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Workshop Program

9:00 AM Introduction
9:05 AM Synchronization in the Connectome: Emergence of Metastable Oscillatory Modes from interactions in the Brain Spacetime Network
Joana Cabral, University of Minho, Portugal (virtual)
A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with the underlying structural connectome remain under debate. I will present a mechanistic hypothesis where transient brain rhythms are a signature of metastable synchronization, occurring at reduced collective frequencies due to delays between brain areas. We consider a system of damped oscillators in the presence of background noise – approximating the short-lived gamma-frequency oscillations generated within neuronal circuits – coupled according to the diffusion weighted tractography between brain areas. Varying the global coupling strength and conduction speed, we identify a critical regime where spatially and spectrally resolved metastable oscillatory modes (MOMs) emerge at sub-gamma frequencies, approximating the MEG power spectra from healthy individuals at rest. Further, we demonstrate that the frequency, duration, and scale of MOMs – as well as the frequency-specific envelope functional connectivity – can be controlled by global parameters, while the connectome structure remains unchanged. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate how interactions between locally generated fast oscillations in the connectome spacetime structure can lead to the emergence of collective brain rhythms organized in space and time.

Cabral, Joana, Francesca Castaldo, Jakub Vohryzek, Vladimir Litvak, Christian Bick, Renaud Lambiotte, Karl Friston, Morten L. Kringelbach, and Gustavo Deco. " Metastable oscillatory modes emerge from synchronization in the brain spacetime connectome" bioRxiv (2022). In Press in Nature Communications Physics.
9:25 AM Spectral Graph Models of brain functional connectivity and neural oscillations
Ashish Raj, University of California San Francisco (virtual)
Understanding the relationship between the functional activity and the structural wiring of the brain is an important question in neuroscience. To address this, various mathematical modeling approaches have been undertaken in the past, which largely consisted of non-linear and biophysically detailed mathematical models with regionally varying model parameters. While such models provide us a rich repertoire of dynamics that can be displayed by the brain, they are computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso- (regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that a linear and analytical model without regionally varying parameters can capture the empirical MEG frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this talk, we will demonstrate spectral graph models that can accurately capture functional dynamics in fMRI and MEG separately.
9:45 AM Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics.
Mac Shine, The University of Sydney, Australia (in-person)
Decades of neurobiological research have disclosed the diverse manners in which the response properties of neurons are dynamically modulated to support adaptive cognitive functions. This neuromodulation is achieved through alterations in the biophysical properties of the neuron. However, changes in cognitive function do not arise directly from the modulation of individual neurons, but are mediated by population dynamics in mesoscopic neural ensembles. Understanding this multiscale mapping is an important but nontrivial issue. Here, we bridge these different levels of description by showing how computational models parametrically map classic neuromodulatory processes onto systems-level models of neural activity. The ensuing critical balance of systems-level activity supports perception and action, although our knowledge of this mapping remains incomplete. In this way, quantitative models that link microscale neuronal neuromodulation to systems-level brain function highlight gaps in knowledge and suggest new directions for integrating theoretical and experimental work.
10:05 AM ---
Vince Calhoun, Georgia Tech, USA (virtual)
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10:30 AM Coffee break
11:00 AM Bursty network dynamics drives Functional Connectivity and marks its reorganization after localized region shut down: a study in-silico and in-vivo
Giovanni Rabuffo, Aix-Marseille University, France (in-person)
Neuroimaging recordings of mammalians’ brains reveal the dynamic formation of distributed resting-state networks. Typical activation patterns are captured by Functional Connectivity (FC) measures. However, brain networks are constantly re-configuring and FC is dominated by few salient events. These events can be associated with short lived bursts of co-activations, where multiple regions across the brain collectively deviate from baseline activity. The origin and the relevance of these bursts is still debated. First, using whole-brain modeling we simulate dynamic FC patterns and we show that spontaneous bursts of co-activations naturally emerge. In the simulations, the bursts originate from localized neuronal fluctuations that propagate through the connectome recruiting down-stream regions in a cascade effect. Then, we study the effects of single region shut-down on the whole-brain network dynamics. We analyze a computational model as well as two empiric datasets where resting state hemodynamic activity was recorded in mice before and after the shut-down of either cortical hubs (via chemogenetic inhibition) or thalamic nuclei (via lesion). In all cases, we show a consistent decrease of spatial correlations' strength, and the proportional loss of bursty network dynamics. Finally, we discuss the multi-scale mechanisms of network response to localized perturbations, which understanding is fundamental for developing predictive brain models.
