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Thin film multilayer materials are very important for a variety of key technologies such as hard drive storage. However, their multilayered nature means it can be difficult to examine them after production and determining properties of individual layers is harder still. Here, methods of preparing multilayer samples for examination using scanning thermal microscopy are compared, showing that both a combination of mechanical and ion beam polishing, and ion beam milling to form a crater produce suitable surfaces for scanning thermal microscopy examination. However, the larger exposed surfaces of the ion beam milled crater are the most promising for distinguishing between the layers and comparison of their thermal transport properties., (Creative Commons Attribution license.)

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Giorgio Gost reports financial support was provided by Lazio Region. Giancarlo Ruocco reports financial support was provided by European Research Council. Viola Folli reports financial support was provided by D-TAILS srl., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Manjunatha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Purpose: Alcohol withdrawal seizures (AWS) are a well-known complication of chronic alcohol abuse, but there is currently little knowledge of their long-term relapse rate and prognosis. The aims of this study were to identify risk factors for AWS recurrence and to study the overall outcome of patients after AWS., Methods: In this retrospective single-center study, we included patients who were admitted to the Emergency Department after an AWS between January 1, 2013 and August 10, 2021 and for whom an electroencephalogram (EEG) was requested. AWS relapses up until April 29, 2022 were researched. We compared history, treatment with benzodiazepines or antiseizure medications (ASMs), laboratory, EEG and computed tomography findings between patients with AWS relapse (r-AWS) and patients with no AWS relapse (nr-AWS)., Results: A total of 199 patients were enrolled (mean age 53 ± 12 years; 78.9% men). AWS relapses occurred in 11% of patients, after a median time of 470.5 days. Brain computed tomography (n = 182) showed pathological findings in 35.7%. Risk factors for relapses were history of previous AWS (p = 0.013), skull fractures (p = 0.004) at the index AWS, and possibly epileptiform EEG abnormalities (p = 0.07). Benzodiazepines or other ASMs, taken before or after the index event, did not differ between the r-AWS and the nr-AWS group. The mortality rate was 2.9%/year of follow-up, which was 13 times higher compared to the general population. Risk factors for death were history of AWS (p < 0.001) and encephalopathic EEG (p = 0.043)., Conclusions: Delayed AWS relapses occur in 11% of patients and are associated with risk factors (previous AWS >24 h apart, skull fractures, and pathological EEG findings) that also increase the epilepsy risk, that is, predisposition for seizures, if not treated. Future prospective studies are mandatory to determine appropriate long-term diagnostic and therapeutic strategies, in order to reduce the risk of relapse and mortality associated with AWS., (© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Cerebellar ataxia, neuropathy and vestibular areflexia syndrome (CANVAS) is an autosomal recessive neurodegenerative disease, usually caused by biallelic AAGGG repeat expansions in RFC1. In this study, we leveraged whole genome sequencing data from nearly 10 000 individuals recruited within the Genomics England sequencing project to investigate the normal and pathogenic variation of the RFC1 repeat. We identified three novel repeat motifs, AGGGC (n = 6 from five families), AAGGC (n = 2 from one family) and AGAGG (n = 1), associated with CANVAS in the homozygous or compound heterozygous state with the common pathogenic AAGGG expansion. While AAAAG, AAAGGG and AAGAG expansions appear to be benign, we revealed a pathogenic role for large AAAGG repeat configuration expansions (n = 5). Long-read sequencing was used to characterize the entire repeat sequence, and six patients exhibited a pure AGGGC expansion, while the other patients presented complex motifs with AAGGG or AAAGG interruptions. All pathogenic motifs appeared to have arisen from a common haplotype and were predicted to form highly stable G quadruplexes, which have previously been demonstrated to affect gene transcription in other conditions. The assessment of these novel configurations is warranted in CANVAS patients with negative or inconclusive genetic testing. Particular attention should be paid to carriers of compound AAGGG/AAAGG expansions when the AAAGG motif is very large (>500 repeats) or the AAGGG motif is interrupted. Accurate sizing and full sequencing of the satellite repeat with long-read sequencing is recommended in clinically selected cases to enable accurate molecular diagnosis and counsel patients and their families., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Importance: The prognosis of overall survival (OS) in patients with glioblastoma (GBM) may depend on the underlying structural connectivity of the brain., Objective: To examine the association between white matter tracts affected by GBM and patients' OS by means of a new tract density index (TDI)., Design, Setting, and Participants: This prognostic study in patients with a histopathologic diagnosis of GBM examined a discovery cohort of 112 patients who underwent surgery between February 1, 2015, and November 30, 2020 (follow-up to May 31, 2023), in Italy and 70 patients in a replicative cohort (n = 70) who underwent surgery between September 1, 2012, and November 30, 2015 (follow-up to May 31, 2023), in Germany. Statistical analyses were performed from June 1, 2021, to May 31, 2023. Thirteen and 12 patients were excluded from the discovery and the replicative sets, respectively, because of magnetic resonance imaging artifacts., Exposure: The density of white matter tracts encompassing GBM., Main Outcomes and Measures: Correlation, linear regression, Cox proportional hazards regression, Kaplan-Meier, and prediction analysis were used to assess the association between the TDI and OS. Results were compared with common prognostic factors of GBM, including age, performance status, O6-methylguanine-DNA methyltransferase methylation, and extent of surgery., Results: In the discovery cohort (n = 99; mean [SD] age, 62.2 [11.5] years; 29 female [29.3%]; 70 male [70.7%]), the TDI was significantly correlated with OS (r = -0.34; P < .001). This association was more stable compared with other prognostic factors. The TDI showed a significant regression pattern (Cox: hazard ratio, 0.28 [95% CI, 0.02-0.55; P = .04]; linear: t = -2.366; P = .02). and a significant Kaplan-Meier stratification of patients as having lower or higher OS based on the TDI (log-rank test = 4.52; P = .03). Results were confirmed in the replicative cohort (n = 58; mean [SD] age, 58.5 [11.1] years, 14 female [24.1%]; 44 male [75.9%]). High (24-month cutoff) and low (18-month cutoff) OS was predicted based on the TDI computed in the discovery cohort (accuracy = 87%)., Conclusions and Relevance: In this study, GBMs encompassing regions with low white matter tract density were associated with longer OS. These findings indicate that the TDI is a reliable presurgical outcome predictor that may be considered in clinical trials and clinical practice. These findings support a framework in which the outcome of GBM depends on the patient's brain organization.

