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Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity., Using differential identifiability and connectome predictive modeling, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. The framework allows us to more robustly identify and characterize functional networks associated to specific cognitive outcomes exhibited in AD.

The identifiability framework (𝕀f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties., Author Summary Functional connectome (FC) fingerprinting recently became a topic of great interest in network neuroscience. We recently proposed a framework to improve brain fingerprint (i.e., identifiability) of FCs, which improves not only test-retest reliability but also the correlation of FCs with fluid intelligence. However, does this improvement in FC fingerprints propagate to the derived network measures? In this work we found that improving the fingerprint (differential identifiability) of the functional connectome also improves the “fingerprint” of its network properties. Furthermore, when using the identifiability framework on the network properties directly, certain network properties like search information and communicability add to the FC fingerprint. Finally, we show that enhancement of the fingerprint in the network measures, in a wide range of cognitive tasks, using the identifiability framework also improves task sensitivity in these measures. We show that regardless of whether you are using functional connectomes or the network properties derived from them, using the 𝕀f framework on the functional connectomes would be a beneficial first step.

Funding Information: The authors are grateful to the Birmingham Surgical Trials Consortium at the University of Birmingham for the use of their servers for secure online data collection. Disclosure: The authors declare no conflict of interest.

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that “clinical fingerprints” can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.

Background: In December 2019, an outbreak of pneumonia, caused by a new type of coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It quickly spread worldwide, resulting in a pandemic. The clinical manifestations of SARS-CoV-2 range from mild non-specific symptoms to severe pneumonia with organ function damage. In addition, up to 60% of patients have liver impairment or dysfunction, confirmed by several studies by the presence of SARS-CoV-2 in the liver tissue.Methods: We report two cases of symptomatic liver cyst requiring fenestration after recent SARS-CoV-2 infection. Both patients had hospital admission due to documented SARS-CoV-2 infection. Recently, after the infection, they developed symptoms caused by an enlarged hepatic cyst: one had abdominal pain, and the other had jaundice. They underwent surgery after two negative swab tests for SARS-CoV-2.Results: Cystic fluid was sent for microbiological test, and real-time fluorescence polymerase chain reaction COVID-19 nucleic-acid assay of the cyst fluid was found to be negative in both cases.Discussion: Although there are no current data that can document a viral contamination of cystic fluid, there are data that document a hepatotropism of COVID-19 virus. Herein we report that after viral clearance at pharyngeal and nasal swab, there is no evidence of viral load in such potential viral reservoir.

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks., Author Summary A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). Here we propose a framework, based on Jensen-Shannon divergence, to define “connectivity distance” and to infer about brain network reconfiguration across different tasks with respect to resting state, and to explore changes in centralized and distributed processing in FCs. Three functional networks (dorsal attention, frontoparietal and DMN) showed major changes in distributed processing and minor changes in centralized processing. Changes in centralized processing depend on the underlying structural connectivity weights and structural path “hiddenness.” These findings suggest that the cognitive “switch” between resting state and task states is a complex interplay between maximally and minimally distant functional connections, and the underlying structure.

Human functional brain connectivity is usually measured either at "rest" or during cognitive tasks, ignoring life's moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.

Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual level., Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing FCs, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the functional magnetic resonance imaging scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required., Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is data set-dependent and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs., Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each data set to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.

Emerging evidence indicates that Huntington's disease (HD) may be described as multi-organ pathology. In this context, we and others have contributed to demonstrate that the disease is characterized by an impairment of the homeostasis of gastro-intestinal (GI) tract. Sphingolipids represent a class of molecules involved in the regulation and maintenance of different tissues and organs including GI system. In this study, we investigated whether the alteration of Sphingosine-1-phosphate (S1P) metabolism, previously described in human HD brains and animal models, is also detectable peripherally in R6/2 HD mice. Our findings indicate, for the first time, that sphingolipid metabolism is perturbed early in the disease in the intestinal tract of HD mice and, its modulation by K6PC-5, a selective activator of S1P synthesis, preserved intestinal integrity and homeostasis. These results further support the evidence that modulation of sphingolipid pathways may represent a potential therapeutic option in HD and suggest that it has also the potential to counteract the peripheral disturbances which may usually complicate the management of the disease and affect patient's quality of life.

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task-dominant, subject dominant or neither, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states., 30 pages; 6 figures; 5 supplementary figures

Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine., Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks., Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space., Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected sub-networks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.

