Stochastic attractor models of visual working memory.

This paper investigates models of working memory in which memory traces evolve according to stochastic attractor dynamics. These models have previously been shown to account for response-biases that are manifest across multiple trials of a visual working memory task. Here we adapt this approach by making the stable fixed points correspond to the multiple items to be remembered within a single-trial, in accordance with standard dynamical perspectives of memory, and find evidence that this multi-item model can provide a better account of behavioural data from continuous-report tasks. Additionally, the multi-item model proposes a simple mechanism by which swap-errors arise: memory traces diffuse away from their initial state and are captured by the attractors of other items. Swap-error curves reveal the evolution of this process as a continuous function of time throughout the maintenance interval and can be inferred from experimental data. Consistent with previous findings, we find that empirical memory performance is not well characterised by a purely-diffusive process but rather by a stochastic process that also embodies error-correcting dynamics.

The emergence of task-relevant representations in a nonlinear decision-making task.

This paper describes the relationship between performance in a decision-making task and the emergence of task-relevant representations. Participants learnt two tasks in which the appropriate response depended on multiple relevant stimuli and the underlying stimulus-outcome associations were governed by a latent feature that participants could discover. We divided participants into good and bad performers based on their overall classification rate and computed behavioural accuracy for each feature value. We found that participants with better performance had a better representation of the latent feature space. We then used representation similarity analysis on Electroencephalographic (EEG) data to identify when these representations emerge. We were able to decode task-relevant representations in a time window emerging 700 ms after stimulus presentation, but only for participants with good task performance. Our findings suggest that, in order to make good decisions, it is necessary to create and extract a low-dimensional representation of the task at hand.

DEIA is essential to advance the goals of translational science: Perspectives from NCATS.

The National Center for Advancing Translational Science (NCATS) seeks to improve upon the translational process to advance research and treatment across all diseases and conditions and bring these interventions to all who need them. Addressing the racial/ethnic health disparities and health inequities that persist in screening, diagnosis, treatment, and health outcomes (e.g., morbidity, mortality) is central to NCATS' mission to deliver more interventions to all people more quickly. Working toward this goal will require enhancing diversity, equity, inclusion, and accessibility (DEIA) in the translational workforce and in research conducted across the translational continuum, to support health equity. This paper discusses how aspects of DEIA are integral to the mission of translational science (TS). It describes recent NIH and NCATS efforts to advance DEIA in the TS workforce and in the research we support. Additionally, NCATS is developing approaches to apply a lens of DEIA in its activities and research - with relevance to the activities of the TS community - and will elucidate these approaches through related examples of NCATS-led, partnered, and supported activities, working toward the Center's goal of bringing more treatments to all people more quickly.

Data for semi-permanent cationic coating for protein separations.

Protein separations and analyses are fundamental to fields of study that include biochemistry, biology, physiology, drug discovery, pharmaceuticals, as well as agricultural and food based industries. Here, we provide the data from a novel phospholipid-cetyltrimethylammonium bromide coating capable of separating cationic and anionic proteins with high efficiency. Capillary electrophoresis separations of protein standards were utilized to characterize the performance of the novel coating. Using capillary electrophoresis with UV absorbance detection a working pH range of 4-9 was identified, with reproducibility in time ≤1% relative standard deviation, and plate counts for proteins as high as 480,000 plates (lysozyme, pH 7). Further details and results from these data are available in the work reported by Crihfield et al. and can be accessed at https://doi.org/10.1016/j.chroma.2019.460397 [1].

Pituitary Adenylate Cyclase-Activating Peptide (PACAP)-Glutamate Co-transmission Drives Circadian Phase-Advancing Responses to Intrinsically Photosensitive Retinal Ganglion Cell Projections by Suprachiasmatic Nucleus.

Results from a variety of sources indicate a role for pituitary adenylate cyclase-activating polypeptide (PACAP) in light/glutamate-induced phase resetting of the circadian clock mediated by the retinohypothalamic tract (RHT). Attempts to block or remove PACAP's contribution to clock-resetting have generated phenotypes that differ in their responses to light or glutamate. For example, previous studies of circadian behaviors found that period-maintenance and early-night phase delays are intact in PACAP-null mice, yet there is a consistent deficit in behavioral phase-resetting to light stimulation in the late night. Here we report rodent stimulus-response characteristics of PACAP release from the RHT, and map these to responses of the suprachiasmatic nucleus (SCN) in intact and PACAP-deficient mouse hypothalamus with regard to phase-resetting. SCN of PACAP-null mice exhibit normal circadian rhythms in neuronal activity, but are "blind" to glutamate stimulating phase-advance responses in late night, although not in early night, consistent with previously reported selective lack of late-night light behavioral responsiveness of these mice. Induction of CREB phosphorylation, a hallmark of the light/glutamate response of the SCN, also is absent in SCN-containing ex vivo slices from PACAP-deficient mouse hypothalamus. PACAP replacement to the SCN of PACAP-null mice restored wild-type phase-shifting of firing-rate patterns in response to glutamate applied to the SCN in late night. Likewise, ex vivo SCN of wild-type mice post-orbital enucleation are unresponsive to glutamate unless PACAP also is restored. Furthermore, we demonstrate that the period of efficacy of PACAP at SCN nerve terminals corresponds to waxing of PACAP mRNA expression in ipRGCs during the night, and waning during the day. These results validate the use of PACAP-deficient mice in defining the role and specificity of PACAP as a co-transmitter with glutamate in ipRGC-RHT projections to SCN in phase advancing the SCN circadian rhythm in late night.

