Biology subjects

Meneghetti, N.

Publications and source records attributed to Meneghetti, N..

6 recordsLinked to original sources

bioRxiv2026

GABAergic Circuit Activation Induces a Therapeutically Responsive State for anti-PD-L1 Immunotherapy in Glioblastoma

Glioblastoma (GBM) disrupts cortical excitatory-inhibitory balance and establishes an immunosuppressive microenvironment that limits therapeutic efficacy. Whether restoring inhibitory signaling can restrain glioma progression and improve responsiveness to immune checkpoint blockade remains unknown. Peritumoral parvalbumin-positive (PV+) interneurons were bidirectionally manipulated by chemogenetics in orthotopic GL261 gliomas to assess tumor growth and neurological function. GABAB signaling was pharmacologically activated with baclofen in GL261 and CT-2A models and combined with anti-PD-L1 blockade in GL261. Therapeutic response, survival, tumor rechallenge, and early myeloid remodeling were evaluated. Human GBM single-cell transcriptomic data were analyzed to examine the relationship between GABAergic and PD-L1 intercellular signaling. PV activation transiently restrained glioma growth, reduced tumor proliferation and preserved cortical function, whereas PV+ silencing increased seizure susceptibility and neurological impairment without accelerating tumor growth. Baclofen monotherapy did not affect survival, whereas its combination with anti-PD-L1 immunotherapy induced complete tumor eradication in 66% of GL261-bearing mice, prolonged survival, and conferred durable protection against tumor rechallenge. Combination therapy also altered the proportions of Arg1+ and CD11c+ cells within the intratumoral F4/80+ compartment. Human single-cell analysis revealed a shared myeloid-centered communication axis linking GABAB and PD-L1 signaling. These findings identify GABAergic signaling as a modulator of GBM progression and demonstrate that combining baclofen with anti-PD-L1 induces durable tumor regression, and prolongs survival in the GL261 model, supporting a neuro-immune framework for combining GABAergic modulation with immunotherapy.

cancer biology↗

bioRxiv2026

Can we trust subthalamic local field potential? Geometrical and dynamical factors constraining the interpretability of extracellular recordings

Local field potentials (LFPs) are widely interpreted as readouts of population synaptic activity, an assumption derived almost entirely from cortical recordings. Whether these principles extend to subcortical structures remains unclear. We address this question in the subthalamic nucleus (STN), where LFPs are routinely recorded and used to guide adaptive deep brain stimulation for Parkinsons disease, using a biophysically detailed population model benchmarked against patient microelectrode recordings. As in the cortex, STN extracellular potentials were dominated by synaptic currents. Differently from the cortex, however, LFPs could not be reliably predicted from these currents or other average population quantities. This dissociation arises from the STN symmetric neuronal morphology and lack of recurrent connectivity, which promote destructive interference among single-neuron contributions, decoupling the LFP from population-level dynamics. This decoupling was not absolute: pathological beta synchrony restored a robust synapse-LFP relationship by consistent underlying dynamics, while the aperiodic slope of the power spectral density tracked STN neuronal morphology, firing rate, and excitatory-inhibitory balance. Together, these findings challenge the prevailing view of LFPs as universal readouts of population activity. Our results show that the interpretability of extracellular signals depends critically on neuronal morphology and synchronization state, and provide a mechanistic framework for the use of STN LFPs as biomarkers in adaptive deep brain stimulation for Parkinsons disease.

neuroscience↗

bioRxiv2025

Gamma oscillations in basal ganglia stem from the interplay between local inhibition and beta synchronization

