Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex

Toro-Serey, Claudio; Tobyne, Sean M.; McGuire, Joseph T.

doi:10.1016/j.neuroimage.2019.116305

Cited by 16 publications

(11 citation statements)

References 71 publications

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“…In the third approach, many clustering algorithms are adopted to construct the connectional topography of the brain, and the normalized cut is one of the excellent clustering algorithms (Heuvel & Pol, 2010;Lee et al, 2012;Salehi et al, 2018;Toro-Serey et al, 2020;Van Den Heuvel et al, 2008;Yeo et al, 2011). So, the normalized cut based mapping method is chosen as a representative approach for comparison, which is a two-level mapping approach.…”

Section: Competitive Connectional Topography Mapping Approachesmentioning

confidence: 99%

“…Existing connectional topography mapping methods can be divided into three main categories, that is, prior connectivity based mapping method (Behrens et al, 2003;Hwang et al, 2017;Jiang et al, 2019;Johansen-Berg et al, 2005;Zhang et al, 2008Zhang et al, , 2010, independent component analysis (ICA) based mapping method (O'Muircheartaigh et al, 2011;O'muircheartaigh et al, 2015;O'muircheartaigh & Jbabdi, 2018;Wu et al, 2015Wu et al, , 2018Yuan et al, 2016), and clustering-based mapping method (Guevara, Román, Houenou, Duclap, & Guevara, 2016;Heuvel & Pol, 2010;Lee et al, 2012;Salehi, Karbasi, Shen, Scheinost, & Constable, 2018;Toro-Serey, Tobyne, & McGuire, 2020;Van Den Heuvel, Mandl, & Pol, 2008;Yeo et al, 2011). In particular, the prior connectivity based mapping method is a hypothesis-driven approach, which needs prior knowledge (i.e., prior brain targets) for mapping connectional topography.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the ICA based mapping method, the clustering-based mapping method is a data-driven approach as well, which is also able to construct meaningful connectional topography in the human brain by clustering functional connectivity profiles derived from fMRI data (Heuvel & Pol, 2010;Lee et al, 2012;Salehi et al, 2018;Toro-Serey et al, 2020;Van Den Heuvel et al, 2008;Yeo et al, 2011), or structural connectivity profiles derived from DTI data (Guevara et al, 2016). The existing clustering-based mapping method commonly constructs the connectional topography at the whole-brain level similar to the ICA based mapping method, which is typically achieved by grouping connectivity profiles utilizing clustering algorithms, such as normalized cut (Toro-Serey et al, 2020;Van Den Heuvel et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Cheng

Liu

2021

Human Brain Mapping

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Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data-driven manner by leveraging the power of co-clustering-based on resting state fMRI (rs-fMRI) data.We propose the co-clustering-based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co-clustering-based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs-fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs-fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres.

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Section: Competitive Connectional Topography Mapping Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Cheng

Liu

2021

Human Brain Mapping

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“…These deepest portions of cortex are also newest with respect to both evolution and development (LeDoux, 2019). It is also notable that these areas also appear to have most of the properties we normally associate with the essence of personhood (Hassabis et al, 2014), including individuality (Peña-Gómez et al, 2018;Toro-Serey et al, 2020), spontaneity (see above), and autonomy (James, 1879).…”

Section: Conscious and Unconscious Cores And Workpaces; Physical Submentioning

confidence: 99%

Integrated World Modeling Theory (IWMT) Revisited

Safron¹

2019

Preprint

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Here, I provide clarifications and discuss further issues relating to Safron (2020), “An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation.” As a synthesis of major theories of complex systems and consciousness, with IWMT we may be able to address some of the most difficult problems in the sciences. This is a claim deserving of close scrutiny and much skepticism. What would it take to solve the Hard problem? One could answer the question of how it is possible that something like subjectivity could emerge from objective brain functioning (i.e., moving from a third person to a first person ontology), but a truly satisfying account might still require solving all the “easy” and “real” problems of consciousness. In this way, IWMT does not claim to definitively solve the Hard problem, as explaining all the particular ways that things feel across all relevant aspects of experience is likely an impossible task. Nonetheless, IWMT does claim to have made major inroads into our understanding of consciousness, and here I will attempt to justify this position by discussing challenging problems and outstanding questions with respect to philosophy, (neuro)phenomenology, computational principles, practical applications, and implications for existing theories of mind and life.

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“…We expected that different operative domains related to the DMN would recruit distinctive sets of areas, including some regions typically assigned to other resting state networks (RSNs). Given the wide range of functions implicated with the mPFC (Delgado et al, 2016; Hiser and Koenigs, 2018; Lieberman et al, 2019; Schneider and Koenigs, 2017; Toro-Serey et al, 2020), we anticipated that this region would express a large variability in its activations, possibly organized in a rostral-caudal arc revolving around the callosal genu (Amodio and Frith, 2006). The PCC might show some internal differentiation as well (Leech et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Default Mode Network spatial configuration varies across task domains

Mancuso

Liloia

et al. 2021

Preprint

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Recent developments in network neuroscience suggest reconsidering what we thought we knew about the Default Mode Network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation maps were then computed. An additional map of task-induced deactivations was also created. A Multidimensional Scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to the involvement of lateral cortices in more external tasks. Further investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the variability of task-induced DMN anatomic redistribution was hard to recapitulate, as none of the maps, or any linear combination of them, could represent the whole space of its dynamical reconfiguration. Altogether, our findings suggest that the DMN may be characterized by a richer functional diversity and a more spatial complexity than previously suggested.

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Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex

Cited by 16 publications

References 71 publications

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Integrated World Modeling Theory (IWMT) Revisited

Default Mode Network spatial configuration varies across task domains

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