Biology subjects

Invernizzi, A.

Publications and source records attributed to Invernizzi, A..

8 recordsLinked to original sources

bioRxiv2023

Neuro-Environmental Interactions: a time sensitive matter

The assessment of resting state (rs) neurophysiological dynamics relies on the control of sensory, perceptual, and behavioral environments to minimize variability and rule-out confounding sources of activation during testing conditions. Here, we investigated how temporally-distal environmental inputs, specifically metal exposures experienced up to several months prior to scanning, affect functional dynamics measured using rs functional magnetic resonance imaging (rs-fMRI). We implemented an interpretable XGBoost-Shapley Additive Explanation (SHAP) model that integrated information from multiple exposure biomarkers to predict rs dynamics in typically developing adolescents. In 124 participants (53% females, ages: 13-25 years) enrolled in the Public Health Impact of Metals Exposure (PHIME) study, we measured concentrations of six metals (manganese, lead, chromium, cupper, nickel and zinc) in biological matrices (saliva, hair, fingernails, toenails, blood and urine) and acquired rs-fMRI scans. Using graph theory metrics, we computed global efficiency (GE) in 111 brain areas (Harvard Oxford Atlas). We used a predictive model based on ensemble gradient boosting to predict GE from metal biomarkers, adjusting for age and biological sex. Model performance was evaluated by comparing predicted versus measured GE. SHAP scores were used to evaluate feature importance. Measured versus predicted rs dynamics from our model utilizing chemical exposures as inputs were significantly correlated (p< 0.001, r = 0.36). Lead, chromium, and copper contributed most to the prediction of GE metrics. Our results indicate that a significant component of rs dynamics, comprising approximately 13% of observed variability in GE, is driven by recent metal exposures. These findings emphasize the need to estimate and control for the influence of past and current chemical exposures in the assessment and analysis of rs functional connectivity.

neuroscience↗

bioRxiv2022

Local neuroplasticity in adult glaucomatous visual cortex

The degree to which the adult human visual cortex retains the ability to functionally adapt to damage at the level of the eye remains ill-understood. Previous studies on cortical neuroplasticity primarily focused on the consequences of foveal visual field defects (VFD), yet these findings may not generalize to peripheral defects such as occur in glaucoma. Moreover, recent findings on neuroplasticity are often based on population receptive field (pRF) mapping, but interpreting these results is complicated in the absence of appropriate control conditions. Here, we used fMRI-based neural modeling to assess putative changes in pRFs associated with glaucomatous VFD. We compared the fMRI-signals and pRF estimates in participants with glaucoma to those of controls with case-matched simulated VFD. We found that the amplitude of the fMRI-signal is reduced in glaucoma compared to control participants and correlated with disease severity. Furthermore, while coarse retinotopic structure is maintained in all participants with glaucoma, we observed local pRF shifts and enlargements in early visual areas, relative to control participants. These differences imply that the adult brain retains local neuroplasticity. This finding has translational relevance, as it is consistent with VFD masking, which prevents glaucoma patients from noticing their VFD and seeking timely treatment.

neuroscience↗

bioRxiv2021

Binocular integrated visual field deficits are associated with changes in local network function in primary open-angle glaucoma: a resting-state fMRI study

Recent brain imaging studies have shown that the degenerative eye damage generally observed in the clinical setting, also extends intracranially. Both structural and functional brain changes have been observed in glaucoma participants, but we still lack an understanding of whether these changes also affect the integrity of cortical functional networks. This is relevant, as functional network integrity may affect the applicability of future treatments, as well as the options for rehabilitation or training. Here, we compare global and local functional connectivity between glaucoma and controls. Moreover, we study the relationship between functional connectivity and visual field (VF) loss. For our study, 20 subjects with primary open-angle glaucoma (POAG) and 24 age-similar healthy participants were recruited to undergo a complete ophthalmic assessment followed by two resting-state (RS) (f)MRI scans. For each scan and for each group, the ROIs with EC values higher than the 95th percentile were considered the most central brain regions ("hubs"). Hubs for which we found a significant difference in EC in both scans between glaucoma and healthy were considered to provide evidence for network changes. In addition, for each participant, behavioural scores were derived based on the notion that a brain regions hub function might relate to the: 1) sensitivity of the worse eye - indicating disease severity, 2) sensitivity of both eyes combined - with one eye potentially compensating for loss in the other, or 3) difference in eye sensitivity - requiring additional network interactions. By correlating each of these VF scores and the EC values, we assessed whether VF defects could be associated with centrality alterations in POAG. Our results show that no functional connectivity disruptions were found at the global brain level in POAG participants. This indicates that in glaucoma global brain network communication is preserved. Furthermore, a positive correlation was found between the EC value of the Lingual Gyrus, identified as a brain hub, and the behavioural score for the VF sensitivity of both eyes combined. The fact that reduced local network functioning is associated with reduced sensitivity of the binocular VF suggests the presence of local brain reorganisation that has a bearing on functional visual abilities.

