Distributed processing; distributed functions?

Fox, Peter T.; Friston, Karl J.

doi:10.1016/j.neuroimage.2011.12.051

Cited by 87 publications

(57 citation statements)

References 153 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This may be particularly fruitful to infer brain–behavior relationships according to the functional integration perspective on brain function (Fox and Friston, 2012). Hence, we discuss the functional organization of the sACC and the putative roles of areas 25, s24, s32 and 33 based on commonalities as well as on differences between co-activation networks.…”

Section: Discussionmentioning

confidence: 99%

Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity

et al. 2015

View full text Add to dashboard Cite

Human subgenual anterior cingulate cortex (sACC) is involved in affective experiences and fear processing. Functional neuroimaging studies view it as a homogeneous cortical entity. However, sACC comprises several distinct cyto- and receptorarchitectonical areas: 25, s24, s32, and the ventral portion of area 33. Thus, we hypothesized that the areas may also be connectionally and functionally distinct. We performed structural post mortem and functional in vivo analyses. We computed probabilistic maps of each area based on cytoarchitectonical analysis of ten post mortem brains. Maps, publicly available via the JuBrain atlas and the Anatomy Toolbox, were used to define seed regions of task-dependent functional connectivity profiles and quantitative functional decoding. sACC areas presented distinct co-activation patterns within widespread networks encompassing cortical and subcortical regions. They shared common functional domains related to emotion, perception and cognition. A more specific analysis of these domains revealed an association of s24 with sadness, and of s32 with fear processing. Both areas were activated during taste evaluation, and co-activated with the amygdala, a key node of the affective network. s32 co-activated with areas of the executive control network, and was associated with tasks probing cognition in which stimuli did not have an emotional component. Area 33 was activated by painful stimuli, and co-activated with areas of the sensorimotor network. These results support the concept of a connectional and functional specificity of the cyto- and receptorarchitectonically defined areas within the sACC, which can no longer be seen as a structurally and functionally homogeneous brain region.

show abstract

Section: Discussionmentioning

confidence: 99%

Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The sidebar on graph theory was contributed by Ed Bullmore and Nicolas Crossley. Portions of this review were adapted from Fox & Friston (2012).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Meta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases

Fox

Lancaster

Laird

et al. 2014

Annu. Rev. Neurosci.

Self Cite

166

126

View full text Add to dashboard Cite

Spatial normalization—applying standardized coordinates as anatomical addresses within a reference space—was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.

show abstract

“…Indeed, given the various anatomical inputs to single regions, there can be several functions associated with such regions, making it challenging to understand how the brain enables behavior (Friston, 2005; Park & Friston, 2013). Solving this challenge rests with our ability to understand brain connectivity because the function of a region depends on its interactions with other brain regions (Fox & Friston, 2012). Brain connectivity is now being explored more frequently using various techniques that range in their ability to quantify neuronal coupling between brain regions (Friston, 2009; Smith et al, 2011; Sporns, 2014).…”

Section: Introductionmentioning

confidence: 99%

Toward a cumulative science of functional integration: A meta‐analysis of psychophysiological interactions

Smith

Gseir

Speer

et al. 2016

Human Brain Mapping

View full text Add to dashboard Cite

Much of the work in cognitive neuroscience is shifting from a focus on single brain regions to a focus on the connectivity between multiple brain regions. These inter-regional connectivity patterns contribute to a wide range of behaviors and are studied with models of functional integration. The rapid expansion of the literature on functional integration offers an opportunity to scrutinize the consistency and specificity of one of the most popular approaches for quantifying connectivity: psychophysiological interaction (PPI) analysis. We performed coordinate-based meta-analyses on 284 PPI studies, which allowed us to test a) whether those studies consistently converge on similar target regions and b) whether the identified target regions are specific to the chosen seed region and psychological context. Our analyses revealed two key results. First, we found that different types of PPI studies—e.g., those using seeds such as amygdala and dorsolateral prefrontal cortex (DLPFC) and contexts such as emotion and cognitive control, respectively—each consistently converge on similar target regions, thus supporting the reliability of PPI as a tool for studying functional integration. Second, we also found target regions that were specific to the chosen seed region and psychological context, indicating distinct patterns of brain connectivity. For example, the DLPFC seed reliably contributed to a posterior cingulate cortex target during cognitive control but contributed to an amygdala target in other contexts. Our results point to the robustness of PPI while highlighting common and distinct patterns of functional integration, potentially advancing models of brain connectivity.

show abstract

Distributed processing; distributed functions?

Cited by 87 publications

References 153 publications

Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity

Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity

Meta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases

Toward a cumulative science of functional integration: A meta‐analysis of psychophysiological interactions

Contact Info

Product

Resources

About