Metabolic connectivity (MC) has been previously proposed as the covariation of static [ 18 F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [ 18 F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio ( SUVR ) vs. [ 18 F]FDG kinetic parameters fully describing the tracer behavior (i.e., K i , K 1 , k 3 ); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR , K i , K 1 , k 3 produced different networks depending on the chosen [ 18 F]FDG parameter ( k 3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: Examining the size and reactivity of the pupils of traumatic brain injury coma patients is fundamental in the Neuro-intensive care unit (ICU). Pupil parameters on admission predict long-term clinical outcomes. However, little is known about the dynamics of pupillary parameters and their potential value for outcome prediction., Methods: This study applied a time-course analysis of pupillary signals (size and photo-reactivity) in acute traumatic brain injury coma patients (n = 20) to predict outcome at 6 months., Results: The time course of pupillary signals was informative in discriminating favorable (F) versus unfavorable (U) outcomes, with the highest correlation within the 1st week notwithstanding pharmacological sedation. Patients with favorable outcome at 6 months showed more consistent in time isochoric and photo-reactive pupils. In contrast, patients with an unfavorable outcome showed more variable measures that tended to stabilize toward pathological values., Interpretation: Time-dependent tracking of pupils' size and reactivity is a promising application for ICU monitoring and long-term prognosis. These findings support the usefulness of automatic tools for the dynamic, quantitative, and objective measurements of pupils., (© 2023 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction., (© 2023. Springer Nature Limited.)

Objective: Brain lesions sometimes induce a failure of recognition of one's own deficits (anosognosia). Lack of deficit awareness may underlie damage of modality-specific systems, for example, visual cortex for visual anosognosia or motor/premotor cortex for motor anosognosia. However, focal lesions induce widespread remote structural and functional disconnection, and anosognosia, independent of modality, may also involve common neural mechanisms., Methods: Here, we study the neural correlates of Anton syndrome (AS), anosognosia of blindness, and compare them with anosognosia for hemiplegia to test whether they share different or common mechanisms. We measured both local damage and patterns of structural-functional disconnection as predicted from healthy normative atlases., Results: AS depends on bilateral striate and extrastriate occipital damage, and disconnection of ventral and dorsal frontoparietal regions involved in attention control. Visual and motor anosognosia each share damage of modality-specific regions, but also involve the disruption of white matter tracts, leading to functional disconnection within dorsal frontal-parietal regions that play critical roles in motor control, visuospatial attention, and multisensory integration., Interpretation: These results reveal the unique shared combination of content-specific and supramodal mechanisms in anosognosia. ANN NEUROL 2023., (© 2023 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Introduction: We describe a systemic neoplastic cryoglobulinemic vasculitis presenting as a large vessel occlusion (LVO) syndrome. We focus on a rare presentation of a rare condition., Case Report: A 68-year-old man was admitted to the Stroke Unit of Padova with a right middle cerebral artery syndrome. A cerebrovascular event was suspected and protocol for revascularization treatment was performed. Neuroimaging provided no evidence for infarcted tissue or medium-large vascular occlusion but hypothesized a vasculitic involvement of the small vessels of the right hemisphere. Further diagnostics demonstrated a microangiopathic involvement of the heart, kidneys, and lungs. Blood tests showed circulating cryoglobulins and further hematological investigation identified a chronic lymphatic leukemia-like lymphoproliferative disorder. High-dose steroid therapy improved the patient's clinical status and no neurological symptoms remained at discharge., Conclusion: We discuss the clinical-radiologic presentation of a small vessel vasculitis that mimics an LVO stroke. This case focuses on the relevance of concomitant multiorgan manifestations in the hyper-acute evaluation of LVO stroke, suggesting the clinical neurologist should consider alternative etiologies as these could provide important clinical implications., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