neuroscience↗

bioRxiv2020

Assessing uncertainty in connective field estimations from resting state fMRI activity

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modelling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data, although this is important since 3T scanners are much more abundant and more often used in clinical research than 7T ones. In this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans interleaved by the acquisition of visual stimulation in 12 healthy participants. Our results show that both approaches reveal good agreement between RS- and visual field (VF)-based maps. Moreover, the 3T observations were similar to those previously reported at 7T. In addition, to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies. HighlightsO_LILocal functional connectivity between visual cortical areas can be estimated from RS-fMRI data at 3T using both standard CF and Bayesian CF modelling. C_LIO_LIBayesian CF modelling quantifies the model uncertainty associated with each CF parameter on RS and VF data, important in particular for future studies on clinical populations. C_LIO_LI3T observations were qualitatively similar to those previously reported at 7T. C_LI

neuroscience↗

bioRxiv2020

Bayesian Connective Field Modeling: a Markov Chain Monte Carlo approach.

The majority of neurons in the human brain process signals from neurons elsewhere in the brain. Connective Field (CF) modeling is a biologically-grounded method to describe this essential aspect of the brains circuitry. It allows characterizing the response of a population of neurons in terms of the activity in another part of the brain. CF modeling translates the concept of the receptive field (RF) into the domain of connectivity by assessing the spatial dependency between signals in distinct cortical visual field areas. Standard CF model estimation has some intrinsic limitations in that it cannot estimate the uncertainty associated with each of its parameters. Obtaining the uncertainty will allow identification of model biases, e.g. related to an over- or under-fitting or a co-dependence of parameters, thereby improving the CF prediction. To enable this, here we present a Bayesian framework for the CF model. Using a Markov Chain Monte Carlo (MCMC) approach, we estimate the underlying posterior distribution of the CF parameters and consequently, quantify the uncertainty associated with each estimate. We applied the method and its new Bayesian features to characterize the cortical circuitry of the early human visual cortex of 12 healthy participants that were assessed using 3T fMRI. In addition, we show how the MCMC approach enables the use of effect size (beta) as a data-driven parameter to retain relevant voxels for further analysis. Finally, we demonstrate how our new method can be used to compare different CF models. Our results show that single Gaussian models are favoured over differences of Gaussians (i.e. center-surround) models, suggesting that the cortico-cortical connections of the early visual system do not possess center-surround organisation. We conclude that our new Bayesian CF framework provides a comprehensive tool to improve our fundamental understanding of the human cortical circuitry in health and disease. Highlights{square} We present and validate a Bayesian CF framework based on a MCMC approach. {square}The MCMC CF approach quantifies the model uncertainty associated with each CF parameter. {square}We show how to use effect size beta as a data-driven threshold to retain relevant voxels. {square}The cortical connective fields of the human early visual system are best described by a single, circular symmetric, Gaussian.