Semi-permanent cationic coating for protein separations.

Capillary electrophoresis has been used extensively for protein separations, but interactions of proteins with the negative charge on the surface of fused silica capillary create band broadening and diminish the separation efficiency. Coatings developed to mask the negative charge of the capillary affect the electroosmotic flow. The method presented in this work addresses these concerns through the use of a two-layer coating of a semi-permanent phospholipid substrate and cetyltrimethylammonium bromide (CTAB). When used alone, phospholipid coating suppresses the electroosmotic flow but cannot be used to simultaneously separate anionic and cationic proteins. When used alone, CTAB creates a dynamic coating that facilitates the separation of cationic proteins with good efficiency, but reduces the separation efficiency of anionic proteins. The use of a hybrid phospholipid-CTAB surface coating alleviates protein adsorption, as demonstrated through a comparison of protein separations obtained with a bare fused silica capillary. The hybrid phospholipid-CTAB surface enables high efficiency separations of cationic and anionic proteins simultaneously. This work verifies the role of the hydrophobic tail of CTAB in developing a stable coating with an electroosmotic flow of 3.14 × 10-4 cm2V-1s-1 (n = 10) from the cathode to the anode at a pH of 7. The coating yields a stable electroosmotic flow even after 2 h of flushing with background electrolyte devoid of CTAB (n = 3) and six consecutive protein injections with no flush sequence between runs. The coating can be used with background electrolytes with pH values ranging from 4 to 8 while maintaining 1% RSD (n = 10) in the electroosmotic flow for each background electrolyte. Six model proteins, lysozyme, ribonuclease A, α-chymotrypsinogen A, enolase, transferrin, and α-1-antitrypsin, with pI values ranging from 4.4 to 11 were used to demonstrate the stability of the phospholipid-CTAB coating, the lack of protein interaction with the wall, and the utility of the coating for the separation of proteins of similar isoelectric points and of protein isoforms.

Functional Peptidomics: Stimulus- and Time-of-Day-Specific Peptide Release in the Mammalian Circadian Clock.

Daily oscillations of brain and body states are under complex temporal modulation by environmental light and the hypothalamic suprachiasmatic nucleus (SCN), the master circadian clock. To better understand mediators of differential temporal modulation, we characterize neuropeptide releasate profiles by nonselective capture of secreted neuropeptides in an optic nerve horizontal SCN brain slice model. Releasates are collected following electrophysiological stimulation of the optic nerve/retinohypothalamic tract under conditions that alter the phase of the SCN activity state. Secreted neuropeptides are identified by intact mass via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). We found time-of-day-specific suites of peptides released downstream of optic nerve stimulation. Peptide release was modified differentially with respect to time-of-day by stimulus parameters and by inhibitors of glutamatergic or PACAPergic neurotransmission. The results suggest that SCN physiology is modulated by differential peptide release of both known and unexpected peptides that communicate time-of-day-specific photic signals via previously unreported neuropeptide signatures.

A validation of dynamic causal modelling for 7T fMRI.

BACKGROUND: There is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM). NEW METHOD: In this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain). RESULTS: Using empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity - as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates; suggesting the 7T data are more informative - and furnish more efficient estimates. COMPARED WITH EXISTING METHODS: In the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization. CONCLUSIONS: In this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently.

Reconstructing anatomy from electro-physiological data.

Here we show how it is possible to make estimates of brain structure based on MEG data. We do this by reconstructing functional estimates onto distorted cortical manifolds parameterised in terms of their spherical harmonics. We demonstrate that both empirical and simulated MEG data give rise to consistent and plausible anatomical estimates. Importantly, the estimation of structure from MEG data can be quantified in terms of millimetres from the true brain structure. We show, for simulated data, that the functional assumptions which are closer to the functional ground-truth give rise to anatomical estimates that are closer to the true anatomy.

