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Segregation of frontoparietal and cerebellar components within saccade and vergence networks using hierarchical independent component analysis of fMRI

Published online by Cambridge University Press:  04 May 2011

YELDA ALKAN ,
BHARAT B. BISWAL ,
PAUL A. TAYLOR  and
TARA L. ALVAREZ
YELDA ALKAN
Affiliation:
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey
BHARAT B. BISWAL*
Affiliation:
Department of Radiology, University of Medicine and Dentistry of New Jersey, Newark, New Jersey
PAUL A. TAYLOR
Affiliation:
Department of Radiology, University of Medicine and Dentistry of New Jersey, Newark, New Jersey
TARA L. ALVAREZ*
Affiliation:
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey
*
*Address correspondence and reprint requests to: Dr. Tara L. Alvarez, Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102. E-mail: tara.l.alvarez@njit.edu and Dr. Bharat B. Biswal, Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102. E-mail: bbiswal@gmail.com
*Address correspondence and reprint requests to: Dr. Tara L. Alvarez, Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102. E-mail: tara.l.alvarez@njit.edu and Dr. Bharat B. Biswal, Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102. E-mail: bbiswal@gmail.com
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Abstract

Purpose: Cortical and subcortical functional activity stimulated via saccade and vergence eye movements were investigated to examine the similarities and differences between networks and regions of interest (ROIs). Methods: Blood oxygenation level-dependent (BOLD) signals from stimulus-induced functional Magnetic Resonance Imaging (MRI) experiments were analyzed studying 16 healthy subjects. Six types of oculomotor experiments were conducted using a block design to study both saccade and vergence circuits. The experiments included a simple eye movement task and a more cognitively demanding prediction task. A hierarchical independent component analysis (ICA) process began by analyzing individual subject data sets with spatial ICA to extract spatial independent components (sIC), which resulted in three ROIs. Using the time series from each of the three ROIs per subject, per oculomotor experiment, a temporal ICA was used to compute individual temporal independent components (tICs). For each of the three ROIs, the individual tICs from multiple subjects were entered into a second temporal ICA to compute group-level tICs for comparison. Results: Two independent spatial maps were observed for each subject (one sIC showing activity in the frontoparietal regions and another sIC in the cerebellum) during the six oculomotor tasks. Analysis of group-level tICs revealed an increased latency in the cerebellar region when compared to the frontoparietal region. Conclusion: Shared neuronal behavior has been reported in the frontal and parietal lobes, which may in part explain the segregation of frontoparietal functional activity into one sIC. The cerebellum uses multiple time scales for motor learning. This may result in an increased latency observed in the BOLD signal of the cerebellar group-level tIC when compared to the frontal and parietal group-level tICs. The increased latency offers a possible explanation to why ICA dissects the cerebellar activity into an sIC. The hierarchical ICA process used to calculate group-level tICs can yield insight into functional connectivity within complex neural networks.

Keywords

SaccadeVergencefMRIPredictionFrontoparietalCerebellumIndependent component analysisEye movementOculomotor
Type
Research Articles
Information
Visual Neuroscience , Volume 28 , Issue 3 , May 2011 , pp. 247 - 261
Copyright
Copyright © Cambridge University Press 2011

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References

Akao, T., Kurkin, S.A., Fukushima, J. & Fukushima, K. (2005). Visual and vergence eye movement-related responses of pursuit neurons in the caudal frontal eye fields to motion-in-depth stimuli. Experimental Brain Research 164, 92–108.Google Scholar