neuroscience↗

bioRxiv2020

Visual field reconstruction using fMRI-based techniques

PurposeTo evaluate the accuracy and reliability of functional magnetic resonance imaging (fMRI)-based techniques to assess the integrity of the visual field (VF). MethodsWe combined fMRI and neurocomputational models, i.e conventional population receptive field (pRF) mapping and a new advanced pRF framework "micro-probing" (MP), to reconstruct the visual field representations of different cortical areas. To demonstrate their scope, both approaches were applied in healthy participants with simulated scotomas (SS) and participants with glaucoma. For the latter group we compared the VFs obtained with standard automated perimetry (SAP) and via fMRI. ResultsUsing SS, we found that the fMRI-based techniques can detect absolute defects in VFs that are larger than 3 deg, in single participants, and based on 12 minutes of fMRI scan time. Moreover, we found that MP results in a less biased estimation of the preserved VF. In participants with glaucoma, we found that fMRI-based VF reconstruction detected VF defects with a correspondence to SAP that was decent, reflected by the positive correlation between fMRI-based sampling density and SAP-based contrast sensitivity loss (SAP) r2=0.44, p=0.0002.This correlation was higher for our new approach (MP) compared to that for the conventional pRF analysis. ConclusionsfMRI-based reconstruction of the VF enables the evaluation of vision loss and provides useful details on the properties of the visual cortex. Translational RelevancefMRI-based VF reconstruction provides an objective alternative to detect VF defects. It may either complement SAP, or could provide VF information in patients unable to perform SAP.

neuroscience↗

bioRxiv2020

Foveal pRF properties in the visual cortex depend on the extent of stimulated visual field

Previous studies demonstrated that alterations in functional MRI derived receptive field (pRF) properties in cortical projection zones of retinal lesions can erroneously be mistaken for cortical large-scale reorganization in response to visual system pathologies. We tested, whether such confounds are also evident in the normal cortical projection zone of the fovea for simulated peripheral visual field defects. We applied fMRI-based visual field mapping of the central visual field at 3 Tesla in eight controls to compare the pRF properties of the central visual field of a reference condition (stimulus radius: 14{degrees}) and two conditions with simulated peripheral visual field defect, i.e., with a peripheral gray mask, stimulating only the central 7{degrees} or 4{degrees} radius. We quantified, for the cortical representation of the actually stimulated visual field, the changes in the position and size of the pRFs associated with reduced peripheral stimulation using conventional and advanced pRF modeling. We found foveal pRF-positions ([≤]3{degrees}) to be significantly shifted towards the periphery (p<0.05, corrected). These pRF-shifts were largest for the 4{degrees} condition [visual area (mean eccentricity shift): V1 (0.9{degrees}), V2 (0.9{degrees}), V3 (1.0{degrees})], but also evident for the 7{degrees} condition [V1 (0.5{degrees}), V2 (0.5{degrees}), V3 (0.9{degrees})]. Further, an overall enlargement of pRF-sizes was observed. These findings indicate the dependence of foveal pRF parameters on the spatial extent of the stimulated visual field. Consequently, our results imply that, previously reported similar findings in patients with actual peripheral scotomas need to be interpreted with caution and indicate the need for adequate control conditions in investigations of visual cortex reorganization. HighlightsO_LIFoveal pRF properties change in controls with restricted peripheral stimulation C_LIO_LIpRFs shift in position and enlarge in size for reduced stimulation extent C_LIO_LIAlterations in pRF characteristics in patients should be interpreted with caution C_LIO_LIAdequate control conditions needed in investigations of visual cortex plasticity C_LI

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bioRxiv2019

Micro-probing enables high-resolution mapping of neuronal subpopulations using fMRI

The characterization of receptive field (RF) properties is fundamental to understanding the neural basis of sensory and cognitive behaviour. The combination of non-invasive imaging, such as fMRI, with biologically inspired neural modeling has enabled the estimation of population RFs directly in humans. However, current approaches require making numerous a priori assumptions, so these cannot reveal unpredicted properties, such as fragmented RFs or subpopulations. This is a critical limitation in studies on adaptation, pathology or reorganization. Here, we introduce micro-probing (MP), a technique for fine-grained and assumption free characterisation of subpopulation RFs. Without specific stimuli or adapted models, MP mapped the bilateral RFs characteristic of observers with a congenital pathway disorder. Moreover, in healthy observers, MP revealed voxels that capture the activity of multiple neuronal subpopulations. Thus, MP provides a versatile framework to visualize, analyze and model, without restrictions, the diverse RFs of cortical subpopulations in health and disease.

neuroscience↗