Stimulus statistics shape oscillations in nonlinear recurrent neural networks.

Rhythmic activity plays a central role in neural computations and brain functions ranging from homeostasis to attention, as well as in neurological and neuropsychiatric disorders. Despite this pervasiveness, little is known about the mechanisms whereby the frequency and power of oscillatory activity are modulated, and how they reflect the inputs received by neurons. Numerous studies have reported input-dependent fluctuations in peak frequency and power (as well as couplings across these features). However, it remains unresolved what mediates these spectral shifts among neural populations. Extending previous findings regarding stochastic nonlinear systems and experimental observations, we provide analytical insights regarding oscillatory responses of neural populations to stimulation from either endogenous or exogenous origins. Using a deceptively simple yet sparse and randomly connected network of neurons, we show how spiking inputs can reliably modulate the peak frequency and power expressed by synchronous neural populations without any changes in circuitry. Our results reveal that a generic, non-nonlinear and input-induced mechanism can robustly mediate these spectral fluctuations, and thus provide a framework in which inputs to the neurons bidirectionally regulate both the frequency and power expressed by synchronous populations. Theoretical and computational analysis of the ensuing spectral fluctuations was found to reflect the underlying dynamics of the input stimuli driving the neurons. Our results provide insights regarding a generic mechanism supporting spectral transitions observed across cortical networks and spanning multiple frequency bands

Diabetes: Part 2. Management of diabetes including hypoglycaemia and complications

No Abstrac

Sustainable diet policy development: implications of multi-criteria and other approaches, 2008-2017

The objective of the present paper is to draw lessons from policy development on sustainable diets. It considers the emergence of sustainable diets as a policy issue and reviews the environmental challenge to nutrition science as to what a 'good' diet is for contemporary policy. It explores the variations in how sustainable diets have been approached by policy-makers. The paper considers how international United Nations and European Union (EU) policy engagement now centres on the 2015 Sustainable Development Goals and Paris Climate Change Accord, which require changes across food systems. The paper outlines national sustainable diet policy in various countries: Australia, Brazil, France, the Netherlands, Qatar, Sweden, UK and USA. While no overarching common framework for sustainable diets has appeared, a policy typology of lessons for sustainable diets is proposed, differentiating (a) orientation and focus, (b) engagement styles and (c) modes of leadership. The paper considers the particularly tortuous rise and fall of UK governmental interest in sustainable diet advice. Initial engagement in the 2000s turned to disengagement in the 2010s, yet some advice has emerged. The 2016 referendum to leave the EU has created a new period of policy uncertainty for the UK food system. This might marginalise attempts to generate sustainable diet advice, but could also be an opportunity for sustainable diets to be a goal for a sustainable UK food system. The role of nutritionists and other food science professions will be significant in this period of policy flux

The Dynamics of ERP and Hemodynamic Responses at Very Short Stimulus Durations

Complementary non-invasive imaging methods on human subjects such as EEG and fMRI can provide new insights into the functioning of the brain and into neurovascular coupling. Particularly, short stimulus durations rather than commonly used standard durations in fMRI experiments are suitable to study the relationship between electrophysiological and vascular measures because of reduction of non-linearities of the hemodynamic response [1]. In this study, using very short stimulus durations (0.1 ms to 5 ms) and measurements with fMRI and EEG we have found that both N75 of the visual evoked potentials and BOLD signal increase and P100 decrease with stimulus duration. In addition, the BOLD signal poststimulus undershoot also tends to deviate more with stimulus duration. These results allow to shed light on whether and which ERP components correlate well with the BOLD signal

Stochastic modulation of oscillatory neural activity.

