Dynamic causal brain circuits during working memory and their functional controllability

Abstract

Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe functional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto-parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition.

Spontaneous Neuronal Oscillations in the Human Insula are Hierarchically Organized Traveling Waves

“…The AI lacks external (layer II) and internal granular (IV) layers, which may subserve its special role of integrating information across wide-spread neuronal networks (Mesulam & Mufson., 1985;Morel et al, 2013;Wylie et al, 2015). By showing that there are distinctive patterns of oscillations for the anterior versus posterior insula that match broader cortex-wide patterns, it supports the notion that the insula is a communication hub and suggests that distinctive patterns of oscillations could play a role in allowing it to engage with other regions, such as the DMN and FPN, across a wide range of cognitive and affective tasks (Cai et al, 2016;Cai et al, 2021;Chen et al, 2016;Lamm & Singer, 2010;Sridharan et al, 2008).…”

“…Similar to the findings in non-human primates, MRI studies in humans have also shown subdivisions in the insula (Ghaziri et al, 2017;Morel, Gallay, Baechler, Wyss, & Gallay, 2013). Previous fMRI studies have shown functional subdivisions within the insula, as the AI was involved in task-related engagement and disengagement with the DMN and FPN across a wide range of cognitive and affective tasks (Cai et al, 2016;Cai, Ryali, Pasumarthy, Talasila, & Menon, 2021;Chen, Cai, Ryali, Supekar, & Menon, 2016;Lamm & Singer, 2010;Sridharan et al, 2008). The AI, along with the anterior cingulate cortex, are known as the salience network, Royer, & Buzsáki, 2012;Patel, Schomburg, Berényi, Fujisawa, & Buzsáki, 2013) in rodents.…”

“…We hypothesize that the left hemisphere insula communicates via both frequency and power gradients whereas the right hemisphere insula communicates via power gradients. Multiplexing of frequency and power gradients and hemispheric asymmetry may help the AI maintain a powerful and flexible role in cognitive control mechanisms as hypothesized previously in human fMRI studies (Cai et al, 2016;Cai et al, 2021;Chen et al, 2016;Lamm & Singer, 2010;Sridharan et al, 2008).…”

“…Temporal uncorrelatedness of theta and beta traveling waves in the human insula suggests putative orthogonal frequency channels of spatiotemporal information transfer within the insula. We hypothesize that these spatiotemporally orthogonal channels enable the insula to operate at the apex of cognitive control networks observed in the human brain (Cai et al, 2021) and initiate dynamic switching, with the DMN and FPN, which has been reported across a wide range of cognitive and affective tasks (Cai et al, 2016;Cai et al, 2021;Chen et al, 2016;Lamm & Singer, 2010;Sridharan et al, 2008).…”

“…Crucially, this finding was replicated across multiple iEEG cohorts. Taken together, lower frequency and higher power oscillations in the AI compared to PI seems to be a putative mechanism that enables the AI to maintain stability and flexibility with other large-scale brain networks (Cai et al, 2016;Cai et al, 2021;Chen et al, 2016;Lamm & Singer, 2010;Sridharan et al, 2008).…”