Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well-being

The menstrual cycle (MC) is a sex hormone-related phenomenon that repeats itself cyclically during the woman's reproductive life. In this explorative study, we hypothesized that coordinated variations of multiple sex hormones may affect the large-scale organization of the brain functional network and that, in turn, such changes might have psychological correlates, even in the absence of overt clinical signs of anxiety and/or depression. To test our hypothesis, we investigated longitudinally, across the MC, the relationship between the sex hormones and both brain network and psychological changes. We enrolled 24 naturally cycling women and, at the early-follicular, peri-ovulatory, and mid-luteal phases of the MC, we performed: (a) sex hormone dosage, (b) magnetoencephalography recording to study the brain network topology, and (c) psychological questionnaires to quantify anxiety, depression, self-esteem, and well-being. We showed that during the peri-ovulatory phase, in the alpha band, the leaf fraction and the tree hierarchy of the brain network were reduced, while the betweenness centrality (BC) of the right posterior cingulate gyrus (rPCG) was increased. Furthermore, the increase in BC was predicted by estradiol levels. Moreover, during the luteal phase, the variation of estradiol correlated positively with the variations of both the topological change and environmental mastery dimension of the well-being test, which, in turn, was related to the increase in the BC of rPCG. Our results highlight the effects of sex hormones on the large-scale brain network organization as well as on their possible relationship with the psychological state across the MC. Moreover, the fact that physiological changes in the brain topology occur throughout the MC has widespread implications for neuroimaging studies

Clinical connectome fingerprints of cognitive decline

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

The structural connectome constrains fast brain dynamics

Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics

Mutations in the SPAST gene causing hereditary spastic paraplegia arerelated to global topological alterations in brain functional networks

Aim: Our aim was to describe the rearrangements of the brain activity related to genetic mutations in the SPAST gene. Methods: Ten SPG4 patients and ten controls underwent a 5 min resting state magnetoencephalography recording and neurological examination. A beamformer algorithm reconstructed the activity of 90 brain areas. The phase lag index was used to estimate synchrony between brain areas. The minimum spanning tree was used to estimate topological metrics such as the leaf fraction (a measure of network integration) and the degree divergence (a measure of the resilience of the network against pathological events). The betweenness centrality (a measure to estimate the centrality of the brain areas) was used to estimate the centrality of each brain area. Results: Our results showed topological rearrangements in the beta band. Specifically, the degree divergence was lower in patients as compared to controls and this parameter related to clinical disability. No differences appeared in leaf fraction nor in betweenness centrality. Conclusion: Mutations in the SPAST gene are related to a reorganization of the brain topology

Neural hypernetwork approach for pulmonary embolism diagnosis

Background Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. A pulmonary embolism is a blockage of the main artery of the lung or one of its branches, frequently fatal. Results Our study uses data on 28 diagnostic features of 1427 people considered to be at risk of pulmonary embolism enrolled in the Department of Internal and Subintensive Medicine of an Italian National Hospital “Ospedali Riuniti di Ancona”. Patients arrived in the department after a first screening executed by the emergency room. The resulting neural hypernetwork correctly recognized 94 % of those developing pulmonary embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space). Conclusion In this work we successfully derived a new integrative approach for the analysis of partial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The novelty of this method is that it does not use clinical parameters extracted by imaging analysis

