Dopamine-induced dissociation of BOLD and neural activity in macaque visual cortex

Neuromodulators determine how neural circuits process information during cognitive states such as wakefulness, attention, learning, and memory [1]. fMRI can provide insight into their function and dynamics, but their exact effect on BOLD responses remains unclear [2, 3 and 4], limiting our ability to interpret the effects of changes in behavioral state using fMRI. Here, we investigated the effects of dopamine (DA) injections on neural responses and haemodynamic signals in macaque primary visual cortex (V1) using fMRI (7T) and intracortical electrophysiology. Aside from DA’s involvement in diseases such as Parkinson’s and schizophrenia, it also plays a role in visual perception [5, 6, 7 and 8]. We mimicked DAergic neuromodulation by systemic injection of L-DOPA and Carbidopa (LDC) or by local application of DA in V1 and found that systemic application of LDC increased the signal-to-noise ratio (SNR) and amplitude of the visually evoked neural responses in V1. However, visually induced BOLD responses decreased, whereas cerebral blood flow (CBF) responses increased. This dissociation of BOLD and CBF suggests that dopamine increases energy metabolism by a disproportionate amount relative to the CBF response, causing the reduced BOLD response. Local application of DA in V1 had no effect on neural activity, suggesting that the dopaminergic effects are mediated by long-range interactions. The combination of BOLD-based and CBF-based fMRI can provide a signature of dopaminergic neuromodulation, indicating that the application of multimodal methods can improve our ability to distinguish sensory processing from neuromodulatory effects

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

Diffusion and multiple orientations from 1.5 MR systems with limited gradient tables

International audienceDiffusion MRI (dMRI) enables the quantification of water diffusion, influenced by the structure of biological tissues, from the acquisition of diffusion weighted magnetic resonance images (DW-MRI). While recent advances enable to recover complex fiber geometries using diffusion measurements along various sampling schemes of high order [4], some older MR systems work with limited gradient tables (ex: maximum of 6 or 12 directions). These systems are designed for Diffusion Tensor Imaging (DTI). Several hospitals and research institutes in the world are limited by these fixed DTI gradient sets. Therefore, groups that want to perform state-of-the-art tractography using high angular resolution diffusion imaging (HARDI) data are pernalized and can only perform DTI tractography on their old system. The Gaussian assumption of the tensor model, in DTI, is an over simplification of the diffusion phenomenon of water molecules in the brain and thus cannot resolve crossing fibers. In this work, we show that new diffusion signal modeling and processing techniques enable to capture complex angular structure of the diffusion process even from a reduced gradient direction set arising from an older MR system

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

Choosing Tractography Parameters to Improve Connectivity Mapping

International audienceDiffusion-weighted imaging (DWI) is often used as a starting point for in vivo white matter (WM) connectivity to reconstruct potential WM pathways between brain areas. Tractography algorithms have many parameters which can influence reconstruction and connectivity. Various choices of parameters have been proposed. But how does one choose the best set of parameters? In this study, we varied three critical parameters while monitoring connectivity score using the Tractometer evaluation system on the International Symposium on Biomedical Imaging (ISBI) Challenge synthetic dataset. The three parameters were: * θ: The maximum deviation angle between two consecutive tractography steps. This addresses the hypothesis of smoothness of the WM pathways. * τ: The spherical function (SF) threshold. This aims at removing noisy propagation directions during the tractography process. * τ init : The initial SF threshold. This aims at removing initial noise at the seeds and to start tractography in a good tangent direction to the WM bundle

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

Studying white matter tractography reproducibility through connectivity matrices

International audienceDiffusion-weighted imaging is often used as a starting point for in vivo white matter (wm) connectivity to reconstruct potential wm pathwaysbetween brain areas. In this study, we investigate the reproducibility of the connectivity matrix, resulting from different tractography parameters. We vary the number of streamlines used to construct the matrix in cortical to cortical connectivity and analyze its effects. We also compare the effect of probabilistic and deterministic local streamline tractography algorithms, seeding both from the wm and from wm-grey matter interface

Microstructure ­driven tractography in the human brain

International audienceIntroduction: Diffusion-weighted (DW) magnetic resonance imaging (MRI) tractography has become the tool of choice to probe the human brain'swhite matter (WM) in vivo. However, the relationship between the resulting streamlines and underlying WM microstructurecharacteristics, such as axon diameter, remains poorly understood. In this work, we reconstruct human brain fascicles using a newapproach to trace WM fascicles while simultaneously characterizing the apparent distribution of axon diameters within the fascicle. Thisprovides the mean to estimate the microstructure characteristics of fascicles while improving their reconstruction in complex tissueconfigurations