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.

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

https://cogns.northwestern.edu/cbmg/TBSSNI2006.pdf

Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

https://hal.inria.fr/hal-02132147/document

New advances in the Clinica software platform for clinical neuroimaging studies

3D Slicer as an image computing platform for the Quantitative Imaging Network.

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.”

/pdf/correspondence-of-the-brain-s-functional-architecture-during-3nxikbjhyt.pdf

Correspondence of the brain's functional architecture during activation and rest.

Advances in functional and structural MR image analysis and implementation as FSL.

Removal of FMRI environment artifacts from EEG data using optimal basis sets.

Resting state networks (RSNs), as imaged by functional MRI, are distributed maps of areas believed to be involved in the function of the “resting” brain, which appear in both resting and task data The current dominant view is that such networks are associated with slow (∼ 0015 Hz), spontaneous fluctuations in the BOLD signal To date, limited work has investigated the frequency characteristics of RSNs; here we investigate a range of issues relating to their spectral and phase characteristics Our results indicate that RSNs, although dominated by low frequencies in the raw BOLD signal, are in fact broadband processes that show temporal coherences across a wide frequency spectrum In addition, we show that RSNs exhibit different levels of phase synchrony at different frequencies These findings challenge the notion that FMRI resting signals are simple “low frequency” spontaneous signal fluctuations

Spectral characteristics of resting state networks.

Resting-state networks (RSNs) in fMRI are ‘activation-like’, spatially structured maps of grey matter brain areas exhibiting temporally correlated signal changes, which are believed to reflect neuronal activities of the ‘resting’ brain and which robustly and consistently appear in both resting and task data. They are purported to reflect the intrinsic energy demands of neuron populations that fire together with a common functional purpose. This chapter introduces the concept of RSNs and why they are of interest to neuroscience, describes their characteristics, reviews the methods used for their analysis, and discusses a few areas of application.

Rami K. Niazy

Papers

Advances in functional and structural MR image analysis and implementation as FSL.

Removal of FMRI environment artifacts from EEG data using optimal basis sets.

Spectral characteristics of resting state networks.

Resting-State Networks