Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI

Paraneoplastic hypoglycaemia secondary to IGF-2 secretion from a metastatic gastrointestinal stromal tumour

We report the case of a 79-year-old male with previous history of non-Hodgkin's lymphoma in remission, who presented acutely to the Accident and Emergency department with recurrent episodes of hypoglycaemia. At the time of presentation, a random glucose was low at 1.4 mmol/l, which upon correction resolved his symptoms. In hindsight, the patient recalled having had similar episodes periodically over the past 2 months to which he did not give much notice. While hospitalized, he continued having episodes of symptomatic hypoglycaemia, requiring treatment with intravenous dextrose and per os steroids. Once stable, he was discharged on oral prednisolone and dietary advice. A computed tomography scan performed during inpatient stay showed multiple deposits in the abdomen. An ultrasound guided biopsy of one of the liver deposits was performed. Immunohistochemistry supported the diagnosis of a gastrointestinal stromal tumour (GIST) positive for CD34 and CD117. The diagnosis of non-islet cell tumour hypoglycaemia (NICTH) secondary to an IGF2 secreting GIST was confirmed with further biochemical investigations (IGF2=105.9 nmol/l; IGF2:IGF1 ratio 23, Upper Level of Normal (ULN) <10). Targeted cytoreductive treatment with Imatinib mesylate following assessment of the tumour's mutational status was successful in preventing hypoglycaemia over a 21-month follow-up observation period

Synergistic Gravity and the Role of Resonances in GRS-Inspired Braneworlds

We consider 5D braneworld models of quasi-localized gravity in which 4D gravity is reproduced at intermediate scales while the extra dimension opens up at both the very short and the very long distances, where the geometry is flat. Our main interest is the interplay between the zero mode of these models, whenever a normalizable zero mode exists, and the effects of zero energy graviton resonant modes coming from the contributions of massive KK modes. We first consider a compactified version of the GRS model and find that quasi-localized gravity is characterized by a scale for which both the resonance and the zero mode have significant contribution to 4D gravity. Above this scale, gravity is primarily mediated by the zero mode, while the resonance gives only minor corrections. Next, we consider an asymmetric version of the standard non-compact GRS model, characterized by different cosmological constants on each AdS side. We show that a resonance is present but the asymmetry, through the form of the localizing potential, can weaken it, resulting in a shorter lifetime and, thus, in a shorter distance scale for 4D gravity. As a third model exhibiting quasi-localization, we consider a version of the GRS model in which the central positive tension brane has been replaced by a configuration of a scalar field propagating in the bulk.Comment: 18 pages, 3 figures, added 1 figure, revised version as published in Class. Quant. Gra

Using late-time optical and near-infrared spectra to constrain Type Ia supernova explosion properties

The late-time spectra of Type Ia supernovae (SNe Ia) are powerful probes of the underlying physics of their explosions. We investigate the late-time optical and near-infrared spectra of seven SNe Ia obtained at the VLT with XShooter at $>$200 d after explosion. At these epochs, the inner Fe-rich ejecta can be studied. We use a line-fitting analysis to determine the relative line fluxes, velocity shifts, and line widths of prominent features contributing to the spectra ([Fe II], [Ni II], and [Co III]). By focussing on [Fe II] and [Ni II] emission lines in the ~7000-7500 \AA\ region of the spectrum, we find that the ratio of stable [Ni II] to mainly radioactively-produced [Fe II] for most SNe Ia in the sample is consistent with Chandrasekhar-mass delayed-detonation explosion models, as well as sub-Chandrasekhar mass explosions that have metallicity values above solar. The mean measured Ni/Fe abundance of our sample is consistent with the solar value. The more highly ionised [Co III] emission lines are found to be more centrally located in the ejecta and have broader lines than the [Fe II] and [Ni II] features. Our analysis also strengthens previous results that SNe Ia with higher Si II velocities at maximum light preferentially display blueshifted [Fe II] 7155 \AA\ lines at late times. Our combined results lead us to speculate that the majority of normal SN Ia explosions produce ejecta distributions that deviate significantly from spherical symmetry.Comment: 17 pages, 12 figure, accepted for publication in MNRA

Reconfiguration of dominant coupling modes in mild traumatic brain injury mediated by δ-band activity: a resting state MEG study

During the last few years, rich-club (RC) organization has been studied as a possible brain-connectivity organization model for large-scale brain networks. At the same time, empirical and simulated data of neurophysiological models have demonstrated the significant role of intra-frequency and inter-frequency coupling among distinct brain areas. The current study investigates further the importance of these couplings using recordings of resting-state magnetoencephalographic activity obtained from 30 mild traumatic brain injury (mTBI) subjects and 50 healthy controls. Intra-frequency and inter-frequency coupling modes are incorporated in a single graph to detect group differences within individual rich-club subnetworks (type I networks) and networks connecting RC nodes with the rest of the nodes (type II networks). Our results show a higher probability of inter-frequency coupling for (δ–γ1), (δ–γ2), (θ–β), (θ–γ2), (α–γ2), (γ1–γ2) and intra-frequency coupling for (γ1–γ1) and (δ–δ) for both type I and type II networks in the mTBI group. Additionally, mTBI and control subjects can be correctly classified with high accuracy (98.6%), whereas a general linear regression model can effectively predict the subject group using the ratio of type I and type II coupling in the (δ, θ), (δ, β), (δ, γ1), and (δ, γ2) frequency pairs. These findings support the presence of an RC organization simultaneously with dominant frequency interactions within a single functional graph. Our results demonstrate a hyperactivation of intrinsic RC networks in mTBI subjects compared to controls, which can be seen as a plausible compensatory mechanism for alternative frequency-dependent routes of information flow in mTBI subjects

