Danielle S. Bassett’s research while affiliated with University of Pennsylvania and other places

Many natural and man-made network systems need to maintain certain patterns, such as working at equilibria or limit cycles, to function properly. Thus, the ability to stabilize such patterns is crucial. Most of the existing studies on stabilization assume that network systems states can be measured online so that feedback control strategies can be used. However, in many real-world scenarios, systems states, e.g., neuronal activity in the brain, are often difficult to measure. In this paper, we take this situation into account and study the stabilization problem of linear network systems with an open-loop control strategy (vibrational control). We derive a graph-theoretic sufficient condition for structural vibrational stabilizability, under which network systems can always be stabilized. We further provide an approach to select the locations in the network for control placement and design corresponding vibrational inputs to stabilize systems that satisfy this condition. Finally, we provide some numerical results that demonstrate the validity of our theoretical findings.

We are delighted to present you the Proceedings of the 2022 CNS meeting. The CNS meeting encourages approaches that combine theoretical, computational, and experimental work in the neurosciences, and provides an opportunity for participants to share their views. The abstracts corresponding to speakers' talks and posters are what you find collected here.

Astrocytes communicate bidirectionally with neurons, enhancing synaptic plasticity and promoting the synchronization of neuronal microcircuits. Despite recent advances in understanding neuron-astrocyte signaling, little is known about astrocytic modulation of neuronal activity at the population level, particularly in disease or following injury. We used high-speed calcium imaging of mixed cortical cultures in vitro to determine how population activity changes after disruption of glutamatergic signaling and mechanical injury. We constructed a multilayer network model of neuron-astrocyte connectivity, which captured distinct topology and response behavior from single-cell-type networks. mGluR5 inhibition decreased neuronal activity, but did not on its own disrupt functional connectivity or network topology. In contrast, injury increased the strength, clustering, and efficiency of neuronal but not astrocytic networks, an effect that was not observed in networks pretreated with mGluR5 inhibition. Comparison of spatial and functional connectivity revealed that functional connectivity is largely independent of spatial proximity at the microscale, but mechanical injury increased the spatial-functional correlation. Finally, we found that astrocyte segments of the same cell often belong to separate functional communities based on neuronal connectivity, suggesting that astrocyte segments function as independent entities. Our findings demonstrate the utility of multilayer network models for characterizing the multiscale connectivity of two distinct but functionally dependent cell populations.

Foreword from the editors. We hosted four keynote speakers: Wolf Singer, Bill Bialek, Danielle Bassett, and Sonja Gruen. They enlightened us about computations in the cerebral cortex, the reduction of high-dimensional data, the emerging field of computational psychiatry, and the significance of spike patterns in motor cortex. From the submissions, we also selected four featured orals as particularly noteworthy. They discussed a new role for cortical oscillations as a tempering mechanism, branch-specific computations in Purkinje cells, low frequency entrainment in processing sign language, and decreasing neural heterogeneity as a unifying sign of epilepsy. An additional 16 submissions were selected for shorter oral presentation in the plenary sessions, touching subjects such a spike and population coding, neural computation and interaction, astrocytic and dopaminergic modulation of plasticity, several kinds of sensory processing, reward learning, respiratory and motor control, neural activity propagation and synchronization, and brain organization in epilepsy and schizophrenia. We were also very pleased by the quality of the 213 presented posters, which drew a strong attendance, and the resulting online interactions between presenters and attendees. The full breadth of computational neuroscience was represented, from theory and method development over data analysis to applications.

Significance To perform biological functions, living systems must break detailed balance by consuming energy and producing entropy. At microscopic scales, broken detailed balance enables a suite of molecular and cellular functions, including computations, kinetic proofreading, sensing, adaptation, and transportation. But do macroscopic violations of detailed balance enable higher-order biological functions, such as cognition and movement? To answer this question, we adapt tools from nonequilibrium statistical mechanics to quantify broken detailed balance in complex living systems. Analyzing neural recordings from hundreds of human subjects, we find that the brain violates detailed balance at large scales and that these violations increase with physical and cognitive exertion. Generally, we provide a flexible framework for investigating broken detailed balance at large scales in complex systems.

