Biology subjects

Mediavilla, L.

Publications and source records attributed to Mediavilla, L..

3 recordsLinked to original sources

bioRxiv2026

Neural correlates of glioma progression using implanted neural interfaces

High-grade glioma is an incurable brain cancer with a median survival of approximately 14 months. Over the last 50 years, small improvements in patient outcomes have been overshadowed by significant progress in most other cancers. Yet, emerging research has revealed that neural circuits play an active and central role in driving glioma growth and proliferation, highlighting the nervous system as a promising avenue for both disease monitoring and therapeutic intervention. Here, we present a platform for chronically monitoring tumor progression using neural recordings in freely behaving mice with gliomas. Using this platform across multiple mouse strains and glioma models, we show that neural recordings can accurately track tumor progression in vivo. Cancer progression was consistently associated with elevated gamma-band neural activity in the tumor microenvironment across both adult glioblastoma (GBM) and pediatric diffuse intrinsic pontine glioma (DIPG) cancer models. Interestingly, lower frequency neural activity exhibited distinct, cell-line specific changes over time: GBM models exhibited decreases in low frequency neural activity whereas DIPG models exhibited increases over time. Finally, using machine learning models applied to chronic neural recordings from tumor-bearing mice treated with or without standard-of-care chemotherapy (temozolomide for GBM), we accurately predicted tumor burden as inferred through in vivo bioluminescence imaging. By fitting low-dimensional mathematical models to gamma-band neural trajectories, we could further predict individual tumor growth rates over a 5-week period with high accuracy. These results establish that pathological neural-tumor interactions can be harnessed to monitor glioma progression in vivo. Coupling this monitoring capability with therapeutic electrical stimulation in the same device could open up a new class of implantable, closed-loop neurotechnologies with the potential to transform glioma treatment.

neuroscience↗

bioRxiv2022

Modelling the contributions to hyperexcitability in a mouse model of Alzheimer's disease

Neuronal hyperexcitability is a feature of Alzheimers disease (AD). Three main mechanisms have been proposed to explain it: i), dendritic degeneration leading to increased input resistance, ii), ion channel changes leading to enhanced intrinsic excitability, and iii), synaptic changes leading to excitation-inhibition (E/I) imbalance. However, the relative contribution of these mechanisms is not fully understood. Therefore, we performed biophysically realistic multi-compartmental modelling of excitability in reconstructed CA1 pyramidal neurons of wild-type and APP/PS1 mice, a well-established animal model of AD. We show that, for synaptic activation, the excitability promoting effects of dendritic degeneration are cancelled out by excitability decreasing effects of synaptic loss. We find an interesting balance of excitability regulation with enhanced degeneration in the basal dendrites of APP/PS1 cells potentially leading to increased excitation by the apical but decreased excitation by the basal Schaffer collateral pathway. Furthermore, our simulations reveal that three additional pathomechanistic scenarios can account for the experimentally observed increase in firing and bursting of CA1 pyramidal neurons in APP/PS1 mice. Scenario 1: increased excitatory burst input; scenario 2: enhanced E/I ratio and scenario 3: alteration of intrinsic ion channels (IAHP down-regulated; INap, INa and ICaT up-regulated) in addition to enhanced E/I ratio. Our work supports the hypothesis that pathological network and ion channel changes are major contributors to neuronal hyperexcitability in AD. Overall, our results are in line with the concept of multi-causality and degeneracy according to which multiple different disruptions are separately sufficient but no single disruption is necessary for neuronal hyperexcitability. In briefUsing a computational model, we find that changes in the extrinsic network and intrinsic biophysical neuronal properties rather than dendritic degeneration alone explain the altered firing behaviour observed in Alzheimers disease (AD). HighlightsO_LISimulations of synaptically driven responses in PCs with AD-related dendritic degeneration. C_LIO_LIDendritic degeneration alone alters PC responses to layer-specific input but additional pathomechanistic scenarios are required to explain neuronal hyperexcitability in AD. C_LIO_LIPossible scenario 1: Burst hyperactivity of the surrounding network can explain hyper-excitability of PCs during AD. C_LIO_LIPossible scenario 2: AD-related increased excitatory input together with decreased inhibitory input (E/I imbalance) can lead to hyperexcitability in PCs. C_LIO_LIPossible scenario 3: Changes in E/I balance combined with altered ion channel properties can account for hyperexcitability in AD. C_LI

neuroscience↗

bioRxiv2019

A general principle of dendritic constancy – a neuron’s size and shape invariant excitability

Reducing neuronal size results in less cell membrane and therefore lower input conductance. Smaller neurons are thus more excitable as seen in their voltage responses to current injections in the soma. However, the impact of a neurons size and shape on its voltage responses to synaptic activation in dendrites is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs and show that these are entirely independent of dendritic length. For a given synaptic density, a neurons response depends only on the average dendritic diameter and its intrinsic conductivity. These results remain true for the entire range of possible dendritic morphologies irrespective of any particular arborisation complexity. Also, spiking models result in morphology invariant numbers of action potentials that encode the percentage of active synapses. Interestingly, in contrast to spike rate, spike times do depend on dendrite morphology. In summary, a neurons excitability in response to synaptic inputs is not affected by total dendrite length. It rather provides a homeostatic input-output relation that specialised synapse distributions, local non-linearities in the dendrites and synaptic plasticity can modulate. Our work reveals a new fundamental principle of dendritic constancy that has consequences for the overall computation in neural circuits.\n\nIn briefWe show that realistic neuron models essentially collapse to point neurons when stimulated by randomly distributed inputs instead of by single synapses or current injection in the soma.\n\nHighlightsO_LIA simple equation that predicts voltage in response to distributed synaptic inputs.\nC_LIO_LIResponses to distributed and clustered inputs are largely independent of dendritic length.\nC_LIO_LISpike rates in various Hodgkin Huxley (HH) like or Leaky Integrate-and-Fire (LIF) models are largely independent of morphology.\nC_LIO_LIPrecise spike timing (firing pattern) depends on dendritic morphology.\nC_LIO_LINeuroMorpho.Org database-wide analysis of the relation between dendritic morphology and electrophysiology.\nC_LIO_LIOur equations set precise input-output relations in realistic dendrite models.\nC_LI

neuroscience↗