Biology subjects

Bifone, A.

Publications and source records attributed to Bifone, A..

4 recordsLinked to original sources

bioRxiv2024

Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.

In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naive controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI1) and after (MRI2) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naive controls no difference was found between MRI1 and MRI2. Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

neuroscience↗

bioRxiv2023

Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo

The idea that sensory stimulation to the embryo (in utero or in ovo) may be crucial for brain development is widespread. Unfortunately, up to now evidence was only indirect because imaging of embryonic brain activity in vivo was not viable. Here we applied for the first time Manganese Enhanced Magnetic Resonance Imaging (MEMRI) to the eggs of domestic chicks. We revealed light-induced brain asymmetry by comparing embryonic brain activity in vivo of eggs that were stimulated by light or maintained in the darkness. Our protocol paves the way to investigation of the effects of a variety of sensory stimulations on brain activity in embryo. SignificanceSensory stimulation in embryo may be crucial for many aspects of brain and behavioral development (e.g., mother language in humans). However, direct evidence of this is lacking because imaging of embryonic brain activity in vivo is not viable. Here we established a new protocol for measuring embryonic brain activity in vivo in domestic chick eggs. We were able to visualize in vivo the effect of light stimulation on the development of structural brain asymmetries of the embryo. The protocol established paves the way for collecting further evidence on the effects of sensory stimulation on embryonic brain activity.

neuroscience↗

bioRxiv2019

Scale-resolved analysis of brain functional connectivity networks with spectral entropy

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.\n\nWe apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

neuroscience↗

bioRxiv2017

Disrupted modular organization of primary sensory brain areas in schizophrenics

Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by Schizophrenia (SCZ) using functional MRI and other neuroimaging methods. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the modular organization of brain connectivity networks. A few studies have shown abnormal distribution of connectivity within and between functional modules, an indication of imbalanced functional segregation ad integration in SCZ patients. However, no major alterations in the modular structure of functional connectivity networks in patients have been reported, and unambiguous identification of the neural substrates involved remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe resolution limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This resolution limit is likely to have hampered the ability to resolve differences between patients and controls in previous cross-sectional studies. Here, we apply a novel, resolution limit-free approach to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients, and in matched healthy controls. Leveraging these important methodological advances, we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings provide support to the hypothesis that cognitive dysfunction in SCZ may arise from deficits occurring already at early stages of sensory processing.

neuroscience↗