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Defining Functional Areas in Individual Human Brains Using Resting Functional Connectivity MRI ☆ ⁎ Alexander L

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Defining Functional Areas in Individual Human Brains Using Resting Functional Connectivity MRI ☆ ⁎ Alexander L www.elsevier.com/locate/ynimg NeuroImage 41 (2008) 45–57 Defining functional areas in individual human brains using resting functional connectivity MRI ☆ ⁎ Alexander L. Cohen,a, Damien A. Fair,a Nico U.F. Dosenbach,b Francis M. Miezin,a,b ⁎ Donna Dierker,c David C. Van Essen,c Bradley L. Schlaggar,a,b,c,d and Steven E. Petersena,b,c,e, aDepartment of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA bDepartment of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA cDepartment of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA dDepartment of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA eDepartment of Psychology, Washington University School of Medicine, St. Louis, MO 63130, USA Received 24 September 2007; revised 8 December 2007; accepted 24 January 2008 Available online 25 March 2008 The cerebral cortex is anatomically organized at many physical scales Introduction starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) In Churchland and Sejnowski's famous diagram showing the studies often focus at the level of areas, networks, and systems. Except levels of neuroanatomical organization, a level labeled “maps” is in restricted domains, (e.g., topographically-organized sensory re- interposed between networks (i.e., columns) and systems (Church- gions), it is difficult to determine area boundaries in the human brain land and Sejnowski, 1991). This level was called “maps” because using fMRI. The ability to delineate functional areas non-invasively many of the most accurately defined entities at this scale (~1 cm) are would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified topographically organized cortical areas. Classic examples include functional areas. Correlations in spontaneous BOLD activity, often the multiple retinotopic maps of primary and extrastriate visual referred to as resting state functional connectivity (rs-fcMRI), are cortex, where each map constitutes a separate representation of the especially promising as a way to accurately localize differences in visual field and contains neurons with a distinctive collection of patterns of activity across large expanses of cortex. In the current functional characteristics. report, we applied a novel set of image analysis tools to explore the Organization at this scale is not limited to visual cortical regions, utility of rs-fcMRI for defining wide-ranging functional area nor to topographically organized somatosensory (Clark et al., 1988) boundaries. We find that rs-fcMRI patterns show sharp transitions or auditory (Langers et al., 2007) maps. Distinct subregions have in correlation patterns and that these putative areal boundaries can be been reported throughout the cortex, including motor (Strick, 1988), reliably detected in individual subjects as well as in group data. and orbitofrontal (Carmichael and Price, 1994) cortex as well as the Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal complete hemispheric partitioning schemes of Brodmann (1909) boundaries across large expanses of cortex without the need for prior and other classical anatomists. Although topography is not seen in information about a region's function or topography. Our approach every region, it can be combined with other attributes, as suggested reliably produces maps of bounded regions appropriate in size and by Passingham et al. (2002) for frontal and motor regions, to provide number for putative functional areas. These findings will hopefully a distinct “fingerprint” that can be used for the identification of stimulate further methodological refinements and validations. individual regions. © 2008 Elsevier Inc. All rights reserved. For the remainder of this report, regions that represent separable functional domains of cortex will be referred to as “functional areas”. “ ” “ ☆ This is in distinction to a more general term region or region of Sentence summary: Resting state functional connectivity can be used in interest”, which may encompass all or part of several functional a semi-automated fashion to delineate putative functional area borders across areas. Because functional areas possess unique combinations of the human cortical surface. inputs, outputs, and internal structure, each functional area is thought ⁎ Corresponding authors. Washington University School of Medicine, Department of Neurology-Campus Box 8111, 660 S. Euclid, St. Louis, MO to make a distinct contribution to information processing. Thus, the 63110, USA. Fax: +1 314 362 6110. study of each area's normal function, developmental trajectory, and E-mail addresses: [email protected], [email protected] modified responses following loss or injury, would be greatly aided (A.L. Cohen), [email protected] (S.E. Petersen). by the ability to accurately and reliably define the location and Available online on ScienceDirect (www.sciencedirect.com). boundaries of functional areas in individual living humans. 1053-8119/$ - see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2008.01.066 46 A.L. Cohen et al. / NeuroImage 41 (2008) 45–57 Four criteria have been proposed for defining cortical areas, boundaries of a large number of functional areas in single individuals. based mainly on studies of non-human primates (Felleman and Van The analyses presented below provide evidence that rs-fcMRI can aid Essen, 1991; Van Essen, 1985): Function (as defined by lesion- in the delineation of a large number of functional area boundaries in behavior or having neurons whose functional properties are distinct individual human cortex. They show that (i) changes in correlation from neighboring cortex), Architectonics (having unique arrange- maps occur abruptly as the seed location moves systematically across ments of cells, myelin density, and/or combinations of chemical the cortical sheet, suggesting the presence of a boundary rather than a markers, etc.), Connections (having a different combination of smooth gradation; (ii) these transitions occur in many locations in inputs and outputs from neighboring cortex), and Topography individual subjects; (iii) customized image processing techniques can (having a topographic map that can be used to define boundaries, for be used to identify putative boundaries and bounded regions across example between primary visual cortex and V2). large expanses of cortex; (iv) the boundaries appear reliable when Unfortunately, the current ability to define functional areas by assessed with independent measurements; (v) the overall map has the these criteria in human cerebral cortex is inadequate. fMRI and appropriate granularity to reflect area-level cortical parcellation. lesion studies provide some localization ability, but their precision is relatively low. Historic architectonic partitioning schemes, such as Methods the rendition of areas introduced by Korbinian Brodmann, unfor- tunately often contain incorrect boundaries and do not address Overview individual variation of location and extent (see Van Essen and Dierker, 2007). Promising methodologies such as architectonic The aim of the analysis stream presented here is to identify measurements in living humans (Scheperjans et al., 2007) and locations on the cortex where the pattern of rs-fcMRI changes connectional studies using DTI (Klein et al., 2007) are currently still rapidly, potentially representing boundaries between functional limited to a small set of areas. Mapping of topographic organization areas. Our approach utilizes established fcMRI voxel-wise correla- is mostly restricted to sensory and motor areas. tion methods, coupled to several novel analysis techniques that are Recently, measures of correlation between resting brain regions (so- performed on a surface representation of the cortex. These include called resting-state functional connectivity, or rs-fcMRI) have demon- established edge detection and image segmentation algorithms used strated promise in describing boundaries between functional areas in in computer vision and image analysis programs. These surface- limited regions of cortex (Margulies et al., 2007). Resting state based operations treat the brain as a 2D sheet, while volume-based functional connectivity is a method for evaluating regional interactions analyses treat the brain as a 3D volume. that occur when a subject is not performing an explicit task (Achard The flowchart in Fig. 1 describes the major steps involved in our et al., 2006; Beckmann et al., 2005; Biswal et al., 1995; Damoiseaux analysis stream. Annotations refer to specific parts of the Methods et al., 2006; Dosenbach et al., 2007; Fair et al., 2007a,b; Fox et al., section that describe each portion of our approach and figures that 2005; Greicius et al., 2003; Lowe et al., 1998; Nir et al., 2006; Salvador display the various intermediate steps of the analysis. This method- et al., 2005). It is based on the discovery that low-frequency (b~0.1Hz) ology can be applied to any set of fcMRI data (see Fair et al., 2007b), BOLD fluctuations in distant, but apparently functionally related grey but the current analysis involves continuous resting state/relaxed matter regions, show strong correlations at rest (Biswal et al., 1995; fixation data. Damoiseaux et al., 2006; Lowe et al., 1998;
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