Basal ganglia rhythms have mainly been studied in the beta band (12-30 Hz), a hallmark of Parkinsons disease (PD), while gamma oscillations (30-100 Hz) in the subthalamic nucleus (STN) have emerged as alternative markers for guiding adaptive deep brain stimulation. However, their underlying mechanisms remains unclear. Using a spiking network model of the basal ganglia, we identified two distinct gamma rhythms: a high-frequency gamma in pallidal (GPe-TI) neurons and a slower gamma in D2 medium spiny neurons (MSNs), both generated through self-inhibition. Under simulated parkinsonian condition, GPe-TI gamma intensity remained stable. In contrast, D2 MSN gamma emerged only in pathological conditions and was strongly modulated by beta activity in both intensity and frequency. Although STN did not generate gamma oscillations directly, gamma activity from GPe-TI population was reflected in simulated STN local field potentials. These results clarify the circuit origins of gamma rhythms and their modulation in PD.

neuroscience↗

bioRxiv2025

Altered cortical network in Parkinson's Disease: the central role of PV interneuron and synaptic remodelling

Parkinson's disease (PD) is traditionally defined by the progressive degeneration of nigrostriatal dopaminergic neurons; however, accumulating evidence highlights extensive cortical dysfunctions as key contributors to motor and non-motor symptoms. Despite this growing recognition, the precise mechanisms underlying cortical network disruptions and their contribution to PD pathophysiology remain poorly understood, particularly in relation to parvalbumin-positive interneurons (PV-INs) and maladaptive plasticity. Here, we investigate the dysregulation of cortical network homeostasis in PD using a 6-hydroxydopamine (6-OHDA) mouse model, focusing on the progressive disruption of parvalbumin-positive interneuron (PV-IN) connectivity, excitatory/inhibitory balance, and neuroinflammatory responses. Using a multimodal approach integrating longitudinal electrophysiology, wide-field calcium imaging, and histological analyses, we revealed striking alterations in cortical activity and connectivity. Specifically, we observed pathological high-gamma hyperactivity during movement, accompanied by severe disruptions in PV-IN connectivity across motor and somatosensory cortices. Histological analyses further revealed synaptic imbalances and microglial dysregulation, suggesting an extensive cortical response to dopaminergic loss. These findings indicate that PV-IN dysfunction drives cortical maladaptive plasticity, leading to network desynchronization and motor deficits. By reframing PD as a disorder of cortical network homeostasis, this study provides novel mechanistic insights and identifies cortical plasticity as a promising therapeutic target for disease modification.

neuroscience↗

bioRxiv2025

A spiking LIF model captures the role of Somatostatin and Parvalbumin neurons in generating oscillations in V1

PurposeOscillations in the primary visual cortex of the mammalian brain have been demonstrated to arise from the balance between excitatory and inhibitory activity. Experimental studies suggest that different inhibitory neuron populations might make specific contributions to such oscillations, but the underlying mechanism has not yet been assessed. MethodsWe modified a standard excitatory-inhibitory spiking neuron model of layer 4 of the primary visual cortex and we investigated the effects on oscillations of the differentiation of inhibitory neurons in somatostatin and parvalbumin neurons. ResultsOur model reproduced the hypothesis that somatostatin and parvalbumin neurons are responsible for beta (15-25)Hz and gamma (40-70)Hz band oscillations, respectively. ConclusionTo date, this is the simplest model accounting for this phenomenon and could therefore be suited to study pathologies in which the two populations have specific roles, such as migraine.

neuroscience↗

bioRxiv2024

Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex

Simulations of large-scale neural activity are powerful tools for investigating neural networks. Calculating measurable brain signals like local field potentials (LFPs) bridges the gap between model predictions and experimental observations. However, accurately simulating LFPs from large-scale models has traditionally required highly detailed multicompartmental neuron models, posing significant computational challenges. Here, we demonstrate that a kernel-based method can efficiently and accurately estimate LFPs in a state-of-the-art multicompartmental model of the mouse primary visual cortex (V1). Beyond its computational efficiency, the kernel method aids analysis by disentangling contributions of individual neuronal populations to the LFP. Using this approach, we found that LFPs in the V1 model were dominated by external synaptic inputs, with local synaptic activity playing a minimal role. Our findings establish the kernel method as a powerful tool for LFP estimation in large-scale network models and for uncovering the synaptic mechanisms underlying brain signals.

neuroscience↗