Network controllability of structural connectomes in the neonatal brain

Sun, Huili; Jiang, Rongtao; Dai, Wei; Dufford, Alexander J.; Noble, Stephanie; Spann, Marisa N.; Gu, Shi; Scheinost, Dustin

doi:10.1038/s41467-023-41499-w

Cited by 8 publications

(2 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HCP 3T data were quality controlled based on motion summary statistics and visual inspection. After exclusion of four high motion subjects and one subject due to software error, the final HCP 3T subset consisted of 95 subjects the final subset utilized included 95 subjects (56% female, mean age = 29.29 ± 3.66, age range = [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Human Connectome Project Datasetmentioning

confidence: 99%

“…Optimal control has been used to understand the link between brain structure and function [21][22][23][24], to study brain network development and executive function [25][26][27], working memory and schizophrenia [28], epilepsy [29,30], psychiatric disorders [31,32], mindfulness training [33], neurostimulation [34,35], and psychedelic research [36,37]. Other work has focused on understanding the metabolic cost of control [38,39].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Controlling the human connectome with spatially diffuse input signals

Betzel,

Puxeddu,

Seguin

et al. 2024

Preprint

View full text Add to dashboard Cite

The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state--a whole-brain pattern of activity--to another. Network control theory offers a framework for understanding the effort -- energy -- associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex -- geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.

show abstract

Section: Human Connectome Project Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Controlling the human connectome with spatially diffuse input signals

Betzel,

Puxeddu,

Seguin

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Methylome-wide association study of adolescent depressive episode with psychotic symptoms and childhood trauma

Sun,

Lin,

Liang

et al. 2025

Journal of Affective Disorders

View full text Add to dashboard Cite

Brain age prediction and deviations from normative trajectories in the neonatal connectome

Sun,

Mehta,

Khaitova

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain’s maturity relative to normative data. The difference between the individual’s predicted and chronological age—or brain age gap (BAG)—represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We use resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predict PMA for term and preterm infants. Predicted ages from each modality are correlated. At the network level, nearly all canonical brain networks—even putatively later developing ones—generate accurate PMA prediction. Additionally, BAGs are associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

show abstract

Network controllability of structural connectomes in the neonatal brain

Cited by 8 publications

References 60 publications

Controlling the human connectome with spatially diffuse input signals

Controlling the human connectome with spatially diffuse input signals

Methylome-wide association study of adolescent depressive episode with psychotic symptoms and childhood trauma

Brain age prediction and deviations from normative trajectories in the neonatal connectome

Contact Info

Product

Resources

About