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Abstract Bioelectronic therapies modulating the vagus nerve are promising for cardiovascular, inflammatory, and mental disorders. Clinical applications are however limited by side-effects such as breathing obstruction and headache caused by non-specific stimulation. To design selective and functional stimulation, we engineered VaStim, a realistic and efficient in-silico model. We developed a protocol to personalize VaStim in-vivo using simple muscle responses, successfully reproducing experimental observations, by combining models with trials conducted on five pigs. Through optimized algorithms, VaStim simulated the complete fiber population in minutes, including often omitted unmyelinated fibers which constitute 80% of the nerve. The model suggested that all Aα-fibers across the nerve affect laryngeal muscle, while heart rate changes were caused by B-efferents in specific fascicles. It predicted that tripolar paradigms could reduce laryngeal activity by 70% compared to typically used protocols. VaStim may serve as a model for developing neuromodulation therapies by maximizing efficacy and specificity, reducing animal experimentation.

Abstract The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features–e.g. diffusion parameters–or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

Abstract Patients with acute exacerbation of lung fibrosis with usual interstitial pneumonia (EUIP) pattern are at increased risk for ventilator-induced lung injury (VILI) and mortality when exposed to mechanical ventilation (MV). Yet, lack of a mechanical model describing UIP-lung deformation during MV represents a research gap. Aim of this study was to develop a constitutive mathematical model for UIP-lung deformation during lung protective MV based on the stress–strain behavior and the specific elastance of patients with EUIP as compared to that of acute respiratory distress syndrome (ARDS) and healthy lung. Partitioned lung and chest wall mechanics were assessed for patients with EUIP and primary ARDS (1:1 matched based on body mass index and PaO2/FiO2 ratio) during a PEEP trial performed within 24 h from intubation. Patient’s stress–strain curve and the lung specific elastance were computed and compared with those of healthy lungs, derived from literature. Respiratory mechanics were used to fit a novel mathematical model of the lung describing mechanical-inflation-induced lung parenchyma deformation, differentiating the contributions of elastin and collagen, the main components of lung extracellular matrix. Five patients with EUIP and 5 matched with primary ARDS were included and analyzed. Global strain was not different at low PEEP between the groups. Overall specific elastance was significantly higher in EUIP as compared to ARDS (28.9 [22.8–33.2] cmH2O versus 11.4 [10.3–14.6] cmH2O, respectively). Compared to ARDS and healthy lung, the stress/strain curve of EUIP showed a steeper increase, crossing the VILI threshold stress risk for strain values greater than 0.55. The contribution of elastin was prevalent at lower strains, while the contribution of collagen was prevalent at large strains. The stress/strain curve for collagen showed an upward shift passing from ARDS and healthy lungs to EUIP lungs. During MV, patients with EUIP showed different respiratory mechanics, stress–strain curve and specific elastance as compared to ARDS patients and healthy subjects and may experience VILI even when protective MV is applied. According to our mathematical model of lung deformation during mechanical inflation, the elastic response of UIP-lung is peculiar and different from ARDS. Our data suggest that patients with EUIP experience VILI with ventilatory setting that are lung-protective for patients with ARDS.

Abstract Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.

Abstract Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of “bursty” dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

Abstract Most human activities require collaborations within and across formal or informal teams. Our understanding of how the collaborative efforts spent by teams relate to their performance is still a matter of debate. Teamwork results in a highly interconnected ecosystem of potentially overlapping components where tasks are performed in interaction with team members and across other teams. To tackle this problem, we propose a graph neural network model to predict a team’s performance while identifying the drivers determining such outcome. In particular, the model is based on three architectural channels: topological, centrality, and contextual, which capture different factors potentially shaping teams’ success. We endow the model with two attention mechanisms to boost model performance and allow interpretability. A first mechanism allows pinpointing key members inside the team. A second mechanism allows us to quantify the contributions of the three driver effects in determining the outcome performance. We test model performance on various domains, outperforming most classical and neural baselines. Moreover, we include synthetic datasets designed to validate how the model disentangles the intended properties on which our model vastly outperforms baselines.

The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.

Abstract Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain’s computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapt across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in male mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.

Abstract Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.

Background/Objectives: Cognitive impairment in spinocerebellar ataxia patients has been reported since the early-disease stage. We aimed to assess cognitive differences in SCA1 and SCA2 patients. Methods: We performed neuropsychological (NPS) and neurophysiological (auditory event-related potentials, aERPs) assessments in 16 SCA1 and 18 SCA2 consecutive patients. Furthermore, clinical information (age at onset, disease duration, motor disability) was collected. Results: NPS tests yielded scores in the normal range in both groups but with lower scores in the Frontal Assessment Battery (p < 0.05) and Visual Analogue Test for Anosognosia for motor impairment (p < 0.05) in SCA1, and the Trail Making Test (p < 0.01), Raven’s progressive matrices (p < 0.01), Stroop (p < 0.05), and emotion attribution tests (p < 0.05) in SCA2. aERPs showed lower N100 amplitude (p < 0.01) and prolonged N200 latency (p < 0.01) in SCA1 compared with SCA2. Clinically, SCA2 had more severe motor disability than SCA1 in the Assessment and Rating of Ataxia Scale. Conclusions: SCA2 showed more significant difficulties in attentional, visuospatial, and emotional function, and greater motor impairment. In contrast, SCA1 showed less cognitive flexibility/phasic ability, probably affected by a more severe degree of dysarthria. The same group revealed less neural activity during nonconscious attentional processing (N100-N200 data), suggesting greater involvement of sensory pathways in discriminating auditory stimuli. NFS did not correlate with NPS findings, implying an independent relationship. However, the specific role of the cerebellum and cerebellar symptoms in NPS test results deserves more focus.

