Biology subjects

Doorn, N.

Publications and source records attributed to Doorn, N..

5 recordsLinked to original sources

bioRxiv2025

Burst suppression: a default brain state associated with loss of network complexity

Burst suppression (BS) is a highly stereotyped EEG pattern observed across a wide range of clinical contexts, from general anesthesia and postanoxic coma to neonatal encephalopathy. Despite its consistent appearance, BS comprises two distinct forms with markedly different implications. BS with identical bursts (IBS) is almost exclusively seen in patients with severe, irreversible encephalopathy and is consistently associated with poor neurological outcome. In contrast, heterogeneous BS (HBS) can appear in reversible conditions such as anesthesia. The mechanisms that give rise to these divergent forms remain elusive. Existing theories impose disease-specific processes on otherwise healthy networks, but such models fail to explain why BS emerges across diverse etiologies and disregard the clinically critical distinction between IBS and HBS. We combined clinical, experimental, and computational approaches to identify shared mechanisms underlying BS. We analyzed EEG recordings from patients with a severe postanoxic encephalopathy and from patients undergoing general anesthesia. These clinical observations were compared with activity recordings from human induced pluripotent stem cell-derived neuronal networks and rodent cortical cultures, and simulations of biophysically grounded neuronal network models. Purely excitatory, low-complexity networks, both in vitro and in silico, spontaneously generated activity virtually indistinguishable from pathological IBS. Introducing inhibitory neurons, modular network structure, or diverse external inputs progressively increased signal complexity and produced HBS-like or continuous activity resembling physiological EEG. Our findings suggest that BS, and particularly IBS, reflects a default dynamic state of simplified excitatory networks that emerges when biological complexity is lost. Different clinical conditions may compromise distinct mechanisms--inhibition, connectivity, or afferent input--yet converge on the same underlying activity pattern. While IBS reflects near-complete loss of complexity, HBS indicates partial preservation. This unified framework explains how diverse etiologies converge on BS and highlights identical forms as signatures of severely reduced network complexity.

neuroscience↗

bioRxiv2024

Automated inference of disease mechanisms in patient-hiPSC-derived neuronal networks

Human induced pluripotent stem cells (hiPSCs)-derived neurons offer a valuable platform for studying neurological disorders in a patient-specific manner. These neurons can be rapidly differentiated into excitatory neuronal networks, whose activity is measurable using multi-electrode arrays (MEAs). Neuronal networks derived from patients exhibit distinct characteristics, reflecting underlying pathological molecular mechanisms. However, elucidating these mechanisms traditionally requires extensive and hypothesis-driven additional experiments. Computational models can link observable network activity to underlying molecular mechanisms by identifying biophysical model parameters that simulate the activity, but this identification process is challenging. Here, we address this challenge by using simulation-based inference (SBI), a machine-learning approach, to automatically identify the full range of model parameters able to explain the patient-derived MEA activity. Our study demonstrates that SBI can accurately identify ground-truth parameters in simulated data, and successfully estimate the parameters that replicate the network activity of healthy hiPSC-derived neuronal networks. Furthermore, we show that SBI can pinpoint molecular mechanisms affected by pharmacological agents and identify key disease mechanisms in neuronal networks derived from patients. These findings underscore the potential of SBI to automate and enhance the identification of disease mechanisms from MEA measurements, offering a robust and scalable method for advancing research with hiPSC-derived neuronal networks.

neuroscience↗

bioRxiv2024

CACNA1A haploinsufficiency leads to reduced synaptic function and increased intrinsic excitability