Alvarez, T.L., Alkan, Y., Gohel, S., Douglas Ward, B. & Biswal, B.B. (2010 a). Functional anatomy of predictive vergence and saccade eye movements in humans: A functional MRI investigation. Vision Research 50, 2163–2175.Google Scholar
Alvarez, T.L., Bhavsar, M., Semmlow, J.L., Bergen, M.T. & Pedrono, C. (2005). Short-term predictive changes in the dynamics of disparity vergence eye movements. Journal of Vision 5, 640–649.Google Scholar
Alvarez, T.L., Semmlow, J.L., Yuan, W. & Munoz, P. (2002). Comparison of disparity vergence system responses to predictable and non-predictable stimulations. Current Psychology of Cognition 21, 343–375.Google Scholar
Alvarez, T.L., Vicci, V.R., Alkan, Y., Kim, E.H., Gohel, S., Barrett, A.M., Chiaravalloti, N. & Biswal, B.B. (2010 b). Vision therapy in adults with convergence insufficiency: Clinical and functional magnetic resonance imaging measures. Optometry & Vision Science 87, E985–E1002.CrossRefGoogle ScholarPubMed
Attwell, D. & Iadecola, C. (2002). The neural basis of functional brain imaging signals. Trends in Neurosciences 25, 621–625.Google Scholar
Bandettini, P.A., Wong, E.C., Hinks, R.S., Tikofsky, R.S. & Hyde, J.S. (1992). Time course EPI of human brain function during task activation. Magnetic Resonance in Medicine 25, 390–397.CrossRefGoogle ScholarPubMed
Batista, A.P., Buneo, C.A., Snyder, L.H. & Andersen, R.A. (1999). Reach plans in eye-centered coordinates. Science 285, 257–260.Google Scholar
Beckmann, C.F. & Smith, S.M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging 23, 137–152.Google Scholar
Binder, J.R., Liebenthal, E., Possing, E.T., Medler, D.A. & Ward, B.D. (2004). Neural correlates of sensory and decision processes in auditory object identification. Nature Neuroscience 7, 295–301.Google Scholar
Biswal, B.B., Kannurpatti, S.S. & Rypma, B. (2007). Hemodynamic scaling of fMRI-BOLD signal: Validation of low-frequency spectral amplitude as a scalability factor. Magnetic Resonance Imaging 25, 1358–1369.Google Scholar
Biswal, B.B. & Ulmer, J.L. (1999). Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. Journal of Computer Assisted Tomography 23, 265–271.Google Scholar
Buttner, U. & Waespe, W. (1984). Purkinje cell activity in the primate flocculus during optokinetic stimulation, smooth pursuit eye movements and VOR-suppression. Experimental Brain Research 55, 97–104.Google Scholar
Calhoun, V.D., Adali, T., McGinty, V.B., Pekar, J.J., Watson, T.D. & Pearlson, G.D. (2001 a). fMRI activation in a visual-perception task: Network of areas detected using the general linear model and independent components analysis. NeuroImage 14, 1080–1088.CrossRefGoogle Scholar
Calhoun, V.D., Adali, T., Pearlson, G.D. & Pekar, J.J. (2001 b). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping 14, 140–151.Google Scholar
Calhoun, V.D., Liu, J. & Adali, T. (2009). A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage 45, S163–S172.Google Scholar
Chen-Harris, H., Joiner, W.M., Ethier, V., Zee, D.S. & Shadmehr, R. (2008). Adaptive control of saccades via internal feedback. The Journal of Neuroscience 28, 2804–2813.Google Scholar
Ciuffreda, K.J. & Tannen, B., ed. (1995). Eye Movement Basics for the Clinician. New York: Mosby.Google Scholar
Colby, C.L., Duhamel, J.R. & Goldberg, M.E. (1996). Visual, presaccadic, and cognitive activation of single neurons in monkey lateral intraparietal area. Journal of Neurophysiology 76, 2841–2852.CrossRefGoogle ScholarPubMed
Comon, P. (1994). Independent component analysis, a new concept? Signal Processing 36, 287–314.Google Scholar
Coubard, O.A. & Kapoula, Z. (2008). Saccades during symmetrical vergence. Graefe’s Archive for Clinical and Experimental Ophthalmology 246, 521–536.CrossRefGoogle ScholarPubMed
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers & Biomedical Research 29, 162–173.Google Scholar