11:20 AM Network Biomarkers of C9orf72 Intrinsic Neurodegeneration
Saurabh Sihag, University of Pennsylvania, USA (virtual)
Frontotemporal degeneration (FTD) and amyotrophic lateral sclerosis (ALS) represent two ends of a neurodegenerative continuum. Mutations in C9orf72 gene is a leading, shared genetic factor that can result in familial FTD, ALS, and/or ALS-FTD. Thus, there exists an opportunity for preventative interventions prior to disease onset in individuals with known genetic mutations. However, pre-symptomatic biomarkers to monitor disease progression in C9orf72 mutation carriers are currently lacking. Prior neuroimaging studies suggest that disruptions of functional and structural connectomes are an early feature of neurodegeneration. In this work, we perform a multi-modal, cross-sectional study of brain connectomes derived from diffusion and functional magnetic resonance imaging (MRI) for a cohort of C9orf72 mutation carriers (pre-symptomatic and symptomatic) and controls from the Human Connectome Project-FTD (HCP-FTD) study. Our results show significant but divergent disruptions in modularity in structural and functional connectomes for C9orf72 mutation carriers with respect to controls, with the pre-symptomatic subjects showing an intermediate effect between controls and symptomatic subjects for both modalities. The divergence in structural and functional connectomes is corroborated by a graph Fourier transform based analysis of structure-function coupling, where we observe significant structure-function decoupling in anatomically plausible brain regions for C9orf72 mutation carriers. Finally, we also observe significant associations between network metrics and plasma neurofilament light chain (NfL) for C9orf72 mutation carriers. Therefore, changes in modularity and structure-function coupling may be a plausible feature of C9orf72 neurodegeneration.
11:40 AM Identification of community structure-based brain states and transitions using functional MRI
Adeel Razi, Monash University, Australia (in-person)
Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct networks. Characterizing the way in which brain regions reconfigure their interactions to give rise to distinct but hidden brain states remains an open challenge. In this work, we propose a Bayesian method for characterizing community structure-based latent brain states and showcase a novel strategy based on posterior predictive discrepancy using the latent block model to detect transitions between community structures in blood oxygen level-dependent (BOLD) time series. The set of estimated parameters in the model includes a latent label vector that assigns network nodes to communities, and also block model parameters that reflect the weighted connectivity within and between communities. Besides extensive in-silico model evaluation, we also provide empirical validation (and replication) using the Human ConnectomeProject (HCP) dataset of 100 healthy adults. Our results obtained through an analysis of task-fMRI data during working memory performance show appropriate lags between external task demands and change-points between brain states, with distinctive community patterns distinguishing fixation, low-demand and high-demand task conditions.
12:00 PM Lunch break
1:30 PM Bridging micro to macro: Exploring graph topologies that support consistent cross-scale dynamics.
Brandon Munn, The University of Sydney, Australia (in-person)
Understanding how macroscale dynamics emerge from underlying micro to mesoscale dynamics is an important question in neuroscience. This presentation outlines a generative model that we iteratively coarse grain to explore how its dynamics change across scales. We simulate this model atop various families of topologies from exponentially decaying geometric, modular, scale-free degree distributed, hierarchical modular, and small-world. Finally, we show that only a small family of topological models are able to reproduce multiscale dynamics observed empirically.
1:50 PM Integrating brain structure and function using multi-modal MRI connectome data
Alex Leow, University of Illinois at Chicago, USA (virtual)
In this talk, I will highlight directions our research team has been pursuing with an overarching goal to integrate structure and function using multi-modal MRI connectomics.

First, we attempted to leverage a function-constrained structural graph variational autoencoder (VAE) that incorporates information from both resting-state functional and structural connectome in an unsupervised fashion. This leads to a joint low-dimensional embedding that establishes a unified spatial coordinate system. When tested on OASIS-3, a large publicly available Alzheimer’s disease (AD) dataset, results showed that a variational formulation may be necessary to encode temporally-varying functional brain dynamics. Further, the joint functional-structural embedding approach is better at characterizing subject sub-populations than approaches that only leverage the structural connectome. If time permits, I will briefly recap our statistical physics based model to infer Excitation-Inhibition balance on the connectome level that was introduced in last year’s workshop and provide some updates using the OASIS-3 dataset.
