Biology subjects

van der Molen, T.

Publications and source records attributed to van der Molen, T..

6 recordsLinked to original sources

bioRxiv2026

A recurrent neural network model of chronic pain development and recovery

Chronic pain presents a leading challenge in the world today for both clinicians and researchers. Because chronic pain is difficult to explain and treat, it is often managed with opioids despite providing limited relief and contributing to dependence and misuse. Persistent pain can be maintained by altered central nervous system processing even in the absence of distinct tissue damage or disease, which may limit the efficacy of conventional pharmacological therapies that target nociceptive signal transmission rather than maladaptive central nervous system dynamics often present in those with chronic pain. Although neuroimaging studies have identified this shift from nociceptive to emotional circuits during pain chronification, a quantitative framework linking these neural changes to longitudinal pain trajectories or recovery is lacking. We present a parsimonious firing-rate model that can account for the development of and recovery from chronic pain, which is based on the theoretical framework established by Wilson and Cowan. The model provides a quantitative explanation of how sensitization, anxiety, and fear maintain pain even after an injury has healed, and how calming stimulus downregulates these processes to facilitate recovery. A study applying the same principles as the model produced an average pain decrease of 3.5 on the Visual Analog Scale (VAS), with all subjects experiencing a reduction in pain. These results, coupled with our model and findings in prior studies, suggest that increasing calming stimulus can reduce pain without necessitating pharmacological or invasive, resource-intensive interventions.

neuroscience↗

bioRxiv2025

Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly

The mouse cortex is a canonical model for studying how functional neural networks emerge, yet it remains unclear which topological features arise from intrinsic cellular organization versus external regional cues. Mouse forebrain organoids provide a powerful system to investigate these intrinsic mechanisms. We generated dorsal (DF) and ventral (VF) forebrain organoids from mouse pluripotent stem cells and tracked their development using longitudinal electrophysiology. DF organoids showed progressively stronger network-wide correlations, while VF organoids developed more refined activity patterns, enhanced small-world topology, and increased modular organization. These differences emerged without extrinsic inputs and may be driven by the increased generation of Pvalb+ interneurons in VF organoids. Our findings demonstrate how variations in cellular composition influence the self-organization of neural circuits, establishing mouse forebrain organoids as a tractable platform to study how neuronal populations shape cortical network architecture.

neuroscience↗

bioRxiv2024

Human Neural Organoid Microphysiological Systems Show the Building Blocks Necessary for Basic Learning and Memory

Brain Microphysiological Systems including neural organoids derived from human induced pluripotent stem cells offer a unique lens to study the intricate workings of the human brain. This paper investigates the foundational elements of learning and memory in neural organoids, also known as Organoid Intelligence by quantifying immediate early gene expression, synaptic plasticity, neuronal network dynamics, and criticality to demonstrate the utility of these organoids in basic science research. Neural organoids showed synapse formation, glutamatergic and GABAergic receptor expression, immediate early gene expression basally and evoked, functional connectivity, criticality, and synaptic plasticity in response to theta-burst stimulation. In addition, pharmacological interventions on GABAergic and glutamatergic receptors, and input specific theta-burst stimulation further shed light on the capacity of neural organoids to mirror synaptic modulation and short-term potentiation, demonstrating their potential as tools for studying neurophysiological and neurological processes and informing therapeutic strategies for diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/613333v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@50a256org.highwire.dtl.DTLVardef@1d1f8c9org.highwire.dtl.DTLVardef@24688borg.highwire.dtl.DTLVardef@4ba3b4_HPS_FORMAT_FIGEXP M_FIG C_FIG Overview of the main components of the experiments conducted. Figure created using BioRender.com.

neuroscience↗

bioRxiv2024

RT-Sort: an action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies

With the use of high density multi electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes both in vivo and in vitro. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high fidelity sequential activations on adjacent electrodes, together with a convolutional neural network based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms{+/-}1.5ms (mean{+/-}STD) after the waveform trough while the recording remains ongoing. RT-Sorts true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sorts performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sorts functionality on different types of recording hardware and electrode configurations.

neuroscience↗

bioRxiv2022

Automated detection of extracellular action potentials from single neurons

Multi-electrode arrays (MEAs) non-invasively record extracellular action potentials (eAPs, also known as spikes) from hundreds of neurons simultaneously. However, because extracellular electrodes sample from the local electrical field, each electrode can detect eAPs from multiple nearby neurons. Interpreting spike trains at individual electrodes of high-density arrays requires spike sorting, a computational process which groups eAPs from single units based on assumptions of how spike waveforms correlate with different neuronal sources. Additionally, when experimental conditions result in changes to eAP waveforms, spike sorting routines may have difficulty correlating eAPs from multiple neurons at single electrodes before and after such waveform changes. We present here a novel, empirical method for unambiguously isolating eAPs from individual, uniquely identifiable neurons, based on automated multi- point detection of action potential propagation. This method is insensitive to changes in eAP waveform morphology because it makes no assumptions about the relationship between spike waveform and neuronal source. Our algorithm for automated detection of action potential propagation produces a fingerprint that uniquely identifies those spikes from each neuron. By unambiguously isolating eAPs from multiple neurons in each recording, on a range of platforms and experimental preparations, our method now enables high-content screening with contemporary MEAs. We outline the limitations and strengths of propagation-based isolation of eAPs from single neurons and propose how our automated method complements spike sorting and could be adapted to in vivo use.

neuroscience↗

bioRxiv2021

Intrinsic network activity in human brain organoids

Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiological behavior of neuronal circuits within organoids remains relatively under-explored. With high-density CMOS microelectrode arrays (26,400 electrodes) and shank electrodes (960 electrodes), we probed broadband and three-dimensional extracellular field recordings generated by spontaneous activity of human brain organoids. These recordings simultaneously captured local field potentials (LFPs) and single-unit activity extracted through spike sorting. From spiking activity, we estimated a directed functional connectivity graph of synchronous neural network activity, which showed a large number of weak functional connections enmeshed within a network skeleton of significantly fewer strong connections. Treatment of the organoid with a benzodiazepine induced a reproducible signature response that shortened the inter-burst intervals, increased the uniformity of the firing pattern within each burst and decreased the population of weakly connected edges. Simultaneously examining the spontaneous LFPs and their phase alignment to spiking showed that spike bursts were coherent with theta oscillations in the LFPs. Our results demonstrate that human brain organoids have self-organized neuronal assemblies of sufficient size, cellular orientation, and functional connectivity to co-activate and generate field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug mechanisms, and the effects of external stimuli upon neuronal networks.

neuroscience↗