Haploinsufficiency of the CACNA1A gene, encoding the pore-forming 1 subunit of P/Q-type voltage-gated calcium channels, is associated with a clinically variable phenotype ranging from cerebellar ataxia, to neurodevelopmental syndromes with epilepsy and intellectual disability. To understand the pathological mechanisms of CACNA1A loss-of-function variants, we characterized a human neuronal model for CACNA1A haploinsufficiency, by differentiating isogenic induced pluripotent stem cell lines into glutamatergic neurons, and investigated the effect of CACNA1A haploinsufficiency on mature neuronal networks through a combination of electrophysiology, gene expression analysis, and in silico modeling. We observed an altered network synchronization in CACNA1A+/- networks alongside synaptic deficits, notably marked by an augmented contribution of GluA2-lacking AMPA receptors. Intriguingly, these synaptic perturbations coexisted with increased non-synaptically driven activity, as characterized by inhibition of NMDA and AMPA receptors on micro-electrode arrays. Single-cell electrophysiology and gene expression analysis corroborated this increased intrinsic excitability through reduced potassium channel function and expression. Moreover, we observed partial mitigation of the CACNA1A+/- network phenotype by 4-aminopyridine, a therapeutic intervention for episodic ataxia type 2. In summary, our study pioneers the characterization of a human induced pluripotent stem cell-derived neuronal model for CACNA1A haploinsufficiency, and has unveiled novel mechanistic insights. Beyond showcasing synaptic deficits, this neuronal model exhibited increased intrinsic excitability mediated by diminished potassium channel function, underscoring its potential as a therapeutic discovery platform with predictive validity.

neuroscience↗

bioRxiv2024

Breaking the Burst: Unveiling Mechanisms Behind Fragmented Network Bursts in Patient-derived Neurons

Fragmented network bursts (NBs) are observed as a phenotypic driver in many patient-derived neuronal networks on multi-electrode arrays (MEAs), but the pathophysiological mechanisms underlying this phenomenon are unknown. Here, we used our previously developed biophysically detailed in silico model to investigate these mechanisms. Fragmentation of NBs in our model simulations occurred only when the level of short-term synaptic depression (STD) was enhanced, suggesting that STD is a key player. Experimental validation with Dynasore, an STD enhancer, induced fragmented NBs in healthy neuronal networks in vitro. Additionally, we showed that strong asynchronous neurotransmitter release, NMDA currents, or short-term facilitation (STF) can support the emergence of multiple fragments in NBs by producing excitation that persists after high-frequency firing stops. Our results provide important insights into disease mechanisms and potential pharmaceutical targets for neurological disorders modeled using hiPSC-derived neurons.

neuroscience↗

bioRxiv2023

Modeling the role of the thalamus in resting-state functional connectivity: nature or structure

The thalamus is a central brain structure that serves as a relay station for sensory inputs from the periphery to the cortex and regulates cortical arousal. Traditionally, it has been regarded as a passive relay that transmits information between brain regions. However, recent studies have suggested that the thalamus may also play a role in shaping functional connectivity (FC) in a task-based context. Based on this idea, we hypothesized that due to its centrality in the network and its involvement in cortical activation, the thalamus may also contribute to resting-state FC, a key neurological biomarker widely used to characterize brain function in health and disease. To investigate this hypothesis, we constructed ten in-silico brain network models based on neuroimaging data (MEG, MRI, and dwMRI), and simulated them including and excluding the thalamus. and raising the noise into thalamus to represent the afferences related to the reticular activating system (RAS) and the relay of peripheral sensory inputs. We simulated brain activity and compared the resulting FC to their empirical MEG counterparts to evaluate models performance. Results showed that a parceled version of the thalamus with higher noise, able to drive damped cortical oscillators, enhanced the match to empirical FC. However, with an already active self-oscillatory cortex, no impact on the dynamics was observed when introducing the thalamus. We also demonstrated that the enhanced performance was not related to the structural connectivity of the thalamus, but to its higher noisy inputs. Additionally, we highlighted the relevance of a balanced signal-to-noise ratio in thalamus to allow it to propagate its own dynamics. In conclusion, our study sheds light on the role of the thalamus in shaping brain dynamics and FC in resting-state and allowed us to discuss the general role of criticality in the brain at the mesoscale level. Author summarySynchrony between brain regions is an essential aspect of coordinated brain function and serves as a biomarker of health and disease. The thalamus, due to its centrality and widespread connectivity with the cortex, is a crucial structure that may contribute to this synchrony by allowing distant brain regions to work together. In this study, we used computational models to investigate the thalamuss role in generating brain synchrony at rest. Our findings suggest that the structural connectivity of the thalamus is not its primary contribution to brain synchrony. Instead, we found that the thalamus plays a critical role in driving cortical activity, and when it is not driving this activity, its impact on brain synchrony is null. Our study provides valuable insights into the thalamocortical networks role in shaping brain dynamics and FC in resting state, laying the groundwork for further research in this area.

neuroscience↗