Stability, affinity and chromatic variants of the glutamate sensor iGluSnFR, bioRxiv, 2017-12-16
AbstractSingle-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of the glutamate sensor iGluSnFR that are functionally brighter; can detect sub-micromolar to millimolar concentrations of glutamate; and have blue, green or yellow emission profiles. These variants allow in vivo imaging where original-iGluSnFR was too dim, reveal glutamate transients at individual spine heads, and permit kilohertz imaging with inexpensive, powerful fiber lasers.
biorxiv neuroscience 0-100-users 2017Intrinsic neuronal dynamics predict distinct functional roles during working memory, bioRxiv, 2017-12-15
AbstractWorking memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF) and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We found that cells with short timescales carried memory information relatively early during memory encoding in lPFC; whereas long timescale cells played a greater role later during processing, dominating coding in the delay period. We also observed a link between functional connectivity at rest and intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predicts complex neuronal dynamics during WM; ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.
biorxiv neuroscience 0-100-users 2017Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex, bioRxiv, 2017-12-15
AbstractCompeting accounts propose that working memory (WM) is subserved either by persistent activity in single neurons or by dynamic (time-varying) activity across a neural population. Here we compare these hypotheses across four regions of prefrontal cortex (PFC) in a spatial WM task, where an intervening distractor indicated the reward available for a correct saccade. WM representations were strongest in ventrolateral PFC (VLPFC) neurons with higher intrinsic temporal stability (time-constant). At the population-level, although a stable mnemonic state was reached during the delay, this tuning geometry was reversed relative to cue-period selectivity, and was disrupted by the distractor. Single-neuron analysis revealed many neurons switched to coding reward, rather than maintaining task-relevant spatial selectivity until saccade. These results imply WM is fulfilled by dynamic, population-level activity within high time-constant neurons. Rather than persistent activity supporting stable mnemonic representations that bridge distraction, PFC neurons may stabilise a dynamic population-level process that supports WM.
biorxiv neuroscience 0-100-users 2017A Quantitative Assessment of Prefrontal Cortex in Humans Relative to Nonhuman Primates, bioRxiv, 2017-12-14
AbstractHumans have the largest cerebral cortex among primates. A long-standing controversy is whether association cortex, particularly prefrontal cortex (PFC), is disproportionately larger in humans compared to nonhuman primates, as some studies report that human PFC is relatively expanded whereas others report uniform PFC scaling. We address this controversy using MRI-derived cortical surfaces of many individual humans, chimpanzees, and macaques. We present two parcellation-based PFC delineations based on cytoarchitecture and function and show that a previously used morphological surrogate (cortex anterior to the genu of the corpus callosum) substantially underestimates PFC extent, especially in humans. We find that the proportion of cortical gray matter occupied by PFC in humans is up to 86% larger than in macaques and 24% larger than in chimpanzees. The disparity is even greater for PFC white matter volume, which is 140% larger in humans compared to macaques and 71% larger than in chimpanzees.
biorxiv neuroscience 0-100-users 2017Estimating the functional dimensionality of neural representations, bioRxiv, 2017-12-14
AbstractRecent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal’s functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns.
biorxiv neuroscience 100-200-users 2017Phasic Activation of Ventral Tegmental, but not Substantia Nigra, Dopamine Neurons Promotes Model-Based Pavlovian Reward Learning, bioRxiv, 2017-12-14
ABSTRACTDopamine (DA) neurons in the ventral tegmental area (VTA) and substantia nigra (SNc) encode reward prediction errors (RPEs) and are proposed to mediate error-driven learning. However the learning strategy engaged by DA-RPEs remains controversial. Model-free associations imbue cueactions with pure value, independently of representations of their associated outcome. In contrast, model-based associations support detailed representation of anticipated outcomes. Here we show that although both VTA and SNc DA neuron activation reinforces instrumental responding, only VTA DA neuron activation during consumption of expected sucrose reward restores error-driven learning and promotes formation of a new cue→sucrose association. Critically, expression of VTA DA-dependent Pavlovian associations is abolished following sucrose devaluation, a signature of model-based learning. These findings reveal that activation of VTA-or SNc-DA neurons engages largely dissociable learning processes with VTA-DA neurons capable of participating in model-based predictive learning, while the role of SNc-DA neurons appears limited to reinforcement of instrumental responses.
biorxiv neuroscience 100-200-users 2017