Biology subjects

Bassett, D. S.

Publications and source records attributed to Bassett, D. S..

23 records · Page 2Linked to original sources

bioRxiv2017

Process reveals structure: How a network is traversed mediates expectations about its architecture

Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of the world around us. How human learners exploit this information remains an essential question. Here, we focus on the temporal constraints that govern such a process. Participants viewed a continuous sequence of images generated by three distinct walks on a modular network. Walks varied along two critical dimensions: their predictability and the density with which they sampled from communities of images. Learners exposed to walks that richly sampled from each community exhibited a sharp increase in processing time upon entry into a new community. This effect was eliminated in a highly regular walk that sampled exhaustively from images in short, successive cycles (i.e., that increasingly minimized uncertainty about the nature of upcoming stimuli). These results demonstrate that temporal organization plays an essential role in how robustly knowledge of network architecture is acquired.

neuroscience↗

bioRxiv2017

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration

The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germaine to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture - which brain regions may functionally interact - and their temporal architecture - how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called nonnegative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine-learning approach automatically clusters temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.

neuroscience↗

bioRxiv2016

Dynamic flexibility in striatal-cortical circuits supports reinforcement learning

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex. Moreover, we found that flexibility in striatal network dynamics correlates with participants learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that not all forms of learning relate to this circuit: episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal-centered networks provide a mechanism for information integration during reinforcement learning.\n\nSignificance StatementLearning from the outcomes of actions-referred to as reinforcement learning-is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of the updating of values for actions or sensory stimuli. Missing from this account, however, is a description of the manner in which different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal-cortical network dynamics that relate to reinforcement learning behavior.

neuroscience↗

bioRxiv2016

Recurring functional interactions predict network architecture of interictal and ictal states in neocortical epilepsy

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.

neuroscience↗

bioRxiv2016

Detecting hierarchical 3-D genome domain reconfiguration with network modularity

Mammalian genomes are folded in a hierarchy of topologically associating domains (TADs), subTADs and looping interactions. The nested nature of chromatin domains has rendered it challenging to identify a sensitive and specific metric for detecting subTADs and quantifying their dynamic reconfiguration across cellular states. Here, we apply graph theoretic principles to quantify hierarchical folding patterns in high-resolution chromatin topology maps. We discover that TADs can be accurately detected using a Louvain-like locally greedy algorithm to maximize network modularity. By varying a resolution parameter in the modularity quality function, we accurately partition the mouse genome across length scales into a hierarchical nested structure of network communities exhibiting a wide range of sizes. To distinguish high probability subTADs from the full detected set, we developed and applied a new hierarchical spatial variance minimization method. Moreover, we identified a large number of dynamically altered communities between pluripotent embryonic stem cells and multipotent neural progenitor cells. Cell type specific boundaries correlate with trends in dynamic occupancy of the architectural protein CTCF, thereby validating their biological relevance. Together, these data demonstrate the utility of metrics from network science in quantifying a nested hierarchy of dynamic 3D chromatin communities across length scales. Our findings are significant toward unraveling the link between higher-order genome folding and gene expression during healthy development and the deregulation of molecular pathways linked to disease.

genomics↗