Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

Abstract

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [ 11 C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (⌬ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. ⌬ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between ⌬ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamineinduced dopamine release capacity.

Activation‐based association profiles differentiate network roles across cognitive loads

“…Furthermore, by decomposing the connectivity into connectional strength and connectional connectivity (namely, nodal connectivity diversity), we unveiled two different properties of the DMN and FPN under different cognitive loads (2‐back and 0‐back), that is, flexibility and strength, respectively (Zuo et al, ). These results agree with our findings that the DMN and the FPN simultaneously showed strong ASS and SASS with other networks, signifying that these two networks globally modulated the functions of the brain in the WM test (Cassidy et al, ; Douw, Wakeman, Tanaka, Liu, & Stufflebeam, ; Godwin, Ji, Kandala, & Mamah, ).…”

Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory

“…Behavioral Results. We used differential WM accuracy [accuracy at the greater WM load minus accuracy at the lower WM load (Δ WM )] as a WM performance measure, as suggested by previous work (31). As in the fMRI study, we tested the association between DRD1-PCI and WM performance using both linear and quadratic terms of the DRD1-PCI and of the DRD2-PCI.…”

“…Behavioral analyses. We used differential WM accuracy [accuracy at the greater WM load minus accuracy at the lower WM load (Δ WM )] as a WM capacity measure (30,31). This measure is associated with the increase of cognitive load in N-back sessions performed outside the scanner.…”

A dopamine gradient controls access to distributed working memory in monkey cortex

“…In order to create a high-resolution, and high-fidelity map of cortical dopamine receptor architecture, we used quantitative in-vitro receptor autoradiography (Zilles and Palomero-Gallagher 2017). PET and SPECT scans provide the advantages of in-vivo measurements, such as information on individual and group differences, but are limited in spatial resolution and signal-to-noise ratio (Cassidy et al 2016;Cools and D'Esposito 2011;Froudist-Walsh et al 2017a;Laruelle et al 1996;Roffman et al 2016) and are often unreliable for cortical measurements (Egerton et al 2010;Farde et al 1988). Gene expression methods have certain advantages, especially RNA sequencing which can provide cell-specific data.…”