In the age of connectomics, it is increasingly important to understand how the nodes and edges of a brain's anatomical network, or "connectome," gives rise to neural signaling and neural function. I will present the first comprehensive brain-wide cell-resolved causal measurements of how neurons signal to one another in response to stimulation in the nematode C. elegans. I will compare this signal propagation atlas to the worm's known connectome to address fundamental questions of structure and function in the brain.

Social experiences can have lasting changes on behavior and affective state. In particular, repeated wins and losses during fighting can facilitate and suppress future aggressive behavior, leading to persistent high aggression or low aggression states. We use a combination of techniques for multi-region neural recording, perturbation, behavioral analysis, and modeling to understand how nodes in the brain’s subcortical “social decision-making network” encode and transform aggressive motivation into action, and how these circuits change following social experience.

The nervous system orchestrates adaptive behaviors by intricately coordinating responses to internal cues and environmental stimuli. This involves integrating sensory input, managing competing motivational states, and drawing on past experiences to anticipate future outcomes. While traditional models attribute this complexity to interactions between the mesocorticolimbic system and hypothalamic centers, the specific nodes of integration have remained elusive. Recent research, including our own, sheds light on the midline thalamus's overlooked role in this process. We propose that the midline thalamus integrates internal states with memory and emotional signals to guide adaptive behaviors. Our investigations into midline thalamic neuronal circuits have provided crucial insights into the neural mechanisms behind flexibility and adaptability. Understanding these processes is essential for deciphering human behavior and conditions marked by impaired motivation and emotional processing. Our research aims to contribute to this understanding, paving the way for targeted interventions and therapies to address such impairments.

Over the past decade we have demonstrated that the fusion of subject-specific structural information of the human brain with mathematical dynamic models allows building biologically realistic brain network models, which have a predictive value, beyond the explanatory power of each approach independently. The network nodes hold neural population models, which are derived using mean field techniques from statistical physics expressing ensemble activity via collective variables. Our hybrid approach fuses data-driven with forward-modeling-based techniques and has been successfully applied to explain healthy brain function and clinical translation including aging, stroke and epilepsy. Here we illustrate the workflow along the example of epilepsy: we reconstruct personalized connectivity matrices of human epileptic patients using Diffusion Tensor weighted Imaging (DTI). Subsets of brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other healthy brain regions and propagate activity through large brain networks. The identification of the EZ is crucial for the success of neurosurgery and presents one of the historically difficult questions in clinical neuroscience. The application of latest techniques in Bayesian inference and model inversion, in particular Hamiltonian Monte Carlo, allows the estimation of the EZ, including estimates of confidence and diagnostics of performance of the inference. The example of epilepsy nicely underwrites the predictive value of personalized large-scale brain network models. The workflow of end-to-end modeling is an integral part of the European neuroinformatics platform EBRAINS and enables neuroscientists worldwide to build and estimate personalized virtual brains.

Which nodes in a brain network causally influence one another, and how do such interactions utilize the underlying structural connectivity? One of the fundamental goals of neuroscience is to pinpoint such causal relations. Conventionally, these relationships are established by manipulating a node while tracking changes in another node. A causal role is then assigned to the first node if this intervention led to a significant change in the state of the tracked node. In this presentation, I use a series of intuitive thought experiments to demonstrate the methodological shortcomings of the current ‘causation via manipulation’ framework. Namely, a node might causally influence another node, but how much and through which mechanistic interactions? Therefore, establishing a causal relationship, however reliable, does not provide the proper causal understanding of the system, because there often exists a wide range of causal influences that require to be adequately decomposed. To do so, I introduce a game-theoretical framework called Multi-perturbation Shapley value Analysis (MSA). Then, I present our work in which we employed MSA on an Echo State Network (ESN), quantified how much its nodes were influencing each other, and compared these measures with the underlying synaptic strength. We found that: 1. Even though the network itself was sparse, every node could causally influence other nodes. In this case, a mere elucidation of causal relationships did not provide any useful information. 2. Additionally, the full knowledge of the structural connectome did not provide a complete causal picture of the system either, since nodes frequently influenced each other indirectly, that is, via other intermediate nodes. Our results show that just elucidating causal contributions in complex networks such as the brain is not sufficient to draw mechanistic conclusions. Moreover, quantifying causal interactions requires a systematic and extensive manipulation framework. The framework put forward here benefits from employing neural network models, and in turn, provides explainability for them.

We consider new measures of centrality in networks which take into account parameters of nodes and group influence of nodes to nodes. Several examples are discussed.

