065714500105Search in Google Scholar PubMed
Pilutti, D. (2016). Non-parametric Bayesian spatial normalization in medical imaging. PhD dissertation, 134.Search in Google Scholar
Power, J.D., Schlaggar, B.L., and Peterson, S.E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 105, 536–551.10.1016/j.neuroimage.2014.10.044Search in Google Scholar PubMed PubMed Central
Rajini, N.H. and Bhavani, R. (2016). Automatic detection and classification of ischemic stroke using k-means clustering and texture features. Emerging Technologies in Intelligent Applications for Image and Video Processing, Chapter 8, IGI Global.10.4018/978-1-4666-9685-3.ch018Search in Google Scholar
Rashid, B., Arbabshirani, M. R., Damaraju, E., Cetin, M.S., Miller, R., Pearlson, G.D., and Calhoun, V.D. (2016). Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage 134, 645–657.10.1016/j.neuroimage.2016.04.051Search in Google Scholar PubMed PubMed Central
Redcay, E., Moran, J. M., Mavros, P. L., Tager-Flusberg, H., Gabrieli, J. D. E., and Whitfield-Gabrieli, S. (2013). Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder. Front. Hum. Neurosci. 7, 573.10.3389/fnhum.2013.00573Search in Google Scholar PubMed PubMed Central
Rehme, A.K., Fink, G.R., von Cramon, D.Y., and Grefkes, C. (2011). The role of the contralesional motor cortex for motor recovery in the early days after stroke assessed with longitudinal fMRI. Cereb. Cortex 21, 756–768.10.1093/cercor/bhq140Search in Google Scholar PubMed
Roche, A. (2011). A four-dimensional registration algorithm with application to joint correction of motion and slice timing in fMRI. IEEE Trans. Med. Imag. 30, 1546–1554.10.1109/TMI.2011.2131152Search in Google Scholar PubMed
Rubinov, M. and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069.10.1016/j.neuroimage.2009.10.003Search in Google Scholar PubMed
Rutter, L., Nadar, S.R., Holroyd, T., Carver, F.W., Apud, J., Weinberger, D.R., and Coppola, R. (2013). Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks. Front. Comput. Neurosci. 7, 1–21.10.3389/fncom.2013.00093Search in Google Scholar PubMed PubMed Central
Samdin, S.B., Ting, C.M., Salleh, S.H., Hamedi, M., and Noor, A.M. (2015). Identifying dynamic effective connectivity states in fMRI based on time-varying vector autoregressive models. International Conference for Innovation in Biomedical Engineering and Life Sciences, 56 of the series IFMBE Proceedings, pp 243–247.10.1007/978-981-10-0266-3_50Search in Google Scholar
Sanai, N., Mirzadeh, Z., and Berger, M.S. (2008). Functional outcome after language mapping for glioma resection. N. Engl. J. Med. 358, 18–27.10.1056/NEJMoa067819Search in Google Scholar PubMed
Sarty, G.E. Computing Brain Activity Maps from fMRI Time-Series Images. Cambridge, ISBN-13: 9780521868266, 198.Search in Google Scholar
Satterthwaite, T.D., Wolf, D.H., Loughead, J., Ruparel, K., Elliott, M.A., Hakonarson, H., Gur, R.C., and Gur, R.E. (2012). Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 60, 623–632.10.1016/j.neuroimage.2011.12.063Search in Google Scholar PubMed PubMed Central
Saur, D., Lange, R., Baumgaertner, A., Schraknepper, V., Willmes, K., Rijntjes, M., and Weiller, C. (2006). Dynamics of language reorganization after stroke. Brain 129(Pt 6), 1371–1384.10.1093/brain/awl090Search in Google Scholar PubMed
Seghier, M.L., Lazeyras, F., Zimine, S., Maier, S., Hanquinet, S., Delavelle, J., Volpe, J.J., and Huppi, P.S. (2004). Combination of event-related fMRI and diffusion tensor imaging in an infant with perinatal stroke. Neuroimage 21, 463–472.10.1016/j.neuroimage.2003.09.015Search in Google Scholar PubMed
