Abstract
The organization of the cerebral cortex into distinct modules may be described along several dimensions, most importantly structure, connectivity and function. Functional neuroimaging provides a powerful tool for the localization of function, which allows testing hypotheses about structure-function relationships. This method is, however, intrinsically less well suited to delineate the organization of a particular brain region. While neuroimaging studies may thus test hypotheses about a functional differentiation between cortical modules, their potential for delineating those in a particular region of interest is limited. Identification of cortical modules by differences in whole-brain connectivity profiles derived from diffusion tensor imaging or resting state correlations have therefore raised much recent interest. As these approaches, however, do not carry task-related information, the functional relevance of the obtained parcellation have so far remained largely elusive.
The emergence of comprehensive databases for functional neuroimaging results provides a novel basis for delineating cortical modules by co-activation networks. Importantly, such approaches are data-driven in that they do not rely on a classification of tasks or paradigms, but merely rely on the spatial pattern of whole brain co-activation profiles. The key idea behind database-informed cortical parcellation is computing the whole-brain co-activation patterns of each individual voxel within a seed region, regardless of the ontological classification of the original experiments. Recording the co-activation likelihoods of all grey-matter voxels outside the region of interest then yields a functional co-activation matrix. This connectivity matrix may then be used to group the seed voxels in such manner, that voxels showing similar co-activation are clustered together and separated from those showing different co-activation profiles. Hereby functional modules may be identified in a data-driven fashion using task-based neuroimaging information. By assessing the functional characteristics and spatial response patterns of those experiments associated with the ensuing clusters, the derived parcellation may be directly related to network properties and task properties.
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References
Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah NJ, Habel U, Schneider F, Zilles K (2005) Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol (Berl) 210(5–6):343–352. doi:10.1007/s00429-005-0025-5
Barnard ST, Pothen A, Simon HD (1995) A spectral algorithm for envelope reduction of sparse matrices. Numer Linear Algebra Appl 2:317–334
Brodmann K (1909) Vergleichende Lokalisationslehre der Großhirnrinde. Barth, Leipzig
Carr L, Iacoboni M, Dubeau MC, Mazziotta JC, Lenzi GL (2003) Neural mechanisms of empathy in humans: a relay from neural systems for imitation to limbic areas. Proc Natl Acad Sci USA 100(9):5497–5502. doi:10.1073/pnas.0935845100
Cauda F, Cavanna AE, D’Agata F, Sacco K, Duca S, Geminiani GC (2011) Functional connectivity and coactivation of the nucleus accumbens: a combined functional connectivity and structure-based meta-analysis. J Cogn Neurosci 23(10):2864–2877. doi:10.1162/jocn.2011.21624
Derrfuss J, Mar RA (2009) Lost in localization: the need for a universal coordinate database. Neuroimage 48(1):1–7. doi:10.1016/j.neuroimage.2009.01.053
Eickhoff SB, Bzdok D (2011) Meta-analyses in basic and clinical neuroscience: state of the art and perspective. In: Ulmer S, Jansen O (eds) fMRI – Basics and clinical applications. 2nd edn. Springer, Heidelberg, Germany
Eickhoff SB, Grefkes C (2011) Approaches for the integrated analysis of structure, function and connectivity of the human brain. Clin EEG Neurosci 42(2):107–121
Eickhoff SB, Rottschy C, Zilles K (2007) Laminar distribution and co-distribution of neurotransmitter receptors in early human visual cortex. Brain Struct Funct 212(3–4):255–267. doi:10.1007/s00429-007-0156-y
Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT (2009) Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp 30(9):2907–2926. doi:10.1002/hbm.20718
Eickhoff SB, Jbabdi S, Caspers S, Laird AR, Fox PT, Zilles K, Behrens TE (2010) Anatomical and functional connectivity of cytoarchitectonic areas within the human parietal operculum. J Neurosci 30(18):6409–6421. doi:30/18/6409 [pii]
Eickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT (2011a) Activation likelihood estimation meta-analysis revisited. Neuroimage. (in press) doi:S1053-8119(11)01062-7 [pii]
Eickhoff SB, Bzdok D, Laird AR, Roski C, Caspers S, Zilles K, Fox PT (2011b) Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation. Neuroimage 57(3):938–949. doi:S1053-8119(11)00509-X [pii]
Evans AC, Collins DL, Milner B (1992) An MRI-based stereotactic atlas from 250 young normal subjects. Soc Neurosci Abstr 18(408)
Fox PT, Lancaster JL (2002) Opinion: mapping context and content: the BrainMap model. Nat Rev Neurosci 3(4):319–321. doi:10.1038/nrn789
Franz SI, Lashley KS (1917) The retention of habits by the rat after destruction of the frontal parts of the cerebrum. Psycholobiology 1:3–18
Friston K (2002) Beyond phrenology: what can neuroimaging tell us about distributed circuitry? Ann Rev Neurosci 25:221–250
Friston KJ, Frith CD, Fletcher P, Liddle PF, Frackowiak RS (1996) Functional topography: multidimensional scaling and functional connectivity in the brain. Cereb Cortex 6(2):156–164
