Biology subjects

Meneghetti, N.

Publications and source records attributed to Meneghetti, N..

4 recordsLinked to original sources

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↗