Sharaev, M.G., Zavyalova, V.V., Ushakov, V.L., Kartashov, S.I., and Velichkovsky, B.M. (2016). Effective Connectivity within the default mode network: dynamic causal modeling of resting-state fMRI data. Front. Hum. Neurosci. 10, 10–14.10.3389/fnhum.2016.00014Search in Google Scholar PubMed PubMed Central
Sharp, D.J., Scott, S.K., and Wise, R.J. (2004). Retrieving meaning after temporal lobe infarction: the role of the basal language area. Ann. Neurol. 56, 836–846.10.1002/ana.20294Search in Google Scholar PubMed
Shen, X., Xu, L., Zhang, Q., and Jia, J. (2014). Multi-modal and multi-spectral registration for natural images. Computer Vision 8692, 309–324.10.1007/978-3-319-10593-2_21Search in Google Scholar
Siddique, N. and Adeli, H. (2013). Computational Intelligence – Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, Wiley, West Sussex, United Kingdom.10.1002/9781118534823Search in Google Scholar
Sladky, R, Friston,K.J., Tröstl, J., Cunnington, R., Moser, E., and Windischberger, C. (2011). Slice-timing effects and their correction in functional MRI. Neuroimage 58, 588–594.10.1016/j.neuroimage.2011.06.078Search in Google Scholar PubMed PubMed Central
Smith, L.I. (2002). A tutorial on principal components analysis. available at http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf.Search in Google Scholar
Smith, S.M., Beckmann, C.F., Andersson, J., Auerbach, E.J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D.A., Griffanti, L., Harms, M.P., et al. (2013). Resting-state fMRI in the human connectome project. Neuroimage 80, 144–168.10.1016/j.neuroimage.2013.05.039Search in Google Scholar PubMed PubMed Central
Stetter, M., Schiebl, I., Otto, T., Sengpiel, F., Hubener, M., Bonhoeffer, T., and Obermayer, K. (2000). Principal component analysis and blind separation of sources for optical imaging of intrinsic signals. Neuroimage 11, 482–490.10.1006/nimg.2000.0551Search in Google Scholar PubMed
Su, H.R. and Lai, S.H. (2015). Non-rigid registration of images with geometric and photometric deformation by using local affine Fourier-moment matching. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 2874–2882.Search in Google Scholar
Su, W.C., Huang, C.S., Chen, C.H., Liu, C.Y., Huang, H.C., and Le, Q.T. (2014). Identifying the modal parameters of a structure from ambient vibration data via the stationary wavelet packet. Comput.-Aided Civil Infrastruct. Eng. 29, 738–757.10.1111/mice.12115Search in Google Scholar
Tabelow, K., Polzehl, J., Voss, H.U., and Spokoiny, V. (2006). Analyzing fMRI experiments with structural adaptive smoothing procedures. Neuroimage 33, 55–62.10.1016/j.neuroimage.2006.06.029Search in Google Scholar PubMed
Talairach, J. and Tournoux, P. (1998). Co-planar Stereotaxic Atlas of the Human Brain. (New York: Thieme), 122.Search in Google Scholar
Tang, C., Zhao, Z., Chen, C., Zheng, X., Sun, F., Zhang, X., Tian, J., Fan, M., Wu, Y., and Jia, J. (2016). Decreased functional connectivity of homotopic brain regions in chronic stroke patients: a resting state fMRI study. PLoS One 11, e0152875.10.1371/journal.pone.0152875Search in Google Scholar PubMed PubMed Central
Thirion B., Varoquaux, G., Dohmatob, E., and Poline, J. B. (2014). Which fMRI clustering gives good brain parcellations. Front. Neurosci. 8, 1–13.10.3389/fnins.2014.00167Search in Google Scholar PubMed PubMed Central
Tie, Y., Rigolo, L., Norton, I.H., Huang, R.Y., Wu, W., Orringer, D., Mukundan, S. Jr, and Golby, A.J. (2014). Defining language networks from resting-state fMRI for surgical planning – a feasibility study. Hum. Brain Mapp 35, 1018–1030.10.1002/hbm.22231Search in Google Scholar PubMed PubMed Central
