The organization of the human cerebral cortex estimated by intrinsic functional connectivity
01 Sep 2011-Journal of Neurophysiology (American Physiological Society)-Vol. 106, Iss: 3, pp 1125-1165
TL;DR: In this paper, the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI data from 1,000 subjects and a clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex.
Abstract: Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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TL;DR: Progress made during the first half of the Human Connectome Project project in refining the methods for data acquisition and analysis provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
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TL;DR: In this article, the authors studied functional brain organization in healthy adults using resting state functional connectivity MRI and proposed two novel brain wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships.
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TL;DR: Using multi-modal magnetic resonance images from the Human Connectome Project and an objective semi-automated neuroanatomical approach, 180 areas per hemisphere are delineated bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults.
Abstract: Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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University of Western Ontario1, York University2, University of Bergen3, The Mind Research Network4, National Institutes of Health5, University of New Mexico6, University of Chieti-Pescara7, Washington University in St. Louis8, Stanford University9, Georgia Institute of Technology10, Oulu University Hospital11, Indiana University12, Otto-von-Guericke University Magdeburg13, Leibniz Institute for Neurobiology14
TL;DR: Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain.
2,332 citations
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TL;DR: An improved methodology for recording human visual topography in striate and extra-striate cortex via fMRI at 3 T and 7 T field strengths is described, as well as the first fit of a two-dimensional map function which jointly models the visuotopic structure within cortical areas V1, V2, and V3 to fMRI visualTopography data.
Abstract: We describe an improved methodology for recording human visual topography in striate and extra-striate cortex via fMRI at 3 T and 7 T field strengths, as well as the first fit of a two-dimensional map function which jointly models the visuotopic structure within cortical areas V1, V2, and V3 to fMRI visual topography data. We discuss five methodological improvements for fMRI visual topography studies. (1) We constructed a custom multi-channel surface coil covering occipital cortex for an improvement in signal-to-noise ratio relative to standard head coil systems. (2) Real-time feedback to subjects, based on psychometrically established eye fixation performance for individual subjects, motivated subjects to maintain fixation. This enabled us to verify accurate long-term fixation and to discard trials where performance was poor. (3) We developed a phase encoding stimulus paradigm where the standard M-factor scaling of a black-and-white checkerboard pattern is replaced with a dynamic spatial noise pattern, in which the correlation length of the noise is matched to cortical magnification factor and thus scales with distance from the center of the visual field. (4) Least-squares optimal quasiisometric brain flattening was used to obtain flat representations of the two-dimensional cortical surface without relaxation cuts through V1 or any other retinotopic area. (5) Finally, we fit a recently developed model of the structure of V1, V2, and V3 visual topography (Balasubramanian et al., Neural Networks, 15:1157–1163, 2002) to our data. This mathematical model allows for shear (i.e., anisotropy) in the cortical map and uses a small number of parameters (two global parameters and one additional parameter each for V1, V2, and V3 shear). Results of this new methodology and data analysis are presented on five human subjects, four collected at 3 T field strength and one collected at 7 T field strength. Presented at Vision Sciences Society 2005 Annual Meeting. Abstract number 901. Support Contributed By: NIH/NIBIB EB001550 Contact info: Jonathan Polimeni, Computer Vision and Computational Neuroscience Lab, 677 Beacon St., Boston, MA, 02215. URL: http://eslab.bu.edu, Email: jonnyreb@cns.bu.edu
11 citations