A Precuneal Causal Loop Mediates External and Internal Information Integration in the Human Brain
Summary (5 min read)
The DMN during tasks: A meta-analytic perspective
- The authors first validated that the DMN is indeed involved during goal-directed tasks, as findings regarding DMN's involvement during tasks are still disputed (Fransson, 2006) .
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- For the easy versus difficult contrast, wide-spread deactivations were found in the DMN.
- Forward inference shows that DMN subregions are active in the tasks.
Differential connectivity between v/dPCu and ICNs
- To explore further the differential response of the dorsal and ventral aspects of the PCu to task demands and the role they may serve in processing internal and external information, the authors next focused on the d/vPCu's connectivity, including structural, resting-state, task-state and effective connectivity.
- The authors demonstrated that there is a disparity in the v/dPCu's structural connectivity (SC) and resting-state (rs) FC (connectivity results for each seed are presented in Supplementary Materials) and the difference follows the pattern of internally and externally oriented cognitive function.
- The authors achieved this by computing the v/dPCu's endogenous taskstate FC (tsFC), i.e., the correlation of timeseries throughout the course of the two tasks, after regressing out the event-related haemodynamic response function (HRF).
- Again, contrary to the well-known DMN "anti-correlation" argument, the authors found a large cluster of positive tsFC with the d/vPCu, centring at the seeds themselves, and covering regions within and beyond the DMN (See supplementary materials for the composite of network domains of the v/dPCu's tsFC).
Cognitive demands modulate the effective coupling between the v/dPCu and internally/externally oriented networks
- The connectivity profiles of the d/vPCu as established so far suggest that the PCu overall has the structural framework necessary and may functionally serve as a platform connecting internal and externally related information.
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- Using PPI the authors found that as cognitive demand increased, eFC increased between the vPCu and IoN and between the dPCu and EoN.
- On the contrary, the vPCu was more connected to the rest of the DMN and interoceptive regions in difficult (vs. easy) conditions, and more connected with visual and primary sensory networks in easy (vs. difficult) conditions .
- Among the "one-state", "deterministic" DCMs, the third model wins over others with consistently higher posterior probability and higher exceedance of model evidence.
Model
- The "exchange" model, which specifies an effective connectivity (EC) modulation of dPCu à vPCu in the easy condition and vPCu à dPCu in the difficult condition; (2) the "forward" model, which specified the EC of dPCu à vPCu to be modulated in both difficult and easy conditions.
- The magnitude of its model evidence did not allow us to draw a safe conclusion of favouring it over the other model (K. E. Stephan et al., 2010) .
DISCUSSION
- The present study investigated the functional differentiation of the DMN and proposed a role for the precuneus (PCu) in mediating external and internal information binding.
- CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
- Braga and Buckner (2017) proposed the DMN encompasses several networks, the spatial pattern of which differs between individuals.
- Based on previous work and their current study, the authors believe the PCu is central for linking the function of the DMN subdivisions, thus playing a key role in integrating the brain's information flow from all sources.
NeuroSynth Meta-analysis of fMRI studies:
- When this work was carried out, the latest Neurosynth database contained 14,371 neuroimaging studies (https://github.com/neurosynth/neurosynth-data), associated with more than 3,200 text-based features and over 410,000 activation peaks that span a wide range of published neuroimaging studies.
- Since their focus is DMN functionality during tasks the authors searched for activation coordinates associated with attentional and executive tasks.
- On the other hand, reverse inference relies on the probability of the specified term being frequently discussed alongside a specific activation [i.e., P(Term|Activation)], thus showing the regions that are selectively associated with the term.
- Key parameters were based on the default values in the publicly available NeuroSynth toolbox (https://github.com/neurosynth/neurosynth).
- A frequency cut-off of 0.001 for article words was used to determine if a study used the term incidentally or purposely.
Tasks selected:
- The authors selected the Relational Processing (RP) task and the N-back working memory (N-back) task from the human connectome project (HCP), as these have two .
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- The copyright holder for this preprint this version posted August 21, 2020.
- Analyses for the two tasks were conducted independently first, and then results were averaged across the two tasks to emphasise domain-general brain activity.
- In the main text, domain-specific terms indicating a certain level of specific cognitive demand (such as 0-back and 2-back in the N-back task) were substituted with the general terms of "easy" and "difficult" to indicate the load of general cognitive abilities.
Image preprocessing:
- The ready-to-use HCP data has already been minimally preprocessed and quality-checked by the distributors (Glasser et al., 2013) and the authors carried out extra preprocessing steps with SPM12 (http//www.fil.ion.ucl.ac.uk/spm).