Rhythmic neural activity plays a central role in neural computation. Oscillatory activity has been associated with myriad functions such as homeostasis, attention, and cognition [1] as well as neurological and psychiatric disorders, including Parkinson’s disease, schizophrenia, and depression [2]. Despite this pervasiveness, little is known about the dynamic mechanisms by which the frequency and power of ongoing cyclical neural activity can be modulated either externally (e.g. external stimulation) or via internally-driven modulatory drive of nearby neurons. While numerous studies have focused on neural rhythms and synchrony, it remains unresolved what mediates frequency transitions whereby the predominant power spectrum shifts from one frequency to another. Here, we provide computational perspectives regarding responses of cortical networks to fast stochastic fluctuations (hereafter “noise”) at frequencies in the range of 10-500 Hz that are mimicked using Poisson shot-noise. Using a sparse and randomly connected network of neurons with time delay, we determine the functional impact of these fluctuations on network topology using mean-field approximations. We show how noise can be used to displace the equilibrium activity state of the population: the noise smoothly shifts the mean activity of the modeled neurons from a regime dominated by inhibition to a regime dominated by excitation. Moreover, we show that noise alone may support frequency transition via a non-nonlinear mechanism that operates in addition to resonance. Surprisingly, stochastic fluctuations non-monotonically modulate network’s oscillations, which are in the beta band. The system’s frequency is first slowed down and then accelerated as the stimulus intensity and/or rate increases. This non-linear effect is caused by combined input-induced linearization of the dynamics and enhanced network susceptibility. Our results provide insights regarding a potentially significant mechanism at play in synchronous neural systems; ongoing activity rhythms can be externally and dynamically modulated, and moreover indicate a candidate mechanism supporting frequency transitions. By altering the oscillation frequency of the network, power can be displaced from one frequency band to another. As such, the action of noise on oscillating neural systems must be regarded as strongly non-linear; its action recruiting more than resonance alone to operate on ongoing dynamics

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

<p>Abstract</p> <p>Background</p> <p>Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.</p> <p>Results</p> <p>Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.</p> <p>Conclusion</p> <p>The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.</p

Gamma and Beta Oscillations in Human MEG Encode the Contents of Vibrotactile Working Memory

Ample evidence suggests that oscillations in the beta band represent quantitative information about somatosensory features during stimulus retention. Visual and auditory working memory (WM) research, on the other hand, has indicated a predominant role of gamma oscillations for active WM processing. Here we reconciled these findings by recording whole-head magnetoencephalography during a vibrotactile frequency comparison task. A Braille stimulator presented healthy subjects with a vibration to the left fingertip that was retained in WM for comparison with a second stimulus presented after a short delay. During this retention interval spectral power in the beta band from the right intraparietal sulcus and inferior frontal gyrus (IFG) monotonically increased with the to-be-remembered vibrotactile frequency. In contrast, induced gamma power showed the inverse of this pattern and decreased with higher stimulus frequency in the right IFG. Together, these results expand the previously established role of beta oscillations for somatosensory WM to the gamma band and give further evidence that quantitative information may be processed in a fronto-parietal network

Fatty acid profile in cord blood of neonates born to optimally controlled gestational diabetes mellitus

OBJECTIVE: To evaluate the fatty acid profile of cord blood phospholipids (PL), cholesteryl esters (CE), triglycerides (TG) and non-esterified fatty acids (NEFA) in neonates born to mothers with gestational diabetes mellitus (GDM) compared to non-diabetic mothers. METHODS: The offspring of 30 pregnant women (15 non-diabetic controls, 15 with diet- or insulin-controlled GDM) were recruited before delivery. Cord blood was collected. After lipid extraction, PL, CE, TG and NEFA were separated by thin layer chromatography and analysed by gas chromatography. RESULTS: In GDM vs. control mothers, maternal glycated haemoglobin (A1C, mean±SD) was not different between groups: 5.3±0.5% vs. 5.3±0.3% (p=0.757), respectively. Cord plasma fatty acids were not different in TG, CE and NEFA between GDM and non-diabetic mothers. However, in PL, levels of palmitate, palmitoleate, oleate, vaccinate and di-homo-gamma-linolenate were significantly lower, with a trend for lower arachidonate (p=0.078), in neonates born to GDM mothers compared to controls. CONCLUSION: In contrast to other studies on cord blood docosahexaenoic acid (DHA) levels in GDM mothers, we did not found lower levels of DHA in cord PL, CE, TG or NEFA in neonates born to GDM compared to non-diabetic mothers

Structural connectivity reproducibility through multiple acquisitions

International audienceIn this study, we investigate the reproducibility of connectivity matrices in cortico-cortical connectivity using probabilistic and deterministic streamline tractography. We show that connectivity matrices computed from probabilistic tractography have higher ratio of inter to intra-subject distances than those computed from deterministic tractography. Moreover, it suggests that the connectivity matrices can be used as a tool to compare tractography algorithms in terms reproducibility and subject specificity