Tobon, G.A. (2011) Spatial alignment of functional regions in fMRI. MSc Thesis, Massachusetts Institute of Technology.Search in Google Scholar
Tomasi, D. and Volkow, N.D. (2012). Resting functional connectivity of language networks: characterization and reproducibility. Mol. Psychiatry 17, 841–854.10.1038/mp.2011.177Search in Google Scholar PubMed PubMed Central
Tsai, Y.H., Yuan, R., Huang, Y.C., Yeh, M.Y., Lin, C.P., and Biswal, B.B. (2013). Disruption of brain connectivity in acute stroke patients with early impairment in consciousness. Front. Psychol. 4, 956.10.3389/fpsyg.2013.00956Search in Google Scholar PubMed PubMed Central
Tuladhar, A.M., Snaphaan, L., Shumskaya, E., Rijpkema, M., Fernandez, G., Norris, D.G., and de Leeuw, F.E. (2013). Default mode network connectivity in stroke patients. PLoS One 8, e66556.10.1371/journal.pone.0066556Search in Google Scholar PubMed PubMed Central
Urbin, M.A., Hong, X., Lang, C., and Carter, A. (2014). Resting-state functional connectivity and its association with multiple domains of upper extremity function in chronic stroke. Neurorehabil. Neural Repair 28, 761–769.10.1177/1545968314522349Search in Google Scholar PubMed PubMed Central
Vahabi, Z., Amirfattahi, R., Ghassemi, F., and Shayegh, F. (2015). Online epileptic seizure prediction using wavelet-based bi-phase correlation of electrical signal tomography. Int. J. Neural. Syst. 25, 1550028.10.1142/S0129065715500288Search in Google Scholar PubMed
Venkataraman, A., Dijk, K.R., Buckner, R.L., and Golland, P. (2009). Exploring functional connectivity in fMRI via clustering. Proc IEEE Int Conf Acoustic Speech Signal Process, Taipei, 441–444.10.1109/ICASSP.2009.4959615Search in Google Scholar PubMed PubMed Central
Vergara, V.M., Mayer, A.R., Damaraju, E., Hutchison, K., and Calhoun, V.D. (2016). The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA. Neuroimage. In press.10.1016/j.neuroimage.2016.03.038Search in Google Scholar PubMed PubMed Central
Wang, C., Qin, W., Zhang, J., Tian, T., Li, Y., Meng, L., Zhang, X., and Yu, C. (2014). Altered functional organization within and between resting-state networks in chronic subcortical infarction. J. Cereb. Blood Flow Metab. 34, 597–605.10.1038/jcbfm.2013.238Search in Google Scholar PubMed PubMed Central
Wang, H., Yajima, A., Liang, R.Y., and Castaneda-Lopez, H. (2015). Bayesian modeling of external corrosion in underground pipelines based on the integration of Markov chain Monte Carlo techniques and clustered inspection data,” Comput.-Aided Civil Infrastruct. Eng. 30, 300–316.10.1111/mice.12096Search in Google Scholar
Weiss, M., Alkemade, A., Keuken, M. C., Müller-Axt, C., Geyer, S., Turner, R., Forstmann BU. (2015). Spatial normalization of ultrahigh resolution 7 T magnetic resonance imaging data of the postmortem human subthalamic nucleus: a multistage approach. Brain Struct. Funct. 220, 1695–1703.10.1007/s00429-014-0754-4Search in Google Scholar PubMed PubMed Central
Whitfield-Gabrieli, S., and Nieto-Castanon, A. (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141.10.1089/brain.2012.0073Search in Google Scholar PubMed
Wu, J.W., Tseng, J. C.R., and Tsai, W.N. (2014). A hybrid linear text segmentation algorithm using hierarchical agglomerative clustering and discrete particle swarm optimization. Integr. Comput. Aided Eng. 21, 35–46.10.3233/ICA-130446Search in Google Scholar
