Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

Seguin, Caio; Jedynak, Maciej; David, Olivier; L., Sina Mansour; Sporns, Olaf; Zalesky, Andrew

doi:10.1101/2022.07.05.498875

Cited by 10 publications

(10 citation statements)

References 109 publications

(106 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, we have seen considerations of space implemented in functional feedforward neural network models (Gozel & Doiron, 2022;Huang et al, 2019;Lee et al, 2020). Here we instantiated our core hypothesis mathematically within the seRNN model by providing two challenges to RNNs during supervised learning: (1) long connections should be minimized where possible -reflective of their metabolic cost (Kaiser & Hilgetag, 2006;Sporns, 2011), and (2) connections can only change their weights as a function of their underlying communication -reflective of signal propagation between neuronal units (Betzel et al, 2022;Seguin, Jedynak, et al, 2022;Seguin, Mansour L, et al, 2022;Shimono & Hatano, 2018). Both challenges are addressed at the local neuronal-level over the course of training which has the effect of continually shaping the networks global structural and functional properties over time.…”

Section: Discussionmentioning

confidence: 99%

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Achterberg

Akarca

Strouse

et al. 2022

Preprint

View full text Add to dashboard Cite

Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. To observe the effect of these processes, we introduce the spatially-embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a 3D Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs, similar to primate cerebral cortices, naturally converge on solving inferences using modular small-world networks, in which functionally similar units spatially configure themselves to utilize an energetically-efficient mixed-selective code. As all these features emerge in unison, seRNNs reveal how many common structural and functional brain motifs are strongly intertwined and can be attributed to basic biological optimization processes. seRNNs can serve as model systems to bridge between structural and functional research communities to move neuroscientific understanding forward.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Achterberg

Akarca

Strouse

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Among the most obvious and tantalizing opportunities is the empirical validation of edge weights estimated here. Datasets in which stimulation is paired with brain-wide recordings make it feasible to estimate directed influence between brain stimulus-target pairs of regions [100, 101]. These estimates could be compared directly to the connection weights inferred here.…”

Section: Discussionmentioning

confidence: 99%

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

Tanner

Faskowitz²,

Teixeira

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are assigned based on diffusion parameters or inferred using a statistical model, although the model generally scales poorly limiting its applications to relatively small networks. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighted schemes. Here, we explore a simple regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participants in at least one shortest path. We also find evidence of asymmetric edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

show abstract

“…Among the most obvious and tantalizing opportunities is the empirical validation of edge weights estimated here. Datasets in which stimulation is paired with brain-wide recordings make it feasible to estimate directed influence between brain stimulus-target pairs of regions [103,104]. These estimates could be compared directly to the connection weights inferred here.…”

Section: Future Directionsmentioning

confidence: 99%

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

Tanner

Faskowitz

Teixeira

et al. 2023

Preprint

View full text Add to dashboard Cite

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features--e.g. diffusion parameters--or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal (combining diffusion and functional MRI data) regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participates in at least one shortest path. We also find evidence of robust asymmetries in edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

show abstract

Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

Cited by 10 publications

References 109 publications

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

Contact Info

Product

Resources

About