The longevity-associated variant (LAV) of the bactericidal/permeability-increasing fold-containing family B member 4 (BPIFB4) has been found significantly enriched in long-living individuals. Neuroinflammation is a key player in Huntington’s disease (HD), a neurodegenerative disorder caused by neural death due to expanded CAG repeats encoding a long polyglutamine tract in the huntingtin protein (Htt). Herein, we showed that striatal-derived cell lines with expanded Htt (STHdh Q111/111) expressed and secreted lower levels of BPIFB4, when compared with Htt expressing cells (STHdh Q7/7), which correlated with a defective stress response to proteasome inhibition. Overexpression of LAV-BPIFB4 in STHdh Q111/111 cells was able to rescue both the BPIFB4 secretory profile and the proliferative/survival response. According to a well-established immunomodulatory role of LAV-BPIFB4, conditioned media from LAV-BPIFB4-overexpressing STHdh Q111/111 cells were able to educate Immortalized Human Microglia—SV40 microglial cells. While STHdh Q111/111 dying cells were ineffective to induce a CD163 + IL-10high pro-resolving microglia compared to normal STHdh Q7/7, LAV-BPIFB4 transduction promptly restored the central immune control through a mechanism involving the stromal cell-derived factor-1. In line with the in vitro results, adeno-associated viral-mediated administration of LAV-BPIFB4 exerted a CXCR4-dependent neuroprotective action in vivo in the R6/2 HD mouse model by preventing important hallmarks of the disease including motor dysfunction, body weight loss, and mutant huntingtin protein aggregation. In this view, LAV-BPIFB4, due to its pleiotropic ability in both immune compartment and cellular homeostasis, may represent a candidate for developing new treatment for HD.

Introduction Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness. Methods Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames. Results Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of “pathological” within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) “pathological” increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1–2) compared to controls. No between-group differences were observed in the total number of frames Conclusion CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation. Funding The European Commission FP7 NeurOmics project; CHDI Foundation; the Medical Research Council UK; the Brain Research Trust; and the Guarantors of Brain.

Introduction Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. Methods Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). Results Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. Discussion Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization.

Although plants are essentially sessile in nature, these organisms are very much in tune with their environment and are capable of a variety of movements. This may come as a surprise to many non-botanists, but not to Charles Darwin, who reported that plants do produce movements. Following Darwin’s specific interest on climbing plants, this paper will focus on the attachment mechanisms by the tendrils. We draw attention to an unsolved problem in available literature: whether during the approach phase the tendrils of climbing plants consider the structure of the support they intend to grasp and plan the movement accordingly ahead of time. Here we report the first empirical evidence that this might be the case. The three-dimensional (3D) kinematic analysis of a climbing plant (Pisum sativum L.) demonstrates that the plant not only perceives the support, but it scales the kinematics of tendrils’ aperture according to its thickness. When the same support is represented in two-dimensions (2D), and thus unclimbable, there is no evidence for such scaling. In these circumstances the tendrils’ kinematics resemble those observed for the condition in which no support was offered. We discuss these data in light of the evidence suggesting that plants are equipped with sensory mechanisms able to provide the necessary information to plan and control a movement.

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FCEEG to second range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals., Author Summary Functional connectivity is governed by a whole-brain organization measurable over multiple timescales by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The relationship across the whole-brain organization captured at the different timescales of EEG and fMRI is largely unknown. Using concurrent EEG-fMRI, we identified spatially independent components consisting of brain connectivity patterns that co-occur in EEG and fMRI over subjects. We observed a component with similar connectivity organization across EEG and fMRI as well as a component with divergent connectivity. The former component governed all EEG frequencies while the latter was modulated by frequency. These findings show that part of functional connectivity organizes in a common spatial layout over several timescales, while a spatially independent part is modulated by frequency-specific information.

Although several agents have been identified to provide therapeutic benefits in Huntington disease (HD), the number of conventionally used treatments remains limited and only symptomatic. Thus, it is plausible that the need to identify new therapeutic targets for the development of alternative and more effective treatments is becoming increasingly urgent. Recently, the sphingosine-1-phosphate (S1P) axis has been reported to be a valid potential novel molecular target for therapy development in HD. Modulation of aberrant metabolism of S1P in HD has been proved to exert neuroprotective action in vitro settings including human HD iPSC-derived neurons. In this study, we investigated whether promoting S1P production by stimulating Sphingosine Kinase 1 (SPHK1) by the selective activator, K6PC-5, may have therapeutic benefit in vivo in R6/2 HD mouse model. Our findings indicate that chronic administration of 0.05 mg/kg K6PC-5 exerted an overall beneficial effect in R6/2 mice. It significantly slowed down the progressive motor deficit associated with disease progression, modulated S1P metabolism, evoked the activation of pro-survival pathways and markedly reduced the toxic mutant huntingtin (mHtt) aggregation. These results suggest that K6PC-5 may represent a future therapeutic option in HD and may potentially counteract the perturbed brain function induced by deregulated S1P pathways.

Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together activities. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of functional connectomes. The individual fingerprinting of functional connectivity profiles is promising due to its potential as a robust neuroimaging biomarker. We evaluated, on two independent multi-site datasets, individual fingerprints in test-retest visit pairs within and across two sites and present a generalized framework based on principal component analysis to improve identifiability. Those components that maximized differential identifiability of a training dataset were used as an orthogonal connectivity basis to reconstruct the functional connectomes of training and validation sets. The optimally reconstructed functional connectomes showed a substantial improvement in individual fingerprinting within and across the two sites relative to the original data. A notable increase in ICC values for functional edges and resting-state networks was also observed. Improvements in identifiability were not found to be affected by global signal regression. Post-hoc analyses assessed the effect of the number of fMRI volumes on identifiability and showed that multi-site differential identifiability was for all cases maximized after optimal reconstruction. The generalizability of the optimal set of orthogonal basis of each dataset was evaluated through a leave-one-out procedure. Overall, results demonstrate that the framework presented in this study systematically improves identifiability in resting-state functional connectomes in multi-site studies., Comment: 28 pages, 11 figures in main text, 5 figures in supplementary