Sensor space group analysis for fNIRS data.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is a method for monitoring hemoglobin responses using optical probes placed on the scalp. fNIRS spatial resolution is limited by the distance between channels defined as a pair of source and detector, and channel positions are often inconsistent across subjects. These challenges can lead to less accurate estimate of group level effects from channel-specific measurements. NEW METHOD: This paper addresses this shortcoming by applying random-effects analysis using summary statistics to interpolated fNIRS topographic images. Specifically, we generate individual contrast images containing the experimental effects of interest in a canonical scalp surface. Random-effects analysis then allows for making inference about the regionally specific effects induced by (potentially) multiple experimental factors in a population. RESULTS: We illustrate the approach using experimental data acquired during a colour-word matching Stroop task, and show that left frontopolar regions are significantly activated in a population during Stroop effects. This result agrees with previous neuroimaging findings. COMPARED WITH EXISTING METHODS: The proposed methods (i) address potential misalignment of sensor locations between subjects using spatial interpolation; (ii) produce experimental effects of interest either on a 2D regular grid or on a 3D triangular mesh, both representations of a canonical scalp surface; and (iii) enables one to infer population effects from fNIRS data using a computationally efficient summary statistic approach (random-effects analysis). Significance of regional effects is assessed using random field theory. CONCLUSIONS: In this paper, we have shown how fNIRS data from multiple subjects can be analysed in sensor space using random-effects analysis.

Estimating anatomical trajectories with Bayesian mixed-effects modeling.

We introduce a mass-univariate framework for the analysis of whole-brain structural trajectories using longitudinal Voxel-Based Morphometry data and Bayesian inference. Our approach to developmental and aging longitudinal studies characterizes heterogeneous structural growth/decline between and within groups. In particular, we propose a probabilistic generative model that parameterizes individual and ensemble average changes in brain structure using linear mixed-effects models of age and subject-specific covariates. Model inversion uses Expectation Maximization (EM), while voxelwise (empirical) priors on the size of individual differences are estimated from the data. Bayesian inference on individual and group trajectories is realized using Posterior Probability Maps (PPM). In addition to parameter inference, the framework affords comparisons of models with varying combinations of model order for fixed and random effects using model evidence. We validate the model in simulations and real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We further demonstrate how subject specific characteristics contribute to individual differences in longitudinal volume changes in healthy subjects, Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD).

Dynamic causal modelling for functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes.

Parametric estimation of cross-frequency coupling.

BACKGROUND: Growing experimental evidence suggests an important role for cross-frequency coupling in neural processing, in particular for phase-amplitude coupling (PAC). Although the details of methods to detect PAC may vary, a common procedure to estimate the significance level is the comparison of observed values to those of at least 100 surrogate time series. When scanning large parts of the frequency spectrum and multiple recording sites, this could amount to very large computation times. NEW METHOD: We demonstrate that the general linear model (GLM) allows for a parametric estimation of significant PAC. Continuous recordings are split into epochs, of a few seconds duration, on which an F-test can be performed. We compared its performance against traditional non-parametric permutation tests in both simulated and experimental data. RESULTS: Our method was able to reproduce findings of phase-amplitude coupling in local field potential recordings obtained from the subthalamic nucleus in patients with Parkinson's disease. We also show that PAC may be detected between the subthalamic nucleus and cortical motor areas. COMPARISON WITH EXISTING METHOD(S): Although the GLM slightly underestimated significance compared to permutation tests in the simulations, for experimental data the two methods produced highly similar results. Computation times were drastically lower for the GLM. Furthermore, we demonstrate that the GLM can be easily extended by including additional predictors such as low-frequency amplitude to test for amplitude-amplitude coupling. CONCLUSIONS: The GLM forms an adequate and computationally efficient approach for detecting cross-frequency coupling with the flexibility to add other explanatory variables of interest.

Efficient gradient computation for dynamical models.

Data assimilation is a fundamental issue that arises across many scales in neuroscience - ranging from the study of single neurons using single electrode recordings to the interaction of thousands of neurons using fMRI. Data assimilation involves inverting a generative model that can not only explain observed data but also generate predictions. Typically, the model is inverted or fitted using conventional tools of (convex) optimization that invariably extremise some functional - norms, minimum descriptive length, variational free energy, etc. Generally, optimisation rests on evaluating the local gradients of the functional to be optimized. In this paper, we compare three different gradient estimation techniques that could be used for extremising any functional in time - (i) finite differences, (ii) forward sensitivities and a method based on (iii) the adjoint of the dynamical system. We demonstrate that the first-order gradients of a dynamical system, linear or non-linear, can be computed most efficiently using the adjoint method. This is particularly true for systems where the number of parameters is greater than the number of states. For such systems, integrating several sensitivity equations - as required with forward sensitivities - proves to be most expensive, while finite-difference approximations have an intermediate efficiency. In the context of neuroimaging, adjoint based inversion of dynamical causal models (DCMs) can, in principle, enable the study of models with large numbers of nodes and parameters.