Cumming, B.G. & Judge, S.J. (1986). Disparity-induced and blur-induced convergence eye movement and accommodation in the monkey. Journal of Neurophysiology 55, 896–914.Google Scholar
Desmurget, M., Pelisson, D., Grethe, J.S., Alexander, G.E., Urquizar, C., Prablanc, C. & Grafton, S.T. (2000). Functional adaptation of reactive saccades in humans: A PET study. Experimental Brain Research 132, 243–259.Google Scholar
Diedrichsen, J., Verstynen, T., Schlerf, J. & Wiestler, T. (2010). Advances in functional imaging of the human cerebellum. Current Opinion in Neurology 23, 382–387.CrossRefGoogle ScholarPubMed
Duhamel, J.R., Colby, C.L. & Goldberg, M.E. (1992). The updating of the representation of visual space in parietal cortex by intended eye movements. Science 255, 90–92.Google Scholar
Dyckman, K.A., Camchong, J., Clementz, B.A. & McDowell, J.E. (2007). An effect of context on saccade-related behavior and brain activity. NeuroImage 36, 774–784.Google Scholar
Esposito, F., Scarabino, T., Hyvarinen, A., Himberg, J., Formisano, E., Comani, S., Tedeschi, G., Goebel, R., Seifritz, E. & Di Salle, F. (2005). Independent component analysis of fMRI group studies by self-organizing clustering. NeuroImage 25, 193–205.Google Scholar
Ethier, V., Zee, D.S. & Shadmehr, R. (2008). Spontaneous recovery of motor memory during saccade adaptation. Journal of Neurophysiology 99, 2577–2583.Google Scholar
Gamlin, P.D. & Yoon, K. (2000). An area for vergence eye movement in primate frontal cortex. Nature 407, 1003–1007.CrossRefGoogle ScholarPubMed
Gamlin, P.D., Yoon, K. & Zhang, H. (1996). The role of cerebro-ponto-cerebellar pathways in the control of vergence eye movements. Eye 10(Pt 2), 167–171.Google Scholar
Genovesio, A. & Ferraina, S. (2004). Integration of retinal disparity and fixation-distance related signals toward an egocentric coding of distance in the posterior parietal cortex of primates. Journal of Neurophysiology 91, 2670–2684.Google Scholar
Gnadt, J.W. & Mays, L.E. (1995). Neurons in monkey parietal area LIP are tuned for eye-movement parameters in three-dimensional space. Journal of Neurophysiology 73, 280–297.Google Scholar
Guo, Y. & Pagnoni, G. (2008). A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage 42, 1078–1093.Google Scholar
Harrison, R.V., Harel, N., Panesar, J. & Mount, R.J. (2002). Blood capillary distribution correlates with hemodynamic-based functional imaging in cerebral cortex. Cerebral Cortex 12, 225–233.Google Scholar
Himberg, J., Hyvarinen, A. & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage 22, 1214–1222.Google Scholar
Huettel, S.A. & McCarthy, G. (2001). Regional differences in the refractory period of the hemodynamic response: An event-related fMRI study. NeuroImage 14, 967–976.CrossRefGoogle ScholarPubMed
Hung, G.K., Semmlow, J.L. & Ciuffreda, K.J. (1983). Identification of accommodative vergence contribution to the near response using response variance. Investigative Ophthalmology & Visual Science 24, 772–777.Google Scholar
Hyvarinen, A., Karhunen, J. & Oja, E. (2001). Independent Component Analysis. New York: John Wiley & Sons.CrossRefGoogle ScholarPubMed
Iadecola, C. (2002). Intrinsic signals and functional brain mapping: Caution, blood vessels at work. Cerebral Cortex 12, 223–224.Google Scholar
Judge, S.J. & Cumming, B.G. (1986). Neurons in the monkey midbrain with activity related to vergence eye movement and accommodation. Journal of Neurophysiology 55, 915–930.Google Scholar
Kannurpatti, S.S., Motes, M.A., Rypma, B. & Biswal, B.B. (2010). Neural and vascular variability and the fMRI-BOLD response in normal aging. Magnetic Resonance Imaging 28, 466–476.Google Scholar
Kim, E.H., Granger-Donetti, B., Vicci, V.R. & Alvarez, T.L. (2010). The relationship between phoria and the ratio of convergence peak velocity to divergence peak velocity. Investigative Ophthalmology & Visual Science 51, 4017–4027.Google Scholar
Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., Veijola, J., Moilanen, I., Isohanni, M., Zang, Y.F. & Tervonen, O. (2009). Functional segmentation of the brain cortex using high model order group PICA. Human Brain Mapping 30, 3865–3886.Google Scholar