A vast body of literature exists showing functional and structural dysfunction within the brains of patients with disorders of consciousness. However, the function (fluorodeoxyglucose FDG-PET metabolism)-structure (MRI-diffusion-weighted images; DWI) relationship and how it is affected in severely brain injured patients remains ill-defined. FDG-PET and MRI-DWI in 25 severely brain injured patients (19 Disorders of Consciousness of which 7 unresponsive wakefulness syndrome, 12 minimally conscious; 6 emergence from minimally conscious state) and 25 healthy control subjects were acquired here. Default mode network (DMN) function-structure connectivity was assessed by fractional anisotropy (FA) and metabolic standardized uptake value (SUV). As expected, a profound decline in regional metabolism and white matter integrity was found in patients as compared with healthy subjects. Furthermore, a function-structure relationship was present in brain-damaged patients between functional metabolism of inferior-parietal, precuneus, and frontal regions and structural integrity of the frontal-inferiorparietal, precuneus-inferiorparietal, thalamo-inferioparietal, and thalamofrontal tracts. When focusing on patients, a stronger relationship between structural integrity of thalamo-inferiorparietal tracts and thalamic metabolism in patients who have emerged from the minimally conscious state as compared with patients with disorders of consciousness was found. The latter finding was in line with the mesocircuit hypothesis for the emergence of consciousness. The findings showed a positive function-structure relationship within most regions of the DMN. Hum Brain Mapp 37:3707-3720, 2016. © 2016 Wiley Periodicals, Inc.

Background: Between pathologically impaired consciousness and normal consciousness exists a scarcely researched transition zone, referred to as emergence from minimally conscious state, in which patients regain the capacity for functional communication, object use, or both. We investigated neural correlates of consciousness in these patients compared with patients with disorders of consciousness and healthy controls, by multimodal imaging. Methods: In this cross-sectional, multimodal imaging study, patients with unresponsive wakefulness syndrome, patients in a minimally conscious state, and patients who had emerged from a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised, were recruited from the neurology department of the Centre Hospitalier Universitaire de Liège, Belgium. Key exclusion criteria were neuroimaging examination in an acute state, sedation or anaesthesia during scanning, large focal brain damage, motion parameters of more than 3 mm in translation and 3° in rotation, and suboptimal segmentation and normalisation. We acquired resting state functional and structural MRI data and 18F-fluorodeoxyglucose (FDG) PET data; we used seed-based functional MRI (fMRI) analysis to investigate positive default mode network connectivity (within-network correlations) and negative default mode network connectivity (between-network anticorrelations). We correlated FDG-PET brain metabolism with fMRI connectivity. We used voxel-based morphometry to test the effect of anatomical deformations on functional connectivity. Findings: We recruited a convenience sample of 58 patients (21 [36%] with unresponsive wakefulness syndrome, 24 [41%] in a minimally conscious state, and 13 [22%] who had emerged from a minimally conscious state) and 35 healthy controls between Oct 1, 2009, and Oct 31, 2014. We detected consciousness-level-dependent increases (from unresponsive wakefulness syndrome, minimally conscious state, emergence from minimally conscious state, to healthy controls) for positive and negative default mode network connectivity, brain metabolism, and grey matter volume (p

Early change in memory performance is a key symptom of many brain diseases, but its underlying mechanism remains largely unknown. While structural MRI has been playing an essential role in revealing potentially relevant brain regions, increasing availability of diffusion MRI data (e.g., Human Connectome Project (HCP)) provides excellent opportunities for exploration of their complex coordination. Given the complementary information held in these two imaging modalities, we hypothesize that studying them as a whole, rather than individually, and exploring their association will provide us valuable insights of the memory mechanism. However, many existing association methods, such as sparse canonical correlation analysis (SCCA), only manage to handle two-way association and thus cannot guarantee the selection of biomarkers and associations to be memory relevant. To overcome this limitation, we propose a new outcome-relevant SCCA model (OSCCA) together with a new algorithm to enable the three-way associations among brain connectivity, anatomic structure and episodic memory performance. In comparison with traditional SCCA, we demonstrate the effectiveness of our model with both synthetic and real data from the HCP cohort.

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed \(\sim \)5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.

Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function., The function of a brain region is determined by the network it is embedded in. Here the authors implement the word2vec algorithm for connectomes generating a vector embedding of the connectivity structure for each node allowing inference about functional relationships between brain regions.

The evaluation of the individual 'fingerprint' of a human functional connectome (FC) is becoming a promising avenue for neuroscientific research, due to its enormous potential inherent to drawing single subject inferences from functional connectivity profiles. Here we show that the individual fingerprint of a human functional connectome can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of brain connectivity modes. We use data from the Human Connectome Project to demonstrate that the optimal reconstruction of the individual FCs through connectivity eigenmodes maximizes subject identifiability across resting-state and all seven tasks evaluated. The identifiability of the optimally reconstructed individual connectivity profiles increases both at the global and edgewise level, also when the reconstruction is imposed on additional functional data of the subjects. Furthermore, reconstructed FC data provide more robust associations with task-behavioral measurements. Finally, we extend this approach to also map the most task-sensitive functional connections. Results show that is possible to maximize individual fingerprinting in the functional connectivity domain regardless of the task, a crucial next step in the area of brain connectivity towards individualized connectomics., 31 pages, 11 figures