The human brain consumes a disproportionate amount of energy to generate neural dynamics. Yet precisely how energetic processes are altered in neurological disorders remains far from understood. Here, we use network control theory to profile the brain's energy landscape, describing the rich dynamical repertoire supported by the structural connectome. This approach allows us to estimate the energy required to activate a circuit, and determine which regions most support that activation. Focusing on temporal lobe epilepsy (TLE), we show that patients require more control energy to activate the limbic network than healthy volunteers, especially ipsilateral to the seizure focus. Further, greater energetic costs are largely localized to the ipsilateral temporo-limbic regions. Importantly, the energetic imbalance between ipsilateral and contralateral temporo-limbic regions is tracked by asymmetric metabolic patterns, which in turn are explained by asymmetric gray matter volume loss. In TLE, failure to meet the extra energy demands may lead to suboptimal brain dynamics and inadequate activation. Broadly, our investigation provides a theoretical framework unifying gray matter integrity, local metabolism, and energetic generation of neural dynamics.

Recent work has combined cognitive neuroscience and control theory to make predictions about cognitive control functions. Here, we test a link between whole-brain theories of semantics and the role of the left inferior frontal gyrus (LIFG) in controlled language performance using network control theory (NCT), a branch of systems engineering. Specifically, we examined whether two properties of node controllability, boundary and modal controllability, were linked to semantic selection and retrieval on sentence completion and verb generation tasks. We tested whether the controllability of the left IFG moderated language selection and retrieval costs and the effects of continuous θ burst stimulation (cTBS), an inhibitory form of transcranial magnetic stimulation (TMS) on behavior in 41 human subjects (25 active, 16 sham). We predicted that boundary controllability, a measure of the theoretical ability of a node to integrate and segregate brain networks, would be linked to word selection in the contextually-rich sentence completion task. In contrast, we expected that modal controllability, a measure of the theoretical ability of a node to drive the brain into specifically hard-to-reach states, would be linked to retrieval on the low-context verb generation task. Boundary controllability was linked to selection and to the ability of TMS to reduce response latencies on the sentence completion task. In contrast, modal controllability was not linked to performance on the tasks or TMS effects. Overall, our results suggest a link between the network integrating role of the LIFG and selection and the overall semantic demands of sentence completion.

Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers’ lives. We disentangle these facets and propose concrete solutions that can be adopted by individuals, academic institutions, and society.

Human brain tissue is a heterogeneous material, consisting of soft outer grey matter tethered internally by stiffer cords of white matter. These white matter tracts conduct electrical impulses between grey matter regions, thereby underpinning neuronal communication. Understanding the material properties of white matter is thus crucial for understanding brain function generally. Efforts to assess white matter microstructure are currently hampered by the inherent limitations of reconstruction by diffusion imaging. Techniques typically represent white matter structures with single scalars that are often difficult to interpret. Here, we address these issues by introducing tools from materials physics for the characterization of white matter microstructure. We investigate structure on a mesoscopic scale by analyzing its homogeneity and determining which regions of the brain are structurally homogeneous, or 'crystalline' in the context of materials physics. We find that crystallinity provides novel information and varies across the brain along interpretable lines of anatomical difference, with highest homogeneity in regions adjacent to the corpus callosum, a large interhemispheric tract. Furthermore, crystallinity is markedly reliable across iterative measurement, yet also varies between individual human volunteers, making it potentially useful for examining individual differences in white matter along several dimensions including sex and age. We also parcellate white matter into 'crystal grains', or contiguous sets of voxels of high structural similarity, and find overlap with a common atlas of distinct white matter areas. Finally, we characterize the shapes of individual diffusion signatures through another tool from materials physics - bond-orientational order parameters - to locate fiber crossings and fascicles. Our results provide new means of assessing white matter microstructure on multiple length scales, and open multiple avenues of future inquiry involving soft matter physics and neuroscience. © 2021 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.

How the brain's white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain's functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; that is, the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a deficiency in the ability to reduce energy through brain activation. In summary, our results provide initial insights into the functional implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.