Individual Variability of the System-Level Organization of the Human Brain

Gordon, Evan M.; Laumann, Timothy O.; Adeyemo, Babatunde; Petersen, Steven E.

doi:10.1093/cercor/bhv239

Cited by 182 publications

(303 citation statements)

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“…, 2015), limiting some potential applications. Since GPIP adapts an initial atlas to individual resting fMRI data, it is unable to identify functional variability that is systemically absent from the group atlas (Laumann et al ., 2016; Gordon et al ., 2015; Mueller et al ., 2013). …”

Section: Discussionmentioning

confidence: 99%

“…However, it is well known that architectonic boundaries and regions of functional specialization do not align precisely with macroscopic morphological features (Brodmann, 1909; von Economo and Koskinas, 1925; Zilles and Amunts, 2010; Amunts et al ., 1999; Penfield and Rasmussen, 1950; Honey et al ., 2009; Gordon et al ., 2015). As a result, a common parcellation will invariably result in errors in defining functionally specialized regions when these areas are mapped back from the atlas to the individual.…”

Section: Introductionmentioning

confidence: 99%

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Individual parcellation of resting fMRI with a group functional connectivity prior

et al. 2017

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Cortical parcellation based on resting fMRI is an important tool for investigating the functional organization and connectivity of the cerebral cortex. Group parcellation based on co-registration of anatomical images to a common atlas will inevitably result in errors in the locations of the boundaries of functional parcels when they are mapped back from the atlas to the individual. This is because areas of functional specialization vary across individuals in a manner that cannot be fully determined from the sulcal and gyral anatomy that is used for mapping between atlas and individual. We describe a method that avoids this problem by refining an initial group parcellation so that for each subject the parcel boundaries are optimized with respect to that subject’s resting fMRI. Initialization with a common parcellation results in automatic correspondence between parcels across subjects. Further, by using a group sparsity constraint to model connectivity, we exploit group similarities in connectivity between parcels while optimizing their boundaries for each individual. We applied this approach with initialization on both high and low density group cortical parcellations and used resting fMRI data to refine across a group of individuals. Cross validation studies show improved homogeneity of resting activity within the refined parcels. Comparisons with task-based localizers show consistent reduction of variance of statistical parametric maps within the refined parcels relative to the group-based initialization indicating improved delineation of regions of functional specialization. This method enables a more accurate estimation of individual subject functional areas, facilitating group analysis of functional connectivity, while maintaining consistency across individuals with a standardized topological atlas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Individual parcellation of resting fMRI with a group functional connectivity prior

et al. 2017

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“…We next examined the extent to which the principal gradient captures the macroscale layout of intrinsic functional connectivity networks. Despite the high reproducibility of largescale resting-state networks (1,(44)(45)(46), there is no clear overarching spatial schema to explain the transition of one network to another. We examined the widely used seven-network parcellation by Yeo et al (2) with respect to the position of each network along the principal gradient (Fig.…”

Section: The Principal Gradient Captures the Spatial Layout Of Large-mentioning

confidence: 99%

Situating the default-mode network along a principal gradient of macroscale cortical organization

Margulies

Ghosh

Goulas

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/ motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.key assumption in neuroscience is that the topographical structure of the cerebral cortex provides an organizing principle that constrains its cognitive processes. Recent advances in the field of human connectomics have revealed multiple largescale networks (1-3), each characterized by distinct functional profiles (4). Some are related to basic primary functions, such as movement or perceiving sounds and images; some serve welldocumented, domain-general functions, such as attention or cognitive control (5-8); and some have functional characteristics that remain less well-understood, such as the default-mode network (DMN) (9, 10). Although the topography of these distinct distributed networks has been described using multiple methods (1-3), the reason for their particular spatial relationship and how this constrains their function remain unclear.Advances in mapping local processing streams have revealed spatial gradients that support increasingly abstract levels of representation, often extending along adjacent cortical regions in a stepwise manner (11). In the visual domain, for example, the ventral occipitotemporal object stream transforms simple visual features, coded by neurons in primary visual cortex, into more complex visual descriptions of objects in anterior inferior temporal cortical regions and ultimately, contributes to multimodal semantic representations in the middle temporal cortex and the most anterior temporal cortex that capture the meaning of what we see, hear, and do (12)(13)(14)(15). Similarly, in the prefrontal cortex, a rostral-caudal gradient has been proposed, whereby goals become increasingly abstract in anterior areas more distant from motor cortex...

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“…This cortical functional parcellation varies substantially across different individuals [7][8][9]. Nevertheless, approaches to defining these are still under development or in early validation stages; neuroimaging studies of brain networks typically still address parcellation at a group level [10], ignoring explicit representations of individual-level variations in parcellations.…”

mentioning

confidence: 99%

“…Dhillon et al proposed an individual sparse PCA with spatial anatomical priors [33]. Gordon et al developed a procedure to match each cortical vertex in each subject to a group averaged parcellation template using the similarity of connectivity profiles [8]. Wang et al designed another group template matching method based on the similarity of BOLD signals, in which the template is updated with each iteration [9].…”

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confidence: 99%

Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex

Matthews

Guo

2016

IEEE Trans. Biomed. Eng.

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Abstract-Objective: Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual -or state -dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects. Methods: We propose a method called "joint K-means" to robustly parcellate the cerebral cortex using functional magnetic resonance imaging (fMRI) data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations. Results: Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are non-uniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal and occipito-parietal cortices. This pattern is reproducible across sessions, between groups and using different numbers of parcels. Conclusion: The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability. Significance: The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences.

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Individual Variability of the System-Level Organization of the Human Brain

Cited by 182 publications

References 45 publications

Individual parcellation of resting fMRI with a group functional connectivity prior

Individual parcellation of resting fMRI with a group functional connectivity prior

Situating the default-mode network along a principal gradient of macroscale cortical organization

Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex

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