Krishnan, V.V., Farazian, F. & Stark, L. (1973). An analysis of latencies and prediction in the fusional vergence system. American Journal of Optometry & Archives of American Academy of Optometry 50, 933–939.Google Scholar
Kusunoki, M. & Goldberg, M.E. (2003). The time course of perisaccadic receptive field shifts in the lateral intraparietal area of the monkey. Journal of Neurophysiology 89, 1519–1527.Google Scholar
Kwong, K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E., Weisskoff, R.M., Poncelet, B.P., Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., et al. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences of the United States of America 89, 5675–5679.Google Scholar
Lee, Y.Y., Granger-Donetti, B., Chang, C. & Alvarez, T.L. (2009). Sustained convergence induced changes in phoria and divergence dynamics. Vision Research 49, 2960–2972.Google Scholar
Leigh, R.J. & Zee, D.S. (2006). The Neurology of Eye Movements. Oxford: Oxford University Press.Google Scholar
Lewis, J.W., Brefczynski, J.A., Phinney, R.E., Janik, J.J. & DeYoe, E.A. (2005). Distinct cortical pathways for processing tool versus animal sounds. The Journal of Neuroscience 25, 5148–5158.CrossRefGoogle ScholarPubMed
Li, Y.O., Adali, T. & Calhoun, V.D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping 28, 1251–1266.Google Scholar
Logothetis, N.K. & Wandell, B.A. (2004). Interpreting the BOLD signal. Annual Review of Physiology 66, 735–769.Google Scholar
Mays, L.E. (1984). Neural control of vergence eye movements: Convergence and divergence neurons in midbrain. Journal of Neurophysiology 51, 1091–1108.CrossRefGoogle ScholarPubMed
McDowell, J.E., Dyckman, K.A., Austin, B.P. & Clementz, B.A. (2008). Neurophysiology and neuroanatomy of reflexive and volitional saccades: Evidence from studies of humans. Brain & Cognition 68, 255–270.Google Scholar
McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J. & Sejnowski, T.J. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping 6, 160–188.Google Scholar
Medendorp, W.P., Goltz, H.C., Vilis, T. & Crawford, J.D. (2003). Gaze-centered updating of visual space in human parietal cortex. The Journal of Neuroscience 23, 6209–6214.Google Scholar
Merriam, E.P., Genovese, C.R. & Colby, C.L. (2003). Spatial updating in human parietal cortex. Neuron 39, 361–373.Google Scholar
Merriam, E.P., Genovese, C.R. & Colby, C.L. (2007). Remapping in human visual cortex. Journal of Neurophysiology 97, 1738–1755.Google Scholar
Nitta, T., Akao, T., Kurkin, S. & Fukushima, K. (2008). Involvement of the cerebellar dorsal vermis in vergence eye movements in monkeys. Cerebral Cortex 18, 1042–1057.Google Scholar
Ohyama, T., Nores, W.L., Medina, J.F., Riusech, F.A. & Mauk, M.D. (2006). Learning-induced plasticity in deep cerebellar nucleus. The Journal of Neuroscience 26, 12656–12663.CrossRefGoogle ScholarPubMed
Oja, E. & Yuan, Z. (2006). The fastICA algorithm revisited: Convergence analysis. IEEE Transactions on Neural Networks 17, 1370–1381.Google Scholar
Optican, L.M. & Robinson, D.A. (1980). Cerebellar-dependent adaptive control of primate saccadic system. Journal of Neurophysiology 44, 1058–1076.Google Scholar
Perlbarg, V., Bellec, P., Anton, J.L., Pelegrini-Issac, M., Doyon, J. & Benali, H. (2007). CORSICA: Correction of structured noise in fMRI by automatic identification of ICA components. Magnetic Resonance Imaging 25, 35–46.Google Scholar
Poggio, G.E. (1995). Mechanisms of stereopsis in monkey visual cortex. Cerebral Cortex 5, 193–204.Google Scholar
Quaia, C., Lefevre, P. & Optican, L.M. (1999). Model of the control of saccades by superior colliculus and cerebellum. Journal of Neurophysiology 82, 999–1018.Google Scholar
Saad, Z.S., DeYoe, E.A. & Ropella, K.M. (2003). Estimation of FMRI response delays. NeuroImage 18, 494–504.Google Scholar
Saad, Z.S., Ropella, K.M., Cox, R.W. & DeYoe, E.A. (2001). Analysis and use of FMRI response delays. Human Brain Mapping 13, 74–93.Google Scholar
