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Transient recurring phenomena are ubiquitous in many scientific fields like neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models (VAR) may be used to characterize peri-event system dynamics associated with such phenomena, and can be learned by exploiting multi-dimensional data gathering samples of the evolution of the system in multiple time windows comprising, each associated with one occurrence of the transient phenomenon, that we will call "trial". However, optimal VAR model order selection methods, commonly relying on the Akaike or Bayesian Information Criteria (AIC/BIC), are typically not designed for multi-trial data. Here we derive the BIC methods for multi-trial ensemble data which are gathered after the detection of the events. We show using simulated bivariate AR models that the multi-trial BIC is able to recover the real model order. We also demonstrate with simulated transient events and real data that the multi-trial BIC is able to estimate a sufficiently small model order for dynamic system modeling., Comment: 12 pages, 4 figures

Water restriction is commonly used to motivate rodents to perform behavioral tasks; however, its on hydration and stress hormone levels are unknown. Here, we report daily body weight and bi-weekly packed red blood cell volume and corticosterone in adult male rats across 80 days for three commonly used water restriction schedules. We also assessed renal adaptation to water restriction using post-mortem histological evaluation of renal medulla. A control group received ad libitum water. After one week of water restriction, rats on all restriction schedules resumed similar levels of growth relative to the control group. Nominal hydration was observed, and water restriction did not drive renal adaptation. An intermittent restriction schedule was associated with an increase in corticosterone relative to the control group. Our results suggest that the water restriction schedules used here will maintain welfare in rats. However, intermittent restriction evokes a stress response which could affect behavioral and neurobiological results. Our results also suggest that stable motivation in behavioral tasks may only be achieved after one week of restriction.

The brainstem noradrenergic locus coeruleus (LC) is reciprocally connected with the prefrontal cortex (PFC). Strong coupling between LC spiking and depolarizing phase of slow (1 – 2 Hz) waves in the PFC field potentials during sleep and anesthesia suggests that the LC drives cortical state transition. Reciprocal LC-PFC connectivity should also allow interactions in the opposing (top-down) direction, but prior work has only studied prefrontal control over LC activity using direct electrical (or optogenetic) stimulation paradigms. Here, we describe the physiological characteristics of naturally occurring top-down prefrontal-coerulear interactions. Specifically, we recorded LC multi-unit activity (MUA) simultaneously with PFC single unit and local field potential (LFP) activity in urethane-anesthetized rats. We observed cross-regional coupling between the phase of ~5 Hz oscillations in LC population spike rate and the power of PFC LFP oscillations within the high Gamma (hGamma) range (60 – 200 Hz). Specifically, transient increases in PFC hGamma power preceded peaks in the ~5 Hz LC-MUA oscillation. Analysis of cross-regional transfer entropy demonstrated that the PFC hGamma transients were predictive of a transient increase in LC-MUA. A ~29 msec delay between these signals was consistent with the conduction velocity from the PFC to the LC. Finally, we showed that PFC hGamma transients are associated with synchronized spiking of a subset (27%) of PFC single units. Our data suggest that, PFC hGamma transients may indicate the timing of the top-down excitatory input to LC, at least under conditions when LC neuronal population activity fluctuates rhythmically at ~5 Hz. Synchronized PFC neuronal spiking that occurs during hGamma transients may provide a previously unknown mode of top-down control over the LC.

Calcium-sensitive MRI contrast agents can only yield quantitative results if the agent concentration in the tissue is known. The agent concentration could be determined by diffusion modeling, if relevant parameters were available. We have established an MRI-based method capable of determining diffusion properties of conventional and calcium-sensitive agents. Simulations and experiments demonstrate that the method is applicable both for conventional contrast agents with a fixed relaxivity value and for calcium-sensitive contrast agents. The full pharmacokinetic time-course of gadolinium concentration estimates was observed by MRI before, during and after intracerebral administration of the agent, and the effective diffusion coefficient D* was determined by voxel-wise fitting of the solution to the diffusion equation. The method yielded whole brain coverage with a high spatial and temporal sampling. The use of two types of MRI sequences for sampling of the diffusion time courses was investigated: Lookndash;Locker-based quantitative T1 mapping, and T1-weighted MRI. The observation times of the proposed MRI method is long (up to 20thinsp;h) and consequently the diffusion distances covered are also long (2ndash;4thinsp;mm). Despite this difference, the D* values in vivo were in agreement with previous findings using optical measurement techniques, based on observation times of a few minutes. The effective diffusion coefficient determined for the calcium-sensitive contrast agents may be used to determine local tissue concentrations and to design infusion protocols that maintain the agent concentration at a steady state, thereby enabling quantitative sensing of the local calcium concentration.

The LFPs is a broadband signal that captures variations of neural population activity over a wide range of time scales. The range of time scales available in LFPs is particularly interesting from the neural coding point of view because it opens up the possibility to investigate whether there are privileged time scales for information processing, a question that has been hotly debated over the last one or two decades.It is possible that information is represented by only a small number of specific frequency ranges, each carrying a separate contribution to the information representation. To shed light on this issue, it is important to quantify the information content of each frequency range of neural activity, and understand which ranges carry complementary or similar information., Comment: To appear in Quian Quiroga and Panzeri (Eds) Principles of Neural Coding, CRC Press, 2012

Supplemental material, JCB916003 Supplementary Information for Neurochemical underpinning of hemodynamic response to neuropsychiatric drugs: A meta- and cluster analysis of preclinical studies by Lewis H Mervin, Ekaterina Mitricheva, Nikos K Logothetis, Angelo Bifone, Andreas Bender and Hamid R Noori in Journal of Cerebral Blood Flow & Metabolism

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Neuropsychiatric disorders are the third leading cause of global disease burden. Current pharmacological treatment for these disorders is inadequate, with often insufficient efficacy and undesirable side effects. One reason for this is that the links between molecular drug action and neurobehavioral drug effects are elusive. We use a big data approach from the neurotransmitter response patterns of 258 different neuropsychiatric drugs in rats to address this question. Data from experiments comprising 110,674 rats are presented in the Syphad database [ www.syphad.org ]. Chemoinformatics analyses of the neurotransmitter responses suggest a mismatch between the current classification of neuropsychiatric drugs and spatiotemporal neurostransmitter response patterns at the systems level. In contrast, predicted drug-target interactions reflect more appropriately brain region related neurotransmitter response. In conclusion the neurobiological mechanism of neuropsychiatric drugs are not well reflected by their current classification or their chemical similarity, but can be better captured by molecular drug-target interactions.