- Specifically, the data was smoothed with a Gaussian kernel of 6 mm FWHM (full-width half maximum) and no low-pass filtering was used as it might reduce signal strength and sensitivity.
- No global signal regression was used, for it may cause anti-correlation artefacts by shifting the distribution of FC towards negative values (Chai et al., 2012; Murphy et al., 2009) .
- To reduce the influence of non-neuronal confounds, eigenvalues were extracted from BOLD (Blood Oxygen Level Dependent) signals within cerebrospinal fluid (CSF) and white matter template masks and were regressed out from target signals during statistical testing.
Activation studies:
- Whole-brain activation was estimated using the standard SPM GLM approach.
- Only the trials with correct responses were explicitly modelled.
- Individual-level GLMs were modelled with difficulty level (2 levels) as the main effect, i.e. difficult (2-back or relational) & easy (0-back or match) conditions, with neuronal nuisance (i.e. CSF, white matter signals), session effects and 6 movement regressors as covariates.
- Group-level GLMs were then constructed to test the significance of the activation in the population level, with age and sex as covariates.
Selection of Regions of Interest (ROI)
- The authors were interested in the functionality of the PCu during attentional demanding goal-directed tasks; therefore, ROIs were selected based on their activation results from the N-back and RP tasks.
- Both activated and deactivated PCu regions associated with increased level of difficulty of the tasks were considered.
- To ensure the PCu region that the authors are considering is actually part of the DMN, they also superimposed it on the DMN canonical mask as defined by the Conn network atlas.
- The seed regions were selected by computing the spatial intersection of their task-related (de)activation clusters (exceeding the cluster-level family-wise-error/FWE-corrected P of 0.05), the PCu as defined by the Conn atlas and the DMN spatial localisation identified by the Conn atlas (Whitfield-Gabrieli & Nieto-Castanon, 2012) .
- Timeseries were extracted from seed regions, and their FC during the course of the experiment was calculated for the two tasks separately.
Functional connectivity studies:
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- The copyright holder for this preprint this version posted August 21, 2020.
Resting-state Seed-based FC of vPCu & dPCu
- Using SPM12, the authors calculated the FC as the partial correlation of the timeseries extracted from the seed regions (v/dPCu) with that from the rest of the brain, after controlling for the effect of non-neuronal confounds (estimated from white matter and CSF) and head movements.
- Baseline FC and differences between the FC of the (v)dPCu at the group level were estimated with one-sample and paired T-tests.
Structural Connectivity of vPCu & dPCu
- The acquisition, preprocessing the diffusion MRI (dMRI) images from the HCP and the generation of diffusion tensor maps has been detailed in published articles (Sotiropoulos et al., 2013) .
- Based on the diffusion tensor images the authors built probabilistic tractography in FSL5 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT).
- The structural connectivity matrix for each individual was obtained using vPCu or dPCu as the seed and the grey matter as the termination mask.
- This matrix was then projected to a standard MNI brain space for further statistical parametric mapping analyses in SPM12.
- Similar to FC analyses, baseline and difference of grey-matter connections of the dPCu and vPCu at the group level, were calculated with one-sample.
Psycho-physiological interactions (PPI)
- PPI was used to evaluate by what amount the cognitive variables during tasks upregulate or downregulate the FC between the seed region and its functionallycoupled regions .
- The implementation of the PPI was similar to the above seed-based FC during tasks, but additionally the GLM model included an interaction term, i.e. a new variable created by dot multiplying the task-related BOLD response and the seed's timeseries.
- Individual-level PPIs were estimated separately for the N-back and the RP task, but the final results were averaged across tasks because the authors wanted to emphasize the common emerging patterns.
- To emphasise that common pattern, the authors then averaged the individual-level modulatory effects between the two tasks, and conducted another group inference based on the individual averaged beta values.
- Results of task-specific PPIs and taskaveraged PPI are both provided.
Anatomical labels and ICN identification based on significant clusters
- To make inferences about the cognitive function of significant regions, the authors used the ly defined ICN-BM network atlas (https://www.nitrc.org/projects/icn_atlas/) for identifying the intrinsic connectivity networks (ICN)s involved in the two tasks.
- The advantage of using the ICN-BM atlas was the nomenclature used not only corresponds to the well-known canonical resting-state connectivity networks, but also to task-based co-activation networks which were generated from a meta-analyses using the BrainMap (BM) dataset (Cole et al., 2016; Smith et al., 2009) .
