Source Modelling of the Human Hippocampus for MEG

Magnetoencephalography (MEG) is a neuroimaging technique which gives direct non-invasive measurements of neuronal activity with high temporal resolution. Given its increasing use in cognitive and clinical research, it is important to characterize, and ideally improve upon, its advantages and limitations. For example, it is conventionally assumed to be insensitive to deep structures because of their distance from the sensors. Consequently, knowledge about their signal contribution is limited. One deep structure of particular interest is the hippocampus which plays a key role in memory and learning, and in organising temporal flow of information across regions. A large body of rodent studies have demonstrated quantifiable oscillatory underpinnings of these functions, now waiting to be addressed in humans. Due to its high temporal resolution, MEG is ideally suited for doing so but faces technical challenges. Firstly, the source-to-sensor distance is large, making it difficult to obtain sufficiently high signal-to-noise ratio (SNR) data. Secondly, most generative models (which describe the relationship between sensors and signal) include only the cortical surface. Thirdly, errors in co-registering data to an anatomical image easily obstruct or blur hippocampal sources. This thesis tested the hypotheses that a) identification and optimisation of acquisition parameters which improve the SNR, b) inclusion of the hippocampus in the generative model, and c) minimisation of co-registration error, together enable reliable inferences about hippocampal activity from MEG data. We found the most important empirical factor in detecting hippocampal activity using the extended generative model to be co-registration error; that this can be minimised using flexible head-casts; and that combining anatomical modelling, head-casts, and a spatial memory task, allows hippocampal activity to be reliably observed. Hence the work confirmed the overall hypothesis to be valid. Additionally, simulation results revealed that for a new generation of MEG sensors, ~5-fold sensitivity improvements can be obtained but critically depend on low sensor location errors. These findings set down a new basis for time-resolved examination of hippocampal function

Bulk spectral function sum rule in QCD-like theories with a holographic dual

We derive the sum rule for the spectral function of the stress-energy tensor in the bulk (uniform dilatation) channel in a general class of strongly coupled field theories. This class includes theories holographically dual to a theory of gravity coupled to a single scalar field, representing the operator of the scale anomaly. In the limit when the operator becomes marginal, the sum rule coincides with that in QCD. Using the holographic model, we verify explicitly the cancellation between large and small frequency contributions to the spectral integral required to satisfy the sum rule in such QCD-like theories.Comment: 16 pages, 2 figure

The Bulk Channel in Thermal Gauge Theories

We investigate the thermal correlator of the trace of the energy-momentum tensor in the SU(3) Yang-Mills theory. Our goal is to constrain the spectral function in that channel, whose low-frequency part determines the bulk viscosity. We focus on the thermal modification of the spectral function, $\rho(\omega,T)-\rho(\omega,0)$. Using the operator-product expansion we give the high-frequency behavior of this difference in terms of thermodynamic potentials. We take into account the presence of an exact delta function located at the origin, which had been missed in previous analyses. We then combine the bulk sum rule and a Monte-Carlo evaluation of the Euclidean correlator to determine the intervals of frequency where the spectral density is enhanced or depleted by thermal effects. We find evidence that the thermal spectral density is non-zero for frequencies below the scalar glueball mass $m$ and is significantly depleted for $m\lesssim\omega\lesssim 3m$.Comment: (1+25) pages, 6 figure

Optic Nerve Head Change in Non-Arteritic Anterior Ischemic Optic Neuropathy and Its Influence on Visual Outcome

To evaluate changes in cup/disc (C/D) diameter ratios and parapapillary atrophy in patients with non-arteritic anterior ischemic optic neuropathy (NA-AION), using morphometric methods.The clinical non-interventional study included 157 patients with unilateral or bilateral NA-AION. Optic disc photographs taken from both eyes at the end of follow-up were morphometrically examined.Follow-up was 86.3±70.3 months. Horizontal and vertical disc diameters (P = 0.30;P = 0.61, respectively), horizontal and vertical C/D ratios (P = 0.47;P = 0.19,resp.), and size of alpha zone and beta zone of parapapillary atrophy (P = 0.27;P = 0.32,resp.) did not differ significantly between affected eyes and contralateral normal eyes in patients with unilateral NA-AION. Similarly, horizontal and vertical disc diameters, horizontal and vertical C/D ratios, and size of alpha zone and beta zone did not vary significantly (all P>0.05) between the unaffected eyes of patients with unilateral NA-AION and the eyes of patients with bilateral NA-AION. Optic disc diameters, C/D ratios, size of alpha zone or beta zone of parapapillary atrophy were not significantly associated with final visual outcome in the eyes affected with NA-AION (all P>0.20) nor with the difference in final visual acuity between affected eyes and unaffected eyes in patients with unilateral NA-AION (all P>0.25).NA-AION did not affect C/D ratios nor alpha zone and beta zone of parapapillary atrophy. Optic disc size was not related to the final visual acuity outcome in NA-AION

