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Workshop Program

9:00 AM Introduction
9:05 AM Spectral Graph Models of brain functional connectivity and neural oscillations
Ashish Raj, University of California San Francisco, USA (in-person)
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.
9:30 AM Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes
Thanos Manos, ETIS lab & Department of Mathematics, CY Cergy Paris Université - CY Tech - Institut des Sciences et Techniques, France (in-person)
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC well aligns with the observed FC when compared to that simulated with traditional structural connectome. Simulations were performed using the open-source framework The Virtual Brain on High Performance Computing infrastructure.
9:55 AM DCM-based Granger Causality for resting-state fMRI
Leonardo Novelli, Monash University, Australia (in-person)
Granger causality, transfer entropy, and Dynamic Causal Modelling (DCM) are widely adopted methods to infer large-scale brain connectivity from neuroimaging time series. The literature often focuses on the differences between these approaches, but I will argue that we have much to gain from combining their strengths. Towards this goal, I will present preliminary results on the analytic derivation of Granger Causality under the spectral DCM model for resting-state fMRI, and discuss future directions.
10:20 AM Coffee break
11:00 AM TBD
Mark Woolrich, University of Oxford, UK (virtual)
TBD
11:25 AM Modeling spreading depression and ischemia in cortex
Bill Lytton, SUNY Downstate Medical Center, USA (in-person)
TBD
11:50 AM Lunch break
1:30 PM Small-world topologies maximize the Dynamics of Functional Connectivity and the emergence of statistical synergy
Patricio Orio, Centro Interdisciplinario de Neurociencias de Valparaíso CINV, Chile (in-person)
Among the complex dynamical features that are commonly described in brain dynamics, the continuously evolving correlation patterns of the brain (Functional Connectivity Dynamics, FCD) has been extensively characterized and described as a marker for aging and psychiatric disorders. More recently, the existence of high-order interdependencies of either synergistic or redundant types has also been shown to change with aging, with brain activity from older healthy subjects showing more redundancy. To understand how different topological features of the structural connectome can be related to these features of the functional connectivity, we performed a systematic assessment using a set of 240-node structural connectomes with varying degrees of integration and segregation. The set of over 40 SCs spanned a broad range of the small-world coefficient omega, sweeping from lattice-like to small-world to random-like networks. The SCs were used to simulate neural activity using a Wilson-Cowan model modified with homeostatic inhibitory plasticity, to ensure an oscillatory regime over a wide range of the global connectivity parameter G. Then, the simulated activity was assessed for the FCD, using the variance of the FCD matrix as an index for dynamical richness, and for the emergence of synergistic interdependencies, by randomly sampled groups of 3, 4, or 5 nodes. The nature of the high-order dependencies was assessed using the O-information metric, that is negative for systems dominated by synergy and positive for those with more redundancy. The networks that maximized both complex dynamical features were those having a positive omega index but close to zero. This indicates networks of the small-world type, which have a balance between their integration and segregation metrics. Our results are in agreement with what has been observed in the brain SC and its dynamics as the brain ages.
1:55 PM A novel individualised resting state network representation enhances trait prediction in UK Biobank
Rezvan Farahibozorg, Oxford University, UK (virtual)
Human brain is a system of networks, each underlying a specific function, and interacting with each other. The discovery of brain networks 'at rest'; i.e., resting state networks (RSNs), has been very influential in characterisation of the functional organisation of the brain. The framework of Probabilistic Functional Modes (PFMs), allows to estimate RSNs simultaneously for populations and every individual therein, thus permitting us to capture individual-specific characteristics beyond what has been possible using standard group-average-based techniques such as ICA. In this talk, we will first describe the latest advances in PFM modelling, including stochastic PFMs for big fMRI data. We will then present a novel type of RSN representation discovered by high-dimensional PFMs (150 modes), which includes both large-scale and fine-grained RSNs simultaneously. We refer to this new decomposition as hybrid-RSNs. We show that hybrid-RSNs are spatially and temporally correlated, and capture functional (i.e., temporal) multiplicity within brain networks, whilst maintaining the distributed spatial organisation of the brain function, without over-splitting it into small units. We validate hybrid-RSNs based on existing individualised parcellations, and further show that they are reproducible across two large datasets (HCP and UK Biobank) and two fMRI data types (volumetric and surface based). We further demonstrate the prediction power of these high-dimensional PFMs for 899 phenotypes in UK Biobank, and show that they can predict personalised traits more accurately and interpretably compared with current standard techniques. Therefore, this RSN representation, which we release publicly, provides a new basis for spontaneous brain activity and brain-behaviour studies.