- When reporting the PPI result, the authors also applied the The spatial overlap reported in the main article was calculated as the ratio of the number of activated voxels over the region/ICN volume which the voxels belong to (Kozák et al., 2017) .
- Other ways of representing the spatial overlap, such as calculating spatial overlaps by taking into account the effect size of each significant voxel, were found to generate similar results and are presented in the Supplementary Materials.
- For identifying the anatomical regions based on the coordinates, the authors adopted the toolboxes of GingerALE (http://brainmap.org/ale/) and Talairach Daemon (http://www.talairach.org/daemon.html) where the Brodmann areas were identified from.
Group analysis and multiple-comparison correction
- For all statistical parametric mapping analyses, random factor effects (RFX) were used (random effect being the intercept of within-subject GLM fitting) and inferences .
- The copyright holder for this preprint this version posted August 21, 2020.
- More stringent voxel-level thresholds were sometimes used because the statistical power in this study was very high and the conventional cluster-forming threshold of P-uncorrected = 0.001 resulted, on some occasions, on clusters that were too extensive to be anatomically meaningful.
- The family-wise error rate was controlled at the cluster level, and a threshold of P-corrected < .05 was used to determine significance among clusters.
Dynamic Causal Modelling (DCM) specification:
- Dynamic causal modelling (DCM) is a generative model in a Bayesian framework for inferring hidden neuronal states from observed fMRI measurements (K. E. Stephan et al., 2010) .
- CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
- The copyright holder for this preprint this version posted August 21, 2020.
- ; https://doi.org/10.1101/2020.08.20.259846 doi: bioRxiv preprint 33 DCM model specified the endogenous connectivity between vPCu and dPCu to be bidirectional; and on top of that the authors modelled all possible configurations of how the task difficulty might influence the endogenous connectivity (Table 1 ).
- Based on that, the authors tried both the one-state, deterministic (the default) and the two-state, stochastic DCM class for modelling local neural dynamics.
DCM estimation:
- To determine the most likely model structure, the authors applied a fixed factor effect (FFX) Bayesian model selection (BMS) procedure to all 11 models estimated across all participants independently for the N-back and the RP task.
- The FFX was used as opposed to a random factor effect (RFX) because the authors hypothesised the mechanism to be general across all subjects.
- Finally, the model with the highest model evidence was.
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References
524 citations
"A Precuneal Causal Loop Mediates Ex..." refers background in this paper
...…all sources converges, endowing a narrative construction of reality: It have also been shown that during movie watching the temporal pattern of the PCu’s activity, compared to other brain regions, can track event boundaries of changing scenes in the most abstract level (Baldassano et al., 2017)....
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...At this brain connectivity hub, information from all sources converges, endowing a narrative construction of reality: It have also been shown that during movie watching the temporal pattern of the PCu’s activity, compared to other brain regions, can track event boundaries of changing scenes in the most abstract level (Baldassano et al., 2017)....
[...]
520 citations
"A Precuneal Causal Loop Mediates Ex..." refers background in this paper
...However, the PCu’s function seems to have an all-encompassing nature since it is activated under all kinds of cognitive demand where other parts of DMN are employed (Laird et al., 2009)....
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508 citations
502 citations
"A Precuneal Causal Loop Mediates Ex..." refers background in this paper
...…hippocampus, which are often implicated in value encoding, emotion, interoception and episodic memory (Chudasama et al., 2013; Euston et al., 2012; Gu et al., 2013; Pessoa, 2008), while the dPCu is more connected with regions in cognitive control networks that are associated with executive,…...
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...10 vPCu is more connected with the vmPFC, ACC and hippocampus, which are often implicated in value encoding, emotion, interoception and episodic memory (Chudasama et al., 2013; Euston et al., 2012; Gu et al., 2013; Pessoa, 2008), while the dPCu is more connected with regions in cognitive control networks that are associated with executive, attentional control and goal-directed behaviour (Figure 3a, 3b)....
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473 citations
"A Precuneal Causal Loop Mediates Ex..." refers methods in this paper
...A previous resting-state FC study showed that the vPCu is functionally closer to the DMN, while the dPCu is more coupled with the superior occipital lobe and cognitive control regions such as the SPL (Zhang & Li, 2012)....
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...A structural connectivity study also showed that from dorsal to ventral parts of the PCu, there is a spectrum of increasing connectivity with DMN regions (such as hippocampus and mPFC) and decreasing connectivity with sensorimotor, visual networks, thalamus and executive regions (such as SPL, prefrontal and premotor areas) (Cunningham et al., 2017)....
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