Networks of stiff biopolymers are an omnipresent structural motif in cells and tissues. A prominent modeling framework for describing biopolymer network mechanics is rigidity percolation theory. This theory describes model networks as nodes joined by randomly placed, springlike bonds. Increasing the amount of bonds in a network results in an abrupt, dramatic increase in elastic moduli above a certain threshold – an example of a mechanical phase transition. While homogeneous networks are well studied, many tissues are made of disparate components and exhibit spatial fluctuations in the concentrations of their constituents. In this talk, I will first discuss recent work in which we explained the structural basis of the shear mechanics of healthy and chemically degraded cartilage by coupling a rigidity percolation framework with a background gel. Our model takes into account collagen concentration, as well as the concentration of peptidoglycans in the surrounding polyelectrolyte gel, to produce a structureproperty relationship that describes the shear mechanics of both sound and diseased cartilage. I will next discuss the introduction of structural correlation in constructing networks, such that sparse and dense patches emerge. I find moderate correlation allows a network to become rigid with fewer bonds, while this benefit is partly erased by excessive correlation. We explain this phenomenon through analysis of the spatial fluctuations in strained networks’ displacement fields. Finally, I will address our work’s implications for non-invasive diagnosis of pathology, as well as rational design of prostheses and novel soft materials.

Behavioural difficulties are seen as hallmarks of many neurodevelopmental conditions. Differences in functional brain organisation have been observed in these conditions, but little is known about how they are related to a child’s profile of behavioural difficulties. We investigated whether behavioural difficulties are associated with how the brain is functionally organised in an intentionally heterogeneous and transdiagnostic sample of 957 children aged 5-15. We used consensus community detection to derive data-driven profiles of behavioural difficulties and constructed functional connectomes from a subset of 238 children with resting-state functional Magnetic Resonance Imaging (fMRI) data. We identified three distinct profiles of behaviour that were characterised by principal difficulties with hot executive function, cool executive function, and learning. Global organisation of the functional connectome did not differ between the groups, but multivariate patterns of connectivity at the level of Intrinsic Connectivity Networks (ICNs), nodes, and hubs significantly predicted group membership in held-out data. Fronto-parietal connector hubs were under-connected in all groups relative to a comparison sample, and children with hot vs cool executive function difficulties were distinguished by connectivity in ICNs associated with cognitive control, emotion processing, and social cognition. This demonstrates both general and specific neurodevelopmental risk factors in the functional connectome. (https://www.medrxiv.org/content/10.1101/2021.09.15.21262637v1)

Macroscopic brain organisation emerges early in life, even prenatally, and continues to develop through adolescence and into early adulthood. The emergence and continual refinement of large-scale brain networks, connecting neuronal populations across anatomical distance, allows for increasing functional integration and specialisation. This process is thought crucial for the emergence of complex cognitive processes. But how and why is this process so diverse? We used structural neuroimaging collected from a large diverse cohort, to explore how different features of macroscopic brain organisation are associated with diverse cognitive trajectories. We used diffusion-weighted imaging (DWI) to construct whole-brain white-matter connectomes. A simulated attack on each child's connectome revealed that some brain networks were strongly organized around highly connected 'hubs'. The more children's brains were critically dependent on hubs, the better their cognitive skills. Conversely, having poorly integrated hubs was a very strong risk factor for cognitive and learning difficulties across the sample. We subsequently developed a computational framework, using generative network modelling (GNM), to model the emergence of this kind of connectome organisation. Relatively subtle changes within the wiring rules of this computational framework give rise to differential developmental trajectories, because of small biases in the preferential wiring properties of different nodes within the network. Finally, we were able to use this GNM to implicate the molecular and cellular processes that govern these different growth patterns.

A rich data representation that finds wide application in industry and research is the so-called knowledge graph - a graph-based structure where entities are depicted as nodes and relations between them as edges. Complex systems like molecules, social networks and industrial factory systems can be described using the common language of knowledge graphs, allowing the usage of graph embedding algorithms to make context-aware predictions in these information-packed environments.

Animals exhibit extraordinary variation in their behavior, yet little is known about the neural mechanisms that generate this diversity. My lab has been taking advantage of the rapid diversification of male courtship behaviors in Drosophila to glean insight into how evolution shapes the nervous system to generate species-specific behaviors. By translating neurogenetic tools from D. melanogaster to closely related Drosophila species, we have begun to directly compare the homologous neural circuits and pinpoint sites of adaptive change. Across species, P1 neurons serve as a conserved node in regulating male courtship: these neurons are selectively activated by the sensory cues indicative of an appropriate mate and their activation triggers enduring courtship displays. We have been examining how different sensory pathways converge onto P1 neurons to regulate a male’s state of arousal, honing his pursuit of a prospective partner. Moreover, by performing cross-species comparison of these circuits, we have begun to gain insight into how reweighting of sensory inputs to P1 neurons underlies species-specific mate recognition. Our results suggest how variation at flexible nodes within the nervous system can serve as a substrate for behavioral evolution, shedding light on the types of changes that are possible and preferable within brain circuits.

