Interpretation of Neuroimaging Data Based on Network Concepts

Abstract

By capturing the actions of distributed brain regions, neuroimaging can give unique insights into the networks underlying complex behavioral and cognitive functions. An approach to interpreting neuroimaging data grounded in emerging ideas in brain network theory is needed to better characterize these large-scale network dynamics. This paper focuses on three concepts germane to this approach to interpretation: "connectivity", "neural context", and "small-world properties". Measures of brain connectivity emphasize the combined action of areas. Functional connectivity analyses focus on interacting neural patterns, whereas effective connectivity analyses uncover directional influences between brain areas. The second concept, neural context, purports that a region's contribution to a function is more fully appreciated in relation to other coactive brain areas. The final concept is the extension of graph theory measures to the estimation of small-world properties. Measures such as clustering and path length can be used to infer the computational capacity of functional networks. These three constructs are central to the interpretation of neuroimaging data that will further unravel how brain network dynamics guide mental function, and are beginning to be applied to the study of neural disorders.

Discrete neural substrates underlie complementary audiovisual speech integration processes

“…The evidence for multiple processes in distinct subregions of mSTC supports and extends the hypothesis that mSTC is a core region ( Miller and D’Esposito, 2005) or neural hub( Hagmann et al, 2008; McIntosh and Korostil, 2008; Sporns, 2010)of a sensory integration network. Multisensory STC is known to be involved in both bottom-up integration of sensory stimuli, receiving afferent projections from early visual and auditory cortex(as well as somatosensory) ( Jones and Powell, 1970; Pandya and Yeterian, 1985; Seltzer and Pandya, 1978) and providing feedback to early sensory cortices ( Pandya and Yeterian, 1985).…”

“…In addition to low -level sensory connections and interactions, mSTC also interacts with other cortical regions associated with higher-level neurocognitive multisensory interactions such as inferior frontal gyrus (including Broca’s area)and inferior parietal sulcus ( Romanski et al, 1999). Given these anatomical and functional connections, viewing mSTC as a neural hub (Hagmann et al, 2008; McIntosh and Korostil, 2008; Sporns, 2010)of the multisensory processing network is warranted. Our results suggest that mSTC is involved with both low-level interactions (temporal synchrony) and higher-level interactions (perceptual fusion), both of which are predicted by this hypothesized role for mSTC( Miller and D’Esposito, 2005).…”

Persistency and flexibility of complex brain networks underlie dual‐task interference

“…One issue in functional connectivity analyses of task data is indirect correlations between processing nodes, which are induced by a common input to the nodes as a result of the external presentation of stimuli [McIntosh and Korostil, 2008;Smith et al, 2011]. External stimulation of one or different modalities evokes temporally correlated activities between brain nodes; however, this coactivation does not necessarily imply direct information flow between these regions.…”

Characterization of anatomical and functional connectivity in the brain: A complex networks perspective

“…Until recently, synchronization has been studied in neurophysiology mainly as a phenomenon occurring between two neurons or two brain areas. What has been lacking until recently was a theoretically supported perspective of synchronization in large-scale complex brain networks (McIntosh and Korostil, 2008). A few years ago a number of major breakthroughs occurred in the mathematical theory of complex networks (Barabasi and Albert, 1999;Watts and Strogatz, 1998).…”