Biology subjects

Tamagnini, F.

Publications and source records attributed to Tamagnini, F..

8 recordsLinked to original sources

bioRxiv2026

An Optimised Method for Robust Golgi Cox Staining in Cortical Neurons

Golgi-based staining remains a valuable method for visualising the full morphology of individual neurons, however conventional workflows can be slow, variable, and technically demanding. Here, we present a modified staining protocol designed to provide a practical workflow for Golgi-based labelling in mouse cortical tissue. The revised workflow combines controlled fixation, dichromate impregnation, silver development, vibratome sectioning, and slide processing conditions that support visualisation of neuronal somata and dendritic architecture. In the tissue examined here, the protocol produced stained tissue with discernible neuronal morphology and was suitable for morphological assessment using standard light microscopy and image analysis software. Overall, this method may be useful for laboratories seeking a straightforward and cost-conscious protocol for neuronal morphology.

neuroscience↗

bioRxiv2025

Psilocybin ameliorates neuropathic pain-like behaviour in mice and facilitates gabapentin-mediated analgesia

Chronic pain states are challenging to control with current drug therapies. Here, we demonstrate that a single dose of psilocybin can produce a sustained anti-nociceptive effect in a model of chronic neuropathic pain in male and female mice. Psilocybin anti-nociceptive effects were mediated by 5-HT2A receptors, although additional mechanisms might also be involved. Furthermore, a single dose of psilocybin caused a significant increase in the anti-nociceptive potential of gabapentin, a widely used treatment for neuropathic pain consistent with the establishment of longer lasting changes in network processing. Overall, these findings present the first preclinical evidence that psilocybin could be a valuable approach for treating chronic pain from nerve injury and serve as a new therapeutic addition for pain management.

neuroscience↗

bioRxiv2024

Longitudinal assessment of the conversion of mild cognitive impairment into Alzheimer's dementia: Observations and mechanisms from neuropsychological testing and electrophysiology

INTRODUCTIONElucidating and better understanding functional biomarkers of Alzheimers disease (AD) is crucial. By analysing a detailed longitudinal dataset, this study aimed to create a model-based toolset to characterise and understand the conversion of mild cognitive impairment (MCI) to AD. METHODSEEG, MRI, and neuropsychological data were collected from participants in San Marino: AD (n = 10), MCI (n = 20), and controls (n = 11). Across two additional years, MCI participants were classified as converters or non-converters. RESULTSWe identified the Stroop Color and Word Test as the largest differentiator for MCI conversion (ROC AUC = 0.795). This was underpinned by disconnectivity in working memory and attention networks. Unsupervised clustering of EEG spectra also differentiated MCI conversion (ROC AUC = 0.710) and was underpinned by reduced excitatory and enhanced inhibitory synaptic efficacy in (prodromal) AD. Combining electrophysiological and neuropsychological assessments increased the accuracy of the differentiation (ROC AUC = 0.880) in comparison to each measure considered individually. CONCLUSIONCombining electrophysiological and neuropsychological assessment with mathematical models can inform the development of non-invasive, low-cost tools for the early diagnosis of AD. HighlightsO_LIWe analysed longitudinal changes in EEG and neuropsychological assessments in MCI C_LIO_LIStroop Color and Word Test error scores were lower in MCI converters C_LIO_LIThe degree of impairment was found to be correlated with functional disconnectivity C_LIO_LIUnsupervised clustering of EEG spectra characterised patterns associated with disease C_LIO_LIMathematical modelling revealed reduced excitatory synaptic efficacy in (prodromal) AD C_LI Research in ContextSystematic review: The authors used PubMed to review the literature on the use of inexpensive modalities, including EEG and neurophysiological testing, for characterising the progression of MCI to AD. Although promising, existing work suggests the full potential of these methods as tools for understanding prodromal AD is still lacking. Interpretation: A novel application of a clustering algorithm to EEG spectra revealed different patient diagnoses could largely be characterised by their cluster assignment. We also found differences in a particular neuropsychological test, the Stroop Color and Word Test. Using mathematical modelling we found there were both network and synaptic mechanisms that underlie these differences. Future directions: Using the methods described herein to build markers for testing MCI to AD conversion on a large independent cohort will be crucial to understanding the full impact and applicability of these approaches. This may ultimately lead to a better characterisation and understanding of the diagnosis and prognosis of AD.

neuroscience↗

bioRxiv2023

Inferring parameters of pyramidal neuronexcitability in mouse models of Alzheimer'sdisease using biophysical modeling and deeplearning

Alzheimers disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We introduce a novel parameter inference technique, deep hybrid modeling (DeepHM), that combines deep learning with biophysical modeling to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks (cGANs) to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrate that DeepHM accurately infers parameter distributions of the conductance-based model and outperforms a Markov chain Monte Carlo method on several test cases using synthetic data. We then use DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimers mouse models compared to their wildtype controls at 12 and 24 months. We find that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.