The Oxford Cognitive Screen (OCS) was developed to measure cognitive impairment in stroke. Here, we test if the OCS administered acutely in stroke patients provides useful information in predicting long-term functional outcome. A group of first-time stroke patients (n = 74) underwent an acute behavioral assessment comprising the OCS and the NIHSS within one-week post-stroke. Functional outcome was evaluated using the Stroke Impact Scale 3.0 (SIS 3.0) and the Geriatric Depression Scale (GDS) at 6 and 12-months post-stroke. We compared the predictive ability of the OCS and NIHSS, separately or in combination, to predict different domains of behavioral impairment at a chronic evaluation. The OCS accounted for 61% of variance of SIS physical domain, 61% of memory domain, 79% of language domain, 70% of participation domain and 70% of recovery domain. The OCS accounted for a greater percentage of outcome variance than demographics and NIHSS. The most informative predictive model included the combination of demographics, OCS and NIHSS data. The OCS, performed early after stroke, is a strong independent predictor of long-term functional outcome and significantly improves the prediction of outcome when considered alongside the NIHSS and demographics., Competing Interests: Declaration of competing interest The authors report no conflicts of interest. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Li-ion batteries are the main power source used in electric propulsion applications (e.g., electric cars, unmanned aerial vehicles, and advanced air mobility aircraft). Analytics-based monitoring and forecasting for metrics such as state of charge and state of health based on battery-specific usage data are critical to ensure high reliability levels. However, the complex electrochemistry that governs battery operation leads to computationally expensive physics-based models; which become unsuitable for prognosis and health management applications. We propose a hybrid physics-informed machine learning approach that simulates dynamical responses by directly implementing numerical integration of principle-based governing equations through recurrent neural networks. While reduced-order models describe part of the voltage discharge under constant or variable loading conditions, model-form uncertainty is captured through multi-layer perceptrons and battery-to-battery aleatory uncertainty is modeled through variational multi-layer perceptrons. In addition, we use a Bayesian approach to merge fleet-wide data in the form of priors with battery-specific discharge cycles, where the battery capacity is fully available or only partially available. We illustrate the effectiveness of our proposed framework using the NASA Prognostics Data Repository Battery dataset, which contains experimental discharge data on Li-ion batteries obtained in a controlled environment., (© 2023. Springer Nature Limited.)

Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks., (© 2023. The Author(s).)

Depression associated with traumatic brain injury (TBI) is believed to be clinically distinct from primary major depressive disorder (MDD) and may be less responsive to conventional treatments. Brain connectivity differences between the dorsal attention network (DAN), default mode network (DMN), and subgenual cingulate have been implicated in TBI and MDD. To characterize these distinctions, we applied precision functional mapping of brain network connectivity to resting-state functional magnetic resonance imaging data from five published patient cohorts, four discovery cohorts ( n = 93), and one replication cohort ( n = 180). We identified a distinct brain connectivity profile in TBI-associated depression that was independent of TBI, MDD, posttraumatic stress disorder (PTSD), depression severity, and cohort. TBI-associated depression was independently associated with decreased DAN-subgenual cingulate connectivity, increased DAN-DMN connectivity, and the combined effect of both. This effect was stronger when using precision functional mapping relative to group-level network maps. Our results support the possibility of a physiologically distinct "TBI affective syndrome," which may benefit from individualized neuromodulation approaches to target its distinct neural circuitry.

Resting state fMRI has been used in many studies to investigate the impact of brain tumours on functional connectivity (FC). However, these studies have so far assumed that FC is stationary, disregarding the fact that the brain fluctuates over dynamic states. Here we utilised resting state fMRI data from 33 patients with high-grade gliomas and 33 healthy controls to examine the dynamic interplay between resting-state networks and to gain insights into the impact of brain tumours on functional dynamics. By employing Hidden Markov Models, we demonstrated that functional dynamics persist even in the presence of a high-grade glioma, and that patients exhibited a global decrease of connections strength, as well as of network segregation. Furthermore, through a multivariate analysis, we demonstrated that patients' cognitive scores are highly predictive of pathological dynamics, thus supporting our hypothesis that functional dynamics could serve as valuable biomarkers for better understanding the traits of high-grade gliomas., (© 2023. The Author(s).)

Background: Resting-state functional-MRI studies identified several cortical gray matter functional networks (GMNs) and white matter functional networks (WMNs) with precise anatomical localization. Here, we aimed at describing the relationships between brain's functional topological organization and glioblastoma (GBM) location. Furthermore, we assessed whether GBM distribution across these networks was associated with overall survival (OS)., Materials and Methods: We included patients with histopathological diagnosis of IDH-wildtype GBM, presurgical MRI and survival data. For each patient, we recorded clinical-prognostic variables. GBM core and edema were segmented and normalized to a standard space. Pre-existing functional connectivity-based atlases were used to define network parcellations: 17 GMNs and 12 WMNs were considered in particular. We computed the percentage of lesion overlap with GMNs and WMNs, both for core and edema. Differences between overlap percentages were assessed through descriptive statistics, ANOVA, post-hoc tests, Pearson's correlation tests and canonical correlations. Multiple linear and non-linear regression tests were employed to explore relationships with OS., Results: 99 patients were included (70 males, mean age 62  years). The most involved GMNs included ventral somatomotor, salient ventral attention and default-mode networks; the most involved WMNs were ventral frontoparietal tracts, deep frontal white matter, and superior longitudinal fasciculus system. Superior longitudinal fasciculus system and dorsal frontoparietal tracts were significantly more included in the edema ( p  < 0.001). 5 main patterns of GBM core distribution across functional networks were found, while edema localization was less classifiable. ANOVA showed significant differences between mean overlap percentages, separately for GMNs and WMNs ( p -values<0.0001). Core-N12 overlap predicts higher OS, although its inclusion does not increase the explained OS variance., Discussion and Conclusion: Both GBM core and edema preferentially overlap with specific GMNs and WMNs, especially associative networks, and GBM core follows five main distribution patterns. Some inter-related GMNs and WMNs were co-lesioned by GBM, suggesting that GBM distribution is not independent of the brain's structural and functional organization. Although the involvement of ventral frontoparietal tracts (N12) seems to have some role in predicting survival, network-topology information is overall scarcely informative about OS. fMRI-based approaches may more effectively demonstrate the effects of GBM on brain networks and survival., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest, (Copyright © 2023 Sansone, Pini, Salvalaggio, Gaiola, Volpin, Baro, Padovan, Anglani, Facchini, Chioffi, Zagonel, D’Avella, Denaro, Lombardi and Corbetta.)

