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Multi-modal artificial dura for simultaneous large-scale optical access and large-scale electrophysiology in non-human primate cortex.

ABSTRACT: Objective.Non-human primates (NHPs) are critical for development of translational neural technologies because of their neurological and neuroanatomical similarities to humans. Large-scale neural interfaces in NHPs with multiple modalities for stimulation and data collection poise us to unveil network-scale dynamics of both healthy and unhealthy neural systems. We aim to develop a large-scale multi-modal interface for NHPs for the purpose of studying large-scale neural phenomena including neural disease, damage, and recovery.Approach.We present a multi-modal artificial dura (MMAD) composed of flexible conductive traces printed into transparent medical grade polymer. Our MMAD provides simultaneous neurophysiological recordings and optical access to large areas of the cortex (∼3 cm²) and is designed to mitigate photo-induced electrical artifacts. The MMAD is the centerpiece of the interfaces we have designed to support electrocorticographic recording and stimulation, cortical imaging, and optogenetic experiments, all at the large-scales afforded by the brains of NHPs. We performed electrical and optical experiments bench-side andin vivowith macaques to validate the utility of our MMAD.Main results.Using our MMAD we present large-scale electrocorticography from sensorimotor cortex of three macaques. Furthermore, we validated surface electrical stimulation in one of our animals. Our bench-side testing showed up to 90% reduction of photo-induced artifacts with our MMAD. The transparency of our MMAD was confirmed both via bench-side testing (87% transmittance) and viain vivoimaging of blood flow from the underlying microvasculature using optical coherence tomography angiography.Significance.Our results indicate that our MMAD supports large-scale electrocorticography, large-scale cortical imaging, and, by extension, large-scale optical stimulation. The MMAD prepares the way for both acute and long-term chronic experiments with complimentary data collection and stimulation modalities. When paired with the complex behaviors and cognitive abilities of NHPs, these assets prepare us to study large-scale neural phenomena including neural disease, damage, and recovery.

SUBMITTER: Griggs DJ

PROVIDER: S-EPMC8523212 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

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Multi-modal artificial dura for simultaneous large-scale optical access and large-scale electrophysiology in non-human primate cortex.

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