An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization

Motion analysis in sport training: the link between technology and pedagogy

Sport is an increasingly popular phenomenon among people probably due to the parallel evolution of the methods of development of physiological, technical and strategic capacities. People who play sports have learned to pay more and more attention to the loads they put on their bodies. This is because it is know that excessive loads during workouts can increase the risk of injuries. As the benefits of sport activity manifest themselves in many fields like in disability, in the presence of clinical pathologies, for recovery prison and especially in schools, it cannot be considered as simple gymnastics, since it involves physical, psychological, and cultural aspects and for these reasons we now increasingly speak of sport pedagogy. Many definitions have been proposed for the word training but all of them are almost always incomplete. This because training is to be understood as a complex pedagogical process in which various factors come into play such as, for example, motor, physical, technical, tactical but above all psychological, neurobiological and social factors. The aim of training is to describe, quantify and evaluate human movement. The analysis of human movement provide information about different aspects of a specific motor task (such as walking, jumping and running), through measuring instruments like cameras or sensors. These allow to obtain quantitative and qualitative descriptions of the observed sport gesture. The purpose of this review is to analyse how the motion analysis, through its different technologies, can help in the description and characterization of sport and training intended as pedagogical processes

Il paesaggio come Museo. Archeologia della costa di Nardò

Il contributo contestualizza il rinvenimento del tesoretto monetale nel sito di Frascone (Nardò, Le), in relazione a un edificio (villa ?) legato ad una proprietà terriera (fundus), che nasce in età romana repubblicana (II sec. a.C.), forse su un insediamento precedente, e continua a vivere fino alla prima età imperiale. Attorno alla seconda metà, o meglio alla fine del III sec. d.C., si registra una “conversione” dell’area, probabilmente una nuova destinazione d’uso, attraverso una consistente fase di riedificazione di strutture semplici, a carattere utilitario, produttivo, forse stagionale, che vede probabilmente il suo periodo di massima attività tra la fine del III ed il IV secolo d.C.; esse potrebbero essere pertinenti un “vicus industriale”, cioè un villaggio dedito ad attività legate al mare, in generale, e alla pesca in particolare, abbandonato in età tardoantica

MR-imaging: a new approach for glioma characterization

Gliomas are the most common primary brain tumors. The diffuse infiltration of white matter tracts by cerebral gliomas is a major cause of their appalling prognosis: tumor cells invade, displace, and possibly destroy WM. An early diagnosis and a comprehensive evaluation of tumor extent and relationships with surrounding anatomical structures are crucial in determining prognosis and treatment planning. Conventional Magnetic Resonance (MR) sequences (e.g. T1- or T2—weighted images) have limited sensitivity and specificity in diagnosing brain tumors,[1] because they do not always allow precise delineation of tumor margins, or tumor differentiation from edema and /or treatment effects. In particular, contrast-enhanced MR images may underestimate lesion margins, which is critical for image-guided tumor resection, radiotherapy planning, and for assessing the response to chemotherapy. On the contrary, Diffusion Tensor Imaging (DTI) can identify peritumoral white—matter abnormalities, by detecting the presence of small areas with tumor—cell infiltration in WM around the edge of the gross tumor, as confirmed by image guided biopsies. In particular the tumor core is characterized by reduced anisotropy and increased isotropy, while, around this area, tumor infiltration shows increased isotropy, but normal anisotropy. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by 3D statistical analysis. In order to investigate the pathological tissues, greyscale digital FLAIR images have been processed. Hence, several well—known statistical quantities have been used to gather meaningful information from the available dataset. The most commonly used indexes of location are mean, mode, median and quartiles. The dispersion (or variability) is given by the variance s2, which is related with its second order moment of the distribution, and its square root, the standard deviation s; dividing the latter by the absolute value of the mean one obtains the coefficient of variation CV, i.e. a non-dimensional measure of spread. Another feature of interest is the heterogeneity, usually characterized by the Gini concentration index and entropy, scaling range from 0 (minimum concentration) up to 1 (maximum concentration). Skewness and kurtosis represent the 3rd and 4th order moments of the distribution, and locate the asymmetry and the “distance” from a perfectly normally distributed variable. Finally, an estimation of the fractal dimension is performed using by box counting. Box counting is a method of gathering data for analyzing complex patterns by breaking a dataset, object, image, etc. into smaller and smaller pieces, typically "box"—shaped, and analyzing the pieces at each smaller scale[2]. This arsenal of instruments allowed us to determine the statistical differences among different gliomas