Huntington's disease is characterized by a complex and heterogeneous pathogenic profile. Studies have shown that disturbance in lipid homeostasis may represent a critical determinant in the progression of several neurodegenerative disorders. The recognition of perturbed lipid metabolism is only recently becoming evident in HD. In order to provide more insight into the nature of such a perturbation and into the effect its modulation may have in HD pathology, we investigated the metabolism of Sphingosine-1-phosphate (S1P), one of the most important bioactive lipids, in both animal models and patient samples. Here, we demonstrated that S1P metabolism is significantly disrupted in HD even at early stage of the disease and importantly, we revealed that such a dysfunction represents a common denominator among multiple disease models ranging from cells to humans through mouse models. Interestingly, the in vitro anti-apoptotic and the pro-survival actions seen after modulation of S1P-metabolizing enzymes allows this axis to emerge as a new druggable target and unfolds its promising therapeutic potential for the development of more effective and targeted interventions against this incurable condition.

Huntington's disease (HD) is the most common neurodegenerative disorder for which no effective cure is yet available. Although several agents have been identified to provide benefits so far, the number of therapeutic options remains limited with only symptomatic treatment available. Over the past few years, we have demonstrated that sphingolipid-based approaches may open the door to new and more targeted treatments for the disease. In this study, we investigated the therapeutic potential of stimulating sphingosine-1-phosphate (S1P) receptor 5 by the new selective agonist A-971432 (provided by AbbVie) in R6/2 mice, a widely used HD animal model. Chronic administration of low-dose (0.1 mg/kg) A-971432 slowed down the progression of the disease and significantly prolonged lifespan in symptomatic R6/2 mice. Such beneficial effects were associated with activation of pro-survival pathways (BDNF, AKT and ERK) and with reduction of mutant huntingtin aggregation. A-971432 also protected blood-brain barrier (BBB) homeostasis in the same mice. Interestingly, when administered early in the disease, before any overt symptoms, A-971432 completely protected HD mice from the classic progressive motor deficit and preserved BBB integrity. Beside representing a promising strategy to take into consideration for the development of alternative therapeutic options for HD, selective stimulation of S1P receptor 5 may be also seen as an effective approach to target brain vasculature defects in the disease.

Communication processes within the human brain at different cognitive states are neither well understood nor completely characterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learning algorithm, starting from a source with no a priori information about the network topology, and cooperatively searching for the target through a pheromone-inspired model. This framework relies on two parameters, namely pheromone perception and edge perception, to define the cognizance and subsequent behaviour of the ants on the network and, overall, the communication processes happening between source and target nodes. Simulations obtained through different configurations allow the identification of path-ensembles that are involved in the communication between node pairs. These path-ensembles may contain different number of paths depending on the perception parameters and the node pair. In order to assess the different communication regimes displayed on the simulations and their associations with functional connectivity, we introduce two network measurements, effective path-length and arrival rate. These communication features are tested as individual as well as combined predictors of functional connectivity during different tasks. Finally, different communication regimes are found in different specialized functional networks. Overall, this framework may be used as a test-bed for different communication regimes on top of an underlaying topology., 22 pages, 4 tables, 6 figures

Alzheimer's disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called disconnection hypothesis suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer's spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.

Alterations of lipid metabolism have been frequently associated with Huntington's disease (HD) over the past years. HD is the most common neurodegenerative disorder, with a complex pathogenic profile, typically characterized by progressive striatal and cortical degeneration and associated motor, cognitive and behavioral disturbances. Previous findings from our group support the idea that disturbed sphingolipid metabolism could represent an additional hallmark of the disease. Although such a defect represents a common biological denominator among multiple disease models ranging from cells to humans through mouse models, more efforts are needed to clearly define its clinical significance and the role it may play in the progression of the disease. In this study, we provided the first evidence of a defective de novo biosynthetic pathway of sphingolipids in multiple HD pre-clinical models. qPCR analysis revealed perturbed gene expression of sphingolipid-metabolizing enzymes in both early and late stage of the disease. In particular, reduction in the levels of sptlc1 and cerS1 mRNA in the brain tissues from manifest HD mice resulted in a significant decrease in the content of dihydroSphingosine, dihydroSphingosine-1-phospahte and dihydroCeramide [C18:0] as assessed by mass spectrometry. Moreover, in vitro studies highlighted the relevant role that aberrant sphingolipid metabolism may have in the HD cellular homeostasis. With this study, we consolidate the evidence of disturbed sphingolipid metabolism in HD and demonstrate for the first time that the de novo biosynthesis pathway is also significantly affected in the disease. This finding further supports the hypothesis that perturbed sphingolipid metabolism may represent a crucial factor accounting for the high susceptibility to disease in HD.

