Biology subjects

Momi, D.

Publications and source records attributed to Momi, D..

3 recordsLinked to original sources

bioRxiv2022

TMS-evoked responses are driven by recurrent large-scale network dynamics

AO_SCPLOWBSTRACTC_SCPLOWA major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. This question finds sharp relief in the case of TMS-EEG evoked potentials (TEPs). TEPs are spatiotemporal waveform patterns with characteristic inflections at [~]50ms, [~]100ms, and [~]150-200ms following a single TMS pulse that disperse from, and later reconverge to, the primary stimulated regions. What parts of the TEP are due to recurrent activity? And what light might this shed on more general principles of brain organization? We studied this using source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from [~]100ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics.

neuroscience↗

bioRxiv2022

Deep Learning-Based Parameter Estimation for Neurophysiological Models of Neuroimaging Data

AO_SCPLOWBSTRACTC_SCPLOWConnectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics observed in whole-brain activity measurements such as fMRI, M/EEG, and related techniques. Estimating physiological parameters by fitting these models to empirical data is challenging however, due to large network sizes, often physiologically detailed fast-timescale system equations, and the need for long (e.g. tens of minutes) simulation runs. Here we introduce a novel approach to connectome-based neural mass model parameter estimation by employing optimization tools developed for deep learning. We cast the system of differential equations representing both neural and haemodynamic activity dynamics as a deep neural network, implemented within a widely used machine learning programming environment (PyTorch). This allows us to use robust industry-standard optimization algorithms, automatic differentiation for computation of gradients, and other useful functionality. The approach is demonstrated using a connectome-based network with nodal dynamics specified by the two-state RWW mean-field neural mass model equations, which we use here as a model of fMRI-measured activity and correlation fluctuations. Additional optimization constraints are explored and prove fruitful, including restricting the model to domains of parameter space near a bifurcation point that yield metastable dynamics. Using these techniques, we first show robust recovery of physiological model parameters in synthetic data and then, as a proof-of-principle, apply the framework to modelling of empirical resting-state fMRI data from the Human Connectome Project database. For resting state activity, the system can be understood as a deep net that receives uncorrelated noise on its input layer, which is transformed into network-wide modelled functional connectivity on its output layer. This is consistent with the prevailing conception in theoretical neuroscience of resting-state functional connectivity patterns as an emergent phenomenon that is driven by (effectively) random activity fluctuations, which are then in turn spatiotemporally filtered by anatomical connectivity and local neural dynamics.

neuroscience↗

bioRxiv2021

Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder

Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. We looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We generated individualized E-field maps on the cortical surface for 121 subjects from the Human Connectome Project database using tetrahedral head models generated from T1-weighted MR images. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects. HighlightsO_LIE-field modelling and functional connectivity used to study TMS targets (dlPFC,OFC) C_LIO_LIConsiderable variability in TMS target E-field patterns seen across subjects C_LIO_LILarge inter-subject differences in target connectivity observed and characterized C_LIO_LIMajor functional networks targeted by dlPFC, OFC TMS were the VAN, FPN and DMN C_LIO_LIInsights can contribute to improved and more personalized TMS therapies in the future C_LI

neuroscience↗