Yating, L.V. (2013). Application of resting-state fMRI methods to acute ischemic stroke. Master Dissertation, 90.Search in Google Scholar
Yildiz, O., Dogan, F.I., Oztekin, I., Mizrak, E., and Vural. F.Y. (2016). A robust normalization method for fMRI data for brain decoding. 2016 24th Signal Processing and Communication Application Conference (SIU), 2269–2272.10.1109/SIU.2016.7496228Search in Google Scholar
Yin, D., Song, F., Xu, D., Sun, L., Zhang, L., Yan, X., and Fan, M. (2014). Altered topological properties of the cortical motor-related network in patients with subcortical stroke revealed by graph theoretical analysis. Hum. Brain Mapp. 35, 3343–3359.10.1002/hbm.22406Search in Google Scholar PubMed PubMed Central
Yoav, H., Eli, S., Dan, B.G., and Dani, L. (2011). Non-rigid dense correspondence with applications for image enhancement. ACM Transactions on Graphics (Proc. SIGGRAPH), Article No. 70, doi:10.1145/1964921.1964965.Search in Google Scholar
Yue, Y., Loh, J.M., and Lindquist, M.A. (2010). Adaptive spatial smoothing of fMRI images. Stat. Interface 3, 3–13.10.4310/SII.2010.v3.n1.a1Search in Google Scholar
Zaragoza, J., Chin, T.J., Brown, M., Suter, D. (2013). As-projective-as-possible image stitching with moving DLT. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1285–1298.10.1109/CVPR.2013.303Search in Google Scholar
Zeng, Z., Xu, J., Wu, S., and Shen, M. (2014). Antithetic method-based particle swarm optimization for a queuing network problem with fuzzy data in concrete transportation systems. Comput.-Aided Civil Infrastruct. Eng. 29, 771–800.10.1111/mice.12111Search in Google Scholar
Zhu, J., Jin, Y., Wang, K., Zhou, Y., Feng, Y., Yu, M., and Jin, X. (2015). Frequency-dependent changes in the regional amplitude and synchronization of resting-state functional MRI in stroke. PLoS One 10, e0123850.10.1371/journal.pone.0123850Search in Google Scholar PubMed PubMed Central
Zhu, Y., Liang, P., Kang, S., Gao, H., and Yang, H. (2016). Disrupted brain connectivity networks in acute ischemic stroke patients. Brain Imaging Behav. 1–10, doi: 10.1007/s11682-016-9525-6.Search in Google Scholar PubMed
©2016 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- The cholinergic system in the cerebellum: from structure to function
- Tanshinones and mental diseases: from chemistry to medicine
- Brain-derived neurotrophic factor: a mediator of inflammation-associated neurogenesis in Alzheimer’s disease
- SIRT1 as a therapeutic target for Alzheimer’s disease
- A systematic review of the neurobiological underpinnings of borderline personality disorder (BPD) in childhood and adolescence
- Neuroprotective properties of mitochondria-targeted antioxidants of the SkQ-type
- Imaging and machine learning techniques for diagnosis of Alzheimer’s disease
- Resting state functional magnetic resonance imaging processing techniques in stroke studies
Articles in the same Issue
- Frontmatter
- The cholinergic system in the cerebellum: from structure to function
- Tanshinones and mental diseases: from chemistry to medicine
- Brain-derived neurotrophic factor: a mediator of inflammation-associated neurogenesis in Alzheimer’s disease
- SIRT1 as a therapeutic target for Alzheimer’s disease
- A systematic review of the neurobiological underpinnings of borderline personality disorder (BPD) in childhood and adolescence
- Neuroprotective properties of mitochondria-targeted antioxidants of the SkQ-type
- Imaging and machine learning techniques for diagnosis of Alzheimer’s disease
- Resting state functional magnetic resonance imaging processing techniques in stroke studies