2:20 PM The bursting brain: a fundamental mechanism underlying multiscale brain function
Giovanni Rabuffo, Aix-Marseille Université, France (in-person)
Neural activity bursts are cascade-like events ubiquitously observed across imaging modalities and scales. A great deal of information is encoded in the aperiodic timing and topographic distribution of these events. In particular, bursts are tightly related to the emergence of large-scale correlations, whereby the spreading of these patterned processes underlie the formation of resting state networks. Furthermore, bursting activity is the manifestation of brain complexity, which makes them candidates for neuronal correlates of higher-level cognition and consciousness. I will present this evidence in several case studies, including the analysis of resting state network reconfiguration after focal brain lesions, the assessment of consciousness levels in humans, and connectome-based simulations, among others. The capability of the brain to burst might be the manifestation of a general mechanism of multiscale communication underlying brain function, cognition, and behavior.
2:45 PM Communication mechanisms in mammalian brain networks: An information theory framework
Alessandra Griffa, EPFL, Switzerland (in-person)
Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain's computational capacities that virtually subtend all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapted across evolution to accomplish increasingly complex functions? In this work we introduce a new framework that leverages information theory principles and multimodal magnetic resonance imaging data to identify structural pathways of the brain network selected for information transmission, and measure the level of parallel communication across different brain circuits. By applying this framework to human, macaque and mouse data, we show a gap in brain network communication strategies between humans and other mammals, with the first tailored toward parallel communication and the latter toward selective information transmission. Parallel communication strategies are specific to individual subjects and appear to act as a major connector of unimodal (sensory, attentional) and transmodal (fronto-parietal, default mode) areas in the human brain, possibly contributing to the evolution of more complex cognitive functions in humans. Our results link the complexity of macroscale brain communication dynamics inferred within a new information theory framework to an evolutionary gap between mammalian lineages.
3:10 PM Coffee break
3:30 PM Adaptation shapes local cortical reactivity: from in-silico simulations to human physiological and pathological responses
Anna Cattani, Boston University, USA (virtual)
The input-output properties of cortical circuits are dramatically affected during sleep in healthy subjects as well as in awake patients with multifocal and focal brain injury, as documented by several human studies employing intracerebral and transcranial perturbations. In all these conditions, direct perturbations unveil cortical bistability, which is the tendency of cortical circuits to fall into an Off-period after an initial activation. Cortical bistability is thought to disrupt the build-up of sustained causal interactions typically observed in healthy wakefulness, and it has important clinical implications, being associated with loss of consciousness or loss of functions. In this talk, we will provide a mechanistic explanation of these findings by means of mean-field theory and simulations of a cortical-like module endowed with activity-dependent adaptation. First, we will show that fundamental aspects of the local responses elicited in humans by direct cortical stimulation can be replicated by systematically varying the relationships between adaptation strength and excitation level in the network. Then, we will reveal a region in the adaptation-excitation parameter space of key relevance for both physiological and pathological conditions, where spontaneous activity and responses to perturbation diverge in their ability to reveal Off-periods. Finally, we will substantiate through simulations of connected cortical-like modules the role of adaptation mechanisms in preventing cortical neurons from engaging in reciprocal causal interactions, as suggested by empirical studies.
3:55 PM Spectral graph theory of brain oscillations
Parul Verma, University of California San Francisco, USA (virtual)
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.
4:20 PM Panel discussion
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