Handl J, Knowles J, Kell DB (2005) Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15):3201–3212. doi:bti517 [pii]
Harlow MJ (1869) Recovery from the passage of an iron bar through the head. Boston Medical and Surgical Journal 3(7)
Hartigan JA, Wong MA (1979) A k-means clustering algorithm. Appl Stat 28:100–108
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACN Computing Surveys 31(3):264–323
Johansen-Berg H, Behrens TE, Robson MD, Drobnjak I, Rushworth MF, Brady JM, Smith SM, Higham DJ, Matthews PM (2004) Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc Natl Acad Sci USA 101(36):13335–13340. doi:10.1073/pnas.0403743101
Kim JH, Lee JM, Jo HJ, Kim SH, Lee JH, Kim ST, Seo SW, Cox RW, Na DL, Kim SI, Saad ZS (2010) Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: functional connectivity-based parcellation method. Neuroimage 49(3):2375–2386. doi:S1053-8119(09)01087-8 [pii]
Laird AR, Eickhoff SB, Li K, Robin DA, Glahn DC, Fox PT (2009a) Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J Neurosci 29(46):14496–14505. doi:29/46/14496 [pii]
Laird AR, Lancaster JL, Fox PT (2009b) Lost in localization? The focus is meta-analysis. Neuroimage 48(1):18–20. doi:S1053-8119(09)00673-9 [pii]
Lancaster JL, Tordesillas-Gutierrez D, Martinez M, Salinas F, Evans A, Zilles K, Mazziotta JC, Fox PT (2007) Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp 28:1194–1205
Leslie KR, Johnson-Frey SH, Grafton ST (2004) Functional imaging of face and hand imitation: towards a motor theory of empathy. Neuroimage 21(2):601–607. doi:10.1016/j.neuroimage.2003.09.038
Lloyd SP (1982) Quantization in PCM. IEEE Trans Inf Theory 28:129–137
Luppino G, Matelli M, Camarda R, Rizzolatti G (1993) Corticocortical connections of area F3 (SMA-proper) and area F6 (pre-SMA) in the macaque monkey. J Comp Neurol 338:114–140
Maunsell JH, van Essen DC (1983) The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. J Neurosci 3(12):2563–2586
Mukamel R, Ekstrom AD, Kaplan J, Iacoboni M, Fried I (2010) Single-neuron responses in humans during execution and observation of actions. Curr Biol 20(8):750–756. doi:S0960-9822(10)00233-2 [pii]
Nanetti L, Cerliani L, Gazzola V, Renken R, Keysers C (2009) Group analyses of connectivity-based cortical parcellation using repeated k-means clustering. Neuroimage 47(4):1666–1677. doi:S1053-8119(09)00626-0 [pii]
Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. Proc Neur Inform Proc Syst 14:849–856
Passingham RE, Stephan KE, Kotter R (2002) The anatomical basis of functional localization in the cortex. Nat Rev Neurosci 3(8):606–616. doi:10.1038/nrn893
Picard N, Strick PL (1996) Motor areas of the medial wall: a review of their location and functional activation. Cereb Cortex 6:342–353
Rizzolatti G, Luppino G (2001) The cortical motor system. Neuron 31:889–901
Rizzolatti G, Wolpert DM (2005) Motor systems. Curr Opin Neurobiol 15:623–625
Robinson JL, Laird AR, Glahn DC, Lovallo WR, Fox PT (2010) Metaanalytic connectivity modeling: delineating the functional connectivity of the human amygdala. Hum Brain Mapp 31(2):173–184. doi:10.1002/hbm.20854
Roy AK, Shehzad Z, Margulies DS, Kelly AM, Uddin LQ, Gotimer K, Biswal BB, Castellanos FX, Milham MP (2009) Functional connectivity of the human amygdala using resting state fMRI. Neuroimage 45(2):614–626. doi:S1053-8119(08)01221-4 [pii]
Saygin ZM, Osher DE, Augustinack J, Fischl B, Gabrieli JD (2011) Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography. Neuroimage 56(3):1353–1361. doi:S1053-8119(11)00265-5 [pii]
Scannell JW, Blakemore C, Young MP (1995) Analysis of connectivity in the cat cerebral cortex. J Neurosci 15(2):1463–1483
Smith SM (2012) The future of FMRI connectivity. Neuroimage. doi:S1053-8119(12)00039-0 [pii]
Solano-Castiella E, Schafer A, Reimer E, Turke E, Proger T, Lohmann G, Trampel R, Turner R (2011) Parcellation of human amygdala in vivo using ultra high field structural MRI. Neuroimage 58(3):741–748. doi:S1053-8119(11)00692-6 [pii]
Steyvers M, Tenenbaum JB, Wagenmakers E-J, Blum B (2003) Inferring causal networks from observations and interventions. Cogn Sci 27(3):453–489. doi:10.1207/s15516709cog2703_6
Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. Thieme, New York
Timm NH (2002) Applied multivariate analysis. Springer, New York
Vogt C, Vogt O (1919) Allgemeinere Ergebnisse unserer Hirnforschung. Journal für Psychologie und Neurologie 25:279–461
Vogt S, Buccino G, Wohlschlager AM, Canessa N, Shah NJ, Zilles K, Eickhoff SB, Freund HJ, Rizzolatti G, Fink GR (2007) Prefrontal involvement in imitation learning of hand actions: effects of practice and expertise. NeuroImage 37:1371–1383
Walters NB, Eickhoff SB, Schleicher A, Zilles K, Amunts K, Egan GF, Watson JD (2007) Observer-independent analysis of high-resolution MR images of the human cerebral cortex: in vivo delineation of cortical areas. Hum Brain Mapp 28(1):1–8. doi:10.1002/hbm.20267
Young MP (1993) The organization of neural systems in the primate cerebral cortex. Proc Biol Sci 252(1333):13–18. doi:10.1098/rspb.1993.0040
Zilles K, Amunts K (2010) Centenary of Brodmann’s map–conception and fate. Nat Rev Neurosci 11(2):139–145. doi:nrn2776 [pii]
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Eickhoff, S.B., Bzdok, D. (2013). Database-Driven Identification of Functional Modules in the Cerebral Cortex. In: Geyer, S., Turner, R. (eds) Microstructural Parcellation of the Human Cerebral Cortex. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37824-9_5
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