A morphospace of functional configuration to assess configural breadth based on brain functional networks

Abstract

The quantification of human brain functional (re-)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to Task transitional reconfiguration, and iii) Within-Task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re-)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN-reconfigurations that result from the brain switching between mental states.

GEFF: Graph embedding for functional fingerprinting

“…Assuming the task-independent nature of GEFF, specific FCs with embeddings that fall far away from the average representation of a given subject might indicate suboptimal quality of its estimation. This also suggests that perhaps we are all hardwired in a similar way and that there are only subtle differences in terms of functional reconfiguration when performing any cognitive task 48,58 . Therefore, perhaps it is not the task but the individual wiring of the person that explains maximal inter-subject variability.…”

Evolutionary paths to lateralization of complex brain functions

“…Morphospaces were first introduced to describe the shape of shells as a function of factors affecting their formation [39,40]. They have been expanded to study other complex systems [41][42][43][44][45][46], including how evolutionary pressures guide the evolution and development of neural or computational substrates [37,47,48]. These maps remind us of phase diagrams that dictate the phase (solid, liquid, etc.)…”

Evolution of Brains and Computers: The Roads Not Taken

“…Additionally, the presence of voids (i.e., empty volumes lacking any candidate system) can provide evidence for constraints or forbidden evolutionary paths. This approach was first introduced within the context of morphological traits of shells [169] and has been later on widely used within Paleobiology and evolutionary biology [21,[170][171][172], and in other different contexts including network science [173][174][175] and computational neuroscience [176][177][178][179][180][181]. Morphospaces provide us with a global picture of possible designs and how they relate to each other (whether they are distant or close) in a feature space.…”