2:10 PM Diffuse neural coupling mediates complex network dynamics through the formation of quasi-critical brain states
Eli Muller, The University of Sydney, Australia (in-person)
The biological mechanisms that allow the brain to balance flexibility and integration remain poorly understood. A potential solution may lie in a unique aspect of neurobiology, which is that numerous brain systems contain diffuse synaptic connectivity. Here, we demonstrate that increasing diffuse cortical coupling within a validated biophysical corticothalamic model traverses the system through a quasi-critical regime in which spatial heterogeneities in input noise support transient critical dynamics in distributed subregions. The presence of quasicritical states coincides with known signatures of complex, adaptive brain network dynamics. Finally, we demonstrate the presence of similar dynamic signatures in empirical whole-brain human neuroimaging data. Together, our results establish that modulating the balance between local and diffuse synaptic coupling in a thalamocortical model subtends the emergence of quasi-critical brain states that act to flexibly transition the brain between unique modes of information processing
2:30 PM Time-varying pattern of the functional connectivity is a strong predictor for Alzheimer’s disease.
Fei Jiang, University of California San Francisco, USA (virtual)
Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. The most popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations preventing them to extract reliable features that associated with neurodegenerative diseases. We utilize the robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution Magnetoencephalography data, which automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. We show the number of brain states, and the number of brain state transitions are strong predictors of Alzheimer's disease (AD) risk. Furthermore, the features from TVDN have high sensitivity and specificity in distinguishing AD and healthy subjects.
2:50 PM Modelling brain dynamics using cortical geometry eigenmodes
James Pang, Monash University, Australia (in-person)
The dynamics of most natural systems are fundamentally constrained by their underlying structure, and the nervous system is no exception. Here, we determine the principles underlying how large-scale dynamics are shaped by the brain's structure and anatomy. Specifically, we derive the eigenmodes of cortical surface geometry and show how they can explain the patterns of task-evoked and resting-state activity at the whole-brain level. Finally, we combine the eigenmodes with a simple model of wave dynamics to provide a mechanistic account for diverse empirical data and physiological phenomena.
3:10 PM Spectral graph theory of brain oscillations
Parul Verma, University of California San Francisco (in-person)
In this work, we explore the properties of a hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography (MEG) recordings. The model consists of coupled excitatory and inhibitory dynamics of the neural ensembles for every brain region, and white-matter structural wiring-based long-range excitatory macroscopic dynamics. We demonstrate that this model, with just a parsimonious set of global and biophysically interpretable model parameters, can display frequency-rich spectra. In particular, we show that even without any oscillations on the regional level, the macroscopic model alone can exhibit oscillations with a frequency in the alpha band. We also show that depending on the parameters, the model can exhibit damped oscillations, limit cycles, or unstable oscillations that blow up with time. We further determined bounds on these parameters to ensure stability of the modeled oscillations. Lastly, we outline a strategy to capture dynamics of MEG in terms of dynamics of these model parameters. These biophysically interpretable model parameters can be employed to investigate correlates of differences in frequency spectra observed in different brain states and neurological diseases.
3:30 PM Coffee break
4:00 PM Large-scale network dynamics and transient spectral events
Mark Woolrich, University of Oxford, UK (virtual)
Functional brain networks display complex spatiotemporal dynamics that span multiple time-scales. In this talk, I will present computational methods that can access the dynamics of large-scale networks at the sub-second time-scales. I will show how these approaches can be used in combination with MEG data to infer fast dynamics of spectrally distinct large-scale phase locking networks in rest and task, to predict disease, and to provide a link between the spontaneous replay of learnt memories and the spontaneous activity of resting state networks.
4:20 PM Whole-brain functional harmonics with negative correlations
Katharina Glomb, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland (virtual)
Graph signal processing is an established method which has recently been adopted for neuroscience. It fits well with the idea that the brain can be conceptualized as a network - or graph - and provides mathematical tools for analyzing functional time series that evolve on top of a fixed structural connectivity. A basic yet versatile idea is that a brain graph can be decomposed into building blocks, or basis functions, that can then be used to understand network organization. These basis functions are also known as harmonic modes, functional harmonics, or, in the case of higher-resolution data, gradients. These modes are theoretically linked to the physical phenomenon of standing waves. In my talk, I will show our recent work on functional harmonics - derived from resting state fMRI - that includes subcortical structures, in particular the cerebellum. I will put emphasis on the role of negative correlations between fMRI-time courses, as the question of how to interpret and handle them is still a matter of debate.
4:40 PM Panel discussion
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