Pulmonary vasculitis identifies a heterogeneous group of diseases characterized by inflammation, damage and necrosis of the wall of pulmonary vessels. The most common approach to classify vasculitis is according to etiology, therefore dividing them into primary and secondary, with a further sub-classification of primary vasculitis based on the size of the affected vessels (large, medium, and small). Pulmonary involvement is frequently observed in patients with systemic vasculitis and radiological presentation is not pathognomonic, but may vary between diseases. The main findings using high-resolution computed tomography (HRCT) include small vessel wall thickening, nodular lesions, cavitary lesions, reticular opacities, ground-glass opacities (GGO), consolidations, interlobular septal thickening, tracheobronchial stenosis, and aneurysmal dilatation of pulmonary arteries, with or without pleural effusion. Radiological diagnosis alone is difficult since signs and symptoms of lung vessel involvement are often non-specific and might overlap with other conditions such as infections, connective tissue diseases and neoplasms. Therefore, the aim of this review is to describe the most common radiological features of lung involvement in pulmonary vasculitis so that, alongside detailed clinical history and laboratory tests, a prompt diagnosis can be performed.

Myeloproliferative neoplasms (MPNs) are subdivided into Philadelphia (Ph) chromosome-positive chronic myeloid leukemia (CML) and Ph-negative MPNs. BCR::ABL1 translocation is essential for the development and diagnosis of CML; on the other hand, the majority of Ph-negative MPNs are characterized by generally mutually exclusive mutations of Janus kinase 2 (JAK2), calreticulin (CALR), or thrombopoietin receptor/myeloproliferative leukemia (MPL). CALR mutations have been described essentially in JAK2 and MPL wild-type essential thrombocythemia and primary myelofibrosis. Rarely coexisting CALR and MPL mutations have been found in Ph-negative MPNs. BCR::ABL1 translocation and JAK2 mutations were initially considered mutually exclusive genomic events, but a discrete number of cases with the combination of these genetic alterations have been reported. The presence of BCR::ABL1 translocation with a coexisting CALR mutation is even more uncommon. Herein, starting from a routinely diagnosed case of CALR-mutated primary myelofibrosis subsequently acquiring BCR::ABL1 translocation, we performed a comprehensive review of the literature, discussing the clinicopathologic and molecular features, as well as the outcome and treatment of cases with BCR::ABL1 and CALR co-occurrence.

IntroductionIdiopathic pulmonary fibrosis (IPF) severely affects the lung leading to aberrant deposition of extracellular matrix and parenchymal stiffness with progressive functional derangement. The limited availability of fresh tissues represents one of the major limitations to study the molecular profiling of IPF lung tissue. The primary aim of this study was to explore the proteomic profiling yield of archived formalin-fixed paraffin-embedded (FFPE) specimens of IPF lung tissues.MethodsWe further determined the protein expression according to respiratory functional decline at the time of biopsy. The total proteins isolated from 11 FFPE samples of IPF patients compared to 3 FFPE samples from a non-fibrotic lung defined as controls, were subjected to label-free quantitative proteomic analysis by liquid chromatography-mass spectrometry (LC-MS/MS) and resulted in the detection of about 400 proteins.ResultsAfter the pairwise comparison between controls and IPF, functional enrichment analysis identified differentially expressed proteins that were involved in extracellular matrix signaling pathways, focal adhesion and transforming growth factor β (TGF-β) signaling pathways strongly associated with IPF onset and progression. Five proteins were significantly over- expressed in the lung of IPF patients with either advanced disease stage (Stage II) or impaired pulmonary function (FVC

Myeloproliferative neoplasms (MPNs) are classified into Philadelphia (Ph) chromosome–positive chronic myeloid leukemia (CML) and Ph-negative MPNs. BCR::ABL1 translocation is the key genetic event of CML, whereas JAK2/MPL/CALR mutations are molecular aberrations of Ph-negative MPNs. Despite initially considered mutually exclusive genetic aberrations, the co-occurrence of BCR::ABL1 and JAK2 has been reported in a limited number of cases. The two genetic alterations may be identified either at the same time or JAK2 aberration may be detected in patients with a previous CML treated with tyrosine kinase inhibitors or, finally, BCR::ABL1 translocation occurs in patients with a history of JAK2-positive MPN. This combination of genomic alterations is potentially confounding with clinical manifestations often misinterpreted either as disease progression or drug resistance, therefore leading to inappropriate patient’s treatment. Our systematic review aims to improve hematologist and pathologist knowledge on this rare subset of patients. Starting from the presentation of two additional cases from our routine daily practice, we focus mainly on clinical, laboratory, and bone marrow histological findings, which may represent useful clues of BCR::ABL1 and JAK2 co-occurrence. The interaction between JAK2 and BCR::ABL1 clones during the disease course as well as therapy and outcome are presented.