An elementary stringy estimate of transport coefficients of large temperature QCD

Modeling QCD at large temperature with a simple holographic five dimensional theory encoding minimal breaking of conformality, allows for the calculation of all the transport coefficients, up to second order, in terms of a single parameter. In particular, the shear and bulk relaxation times are provided. The result follows by deforming the AdS background with a scalar dual to a marginally relevant operator, at leading order in the deformation parameter.Comment: 11 pages; v2: comments and references adde

Approximation of corner polyhedra with families of intersection cuts

We study the problem of approximating the corner polyhedron using intersection cuts derived from families of lattice-free sets in $\mathbb{R}^n$. In particular, we look at the problem of characterizing families that approximate the corner polyhedron up to a constant factor, which depends only on $n$ and not the data or dimension of the corner polyhedron. The literature already contains several results in this direction. In this paper, we use the maximum number of facets of lattice-free sets in a family as a measure of its complexity and precisely characterize the level of complexity of a family required for constant factor approximations. As one of the main results, we show that, for each natural number $n$, a corner polyhedron with $n$ basic integer variables and an arbitrary number of continuous non-basic variables is approximated up to a constant factor by intersection cuts from lattice-free sets with at most $i$ facets if $i> 2^{n-1}$ and that no such approximation is possible if $i \leq 2^{n-1}$. When the approximation factor is allowed to depend on the denominator of the fractional vertex of the linear relaxation of the corner polyhedron, we show that the threshold is $i > n$ versus $i \leq n$. The tools introduced for proving such results are of independent interest for studying intersection cuts

Neural Competitive Queuing of Ordinal Structure Underlies Skilled Sequential Action.

Fluent retrieval and execution of movement sequences is essential for daily activities, but the neural mechanisms underlying sequence planning remain elusive. Here participants learned finger press sequences with different orders and timings and reproduced them in a magneto-encephalography (MEG) scanner. We classified the MEG patterns for each press in the sequence and examined pattern dynamics during preparation and production. Our results demonstrate the "competitive queuing" (CQ) of upcoming action representations, extending previous computational and non-human primate recording studies to non-invasive measures in humans. In addition, we show that CQ reflects an ordinal template that generalizes across specific motor actions at each position. Finally, we demonstrate that CQ predicts participants' production accuracy and originates from parahippocampal and cerebellar sources. These results suggest that the brain learns and controls multiple sequences by flexibly combining representations of specific actions and interval timing with high-level, parallel representations of sequence position

Using generative models to make probabilistic statements about hippocampal engagement in MEG

Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports it on both the cortical and hippocampal surfaces. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is -20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts

Sum Rules from an Extra Dimension

Using the gravity side of the AdS/CFT correspondence, we investigate the analytic properties of thermal retarded Green's functions for scalars, conserved currents, the stress tensor, and massless fermions. We provide some results concerning their large and small frequency behavior and their pole structure. From these results, it is straightforward to prove the validity of various sum rules on the field theory side of the duality. We introduce a novel contraction mapping we use to study the large frequency behavior of the Green's functions.Comment: v2: 23 pages (plus appendix), revised presentation, discussion of branch cuts moved to appendix, and some minor changes; v1: 24 pages (plus appendix

Holographic non-relativistic fermionic fixed point by the charged dilatonic black hole

Driven by the landscape of garden-variety condensed matter systems, we have investigated how the dual spectral function behaves at the non-relativistic as well as relativistic fermionic fixed point by considering the probe Dirac fermion in an extremal charged dilatonic black hole with zero entropy. Although the pattern for both of the appearance of flat band and emergence of Fermi surface is qualitatively similar to that given by the probe fermion in the extremal Reissner-Nordstrom AdS black hole, we find a distinctly different low energy behavior around the Fermi surface, which can be traced back to the different near horizon geometry. In particular, with the peculiar near horizon geometry of our extremal charged dilatonic black hole, the low energy behavior exhibits the universal linear dispersion relation and scaling property, where the former indicates that the dual liquid is a Fermi one while the latter implies that the dual liquid is not exactly of Landau Fermi type