The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

Rawls, Eric; Kummerfeld, Erich; Mueller, Bryon A.; Ma, Sisi; Zilverstand, Anna

doi:10.1016/j.neuroimage.2022.119211

Cited by 12 publications

(10 citation statements)

References 121 publications

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“…Causal centrality was defined as the distributional divergence induced by intervention on the edges of the causal graph (Janzing et al 2013;Pearl 2010). This interventional approach is conceptually similar to "lesioning" (Deco et al 2017;Irimia and van Horn 2014), "hubopathy" (Bell and Shine 2016) and "vulnerability analysis" (Iturria-Medina et al 2008;Rawls et al 2022) in network (neuro)science, which measure the effect of edge/node removal on the network integrity and performance (Gol'dshtein et al 2004). Notably, since causal centrality is conditioned on the generative model of the DCM associated with the graph, it explicitly accounts for the network dynamics.…”

Section: Discussionmentioning

confidence: 99%

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

Zarghami

2023

Preprint

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Network representation has been a groundbreaking concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against major graph-theoretical centralities. The results showed that the subcortical structures of the eDMN are more causally central than the cortical regions, even though the (dynamics-free) graph-theoretical centralities unanimously favor the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality - to study other neurotypical and pathological functional networks - are discussed, and some future lines of research are outlined.

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Section: Discussionmentioning

confidence: 99%

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

Zarghami

2023

Preprint

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“…Time series data from these selected parcellations ( Table 3 , details in Supplementary Material D and E ) were extracted. Second, to generate causal models of LDLPFC’s directional influence on addiction networks we used the CDA method Greedy Adjacencies and Non-Gaussian Orientations (GANGO [5]). GANGO allowed us to identify nodal adjacencies [6–8] and edge orientations [9].…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…GANGO allowed us to identify nodal adjacencies [6–8] and edge orientations [9]. This method has been used to generate causal models of AUD [10] and fMRI data [5]. Third, to quantify the strength of each connection we used the GANGO causal models to determine the structure of a structural equation model (SEM), and fit that SEM to the time series data.…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…inhibitory control, working memory). A following paper by the same group [36] detailed how CDA can be used to determine whole-brain causal connectomes from rest fMRI data determining (i) edges or connections between regions and (ii) edge orientation [37,38]. In the current study, we applied this CDA methodology [36] to determine whether the causal relationship between the left dorsolateral prefrontal cortex (LDLPFC, tDCS stimulation site [39]) and theoretically defined addiction networks known to support abstinence [16] changes following intervention.…”

Section: Introductionmentioning

confidence: 99%

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Frontal tDCS reduces alcohol relapse rates by increasing connections from left dorsolateral prefrontal cortex to addiction networks

Camchong

Fiecas

Gilmore

et al. 2022

Preprint

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BackgroundBrain-based interventions are needed to address persistent relapse in alcohol use disorder (AUD). Neuroimaging evidence suggests higher fronto-striatal connectivity as well as higher within-network connectivity of theoretically defined addiction networks is associated with reduced relapse rates and extended abstinence during follow-up periods.Objective/HypothesisA longitudinal randomized double-blind sham-controlled clinical trial investigated whether a non-invasive neuromodulation intervention delivered during early abstinence can (i) modulate connectivity of addiction networks supporting abstinence and (ii) improve relapse rates. Hypotheses: Active transcranial direct current stimulation (tDCS) will (i) increase connectivity of addiction networks known to support abstinence and (ii) reduce relapse rates.MethodsShort-term abstinent AUD participants (n=60) were assigned to 5 days of either active tDCS or sham during cognitive training. Causal discovery analysis (CDA) examined the directional influence from left dorsolateral prefrontal cortex (LDLPFC, stimulation site) to addiction networks that support abstinence.ResultsActive tDCS had an effect on the average strength of CDA-determined connectivity from LDLPFC to the incentive salience and negative emotionality addiction networks - increasing in the active tDCS group and decreasing in the sham group. Active tDCS had an effect on relapse rates 1-month following the intervention, with lower probability of relapse in the active tDCS vs. sham groups. Active tDCS showed an unexpected sex-dependent effect on relapse rates.ConclusionOur results suggest that LDLPFC stimulation delivered during early abstinence has an effect on addiction networks supporting abstinence and on relapse rates. The unexpected sex-dependent neuromodulation effects need to be further examined in larger clinical trials.

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“…As mentioned above, architectural models of cognition rely on the presence of some kind of underlying network system, but the structure and mechanisms are often only loosely tied to structures present in the brain. There is some evidence to suggest that a single high level network structure is supporting activity across tasks (Stocco et al, 2021 ; Rawls et al, 2022 ), and lower level analyses have found structural similarities in network activity across both states (Cole et al, 2014 ; Krienen et al, 2014 ; Bolt et al, 2017 ), but the degree to which a single, broad scope architecture is involved in the patterns of LFO activity at rest is still unclear.…”

Section: Introductionmentioning

confidence: 99%

The Structured Mind at Rest: Low-Frequency Oscillations Reflect Interactive Dynamics Between Spontaneous Brain Activity and a Common Architecture for Task Control

2022

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The Common Model of Cognition (CMC) has been proposed as a high level framework through which functional neuroimaging data can be predicted and interpreted. Previous work has found the CMC is capable of predicting brain activity across a variety of tasks, but it has not been tested on resting state data. This paper adapts a previously used method for comparing theoretical models of brain structure, Dynamic Causal Modeling, for the task-free environment of resting state, and compares the CMC against six alternate architectural frameworks while also separately modeling spontaneous low-frequency oscillations. For a large sample of subjects from the Human Connectome Project, the CMC provides the best account of resting state brain activity, suggesting the presence of a general purpose structure of connections in the brain that drives activity when at rest and when performing directed task behavior. At the same time, spontaneous brain activity was found to be present and significant across all frequencies and in all regions. Together, these results suggest that, at rest, spontaneous low-frequency oscillations interact with the general cognitive architecture for task-based activity. The possible functional implications of these findings are discussed.

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The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

Cited by 12 publications

References 121 publications

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

Frontal tDCS reduces alcohol relapse rates by increasing connections from left dorsolateral prefrontal cortex to addiction networks

The Structured Mind at Rest: Low-Frequency Oscillations Reflect Interactive Dynamics Between Spontaneous Brain Activity and a Common Architecture for Task Control

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