Huntington disease (HD) is a neurodegenerative disorder for which new treatments are urgently needed. Pridopidine is a new dopaminergic stabilizer, recently developed for the treatment of motor symptoms associated with HD. The therapeutic effect of pridopidine in patients with HD has been determined in two double-blind randomized clinical trials, however, whether pridopidine exerts neuroprotection remains to be addressed. The main goal of this study was to define the potential neuroprotective effect of pridopidine, in HD in vivo and in vitro models, thus providing evidence that might support a potential disease-modifying action of the drug and possibly clarifying other aspects of pridopidine mode-of-action. Our data corroborated the hypothesis of neuroprotective action of pridopidine in HD experimental models. Administration of pridopidine protected cells from apoptosis, and resulted in highly improved motor performance in R6/2 mice. The anti-apoptotic effect observed in the in vitro system highlighted neuroprotective properties of the drug, and advanced the idea of sigma-1-receptor as an additional molecular target implicated in the mechanism of action of pridopidine. Coherent with protective effects, pridopidine-mediated beneficial effects in R6/2 mice were associated with an increased expression of pro-survival and neurostimulatory molecules, such as brain derived neurotrophic factor and DARPP32, and with a reduction in the size of mHtt aggregates in striatal tissues. Taken together, these findings support the theory of pridopidine as molecule with disease-modifying properties in HD and advance the idea of a valuable therapeutic strategy for effectively treating the disease.

The human connectome is constructed by translating neuron activities into a complex network with nodes and edges. Although the topology of such networks is well studied, the formation rules and dynamics are not fully understood. It is challenging to develop a generative model for large scale dynamic complex networks due to the computational obstacle and nontrivial network structure. To study the node-based and subject-based network dynamics in resting-state brain, we divide the brain scan session into several sliding windows and generate a network for each segment. Then, an action-based model generator, which generates edges according to a topology manipulating actions, is fitted to the network series. This model, presumably as the first applicable approach for synthesizing the brain network dynamics, is shown capable of synthesizing a network series of the action-based model. Also, the estimated parameters shows that the actions of brain regions are related to their functionality.

Disruption of blood-brain barrier (BBB) integrity is a common feature for different neurological and neurodegenerative diseases. Although the interplay between perturbed BBB homeostasis and the pathogenesis of brain disorders needs further investigation, the development and validation of a reliable procedure to accurately detect BBB alterations may be crucial and represent a useful tool for potentially predicting disease progression and developing targeted therapeutic strategies. Here, we present an easy and efficient procedure for evaluating BBB leakage in a neurodegenerative condition like that occurring in a preclinical mouse model of Huntington disease, in which defects in the permeability of BBB are clearly detectable precociously in the disease. Specifically, the high molecular weight fluorescein isothiocyanate labelled (FITC)-albumin, which is able to cross the BBB only when the latter is impaired, is acutely infused into a mouse jugular vein and its distribution in the vascular or parenchymal districts is then determined by fluorescence microscopy. Accumulation of green fluorescent-albumin in the brain parenchyma functions as an index of aberrant BBB permeability and, when quantitated by using Image J processing software, is reported as Green Fluorescence Intensity.

Huntington’s disease is characterized by a complex and heterogeneous pathogenic profile. Studies have shown that disturbance in lipid homeostasis may represent a critical determinant in the progression of several neurodegenerative disorders. The recognition of perturbed lipid metabolism is only recently becoming evident in HD. In order to provide more insight into the nature of such a perturbation and into the effect its modulation may have in HD pathology, we investigated the metabolism of Sphingosine-1-phosphate (S1P), one of the most important bioactive lipids, in both animal models and patient samples. Here, we demonstrated that S1P metabolism is significantly disrupted in HD even at early stage of the disease and importantly, we revealed that such a dysfunction represents a common denominator among multiple disease models ranging from cells to humans through mouse models. Interestingly, the in vitro anti-apoptotic and the pro-survival actions seen after modulation of S1P-metabolizing enzymes allows this axis to emerge as a new druggable target and unfolds its promising therapeutic potential for the development of more effective and targeted interventions against this incurable condition.

Neuromedin U (NmU) is a pleiotropic hypothalamic neuropeptide involved in the gut-brain axis. It acts via both a Gαq/11-coupled receptor (NMUR1) and a Gαi-coupled receptor (NMUR2) in different cell types. Expression of both receptors was reported in platelets, but their significance for NmU signaling remains elusive. We studied the potential effects of NmU on human platelet activation. In platelet-rich plasma (PRP), NmU alone (up to 10μM) did not induce any measurable aggregation, but at nanomolar concentrations, it potentiated platelet aggregation by low (mean 0.47μM) ADP concentrations (from 25.9±3.6% to 74.8±2.7% maximal aggregation for ADP vs. ADP+NmU, 100nM, mean±SEM, n=13), accompanied by platelet P-selectin expression and intracellular calcium mobilization. Accordingly, platelet preincubation with NmU for 2min sensitized platelets for subsequent activation by ADP. When P2Y1 was inactivated by 50μM MRS2179, NmU comparably potentiated ADP-induced PRP aggregation, suggestive of cooperative activation with Gαi-coupled P2Y12. Likewise, NmU potentiated platelet aggregation by Gαi-operated epinephrine at subthreshold concentrations (99ng/ml, mean), but not that by Gαq-dependent serotonin (20μM). Platelet aggregation by NmU/epinephrine combination was fully inhibited by the Gαq inhibitor YM-254890 (1μM). qPCR detection and western blot analysis substantiated platelet expression of NMUR1 in different donors, a finding collectively complying with functionally relevant Gαq/11-mediated activation of platelet NMUR1 by NmU. Our findings advocate further studies on platelet sensitization by NmU, released during vascular activation and injury, to define its role as a modifier of platelet responsiveness to the physiological activation signals, operational in cardiovascular health and disease.