Reading front to back: MEG evidence for early feedback effects during word recognition.

Magnetoencephalography studies in humans have shown word-selective activity in the left inferior frontal gyrus (IFG) approximately 130 ms after word presentation ( Pammer et al. 2004; Cornelissen et al. 2009; Wheat et al. 2010). The role of this early frontal response is currently not known. We tested the hypothesis that the IFG provides top-down constraints on word recognition using dynamic causal modeling of magnetoencephalography data collected, while subjects viewed written words and false font stimuli. Subject-specific dipoles in left and right occipital, ventral occipitotemporal and frontal cortices were identified using Variational Bayesian Equivalent Current Dipole source reconstruction. A connectivity analysis tested how words and false font stimuli differentially modulated activity between these regions within the first 300 ms after stimulus presentation. We found that left inferior frontal activity showed stronger sensitivity to words than false font and a stronger feedback connection onto the left ventral occipitotemporal cortex (vOT) in the first 200 ms. Subsequently, the effect of words relative to false font was observed on feedforward connections from left occipital to ventral occipitotemporal and frontal regions. These findings demonstrate that left inferior frontal activity modulates vOT in the early stages of word processing and provides a mechanistic account of top-down effects during word recognition.

Decoding oscillatory representations and mechanisms in memory.

A fundamental goal in memory research is to understand how information is represented in distributed brain networks and what mechanisms enable its reactivation. It is evident that progress towards this goal will greatly benefit from multivariate pattern classification (MVPC) techniques that can decode representations in brain activity with high temporal resolution. Recently, progress along these lines has been achieved by applying MVPC to neural oscillations recorded with electroencephalography (EEG) and magnetoencephalography (MEG). We highlight two examples of methodological approaches for MVPC of EEG and MEG data that can be used to study memory function. The first example aims at understanding the dynamic neural mechanisms that enable reactivation of memory representations, i.e., memory replay; we discuss how MVPC can help uncover the physiological mechanisms underlying memory replay during working memory maintenance and episodic memory. The second example aims at understanding representational differences between various types of memory, such as perceptual priming and conscious recognition memory. We also highlight the conceptual and methodological differences between these two examples. Finally, we discuss potential future applications for MVPC of EEG/MEG data in studies of memory. We conclude that despite its infancy and existing methodological challenges, MVPC of EEG and MEG data is a powerful tool with which to assess mechanistic models of memory.

Post-hoc selection of dynamic causal models.

Dynamic causal modelling (DCM) was originally proposed as a hypothesis driven procedure in which a small number of neurobiologically motivated models are compared. Model comparison in this context usually proceeds by individually fitting each model to data and then approximating the corresponding model evidence with a free energy bound. However, a recent trend has emerged for comparing very large numbers of models in a more exploratory manner. This led Friston and Penny (2011) to propose a post-hoc approximation to the model evidence, which is computed by optimising only the largest (full) model of a set of models. The evidence for any (reduced) submodel is then obtained using a generalisation of the Savage-Dickey density ratio (Dickey, 1971). The benefit of this post-hoc approach is a huge reduction in the computational time required for model fitting. This is because only a single model is fitted to data, allowing a potentially huge model space to be searched relatively quickly. In this paper, we explore the relationship between the free energy bound and post-hoc approximations to the model evidence in the context of deterministic (bilinear) dynamic causal models (DCMs) for functional magnetic resonance imaging data.

A dynamical pattern recognition model of γ activity in auditory cortex.

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75-150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain.

A general Bayesian treatment for MEG source reconstruction incorporating lead field uncertainty.

There is uncertainty introduced when a cortical surface based model derived from an anatomical MRI is used to reconstruct neural activity with MEG data. This is a specific case of a problem with uncertainty in parameters on which M/EEG lead fields depend non-linearly. Here we present a general mathematical treatment of any such problem with a particular focus on co-registration. We use a Metropolis search followed by Bayesian Model Averaging over multiple sparse prior source inversions with different headlocation/orientation parameters. Based on MEG data alone we can locate the cortex to within 4mm at empirically realistic signal to noise ratios. We also show that this process gives improved posterior distributions on the estimated current distributions, and can be extended to make inference on the locations of local maxima by providing confidence intervals for each source.

Comparing dynamic causal models using AIC, BIC and free energy.

In neuroimaging it is now becoming standard practise to fit multiple models to data and compare them using a model selection criterion. This is especially prevalent in the analysis of brain connectivity. This paper describes a simulation study which compares the relative merits of three model selection criteria (i) Akaike's Information Criterion (AIC), (ii) the Bayesian Information Criterion (BIC) and (iii) the variational Free Energy. Differences in performance are examined in the context of General Linear Models (GLMs) and Dynamic Causal Models (DCMs). We find that the Free Energy has the best model selection ability and recommend it be used for comparison of DCMs.