Schmid, A., Rees, G., Frith, C. & Barnes, G. (2001). An fMRI study of anticipation and learning of smooth pursuit eye movements in humans. Neuroreport 12, 1409–1414.CrossRefGoogle ScholarPubMed
Schmithorst, V.J. & Brown, R.D. (2004). Empirical validation of the triple-code model of numerical processing for complex math operations using functional MRI and group Independent Component Analysis of the mental addition and subtraction of fractions. NeuroImage 22, 1414–1420.Google Scholar
Seifritz, E., Esposito, F., Hennel, F., Mustovic, H., Neuhoff, J.G., Bilecen, D., Tedeschi, G., Scheffler, K. & Di Salle, F. (2002). Spatiotemporal pattern of neural processing in the human auditory cortex. Science 297, 1706–1708.Google Scholar
Semmlow, J.L., Chen, Y.F., Granger-Donetti, B. & Alvarez, T.L. (2009). Correction of saccade-induced midline errors in responses to pure disparity vergence stimuli. Journal of Eye Movement Research 2, 1–13.Google Scholar
Semmlow, J.L., Chen, Y.F., Pedrono, C. & Alvarez, T. (2008). Saccadic behavior during the response to pure disparity vergence stimuli I: General properties. Journal of Eye Movement Research 1, 1–11.Google Scholar
Semmlow, J.L., Yuan, W. & Alvarez, T.L. (2002). Short-term adaptive control processes in vergence eye movements. Current Psychology of Cognition 21, 243–261.Google Scholar
Shutoh, F., Ohki, M., Kitazawa, H., Itohara, S. & Nagao, S. (2006). Memory trace of motor learning shifts transsynaptically from cerebellar cortex to nuclei for consolidation. Neuroscience 139, 767–777.Google Scholar
Sommer, M.A. & Wurtz, R.H. (2006). Influence of the thalamus on spatial visual processing in frontal cortex. Nature 444, 374–377.Google Scholar
Sui, J., Adali, T., Pearlson, G., Yang, H., Sponheim, S.R., White, T. & Calhoun, V.D. (2010). A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia. NeuroImage 51, 123–134.Google Scholar
Takagi, M., Tamargo, R. & Zee, D.S. (2003). Effects of lesions of the cerebellar oculomotor vermis on eye movements in primate: Binocular control. Progress in Brain Research 142, 19–33.Google Scholar
Talairach, J. & Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the Human Brain. New York: Thieme.Google Scholar
Thach, W.T. (2007). On the mechanism of cerebellar contributions to cognition. Cerebellum 6, 163–167.CrossRefGoogle ScholarPubMed
Umeno, M.M. & Goldberg, M.E. (1997). Spatial processing in the monkey frontal eye field. I. Predictive visual responses. Journal of Neurophysiology 78, 1373–1383.Google Scholar
Umeno, M.M. & Goldberg, M.E. (2001). Spatial processing in the monkey frontal eye field. II. Memory responses. Journal of Neurophysiology 86, 2344–2352.CrossRefGoogle ScholarPubMed
Varoquaux, G., Sadaghiani, S., Pinel, P., Kleinschmidt, A., Poline, J.B. & Thirion, B. (2010). A group model for stable multi-subject ICA on fMRI data sets. NeuroImage 51, 288–299.Google Scholar
Westheimer, G. (1954). Eye movement responses to a horizontally moving visual stimulus. A.M.A. Archives of Ophthalmology 52, 932–941.Google Scholar
Windischberger, C., Lamm, C., Bauer, H. & Moser, E. (2002). Consistency of inter-trial activation using single-trial fMRI: Assessment of regional differences. Brain Research. Cognitive Brain Research 13, 129–138.Google Scholar
Wurtz, R.H. (2008). Neuronal mechanisms of visual stability. Vision Research 48, 2070–2089.Google Scholar
Xu-Wilson, M., Chen-Harris, H., Zee, D.S. & Shadmehr, R. (2009). Cerebellar contributions to adaptive control of saccades in humans. The Journal of Neuroscience 29, 12930–12939.Google Scholar
Zee, D.S., Yee, R.D., Cogan, D.G., Robinson, D.A. & Engel, W.K. (1976). Ocular motor abnormalities in hereditary cerebellar ataxia. Brain 99, 207–234.CrossRefGoogle ScholarPubMed
Zhang, H. & Gamlin, P.D. (1998). Neurons in the posterior interposed nucleus of the cerebellum related to vergence and accommodation. I. Steady-state characteristics. Journal of Neurophysiology 79, 1255–1269.Google Scholar