Introduction: To evaluate the access to treatments with intravenous thrombolysis (IVT) and/or mechanical thrombectomy (MT) in acute ischemic stroke patients admitted to stroke units (SUs) of Veneto region (Italy) according to current "hub-and-spoke" model from 2017 to 2021., Patients and Methods: We retrospectively analyzed data on treatments with IVT and/or MT for stroke patients admitted to the 23 SUs (6 Hubs and 17 Spokes) of the 6 macro-areas including 9 local sanitary units (LSUs) and 2 hospitals., Results: We reported 6093 treatments with IVT alone, 1114 with IVT plus MT, and 921 with MT alone. Number of stroke unit (SU) beds/100,000 inhabitants ranges from 2.3 to 2.8, and no difference was found among different macro-areas. Number of treatments/100,000 inhabitants/year ranges from 19 to 34 for IVT alone, from 2 to 7 for IVT plus MT, and from 2 to 5 for MT alone. Number of IVT alone/SU bed/year ranges from 9 to 21 in the Hub and from 6 to 12 in the Spokes. Rate of IVT plus MT in patients directly arrived in the same LSU's Hub ranges from 50 to 81%, likewise the one of MT alone ranges from 49 to 84%., Conclusions: Treatment target rates of IVT and MT set by Action Plan for Stroke in Europe 2018-2030 has been globally exceeded in the Veneto region. However, the target rate of MT and access revascularization treatments is heterogeneous among different macro-areas. Further efforts should be made to homogenize the current territorial organization., (© 2023. Fondazione Società Italiana di Neurologia.)

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly popular technique. This technique can assess several features of brain connectivity, such as inter-regional temporal correlation (functional connectivity), from which graph measures of network organization can be derived. However, these measures are prone to a certain degree of variability depending on the analytical steps during preprocessing. Many studies have investigated the effect of different preprocessing steps on functional connectivity measures; however, no study investigated whether different structural reconstructions lead to different functional connectivity metrics. Here, we evaluated the impact of different structural segmentation strategies on functional connectivity outcomes. To this aim, we compared different metrics computed after two different registration strategies. The first strategy used structural information from the 3D T1-weighted image (unimodal), while the second strategy implemented a multimodal approach, where an additional registration step used the information from the T2-weighted image. The impact of these different approaches was evaluated on a sample of 58 healthy adults. As expected, different approaches led to significant differences in structural measures (i.e., cortical thickness, volume, and gyrification index), with the maximum impact on the insula cortex. However, these differences were only slightly translated to functional metrics. We reported no differences in graph measures and seed-based functional connectivity maps, but slight differences in the insula when we compared the mean functional strength for each parcel. Overall, these results suggested that functional metrics are only slightly different when using a unimodal compared to a multimodal approach, while the structural output can be significantly affected., (© 2023. The Author(s).)

Liquid biopsy (LB) for prostate cancer (PCa) detection could represent an alternative to biopsy. Seminal fluid (SF) is a source of PCa-specific biomarkers, as 40% of ejaculate derives from the prostate. We tested the feasibility of an SF-based LB by evaluating the yield of semen self-sampling in a cohort of >750 patients with clinically localized PCa. The overall SF collection yield was 18.2% (39% when considering only compliant patients), with about a half of the patients (53.15%) not consenting to SF donation. Independent favorable predictors for SF collection were younger age and lower prostate volume. We implemented a protocol to enrich prostate-derived cells by multi-color flow cytometry and applied it on SF and urine samples from 100 patients. The number of prostate-enriched cells (SYTO-16+ PSMA+ CD45-) was variable, with higher numbers of cells isolated from SF than urine ( p value < 0.001). Putative cancer cells (EpCAM high ) were 2% of isolated cells in both specimens. The fraction of EpCAM high cells over prostate-enriched cells (PSMA+) significantly correlated with patient age in both semen and urine, but not with other clinical parameters, such as Gleason Score, ISUP, or TNM stage. Hence, enumeration of prostate-derived cells is not sufficient to guide PCa diagnosis; additional molecular analyses to detect patient-specific cancer lesions will be needed.

Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores-a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance. This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework's range of assessments and predictive power to increase even further through future crowdsourcing., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

