Serial dependence and representational momentum in single-trial perceptual decisions.

The human brain has evolved to predict and anticipate environmental events from their temporal dynamics. Predictions can bias perception toward the recent past, particularly when the environment contains no foreseeable changes, but can also push perception toward future states of sensory input, like when anticipating the trajectory of moving objects. Here, we show that perceptual decisions are simultaneously influenced by both past and future states of sensory signals. Using an orientation adjustment task, we demonstrate that single-trial errors are displaced toward previous features of behaviorally relevant stimuli and, at the same time, toward future states of dynamic sensory signals. These opposing tendencies, consistent with decisional serial dependence and representational momentum, involve different types of processing: serial dependence occurs beyond objecthood whereas representational momentum requires the representation of a single object with coherent dynamics in time and space. The coexistence of these two phenomena supports the independent binding of stimuli and decisions over time.

Modeling time-varying brain networks with a self-tuning optimized Kalman filter.

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge.

Time-varying effective EEG source connectivity: the optimization of model parameters.

Adaptive estimation methods based on general Kalman filter are powerful tools to investigate brain networks dynamics given the non-stationary nature of neural signals. These methods rely on two parameters, the model order p and adaptation constant c, which determine the resolution and smoothness of the time-varying multivariate autoregressive estimates. A sub-optimal filtering may present consistent biases in the frequency domain and temporal distortions, leading to fallacious interpretations. Thus, the performance of these methods heavily depends on the accurate choice of these two parameters in the filter design. In this work, we sought to define an objective criterion for the optimal choice of these parameters. Since residual- and information-based criteria are not guaranteed to reach an absolute minimum, we propose to study the partial derivatives of these functions to guide the choice of p and c. To validate the performance of our method, we used a dataset of human visual evoked potentials during face perception where the generation and propagation of information in the brain is well understood and a set of simulated data where the ground truth is available.

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.

Different colors of light lead to different adaptation and activation as determined by high-density EEG.

Light adaptation is crucial for coping with the varying levels of ambient light. Using high-density electroencephalography (EEG), we investigated how adaptation to light of different colors affects brain responsiveness. In a within-subject design, sixteen young participants were adapted first to dim white light and then to blue, green, red, or white bright light (one color per session in a randomized order). Immediately after both dim and bright light adaptation, we presented brief light pulses and recorded event-related potentials (ERPs). We analyzed ERP response strengths and brain topographies and determined the underlying sources using electrical source imaging. Between 150 and 261 ms after stimulus onset, the global field power (GFP) was higher after dim than bright light adaptation. This effect was most pronounced with red light and localized in the frontal lobe, the fusiform gyrus, the occipital lobe and the cerebellum. After bright light adaptation, within the first 100 ms after light onset, stronger responses were found than after dim light adaptation for all colors except for red light. Differences between conditions were localized in the frontal lobe, the cingulate gyrus, and the cerebellum. These results indicate that very short-term EEG brain responses are influenced by prior light adaptation and the spectral quality of the light stimulus. We show that the early EEG responses are differently affected by adaptation to different colors of light which may contribute to known differences in performance and reaction times in cognitive tests.

Neural correlates of priming on occluded figure interpretation in human fusiform cortex.

The visual system rapidly completes a partially occluded figure. We probed the completion process by using priming in combination with neuroimaging techniques. Priming leads to more efficient visual processing and thus a reduction in neural activity in relevant brain areas. These areas were studied with high spatial resolution and temporal accuracy with focus on early perceptual processing. We recorded magnetoencephalographic responses from 10 human volunteers in a primed same-different task for test figures. The test figures were preceded by a sequence of two figures, a prime or control figure followed by an occluded figure. The prime figures were one of three possible interpretations of the occluded figures: global and local completions and mosaic interpretation. A significant priming effect was evident: in primed trials as compared with control trials, subjects responded faster and the latency was shorter in the magnetoencephalographic signal for the largest peak between 50 and 300 ms after the occluded figure onset. Tomographic and statistical parametric mapping analyses revealed stages of activation in occipitotemporal areas during occluded figure processing. Notably, we found significantly reduced activation in the right fusiform cortex between 120 and 200 ms after occluded figure onset for primed trials as compared with control trials. We also found significant spatiotemporal differences of local, global and mosaic interpretations for individual subjects but not across subjects. We conclude that modulation of activity in the right fusiform cortex may be a neural correlate of priming in the interpretation of an occluded figure, and that this area acts as a hub for different occluded figure interpretations in this early stage of perception.

Disposition of morphine in rat brain: relationship to biological activity.

The levels of morphine in plasma and brain subcortex of rats were determined by gas-liquid chromatography and radioimmunoassay at various time intervals after a single i.v. injection of graded doses of morphine (0.44, 1.04 and 2.25 mg/kg). The disappearance curve of morphine plasma levels appeared to be composed of at least two exponential terms: a rapid disappearance during the 1st hr, which was dependent on the unit dose of morphine injected and a slower disappearance rate after the 1st hr. The highest morphine levels in the brain were measured already 5 min after its injection and these levels only slightly decreased over the next 2 hr. Thereafter a more rapid disappearance of brain morphine was observed. The brain/plasma ratio gradually increased from 0.18 at 5 min after injection to about 1.0 at 60 and 120 min and decreased to approximately 0.4 at 4 hr after injection. This may suggest that morphine is retained in the brain during the 1st hr after injection and that it might be removed from the brain between 2 and 4 hr, possibly by an active process. The relationship between brain morphine levels and the degree of antinociception as assessed with the hot plate procedure appeared to be rather complicated. Shortly after morphine injection, but also at 60 and 120 min after injection, the antinociceptive effect was much lower than might be expected from the levels of morphine in the brain. Several possible explanations for this phenomenon are discussed. A high dose of naltrexone, which completely blocked the antinociceptive effect of morphine, did not affect brain and plasma levels of morphine. Rifampicin, which attenuates development of morphine tolerance, increased brain levels of morphine but did not change plasma levels. Pretreatment with the tripeptide prolyl-leucyl-glycinamide, which facilitates development of morphine tolerance, decreased brain morphine levels and increased plasma levels at 3 hr after morphine injection. This suggests that tolerance development and the rate with which morphine is removed from the brain may have at least some common underlying mechanisms.

Adrenocorticotrophic hormone fragments mimic the effect of morphine in vitro.

1 Fragments of the N terminal part of adrenocorticotrophic hormone (ACTH) inhibited the electrically evoked contractions of the mouse vas deferens. This inhibition could be antagonized by naloxone. 2 The same fragments displaced radiolabelled morphine from morphine antiserum. 3 Structure-activity relationship studies showed that in both assay systems the active core is located within the sequence ACTH 7--10. 4 It is postulated that the Trp9 residue and the peptide bond between Trp9 and Gly10 are particularly important for interaction of ACTH fragments with morphine receptors.