neuroscience↗

bioRxiv2022

Alterations to parvalbumin-expressing interneuron function and associated network oscillations in the hippocampal - medial prefrontal cortex circuit during natural sleep in APPNL-G-F mice

In the early stages of Alzheimers disease (AD), the accumulation of the peptide amyloid-{beta} (A{beta}) damages synapses and disrupts neuronal activity and leads to disruption of neuronal oscillations associated with cognition. This is thought to be largely due to impairments in CNS synaptic inhibition, particularly via parvalbumin (PV)-expressing interneurons that essential for generating several key oscillations. Research in this field has largely been conducted in mouse models that over-express humanised, mutated forms of AD-associated genes that produce exaggerated pathology. This has prompted the development and use of knock-in mouse lines that express these genes at an endogenous level, such as the AppNL-G-F/NL-G-F mouse model used in the present study. These mice appear to model the early stages of A{beta}-induced network impairments, yet an in-depth characterisation of these impairments in currently lacking. Therefore, using 16 month-old AppNL-G-F/NL-G-F mice, we analysed neuronal oscillations found in the hippocampal - medial prefrontal cortex (mPFC) during awake behaviour, rapid eye movement (REM) and non-REM (NREM) sleep to assess the extent of network dysfunction. No alterations to gamma oscillations were found to occur in the hippocampus or mPFC during either awake behaviour, REM or NREM sleep. However, during NREM sleep an increase in the amplitude of mPFC spindles and decrease in the power of hippocampal SWRs was identified. The former was associated with a decrease in the density of mPFC PV-expressing interneurons and the latter was accompanied by an increase in the synchronisation of PV-expressing interneuron activity, as measured using two-photon Ca2+ imaging. Furthermore, although changes were detected in local network function of mPFC and hippocampus, long-range communication between these regions appeared intact. Altogether, our results suggest that these NREM sleep-specific impairments represent the early stages of circuit breakdown in response to amyloidopathy.

neuroscience↗

bioRxiv2020

Synaptic dysfunction caused by truncated tau is associated with hyperpolarization-activated cyclic nucleotide-gated channelopathy

Progressive neurodegeneration in tauopathies is mediated through an elusive mechanism. Here, we show that hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are functionally linked to disease-associated abnormalities in tau. Selective rises in the proportion of HCN-positive neurons are detected both in post-mortem human brain from Alzheimers disease and in the Tau35 mouse model of tauopathy. Tau35 mice develop progressive abnormalities including increased phosphorylated tau, enhanced HCN channel expression and decreased dendritic branching, as well as reduced synapse density that is accompanied by vesicle clustering defects. Notably, altered spine density and increased HCN channel expression in Tau35 neurons correlates with functional abnormalities in network properties, including enhanced hyperpolarization-induced membrane voltage sag and changes in the frequency and kinetics of spontaneous excitatory postsynaptic currents. Our findings are consistent with pathological changes in tauopathies impacting on HCN channels to drive network-wide structural and functional synaptic deficits, providing new targets for therapeutic intervention.

neuroscience↗

bioRxiv2019

EEG microstate complexity for aiding early diagnosis of Alzheimer's disease

IntroductionElectroencephalogram (EEG) is a potentially useful clinical tool for aiding diagnosis of Alzheimers disease (AD). We hypothesized we can increase the accuracy of EEG for aiding diagnosis of AD using microstates, which are epochs of quasi-stability at the millisecond scale. MethodsEEG was collected from two independent cohorts of AD and control participants and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. Microstates were analysed, including a novel measure of complexity. ResultsMicrostate complexity significantly decreased in AD, and when combined with a spectral EEG measure, could classify AD with sensitivity and specificity >80%. These results were validated on an independent cohort and were also found to be generalizable to predict progression from MCI to AD. Additionally, microstates associated with the frontoparietal network were altered in AD. DiscussionEEG has the potential to be a non-invasive functional biomarker that predicts progression from MCI to AD.

neuroscience↗

bioRxiv2019

Imbalance in the response of pre- and post-synaptic components to amyloidopathy

Alzheimers disease (AD)-associated synaptic dysfunction drives the progression of pathology from its earliest stages. A{beta} species, both soluble and in plaque deposits, have been causally related to the progressive, structural and functional impairments observed in AD. It is, however, still unclear how A{beta} plaques develop over time and how they progressively affect local synapse density and turnover. Here we observed, in a mouse model of AD, that A{beta} plaques grow faster in the earlier stages of the disease and if their initial area is > 500 {micro}m2; this may be due to deposition occurring in the diffuse part of the plaque. In addition, synaptic turnover is higher in the presence of amyloid pathology and this is paralleled by a reduction in pre-but not post-synaptic densities. Plaque proximity does not appear to have an impact on synaptic dynamics. These observations indicate an imbalance in the response of the pre- and post-synaptic terminals and that therapeutics, alongside targeting the underlying pathology, need to address changes in synapse dynamics.

neuroscience↗