Control energy assessment of spatial interactions among macro‐scale brain networks

Abstract

Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro-scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macroscale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro-scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task-based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, diverse energy consumption patterns of macro-scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro-scale brain networks.

Using network control theory to study the dynamics of the structural connectome

“…Here, we present a protocol for applying NCT to two different structural connectomes: one defined using undirected connectivity estimated in the human brain [21,22] and the other using directed connectivity estimated in the mouse brain [23][24][25]. Briefly, we detail two common applications of NCT that we-as well as other groups [26][27][28][29][30][31][32]-have deployed that focus on (i) quantifying the amount of energy that is required to complete transitions between specific brain states (Figure 1) and (ii) examining regions' general capacity to control dynamics (Figure 2). The former approach is useful for researchers interested in examining how dynamics can be controlled to move from one place on the network to another, while the latter is useful for researchers interested in analyzing topographic maps of control.…”

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

“…To some extent, FC can reflect the functional interaction between different brain regions. It has been widely employed [ 27 , 29 , 33 ].…”

Mesoscopic patterns of functional connectivity alterations in autism by contrast subgraphs

“…Nonetheless, the function of network nodes and node clusters likely depends critically upon the scale at which a network is constructed and analyzed. Accordingly, we might expect networks to be optimized to perform scalespecific functions (Yuan et al, 2022), and focusing on a particular scale gives a unique insight into the network architecture underpinning those functions. Therefore, the open problem is finding a coherent approach to highlight connectivity alterations across multiple scales while retaining a global perspective on the network level.…”