The study of pollutant effects is extremely important to address the epochal challenges we are facing, where world populations are increasingly moving from rural to urban centers, revolutionizing our world into an urban world. These transformations will exacerbate pollution, thus highlighting the necessity to unravel its effect on human health. Epidemiological studies have reported that pollution increases the risk of neurological diseases, with growing evidence on the risk of neurodegenerative disorders. Air pollution and water pollutants are the main chemicals driving this risk. These chemicals can promote inflammation, acting in synergy with genotype vulnerability. However, the biological underpinnings of this association are unknown. In this review, we focus on the link between pollution and brain network connectivity at the macro-scale level. We provide an updated overview of epidemiological findings and studies investigating brain network changes associated with pollution exposure, and discuss the mechanistic insights of pollution-induced brain changes through neural networks. We explain, in detail, the pollutome-connectome axis that might provide the functional substrate for pollution-induced processes leading to cognitive impairment and neurodegeneration. We describe this model within the framework of two pollutants, air pollution, a widely recognized threat, and polyfluoroalkyl substances, a large class of synthetic chemicals which are currently emerging as new neurotoxic source., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Recent evidence suggests that, in the absence of any task, spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. These "resting-state" activity patterns may underlie the maintenance and consolidation (replay) of information states coding for ecological stimuli and behaviors. In this study, we examine whether replay patterns occur in resting-state activity in association cortex grouped into high-order cognitive networks not directly processing sensory inputs or motor outputs. Fifteen participants (7 females) performed four hand movements during an fMRI study. Three movements were ecological. The fourth movement as control was less ecological. Before and after the task scans, we acquired resting-state fMRI scans. The analysis examined whether multivertex task activation patterns for the four movements computed at the cortical surface in different brain networks resembled spontaneous activity patterns measured at rest. For each movement, we computed a vector of r values indicating the strength of the similarity between the mean task activation pattern and frame-by-frame resting-state patterns. We computed a cumulative distribution function of r 2 values and used the 90th percentile cutoff value for comparison. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, rest-task pattern correlation was more likely for less ecological movement in the ventral attention network. These findings show that spontaneous activity patterns in human attention networks code for hand movements. SIGNIFICANCE STATEMENT fMRI indirectly measures neural activity noninvasively. Resting-state (spontaneous) fMRI signals measured in the absence of any task resemble signals evoked by task performance both in topography and inter-regional (functional) connectivity. However, the function of spontaneous brain activity is unknown. We recently showed that spatial activity patterns evoked by visual and motor tasks in visual and motor cortex, respectively, occur at rest in the absence of any stimulus or response. Here we show that activity patterns related to hand movements replay at rest in frontoparietal regions of the human attention system. These findings show that spontaneous activity in the human cortex may mediate the maintenance and consolidation of information states coding for ecological stimuli and behaviors., (Copyright © 2023 the authors.)

Deep brain stimulation (DBS) has emerged as an invasive neuromodulation technique for the treatment of several neurological disorders, but the mechanisms underlying its effects remain partially elusive. In this context, the application of Transcranial Magnetic Stimulation (TMS) in patients treated with DBS represents an intriguing approach to investigate the neurophysiology of cortico-basal networks. Experimental studies combining TMS and DBS that have been performed so far have mainly aimed to evaluate the effects of DBS on the cerebral cortex and thus to provide insights into DBS's mechanisms of action. The modulation of cortical excitability and plasticity by DBS is emerging as a potential contributor to its therapeutic effects. Moreover, pairing DBS and TMS stimuli could represent a method to induce cortical synaptic plasticity, the therapeutic potential of which is still unexplored. Furthermore, the advent of new DBS technologies and novel treatment targets will present new research opportunities and prospects to investigate brain networks. However, the application of the combined TMS-DBS approach is currently limited by safety concerns. In this review, we sought to present an overview of studies performed by combining TMS and DBS in neurological disorders, as well as available evidence and recommendations on the safety of their combination. Additionally, we outline perspectives for future research by highlighting knowledge gaps and possible novel applications of this approach.

Introduction: Navigated transcranial magnetic stimulation (nTMS) has emerged as one of the most innovative techniques in neurosurgical practice. However, nTMS motor mapping involves rigorous steps, and the importance of an accurate execution method has not been emphasized enough. In particular, despite strict adherence to procedural protocols, we have observed high variability in map activation according to the choice of stimulation intensity (SI) right from the early stage of hotspot localization. We present a retrospective analysis of motor mappings performed between March 2020 and July 2022, where the SI was only chosen with rigorous care in the most recent ones, under the guide of an expert neurophysiologist., Materials and Methods: In order to test the ability to reduce inaccurate responses and time expenditure using selective SI, data were collected from 16 patients who underwent mapping with the random method (group A) and 15 patients who underwent mapping with the proposed method (group B). The parameters considered were resting motor threshold (%), number of stimuli, number of valid motor evoked potentials (MEPs), number of valid MEPs considered true positives (TPs), number of valid MEPs considered false positives (FPs), ratio of true-positive MEPs to total stimuli, ratio of true-positive MEPs to valid MEPs, minimum amplitude, maximum amplitude and mapping time for each patient., Results: The analysis showed statistically significant reductions in total stimulus demand, procedural time and number of false-positive MEPs. Significant increases were observed in the number of true-positive MEPs, the ratio of true-positive MEPs to total stimuli and the ratio of true-positive MEPs to valid MEPs. In the subgroups analyzed, there were similar trends, in particular, an increase in true positives and a decrease in false-positive responses., Conclusions: The precise selection of SI during hotspot search in nTMS motor mapping could provide reliable cortical maps in short time and with low employment of resources. This method seems to ensure that a MEP really represents a functionally eloquent cortical point, making mapping more intuitive even in less experienced centers.

