Closing the mechanistic gap: the value of microarchitecture in understanding cognitive networks. Issue 10 (October 2022)

• Record Type: Journal Article
• Authors: Paquola, Casey; Amunts, Katrin; Evans, Alan; Smallwood, Jonathan; Bernhardt, Boris
• Is Part Of: Trends in cognitive sciences. Volume 26:Issue 10(2022)
• Page Start: 873
• Page End: 886
• Publication Date: 2022-10
• Subjects: microstructure -- functional networks -- higher-order cognition -- microcircuits -- multiscale; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2022.07.001
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract : Cognitive neuroscience aims to provide biologically relevant accounts of cognition. Contemporary research linking spatial patterns of neural activity to psychological constructs describes 'where' hypothesised functions occur, but not 'how' these regions contribute to cognition. Technological, empirical, and conceptual advances allow this mechanistic gap to be closed by embedding patterns of functional activity in macro- and microscale descriptions of brain organisation. Recent work on the default mode network (DMN) and the multiple demand network (MDN), for example, highlights a microarchitectural landscape that may explain how activity in these networks integrates varied information, thus providing an anatomical foundation that will help to explain how these networks contribute to many different cognitive states. This perspective highlights emerging insights into how microarchitecture can constrain network accounts of human cognition. Highlights: Characterising the mechanisms of human cognition can benefit from integrating local microstructural properties with the macroscale organisation of the brain. Digitised datasets of the human brain, including histology, brain maps, transcriptomics, and high-resolution imaging, provide unprecedented opportunities to link microarchitectural features to functional networks that contribute to complex thought. Microarchitectural heterogeneity of higher cognitive networks [e.g., the default mode network (DMN) and multiple demand network (MDN)] underpins their broad involvements in cognition. The positioning of the DMN and the MDN with respect to hierarchical and parallel processing streams supports and sheds light onto their functional differences. Complex functional dynamics and the inter-relationship between functional networks may be better understood by taking a multidimensional perspective informed by changes in local microarchitecture.