The last decade in the field of neuroscience has been marked by intense debate on the meaning of the term fear. Whereas some have argued that fear (as well as other emotions) relies on cognitive capacities that are unique to humans, others view it as a negative state constructed from essential building blocks. This latter definition posits that fear states are associated with varying readouts that one could consider to be parallel processes or serial events tied to a specific hierarchy. Within this framework, innate defensive behaviors are considered to be common displays of fear states that lie under the control of hard-wired brain circuits. As a general rule, these defensive behaviors can be classified as either reactive or cognitive based on a thread imminence continuum. However, while evidence of the neuronal circuits that lead to these divergent behavioral strategies has accrued over the last decades, most literature has considered these responses in isolation. As a result, important misconceptions have arisen regarding how fear circuits are distributed in the brain and the contribution of specific nodes within these circuits to defensive behaviors. To mitigate the status quo, I will conduct a systematic comparison of brain circuits driving the expression of freezing and active avoidance behavior, which I will use as well-studied proxies of reactive and cognitive fear, respectively. In addition, I propose that by integrating associative information with interoceptive and exteroceptive signals the central nucleus of the amygdala plays a crucial role in biasing the selection of defensive behaviors.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g. specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade both our cognitive and neural networks. Moreover, calculating node centrality (absolute strength and bridge strength) and using two separate community detection algorithms (Walktrap and Clique Percolation), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

The Group Iterative Multiple Model Estimation (GIMME) framework has emerged as a promising method for characterizing connections between brain regions in functional neuroimaging data. Two of the most appealing features of this framework are its ability to estimate the directionality of connections between network nodes and its ability to determine whether those connections apply to everyone in a sample (group-level) or just to one person (individual-level). However, there are outstanding questions about the validity and stability of these estimates, including: 1) how recovery of connection directionality is affected by features of data sets such as scan length and autoregressive effects, which may be strong in some imaging modalities (resting state fMRI, fNIRS) but weaker in others (task fMRI); and 2) whether inferences about directionality at the group and individual levels are stable across time. This talk will provide an overview of the GIMME framework and describe relevant results from a large-scale simulation study that assesses directionality recovery under various conditions and a separate project that investigates the temporal stability of GIMME’s inferences in the Human Connectome Project data set. Analyses from these projects demonstrate that estimates of directionality are most precise when autoregressive and cross-lagged relations in the data are relatively strong, and that inferences about the directionality of group-level connections, specifically, appear to be stable across time. Implications of these findings for the interpretation of directional connectivity estimates in different types of neuroimaging data will be discussed.

Courtship behavior is an innate model for many types of brain computations including sensory detection, learning and memory, and internal state modulation. Despite the robustness of the behavior, we have little understanding of the underlying neural circuits and mechanisms. The Stowers’ lab is leveraging the ability of specialized olfactory cues, pheromones, to specifically activate and therefore identify and study courtship circuits in the mouse. We are interested in identifying general circuit principles (specific brain nodes and information flow) that are common to all individuals, in order to additionally study how experience, gender, age, and internal state modulate and personalize behavior. We are solving two parallel sensory to motor courtship circuits, that promote social vocal calling and scent marking, to study information processing of behavior as a complete unit instead of restricting focus to a single brain region. We expect comparing and contrasting the coding logic of two courtship motor behaviors will begin to shed light on general principles of how the brain senses context, weighs experience and responds to internal state to ultimately decide appropriate action.

Immune cells and their derived molecules have major impact on brain function. Mice deficient in adaptive immunity have impaired cognitive and social function compared to that of wild-type mice. Importantly, replenishment of the T cell compartment in immune deficient mice restored proper brain function. Despite the robust influence on brain function, T cells are not found within the brain parenchyma, a fact that only adds more mystery into these enigmatic interactions between T cells and the brain. Our results suggest that meningeal space, surrounding the brain, is the site where CNS-associated immune activity takes place. We have recently discovered a presence of meningeal lymphatic vessels that drain CNS molecules and immune cells to the deep cervical lymph nodes. This communication between the CNS and the peripheral immunity is playing a key role in neurophysiology and in several CNS disorders. Interestingly, meningeal lymphatics are impaired in aging and their dysfunction may be related to age-related cognitive decline as well as to Alzheimer’s pathology. In addition to providing new insights into age-related disorders, meningeal lymphatics may also serve as a novel therapeutic target for these diseases and are worth of in-depth mechanistic exploration.

Animals exhibit extraordinary variation in their behaviour, yet little is known about the neural mechanisms that generate this diversity. My lab has been taking advantage of the rapid diversification of male courtship behaviours in Drosophila to gain insight into how evolution shapes the nervous system to generate species-specific behaviours. By translating neurogenetic tools from D. melanogaster to closely related Drosophila species, we have begun to directly compare the homologous neural circuits and pinpoint sites of adaptive change. Across species, P1 interneurons serve as a conserved and key node in regulating male courtship: these neurons are selectively activated by the sensory cues carried by an appropriate mate and their activation triggers enduring courtship displays. We have been examining how different sensory pathways converge onto P1 neurons to regulate a male’s state of arousal, honing his pursuit of a prospective partner. Moreover, by performing cross-species comparison of these circuits, we have begun to gain insight into how reweighting of sensory inputs to P1 neurons underlies species-specific mate recognition. Our results suggest how variation at flexible nodes within the nervous system can serve as a substrate for behavioural evolution, shedding light on the types of changes that are possible and preferable within brain circuits.