Background. In deep-seated brain tumors, adequate preoperative planning is mandatory to assess the best surgical corridor to obtain maximal safe resection. Functional diffusor tensor imaging (DTI) tractography based on navigated transcranial magnetic stimulation (nTMS) motor mapping has proven to be a valid preoperative examination method in adults. The aim of this paper is to present the application of nTMS and functional DTI tractography in a series of pediatric diencephalic tumors. Material and methods. Three patients affected by thalamic (one) and thalamopeduncular tumor (two) were successfully examined with nTMS motor mapping and DTI tractography between October 2020 and October 2021 (F:M 3:0, mean age 12 years ± 0.8). Cortical representation of leg, hand and mouth were determined in the affected hemisphere and the positive stimulation spots were set as seeds point for tractography. Results. Mapping of the motor cortex and tracts reconstruction for leg and hand were successful in all patients, while facial function was properly mapped in one patient only. In all cases, the procedure was well tolerated and no adverse events were recorded. Spatial relationships between tumor and functional tissue guided the surgical planning. Extent of the resection varied from 96.1% to 100% with a postoperative new motor deficit in one patient. Conclusions. nTMS and DTI fiber tracking is a feasible, effective and well-tolerated method to identify motor pathway in deep-seated lesion in pediatric population.

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm ., (© 2023. The Author(s).)

Introduction: Neuropsychological studies infer brain-behavior relationships from focal lesions like stroke and tumors. However, these pathologies impair brain function through different mechanisms even when they occur at the same brain's location. The aim of this study was to compare the profile of cognitive impairment in patients with brain tumors vs. stroke and examine the correlation with lesion location in each pathology., Methods: Patients with first time stroke (n = 77) or newly diagnosed brain tumors (n = 76) were assessed with a neuropsychological battery. Their lesions were mapped with MRI scans. Test scores were analyzed using principal component analysis (PCA) to measure their correlation, and logistic regression to examine differences between pathologies. Next, with ridge regression we examined whether lesion features (location, volume) were associated with behavioral performance., Results: The PCA showed a similar cognitive impairment profile in tumors and strokes with three principal components (PCs) accounting for about half of the individual variance. PC1 loaded on language, verbal memory, and executive/working memory; PC2 loaded on general performance, visuo-spatial attention and memory, and executive functions; and, PC3 loaded on calculation, reading and visuo-spatial attention. The average lesion distribution was different, and lesion location was correlated with cognitive deficits only in stroke. Logistic regression found language and calculation more affected in stroke, and verbal memory and verbal fluency more affected in tumors., Conclusions: A similar low dimensional set of behavioral impairments was found both in stroke and brain tumors, even though each pathology caused some specific deficits in different domains. The lesion distribution was different for stroke and tumors and correlated with behavioral impairment only in stroke., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Alzheimer's disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated., Methods: We investigated the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71±10), 22 AD (age: 72±6), and 20 controls (age: 72±6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN), and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a bootstrapping correlation and interaction analyses., Results: Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, and the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a significant association in the whole group with cogPC1 and cogPC2 and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation)., Conclusions: A low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components., (© 2022. The Author(s).)

During visual exploration, eye movements are controlled by multiple stimulus- and goal-driven factors. We recently showed that the dynamics of eye movements -how/when the eye move- during natural scenes' free viewing were similar across individuals and identified two viewing styles: static and dynamic, characterized respectively by longer or shorter fixations. Interestingly, these styles could be revealed at rest, in the absence of any visual stimulus. This result supports a role of intrinsic activity in eye movement dynamics. Here we hypothesize that these two viewing styles correspond to different spontaneous patterns of brain activity. One year after the behavioural experiments, static and dynamic viewers were called back to the lab to record high density EEG activity during eyes open and eyes closed. Static viewers show higher cortical inhibition, slower individual alpha frequency peak, and longer memory of alpha oscillations. The opposite holds for dynamic viewers. We conclude that some properties of spontaneous activity predict exploratory eye movement dynamics during free viewing., (© 2022. The Author(s).)

Attention is a core cognitive function that filters and selects behaviourally relevant information in the environment. The cortical mapping of attentional systems identified two segregated networks that mediate stimulus-driven and goal-driven processes, the Ventral and the Dorsal Attention Networks (VAN, DAN). Deep brain electrophysiological recordings, behavioral data from phylogenetic distant species, and observations from human brain pathologies challenge purely corticocentric models. Here, we used advanced methods of functional alignment applied to resting-state functional connectivity analyses to map the subcortical architecture of the Ventral and Dorsal Attention Networks. Our investigations revealed the involvement of the pulvinar, the superior colliculi, the head of caudate nuclei, and a cluster of brainstem nuclei relevant to both networks. These nuclei are densely connected structural network hubs, as revealed by diffusion-weighted imaging tractography. Their projections establish interrelations with the acetylcholine nicotinic receptor as well as dopamine and serotonin transporters, as demonstrated in a spatial correlation analysis with a normative atlas of neurotransmitter systems. This convergence of functional, structural, and neurochemical evidence provides a comprehensive framework to understand the neural basis of attention across different species and brain diseases., (© 2022. The Author(s).)

Gliomas are amongst the most common primary brain tumours in adults and are often associated with poor prognosis. Understanding the extent of white matter (WM) which is affected outside the tumoral lesion may be of paramount importance to explain cognitive deficits and the clinical progression of the disease. To this end, we explored both direct (i.e., tractography based) and indirect (i.e., atlas-based) approaches to quantifying WM structural disconnections in a cohort of 44 high- and low-grade glioma patients. While these methodologies have recently gained popularity in the context of stroke and other pathologies, to our knowledge, this is the first time they are applied in patients with brain tumours. More specifically, in this work, we present a quantitative comparison of the disconnection maps provided by the two methodologies by applying well-known metrics of spatial similarity, extension, and correlation. Given the important role the oedematous tissue plays in the physiopathology of tumours, we performed these analyses both by including and excluding it in the definition of the tumoral lesion. This was done to investigate possible differences determined by this choice. We found that direct and indirect approaches offer two distinct pictures of structural disconnections in patients affected by brain gliomas, presenting key differences in several regions of the brain. Following the outcomes of our analysis, we eventually discuss the strengths and pitfalls of these two approaches when applied in this critical field., (© 2022. The Author(s).)

Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure-function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke., (© 2022. The Author(s).)

Spontaneous brain activity, measured with resting state fMRI (R-fMRI), is correlated among regions that are co-activated by behavioral tasks. It is unclear, however, whether spatial patterns of spontaneous activity within a cortical region correspond to spatial patterns of activity evoked by specific stimuli, actions, or mental states. The current study investigated the hypothesis that spontaneous activity in motor cortex represents motor patterns commonly occurring in daily life. To test this hypothesis 15 healthy participants were scanned while performing four different hand movements. Three movements (Grip, Extend, Pinch) were ecological involving grip and grasp hand movements; one control movement involving the rotation of the wrist was not ecological and infrequent (Shake). They were also scanned at rest before and after the execution of the motor tasks (resting-state scans). Using the task data, we identified movement-specific patterns in the primary motor cortex. These task-defined patterns were compared to resting-state patterns in the same motor region. We also performed a control analysis within the primary visual cortex. We found that spontaneous activity patterns in the primary motor cortex were more like task patterns for ecological than control movements. In contrast, there was no difference between ecological and control hand movements in the primary visual area. These findings provide evidence that spontaneous activity in human motor cortex forms fine-scale, patterned representations associated with behaviors that frequently occur in daily life., (© 2022. The Author(s).)

The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01-0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome., (© 2022. The Author(s).)

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18 F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis., Competing Interests: Declaration of competing Interests The authors declare no competing interests., (Copyright © 2022. Published by Elsevier Inc.)

Quantification of brain [ 18 F] fluorodeoxyglucose ([ 18 F]FDG) positron emission tomography (PET) data requires an input function. A noninvasive alternative to gold-standard arterial sampling is the image-derived input function (IDIF), typically extracted from the internal carotid arteries (ICAs), which are however difficult to segment and subjected to spillover effects. In this work, we evaluated the feasibility of extracting the IDIF from two different vascular sites, i.e., 1) common carotids (CCA) and 2) superior sagittal sinus (SSS), other than 3) ICA in a large group of glioma patients undergoing a dynamic [ 18 F]FDG PET acquisition on a hybrid PET/MR scanner. Comparisons are drawn between the different IDIFs in terms of peak amplitude and shape, as well as between the estimates of fractional uptake rate (Kr) obtained from the different extraction sites in terms of a) grey/white matter average absolute values, b) ratio of grey-to-white matter, and c) spatial patterns for the hemisphere contralateral to the lesion. Clinical Relevance - This work points towards new feasible IDIF extraction sites (CCA in particular) which could allow for fully noninvasive absolute PET quantification in clinical populations.

The gold-standard approach to quantifying dynamic PET images relies on using invasive measures of the arterial plasma tracer concentration. An attractive alternative is to employ an image-derived input function (IDIF), corrected for spillover effects and rescaled with venous plasma samples. However, venous samples are not always available for every participant. In this work, we used the nonlinear mixed-effects modeling approach to develop a model which infers venous tracer kinetics by using venous samples obtained from a population of healthy individuals and integrating subject-specific covariates. Population parameters (fixed effects), their between-subject variability (random effects), and the effects of covariates were estimated. The selected model will allow to reliably infer venous tracer kinetics in subjects with missing measurements. Clinical relevance - The derived model will be relevant for fully noninvasive dynamic FDG PET quantification using image-derived input functions in both healthy and patient populations when hemodynamics is not impaired.

The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white-matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants., (© 2022. The Author(s).)

The ward climate or atmosphere refers to its material, emotional and social conditions. A good ward climate in psychiatric settings can influence the mood, behaviour and self-concept of patients and staff members and improve patient outcomes. Many studies have examined the relationship between ward climate and aggression, but only a few have investigated the effect of a ward's environment, rules and activities. This multicentric observational study aimed to assess the relationship between the rules/activities and the climate of four acute psychiatric units of Northern Italy. The Essen Climate Evaluation Scheme (EssenCES) questionnaire, which was administered to patients and staff, was used to evaluate the different dimensions of ward atmosphere. There was a good response rate (79%) in patients and staff members who completed the questionnaire (114 patients and 109 staff). Safety perception appeared to be quite different in patients and staff. The patients who were authorized to have more visiting hours and more time to use their mobile phone had higher scores on Experienced Safety subscale. A negative correlation between the Therapeutic Hold and Experienced Safety subscales was found in the staff members, and this was due to their negative perception. The ward climate seemed to be affected by the unit's rules, especially with respect to visits and the smartphones use. Nurses need to be aware of the importance of ward climate and how their own perception may differ from and that of patients: this gap could lead to decisions detached from the patients' needs., (© 2022 The Authors. International Journal of Mental Health Nursing published by John Wiley & Sons Australia, Ltd.)