Dynamical models implemented on the large scale architecture of the human brain may shed light on how a function arises from the underlying structure. This is the case notably for simple abstract models, such as the Ising model. We compare the spin correlations of the Ising model and the empirical functional brain correlations, both at the single link level and at the modular level, and show that their match increases at the modular level in anesthesia, in line with recent results and theories. Moreover, we show that at the peak of the specific heat (the critical state), the spin correlations are minimally shaped by the underlying structural network, explaining how the best match between the structure and function is obtained at the onset of criticality, as previously observed. These findings confirm that brain dynamics under anesthesia shows a departure from criticality and could open the way to novel perspectives when the conserved magnetization is interpreted in terms of a homeostatic principle imposed to neural activity.

Whereas Huntington’s disease (HD) is unequivocally a neurological disorder, a critical mass of emerging studies highlights the occurrence of peripheral pathology like cardiovascular defects in both animal models and humans. The overt impairment in cardiac function is normally expected to be associated with peripheral vascular dysfunction, however whether this assumption is reasonable or not in HD is still unknown. In this study we functionally characterized the vascular system in R6/2 mouse model (line 160 CAG), which recapitulates several features of human pathology including cardiac disease. Vascular reactivity in different arterial districts was determined by wire myography in symptomatic R6/2 mice and age-matched wild type (WT) littermates. Disease stage was assessed by using well-validated behavioural tests like rotarod and horizontal ladder task. Surprisingly, no signs of vascular dysfunction were detectable in symptomatic mice and no link with motor phenotype was found.

Blood-brain barrier (BBB) breakdown, due to the concomitant disruption of the tight junctions (TJs), normally required for the maintenance of BBB function, and to the altered transport of molecules between blood and brain and vice-versa, has been suggested to significantly contribute to the development and progression of different brain disorders including Huntington’s disease (HD). Although the detrimental consequence the BBB breakdown may have in the clinical settings, the timing of its alteration remains elusive for many neurodegenerative diseases. In this study we demonstrate for the first time that BBB disruption in HD is not confined to established symptoms, but occurs early in the disease progression. Despite the obvious signs of impaired BBB permeability were only detectable in concomitance with the onset of the disease, signs of deranged TJs integrity occur precociously in the disease and precede the onset of overt symptoms. To our perspective this finding may add a new dimension to the horizons of pathological mechanisms underlying this devastating disease, however much remains to be elucidated for understanding how specific BBB drug targets can be approached in the future.

Transcranial magnetic stimulation (TMS) in combination with neuroimaging techniques allows to measure the effects of a direct perturbation of the brain. When coupled with high-density electroencephalography (TMS/hd-EEG), TMS pulses revealed electrophysiological signatures of different cortical modules in health and disease. However, the neural underpinnings of these signatures remain unclear. Here, by applying multimodal analyses of cortical response to TMS recordings and diffusion magnetic resonance imaging (dMRI) tractography, we investigated the relationship between functional and structural features of different cortical modules in a cohort of awake healthy volunteers. For each subject, we computed directed functional connectivity interactions between cortical areas from the source-reconstructed TMS/hd-EEG recordings and correlated them with the correspondent structural connectivity matrix extracted from dMRI tractography, in three different frequency bands (alpha, beta, gamma) and two sites of stimulation (left precuneus and left premotor). Each stimulated area appeared to mainly respond to TMS by being functionally elicited in specific frequency bands, that is, beta for precuneus and gamma for premotor. We also observed a temporary decrease in the whole-brain correlation between directed functional connectivity and structural connectivity after TMS in all frequency bands. Notably, when focusing on the stimulated areas only, we found that the structure-function correlation significantly increases over time in the premotor area controlateral to TMS. Our study points out the importance of taking into account the major role played by different cortical oscillations when investigating the mechanisms for integration and segregation of information in the human brain.

The combined use of Functional Electrical Stimulation (FES) and robotic technologies is advocated to improve rehabilitation outcomes after stroke. This work describes an arm rehabilitation system developed within the European project RETRAINER. The system consists of a passive 4-degrees-of-freedom exoskeleton equipped with springs to provide gravity compensation and electromagnetic brakes to hold target positions. FES is integrated in the system to provide additional support to the most impaired muscles. FES is triggered based on the volitional EMG signal of the same stimulated muscle; in order to encourage the active involvement of the patient the volitional EMG is also monitored throughout the task execution and based on it a happy or sad emoji is visualized at the end of each task. The control interface control of the system provides a GUI and multiple software tools to organize rehabilitation exercises and monitor rehabilitation progress. The functionality and the usability of the system was evaluated on four stroke patients. All patients were able to use the system and judged positively its wearability and the provided support. They were able to trigger the stimulation based on their residual muscle activity and provided different levels of active involvement in the exercise, in agreement with their level of impairment. A randomized controlled trial aimed at evaluating the effectiveness of the RETRAINER system to improve arm function after stroke is currently ongoing.

Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA , which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connICA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a “resting” human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings. Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage.

