Network connectivity correlates of variability in fluid intelligence performance

Abstract

Abstract\ud Abstract reasoning requires a pattern of spatial and temporal coordination among regions across the entire brain. Recent evidence suggests a very high similarity between spontaneous and evoked brain activity in humans, implying that a fine characterization of brain dynamics recorded during resting-state might be informative for the understanding of evoked behavior. In a recent work, we listed and detailed the sets of regions showing robust co-activation during the solution of fluid intelligence (gf) tasks, decomposing such meta-analytic maps in stimulus- and reasoning stage-specific sub-maps. However, while anatomical overlap with well-known resting-state fMRI networks (RSNs) has been documented, we here propose a quantitative validation of such findings via functional connectivity analysis in a sample of healthy participants. Results highlight a striking degree of similarity between the connectivity profile of the gf network and that of the dorsal attention network, with additional overlap with the left and right fronto-parietal control networks. Interestingly, a strong negative correlation with structures of the default mode network (DMN) was also identified. Results of regression models built on two independent fMRI datasets confirmed the negative correlation between gf regions and medial prefrontal structures of the DMN as a significant predictor of individual gf scores. These might suggest a framework to interpret previously reported aging-related decline in both gf and the correlation between “task-positive” networks and DMN, possibly pointing to a common neurophysiological substrate

Impact of network‐targeted multichannel transcranial direct current stimulation on intrinsic and network‐to‐network functional connectivity

“…Several studies have also investigated the impact of tDCS by combining TMS‐EEG (Pellicciari et al., 2013; Romero Lauro et al., 2014; Varoli et al., 2018) or by means of neuroimaging measures (Amadi, Ilie, Johansen‐Berg, & Stagg, 2014; Polanía, Paulus, & Nitsche, 2012a, 2012b; Sehm, Kipping, Schäfer, Villringer, & Ragert, 2013). Given the shift in focus toward network‐based approaches for the study of human cognition (Pisoni et al., 2018; Santarnecchi et al., 2017; Santarnecchi, Momi, et al., 2018; Santarnecchi, Sprugnoli, et al., 2018; Spreng et al., 2016) —and more recently even for the diagnosis of neuropsychiatric conditions (Fox et al., 2014)— we tested the impact of a tDCS montage optimized to concurrently modulate multiple nodes of the SMN (and its negatively correlated regions) instead of just left M1. Using a multifocal tDCS solution previously tested by our group (Fischer et al., 2017), here we document a greater modulation of FC involving both left and right M1 during net‐tDCS compared to standard tDCS, similar to that observed in our previous study where an increase in excitability of right M1 was found solely for net‐tDCS.…”

“…Moreover, the negative connectivity (or “anticorrelation”) between brain networks has been also promoted as a crucial aspect of the functional organization of the human brain, with relevance for cognitive performance (Fox et al., 2005). For instance, recent reports have highlighted how the strength of the negative connectivity between regions of the dorsal attention network (DAN) and the DMN is among the best predictors of individual variability in intelligence levels (Santarnecchi et al., 2017). Recent work by our group has shown the possibility to selectively modify resting‐state fMRI network‐to‐network coupling by means of multisite TMS using cortico‐cortical paired associative stimulation (cc‐PAS; Santarnecchi, Momi, et al., 2018); however, the possibility to modulate inter‐network dynamics by means of network‐targeted tDCS has not been demonstrated yet.…”

Stimuli, presentation modality, and load‐specific brain activity patterns during n‐back task

“…Even though previous studies have reported deactivation coordinates for single analysis, to the best of our knowledge here we originally provide an ALE map showing the neural deactivation during the n-back task, revealing a signifi- In line with the "deactivation of the DMN," we identified a high resemblance between the connectivity profile of regions activated during n-back processing and the DAN. Interestingly, the interplay between these two networks has been suggested as a major candidate biomarker for normal and pathological aging (Spreng & Schacter, 2012;Spreng, Stevens, Viviano, & Schacter, 2016) and has been correlated with cognition in healthy young participants Santarnecchi, Emmendorfer, Tadayon, et al, 2017). A clear overlap with DMN and DAN can suggest the modulation of their interplay as a candidate target for brain stimulation interventions based on transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), able to modulate network-level activity and elicit cognitive enhancement (Ruffini, Wendling, Sanchez-Todo, & Santarnecchi, 2018;Santarnecchi et al, 2018).…”

Whole-brain white matter organization, intelligence, and educational attainment

“…A consistent finding is that functional and structural brain networks with higher global efficiency, i.e. networks with shorter connections between any pair of nodes in the network, are associated with higher scores on assessments of general intelligence in both children and adults (Santarnecchi et al 2017; Pineda-Pardo et al 2016; Kim et al 2016; van den Heuvel et al 2009).…”