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Though the assessment of cognitive functions is proven to be a reliable prognostic indicator in patients with brain tumors, some of these functions, such as cognitive control, are still rarely investigated. The objective of this study was to examine proactive and reactive control functions in patients with focal brain tumors and to identify lesioned brain areas more at "risk" for developing impairment of these functions. To this end, a group of twenty-two patients, candidate to surgery, were tested with an AX-CPT task and a Stroop task, along with a clinical neuropsychological assessment, and their performance was compared to that of a well-matched healthy control group. Although overall accuracy and response times were similar for patients and control groups, the patient group failed more on the BX trials of the AX-CPT task and on the incongruent trials of the Stroop task, specifically. Behavioral results were associated with the damaged brain areas, mostly distributed in right frontal regions, by means of a lesion-symptom mapping multivariate approach. This analysis showed that a white matter cluster in the right prefrontal area was associated with lower d'-context values on the AX-CPT, which reflected the fact that these patients rely more on later information (reactive processes) to respond to unexpected and conflicting stimuli, than on earlier contextual cues (proactive processes). Taken together, these results suggest that patients with brain tumors present an imbalance between proactive and reactive control strategies in high interfering conditions, in association with right prefrontal white matter lesions., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Functional neurological disorders (FND) are disabling medical conditions commonly seen in neurological practice. Neurologists play an essential role in managing FND, from establishing a diagnosis to coordination of multidisciplinary team-based treatment for patients. With this study, we investigated the knowledge and the clinical experience of Italian neurologists in managing patients with FND., Methods: Members of the Italian Society of Neurology were invited via e-mail to participate in this ad hoc online survey; 492 questionnaires were returned completed., Results: The term "Functional neurological disorders" in reference to FND was used more frequently than other psychological (e.g., psychogenic or conversion), or descriptive terms (e.g., non-organic or stress-related). When speaking with patients, the respondents stated that they preferred explaining symptoms based on abnormal functioning of the nervous system than discussing mental illness and that they would refer their patient to a psychologist rather than to a psychiatrist. Few considered that physiotherapy and psychiatric interventions are useful approaches to treating FND. Some believed that patients simulate their symptoms., Conclusions: Overall, the responses suggest that knowledge about scientific advances in FND is somewhat sparse. A psychiatric-centered view of FND opens the way to an approach in which neurobiological and psychological aspects constitute essential factors of the condition. In this context, professional education could improve understanding of FND and optimize patient management., (© 2021. The Author(s).)

Assessment of impaired/preserved cortical regions in brain tumours is typically performed via intraoperative direct brain stimulation of eloquent areas or task-based functional MRI. One main limitation is that they overlook distal brain regions or networks that could be functionally impaired by the tumour. This study aims (i) to investigate the impact of brain tumours on the cortical synchronization of brain networks measured with resting-state functional magnetic resonance imaging (resting-state networks) both near the lesion and remotely and (ii) to test whether potential changes in resting-state networks correlate with cognitive status. The sample included 24 glioma patients (mean age: 58.1 ± 16.4 years) with different pathological staging. We developed a new method for single subject localization of resting-state networks abnormalities. First, we derived the spatial pattern of the main resting-state networks by means of the group-guided independent component analysis. This was informed by a high-resolution resting-state networks template derived from an independent sample of healthy controls. Second, we developed a spatial similarity index to measure differences in network topography and strength between healthy controls and individual brain tumour patients. Next, we investigated the spatial relationship between altered networks and tumour location. Finally, multivariate analyses related cognitive scores across multiple cognitive domains (attention, language, memory, decision making) with patterns of multi-network abnormality. We found that brain gliomas cause broad alterations of resting-state networks topography that occurred mainly in structurally normal regions outside the tumour and oedema region. Cortical regions near the tumour often showed normal synchronization. Finally, multi-network abnormalities predicted attention deficits. Overall, we present a novel method for the functional localization of resting-state networks abnormalities in individual glioma patients. These abnormalities partially explain cognitive disabilities and shall be carefully navigated during surgery., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Glioblastoma (GBM) is the most commonly occurring primary malignant brain tumor, and it carries a dismal prognosis. Focusing on the tumor microenvironment may provide new insights into pathogenesis, but no clinical tools are available to do this. We hypothesized that the infiltration of different leukocyte populations in the tumoral and peritumoral brain tissues may be measured by magnetic resonance imaging (MRI)., Methods: Pre-operative MRI was combined with immune phenotyping of intraoperative tumor tissue based on flow cytometry of myeloid cell populations that are associated with immune suppression, namely, microglia and bone marrow-derived macrophages (BMDM). These cell populations were measured from the central and marginal areas of the lesion identified intraoperatively with 5-aminolevulinic acid-guided surgery. MRI features (volume, mean and standard deviation of signal intensity, and fractality) were derived from all MR sequences (T1w, Gd+ T1w, T2w, FLAIR) and ADC MR maps and from different tumor areas (contrast- and non-contrast-enhancing tumor, necrosis, and edema). The principal components of MRI features were correlated with different myeloid cell populations by Pearson's correlation., Results: We analyzed 126 samples from 62 GBM patients. The ratio between BMDM and microglia decreases significantly from the central core to the periphery. Several MRI-derived principal components were significantly correlated (p <0.05, r range: [-0.29, -0.41]) with the BMDM/microglia ratio collected in the central part of the tumor., Conclusions: We report a significant correlation between structural MRI clinical imaging and the ratio of recruited vs. resident macrophages with different immunomodulatory activities. MRI features may represent a novel tool for investigating the microenvironment of GBM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Salvalaggio, Silvestri, Sansone, Pinton, Magri, Briani, Anglani, Lombardi, Zagonel, Della Puppa, Mandruzzato, Corbetta and Bertoldo.)