Modes of operation: A topographic neural gradient supporting stimulus dependent and independent cognition

Murphy, Charlotte; Wang, Hao-Ting; Konu, Delali; Lowndes, Rebecca; Margulies, Daniel S.; Jefferies, Elizabeth; Smallwood, Jonathan

doi:10.1016/j.neuroimage.2018.11.009

Cited by 116 publications

(128 citation statements)

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“…Gradients can also be used as a coordinate system, and stratify cortical features that are not per se gradient-based. Examples include surface-485 based geodesic distance measures from sensory-motor regions to other regions of cortex (Margulies et al, 2016), task-based functional activation patterns and meta-analytical data (Murphy et al, 2018(Murphy et al, , 2019Margulies et al, 2016), as well as MRI-based cortical thickness and PET-derived amyloid beta uptake measures (Lowe et al, 2019). As such, using manifolds as a new coordinate 490 system (Huntenburg et al, 2018) may complement widely used parcellation approaches (Schaefer et al, 2017;Yeo et al, 2011;Glasser et al, 2016) and be can be useful for dimensionality reduction of findings and for the interpretation and communication of results.…”

Section: Discussionmentioning

confidence: 99%

“…The ability to describe the brain wide neural activity in a single manifold offers the possibility to understand how the integrated nature of neural processing gives rise to function and dysfunction. Adopting a macroscale perspective on 50 cortical organization has already provided important insights into how cortexwide patterns relate to cortical dynamics and high level cognition (Sormaz et al, 2018;Murphy et al, 2018Murphy et al, , 2019Shine et al, 2019). Furthermore, several studies have leveraged gradients as an analytical framework to describe atypical macroscale organization across clinical conditions, 55 for example, by showing perturbations in functional connectome gradients in autism (Hong et al, 2019) and schizophrenia (Tian et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…We also provide the evaluated FC and MPC gradients across these different spatial scales. Such gradients can be used to, for example stratify other imaging measures, including 380 functional activation and connectivity patters (Hong et al, 2019;Murphy et al, 2018), meta-analytical syntheses (Murphy et al, 2019;Margulies et al, 2016), cortical thickness measures or Amyloid-beta PET uptake data (Lowe et al, 2019).…”

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confidence: 99%

“…Hong, S.J., Vos de Wael, R., Bethlehem, R.A., Lariviere, S., Paquola, C., Valk, S.L., Milham, M.P., Di Martino, A.,Margulies, D.S., Smallwood, J., et al, 2019. Atypical functional connectome hierarchy in autism.…”

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confidence: 99%

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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Wael

Benkarim

Paquola

et al. 2019

Preprint

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Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact manner through the use of multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. More generally, its macroscale perspective on brain organization offers novel possibilities to investigate the complex relationships between brain structure, function, and cognition in a quantified manner. Here, we present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Our toolbox also allows for controlled association studies between gradients with other brain-level features, adjusted with respect to several null models that account for spatial autocorrelation. The toolbox is implemented in both Python and Matlab, programming languages widely used by the neuroimaging and network neuroscience communities. Several use-case examples and validation experiments demonstrate the usage and consistency of our tools * Corresponding author Email address: boris.bernhardt@mcgill.ca (Boris C. Bernhardt) 1 Authors contributed equally to this work.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Wael

Benkarim

Paquola

et al. 2019

Preprint

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108

256

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“…Although these studies provide evidence of associations between the DMN and off-task thought, the broader view of this system as task-negative has been challenged (Spreng, 2012). For example, recent studies of connectivity during cognitive tasks have shown that the DMN communicates selectively with task positive systems (Krieger-Redwood et al, 2016), and that DMN activity can increase during cognitive task that require internally represented information or task rules (Crittenden, Mitchell, & Duncan, 2015;Murphy et al, 2018;2019;Vatansever, Menon, & Stamatakis, 2017). If the functions of the DMN are not task-negative per se then this would undermine a simple oneto-one mapping of DMN activity and content processed in the off-task state.…”

Section: Introductionmentioning

confidence: 99%

Hello, is that me you are looking for? A re-examination of the role of the DMN in off-task thought

Murphy

Poerio

Sormaz

et al. 2019

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Neural activity within the default mode network (DMN) is widely assumed to relate to processing during off-task states, however it remains unclear whether this association emerges from a shared role in self or social cognition. In the current study, we examine the possibility that the role of the DMN in ongoing thought emerges from contributions to specific features of off-task experience such as self-relevant or social content. A group of participants described their experiences while performing a laboratory task over a period of days. In a different session, neural activity was measured while participants performed self/other judgements. Despite the prominence of social and personal content in off-task reports, there was no association with neural activity during off-task trait adjective judgements. Instead, during both self and other judgements we found recruitment of caudal posterior cingulate cortex -a core DMN hub -was above baseline for individuals whose laboratory experiences were characterised as detailed. These data provide little support for a role of the DMN in self or other content in the off-task state and instead suggest a role in how on-going thought is represented.3

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Dynamic brain network configurations during rest and an attention task with frequent occurrence of mind wandering

Denkova

Nomi

Uddin

et al. 2019

Human Brain Mapping

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Mind wandering (MW) has become a prominent topic of neuroscientific investigation due to the importance of understanding attentional processes in our day‐to‐day experiences. Emerging evidence suggests a critical role for three large‐scale brain networks in MW: the default network (DN), the central executive network (CEN), and the salience network (SN). Advances in analytical methods for neuroimaging data (i.e., dynamic functional connectivity, DFC) demonstrate that the interactions between these networks are not static but dynamically fluctuate over time (Chang & Glover, 2010, NeuroImage, 50(1), 81–98). While the bulk of the evidence comes from studies involving resting‐state functional MRI, a few studies have investigated DFC during a task. Direct comparison of DFC during rest and task with frequent MW is scarce. The present study applies the DFC method to neuroimaging data collected from 30 participants who completed a resting‐state run followed by two runs of sustained attention to response task (SART) with embedded probes indicating a high prevalence of MW. The analysis identified five DFC states. Differences between rest and task were noted in the frequency of three DFC states. One DFC state characterized by negative DN–CEN/SN connectivity along with positive CEN–SN connectivity was more frequently observed during task vs. rest. Two DFC states, one of which was characterized by weaker connectivity between networks, were more frequently observed during rest than task. These findings suggest that the dynamic relationships between brain networks may vary as a function of whether ongoing cognitive activity unfolds in an “unconstrained” manner during rest or is “constrained” by task demands.

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Modes of operation: A topographic neural gradient supporting stimulus dependent and independent cognition

Cited by 116 publications

References 40 publications

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Hello, is that me you are looking for? A re-examination of the role of the DMN in off-task thought

Dynamic brain network configurations during rest and an attention task with frequent occurrence of mind wandering

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