From connectome to cognition: The search for mechanism in human functional brain networks

Abstract

Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for “network mechanisms”, which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or “effective” connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain “stable” across task domains and more “flexible” components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.

Graph Theoretic Analysis of Resting State Functional MR Imaging

“…It is also possible, as it commonly done, to rely heavily on reverse inference [87, 88] to interpret the graph statistics we observe and previously known ideas from cognitive neuroscience. To identify specific value of graph theory, we should search for consilience between graph theoretic analysis and other neuroscientific approaches [89]. …”

Short-term test-retest reliability of the human intrinsic functional connectome

“…This yields between 100 trillion and 1 quadrillion synapses, depending on a person's age [1]. Current research in neuroscience suggests that it is the architecture and dynamic interactions of neurons that give rise to complex phenomena, such as cognition and emotion [2,3,4]. This has been called the "functional connectome" and over the past three decades, several studies have characterized it in vivo (see for example [5]).…”

Characterisation of Functional Brain Networks underlying Cognitive Reasoning and Intelligence

“…This change in outlook is analogous to that which has occurred in animal physiology research, which has undergone a shift from investigating activity patterns of individual spiking neurons to analysing complex spatiotemporal patterns of synchronisation and desynchronization across larger neuronal populations (Kumar et al, 2010;Mill et al, 2017). Thanks to these technical and conceptual advances, it is clear that the computational power of the brain is more than the sum of its segregated parts, and instead reflects the dynamic integration of specialised processes by widespread neural networks (Sporns and Zwi, 2004).…”

“…Much like other brain imaging data, connectomes can be compared across cognitive states, between participant groups (e.g., patients versus healthy controls), or with respect to a given behavioural measure (e.g., intelligence scores). The insights that can be gained from connectomic analysis can be considered within three broad categories: spatial topology, directionality and temporal dynamics (Mill et al, 2017). Topology is perhaps the richest domain, and refers to the spatial configuration of networks.…”

“…An important unresolved question is how selective, 'flexible' task driven reconfigurations emerge amongst 'stable' intrinsic brain topology. Moreover, it is critical to understand how such global and selective changes are related to behaviour (Bolt et al, 2017;Mill et al, 2017).…”