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Open AccessJournal ArticleDOI

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

TLDR
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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Citations
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The cerebellar cognitive affective/Schmahmann syndrome scale

TL;DR: A 10-item scale providing total raw score, cut-offs for each test, and pass/fail criteria that determined 'possible', 'probable', and 'definite' CCAS is derived, useful for expedited clinical assessment of CCAS in patients with cerebellar disorders.
Journal ArticleDOI

Specifying the core network supporting episodic simulation and episodic memory by activation likelihood estimation.

TL;DR: The present article specifies this core network via Activation Likelihood Estimation (ALE) to determine the set of brain regions that allow us to experience past and hypothetical episodes, thus providing an important foundation for studying the regions' specialized contributions and interactions.
Journal ArticleDOI

Functional gradients of the cerebellum.

TL;DR: Application of diffusion map embedding to resting-state data from the Human Connectome Project dataset shows for the first time that cerebellar functional regions follow a gradual organization which progresses from primary (motor) to transmodal (DMN, task-unfocused) regions.
Journal ArticleDOI

Contributions of episodic retrieval and mentalizing to autobiographical thought: evidence from functional neuroimaging, resting-state connectivity, and fMRI meta-analyses

TL;DR: The results suggest the default network is a heterogeneous brain system whose subsystems support distinct component processes of autobiographical thought.
Journal ArticleDOI

Quantitative assessment of structural image quality.

TL;DR: Data quality both inflated and obscured associations with age during adolescence, indicating that reliable measures of data quality can be automatically derived from T1‐weighted volumes, and that failing to control for dataquality can systematically bias the results of studies of brain maturation.
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Journal ArticleDOI

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