Greater repertoire and temporal variability of cross-frequency coupling (CFC) modes in resting-state neuromagnetic recordings among children with reading difficulties

Cross-frequency, phase-to-amplitude coupling (PAC) between neuronal oscillations at rest may serve as the substrate that supports information exchange between functionally specialized neuronal populations both within and between cortical regions. The study utilizes novel algorithms to identify prominent instantaneous modes of cross-frequency coupling and their temporal stability in resting state magnetoencephalography (MEG) data from 25 students experiencing severe reading difficulties (RD) and 27 age-matched non-impaired readers (NI). Phase coherence estimates were computed in order to identify the prominent mode of PAC interaction for each sensor, sensor pair, and pair of frequency bands (from δ to γ) at successive time windows of the continuous MEG record. The degree of variability in the characteristic frequency-pair PACf1−f2 modes over time was also estimated. Results revealed a wider repertoire of prominent PAC interactions in RD as compared to NI students, suggesting an altered functional substrate for information exchange between neuronal assemblies in the former group. Moreover, RD students showed significant variability in PAC modes over time. This temporal instability of PAC values was particularly prominent: (a) within and between right hemisphere temporo-parietal and occipito-temporal sensors and, (b) between left hemisphere frontal, temporal, and occipito-temporal sensors and corresponding right hemisphere sites. Altered modes of neuronal population coupling may help account for extant data revealing reduced, task-related neurophysiological and hemodynamic activation in left hemisphere regions involved in the reading network in RD. Moreover, the spatial distribution of pronounced instability of cross-frequency coupling modes in this group may provide an explanation for previous reports suggesting the presence of inefficient compensatory mechanisms to support reading

Improving the detection of mtbi via complexity analysis in resting - state magnetoencephalography

Diagnosis of mild Traumatic Brain Injury (mTBI) is difficult due to the variability of obvious brain lesions using imaging scans. A promising tool for exploring potential biomarkers for mTBI is magnetoencephalography which has the advantage of high spatial and temporal resolution. By adopting proper analytic tools from the field of symbolic dynamics like Lempel-Ziv complexity, we can objectively characterize neural network alterations compared to healthy control by enumerating the different patterns of a symbolic sequence. This procedure oversimplifies the rich information of brain activity captured via MEG. For that reason, we adopted neural-gas algorithm which can transform a time series into more than two symbols by learning brain dynamics with a small reconstructed error. The proposed analysis was applied to recordings of 30 mTBI patients and 50 normal controls in δ frequency band. Our results demonstrated that mTBI patients could be separated from normal controls with more than 97% classification accuracy based on high complexity regions corresponding to right frontal areas. In addition, a reverse relation between complexity and transition rate was demonstrated for both groups. These findings indicate that symbolic complexity could have a significant predictive value in the development of reliable biomarkers to help with the early detection of mTBI

DGP Cosmology with a Non-Minimally Coupled Scalar Field on the Brane

We construct a DGP inspired braneworld scenario where a scalar field non-minimally coupled to the induced Ricci curvature is present on the brane. First we investigate the status of gravitational potential with non-minimal coupling and observational constraints on this non-minimal model. Then we further deepen the idea of embedding of FRW cosmology in this non-minimal setup. Cosmological implications of this scenario are examined with details and the quintessence and late-time expansion of the universe within this framework are examined. Some observational constraints imposed on this non-minimal scenario are studied and relation of this model with dark radiation formalism is determined with details.Comment: 26 pages, 3 eps figure

Classifying children with reading difficulties from non-impaired readers via symbolic dynamics and complexity analysis of MEG resting-state data

Magnetoencephalography (MEG) is a brain imaging method affording real-time temporal, and adequate spatial resolution to reveal aberrant neurophysiological function associated with dyslexia. In this study we analyzed sensor-level resting-state neuromagnetic recordings from 25 reading-disabled children and 27 non-impaired readers under the notion of symbolic dynamics and complexity analysis. We compared two techniques for estimating the complexity of MEG time-series in each of 8 frequency bands based on symbolic dynamics: (a) Lempel-Ziv complexity (LZC) entailing binarization of each MEG time series using the mean amplitude as a threshold, and (b) An approach based on the neural-gas algorithm (NG) which has been used by our group in the context of various symbolization schemes. The NG approach transforms each MEG time series to more than two symbols by learning the reconstructed manifold of each time series with a small error. Using this algorithm we computed a complexity index (CI) based on the distribution of words up to a predetermined length. The relative performance of the two complexity indexes was assessed using a classification procedure based on k-NN and Support Vector Machines. Results revealed the capacity of CI to discriminate impaired from non-impaired readers with 80% accuracy. Corresponding performance of LZC values did not exceed 55%. These findings indicate that symbolization of MEG recordings with an appropriate neuroinformatic approach, such as the proposed CI metric, may be of value in understanding the neural dynamics of dyslexia