Huntington Disease (HD) is a genetic neurodegenerative disorder characterized by broad types of cellular and molecular dysfunctions that may affect both neuronal and non-neuronal cell populations. Among all the molecular mechanisms underlying the complex pathogenesis of the disease, alteration of sphingolipids has been identified as one of the most important determinants in the last years. In the present study, besides the purpose of further confirming the evidence of perturbed metabolism of gangliosides GM1, GD1a, and GT1b the most abundant cerebral glycosphingolipids, in the striatal and cortical tissues of HD transgenic mice, we aimed to test the hypothesis that abnormal levels of these lipids may be found also in the corpus callosum white matter, a ganglioside-enriched brain region described being dysfunctional early in the disease. Semi-quantitative analysis of GM1, GD1a, and GT1b content indicated that ganglioside metabolism is a common feature in two different HD animal models (YAC128 and R6/2 mice) and importantly, demonstrated that levels of these gangliosides were significantly reduced in the corpus callosum white matter of both models starting from the early stages of the disease. Besides corroborating the evidence of aberrant ganglioside metabolism in HD, here, we found out for the first time, that ganglioside dysfunction is an early event in HD models and it may potentially represent a critical molecular change influencing the pathogenesis of the disease.

Loss of cortical integration and changes in the dynamics of electrophysiological brain signals characterize the transition from wakefulness towards unconsciousness. In this study, we arrive at a basic model explaining these observations based on the theory of phase transitions in complex systems. We studied the link between spatial and temporal correlations of large-scale brain activity recorded with functional magnetic resonance imaging during wakefulness, propofol-induced sedation and loss of consciousness and during the subsequent recovery. We observed that during unconsciousness activity in frontothalamic regions exhibited a reduction of long-range temporal correlations and a departure of functional connectivity from anatomical constraints. A model of a system exhibiting a phase transition reproduced our findings, as well as the diminished sensitivity of the cortex to external perturbations during unconsciousness. This framework unifies different observations about brain activity during unconsciousness and predicts that the principles we identified are universal and independent from its causes. Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Christian Albrechts University zu Kiel; Alemania. Goethe Universitat Frankfurt; Alemania Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Siniatchkin, Michael. Christian Albrechts University zu Kiel; Alemania Fil: Amico, Enrico. Université de Liège; Bélgica Fil: Brichant, Jean Francois. Université de Liège; Bélgica Fil: Bonhomme, Vincent. Université de Liège; Bélgica Fil: Noirhomme, Quentin. Université de Liège; Bélgica Fil: Laufs, Helmut. Goethe Universitat Frankfurt; Alemania Fil: Laureys, Steven. Université de Liège; Bélgica

Summary Introduction Immune dysfunction, promoted by pro-inflammatory cytokines, plays a pivotal role in neurodegeneration associated with Huntington's disease. Aims The aim of this study was to investigate the emerging immunoregulatory and antiinflammatory properties of Sertoli cells in Huntington's disease. Methods The experimental R6/2 mouse model of Huntington's disease was treated by a single intraperitoneal injection of microencapsulated prepubertal porcine Sertoli cells and lifespan, motor performance and striatal inflammatory pattern have been evaluated. Results The results of this study demonstrated that a single intraperitoneal injection of microencapsulated prepubertal porcine Sertoli cells uniquely improved performances and extended the life expectancy of R6/2 Huntington's disease mice, by immune dysfunction modulation in brain. Conclusions This study highlights the immunomodulatory and trophic role of Sertoli cells that could be of help in the treatment of neurodegenerative disorders.

To date, hampered physiological function after exposure to microgravity has been primarily attributed to deprived peripheral neuro-sensory systems. For the first time, this study elucidates alterations in human brain function after long-duration spaceflight. More specifically, we found significant differences in resting-state functional connectivity between motor cortex and cerebellum, as well as changes within the default mode network. In addition, the cosmonaut showed changes in the supplementary motor areas during a motor imagery task. These results highlight the underlying neural basis for the observed physiological deconditioning due to spaceflight and are relevant for future interplanetary missions and vestibular patients. Electronic supplementary material The online version of this article (doi:10.1007/s00429-015-1054-3) contains supplementary material, which is available to authorized users.

The digital parallel frequency domain design is a powerful approach that offers the possibility to implement a variety of different applications in fluorescence spectroscopy and microscopy. It allows lifetime measurements in a cuvette, fluorescence lifetime imaging and FCS as well as multi frequency and multi wavelength tissue imaging in small portable medical devices. It dramatically reduces the acquisition time from the several minutes scale to the seconds scale, and performs every signal process in a digital fashion, avoiding any RF emission and it is extremely inexpensive. This development is the result of a systematic study carried on the previous design known as the FLIMbox. The extensive work done in maximizing the performance of the original FLIMbox led us to develop a new hardware solution with exciting and promising results and potentials that were not possible in the previous hardware realizations. The new design permits acquisition of the full harmonic content of the sample response when it is excited with a pulsed light source in one single measurement using the digital mixing principle that was developed in the original FLIMbox. Furthermore, it is very stable, has very low power requirements and higher precision, and allows the multi exponential analysis. The FLIMbox can be synchronized with lasers that are intrinsically modulated or can generate a frequency to amplitude modulate a laser diode or LED. It provides up to four input channels, it has a saturation feedback control to avoid any time information loss, and it is only limited by the number of photons collected rather than by the sampling window implementation scheme. Work supported by NIGMS, 8P41GM103540.

Background Huntington Disease (HD) is a neurodegenerative disorder resulting from the expansion of polyglutamine stretch in the huntingtin protein (Htt). Mutant HTT (mHtt) leads to progressive impairment of several molecular pathways that have been linked to disease pathogenesis. Defects in the production of a number of neurotrophic factors have been described as important determinants contributing to the development of HD. We have previously demonstrated that production of transforming growth factor-β1 (TGF-β1) is also deregulated in HD. Peripheral levels of TGF-β1 were markedly reduced early in the disease and returned to normal levels with disease severity. However, the cause and the biochemical origin of such abnormalities are still unclear. Results We report here that the abnormal production of peripheral TGF-β1 depends on the changes in the percentage of TGF-β1-producing macrophages along disease course. Variation in the number of TGF-β1-producing macrophages resulted from differential activation state of the same cells, which displayed phenotypic and functional heterogeneity throughout the clinical course of HD. We further demonstrated that, similar to the periphery, the number of TGF-β1-immunoreactive cells in human post-mortem brain with HD, varied with neuropathological changes. Conclusions Our data indicate that reduced bioavailability of TGF-β1 in the serum of HD subjects is attributable to the variation of the number of TGF-β1-producing macrophages. Macrophages display a differential ability to produce TGF-β1, which reflects diversity in cells polarization throughout the disease course. Besides elucidating the biochemical origin of TGF-β1 fluctuations in HD, our study highlights an interesting parallelism between periphery and central compartment and underlines the potential of TGF-β1 as a possible indicator suitable for prediction of disease onset in HD.

Background MUNDUS is an assistive framework for recovering direct interaction capability of severely motor impaired people based on arm reaching and hand functions. It aims at achieving personalization, modularity and maximization of the user’s direct involvement in assistive systems. To this, MUNDUS exploits any residual control of the end-user and can be adapted to the level of severity or to the progression of the disease allowing the user to voluntarily interact with the environment. MUNDUS target pathologies are high-level spinal cord injury (SCI) and neurodegenerative and genetic neuromuscular diseases, such as amyotrophic lateral sclerosis, Friedreich ataxia, and multiple sclerosis (MS). The system can be alternatively driven by residual voluntary muscular activation, head/eye motion, and brain signals. MUNDUS modularly combines an antigravity lightweight and non-cumbersome exoskeleton, closed-loop controlled Neuromuscular Electrical Stimulation for arm and hand motion, and potentially a motorized hand orthosis, for grasping interactive objects. Methods The definition of the requirements and of the interaction tasks were designed by a focus group with experts and a questionnaire with 36 potential end-users. Five end-users (3 SCI and 2 MS) tested the system in the configuration suitable to their specific level of impairment. They performed two exemplary tasks: reaching different points in the working volume and drinking. Three experts evaluated over a 3-level score (from 0, unsuccessful, to 2, completely functional) the execution of each assisted sub-action. Results The functionality of all modules has been successfully demonstrated. User’s intention was detected with a 100% success. Averaging all subjects and tasks, the minimum evaluation score obtained was 1.13 ± 0.99 for the release of the handle during the drinking task, whilst all the other sub-actions achieved a mean value above 1.6. All users, but one, subjectively perceived the usefulness of the assistance and could easily control the system. Donning time ranged from 6 to 65 minutes, scaled on the configuration complexity. Conclusions The MUNDUS platform provides functional assistance to daily life activities; the modules integration depends on the user’s need, the functionality of the system have been demonstrated for all the possible configurations, and preliminary assessment of usability and acceptance is promising. ISSN:1743-0003

Background Curcumin, the active ingredient in turmeric, has been validated to harbour important beneficial effects for a multitude of inflammatory-based diseases and also for neurodegenerative processes similar to those occurring in HD. To this regard, it has been recently described that curcumin significantly improves neuropathology and positively affects some of the neurochemical and neurobehavioral disease-related defects in a mouse model of HD. Although the mechanism of its neuroprotective action is still not completely understood, it has been hypothesised to act majorly through anti-inflammatory and anti-oxidative activities. Aim Here, our purpose is to investigate whether such specific properties of curcumin may potentially play a role in the control of the gradual body weight loss and in the accumulation of redox metals commonly observed in HD. We also want to examine whether the described beneficial effect of curcumin in HD is a shared mechanism across multiple HD models and not confined only to a single or few systems. Methods Curcumin along with bioperine (bioavailability enhancing-agent) was orally administered to females 10 days before pregnancy and during the entire gestation period up to the end of lactation. Starting from the third week of age, pups were daily treated with 40 mg/kg curcumin for 7 weeks. Results In line with other studies, so far, our preliminary data confirmed that curcumin has an outstanding safety profile with no adverse effect also in R6/2 mice. Evaluation of any potential beneficial effects of the treatment is in progress. Conclusion Curcumin is well tolerated also in R6/2 HD mice and, based on its therapeutic properties, it could conceivably represent an attractive possibility to develop a valuable dietary supplement in neurodegenerative diseases such as HD.

Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.