Sequence Of Stimuli Research Articles

Pre- and Post-Season Electroencephalography Measures of Brain Vital Signs in Youth Football Players

Amid growing concern of potential brain trauma caused by repetitive head impacts (RHI) in youth football, there is an emerging need to develop objective, physiologic assessments of brain function that can identify sub-concussive impairment. Electroencephalography (EEG) may be a viable tool to evaluate neurologic dysfunction associated with RHI. PURPOSE: To evaluate the neurophysiologic activity of youth football players in association with RHI. METHODS: EEG data were captured from nine middle school football players (13.1 ± 0.5 yr) before (PRE) and after (POST) one season using a portable 8-channel EEG cap with three electrodes (Fz, Cz and Pz) while subjects listened to an auditory stimulus sequence (~5 min). Amplitudes (A) and latencies (L) of event-related potentials (ERP) corresponding to auditory sensation (N100A, N100L), basic attention (P300A, P300L), and cognitive processing (N400A, N400L) were converted to normalized brain vital signs scores (0-100 scale). Larger ERP amplitudes equate to higher scores and delayed latencies equate to lower scores. RHI were measured during the season via accelerometry (Head Impact Telemetry System). EEG data from three subjects were of insufficient quality for analysis; thus, results were limited to the six remaining subjects. RESULTS: Scores for N400L decreased significantly (P = 0.031) from PRE (63.9 ± 7.6) to POST (38.2 ± 16.8). There were no significant changes in N100A (P = 0.971), N100L (P = 0.308), P300A (P = 0.562), P300L (P = 0.183), or N400A (P = 0.685) scores. On average, players sustained 134 ± 66 head impacts during the season. Head impact frequency was not significantly associated with any brain vital signs score (P = 0.169-0.783). CONCLUSION: In this small sample of youth football players, cognitive processing was delayed following a single season as measured by the significant reduction in N400 latency scores. However, this change was not associated with RHI incurred by the players. While these data should be interpreted with caution, they provide preliminary evidence for the potential value of using the brain vital signs framework to evaluate brain function and sub-concussive impairment in collision-sport athletes. This work was supported by a grant from the T. Denny Sanford Pediatric Collaborative Research Fund between Mayo Clinic and Sanford Health.

Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity

We demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network contains units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections rendering each unit bi-stable. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding of the incoming information, with short-term synaptic depression ensuring an itinerancy between sets of active units. We assess the ability of such a network to encode the identity of sequences of stimuli, so as to provide a template for sequence recall, or decisions based on accumulation of evidence. Across a range of parameters, such networks produce the primacy (better final encoding of the earliest stimuli) and recency (better final encoding of the latest stimuli) observed in human recall data and can retain the information needed to make a binary choice based on total number of presentations of a specific stimulus. Similarities and differences in the final states of the network produced by different sequences lead to predictions of specific errors that could arise when an animal or human subject generalizes from training data, when the training data comprises a subset of the entire stimulus repertoire. We suggest that such networks can provide the general purpose computational engines needed for us to solve many cognitive tasks.

Action-induced adjustment of prediction explains no visual mismatch negativity to self-generated deviants

Visual mismatch negativity (VMMN) is an event-related brain potential component elicited by infrequent deviant events embedded among frequent standard events in a visual stimulus sequence. Although VMMN is known to be elicited by deviant events in a stimulus-driven manner, it was recently shown that VMMN can be drastically affected by the participant's voluntary action. With a paradigm in which participants were required to press one button frequently (about 90%) and another button infrequently (10%) in random order, and produce a visual stimulus sequence consisting of deviant and standard events, VMMN was elicited by deviant events triggered by the frequently-performed button press that should generate a standard event, but not by deviant events triggered by the infrequently-performed button press that should generate a corresponding deviant event. The present study replicated these previous findings and further demonstrated that VMMN was elicited by deviant events triggered by the infrequently-performed button press that should generate a different deviant event. These results support the hypothesis that VMMN-generating processes can be adjusted according to the participant's voluntary action, and rule out an alternative hypothesis that VMMN-generating processes are terminated when participants change their action. This adjustment is thought to be implemented so that self-generated deviants that carry no novel information are discarded, while externally-generated deviants that may carry novel information are selectively detected.

A neuromechanistic model for rhythmic beat generation.

When listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise.

Red color facilitates the detection of facial anger — But how much?

The color red seems to be consistently associated with the concept of anger. Beyond semantic associations, it has been suggested that the color red enhances our ability to perceive anger in faces. However, previous studies often lack proper color control or the results are confounded by the presence of several emotions. Moreover, the magnitude of the (potential) effect of red has not been quantified. To address these caveats, we quantified the effect of facial color and background color on anger with psychometric functions measured with the method-of-constant-stimuli while facial hue or surround hue was varied in CIELAB color space. Stimulus sequences were generated by morphing between neutral and angry faces. For the facial color, the average chromaticity of the faces was shifted by ΔE 12/20 in red, yellow, green and blue directions. For the background color, the hue was either neutral or saturated red, green or blue. Both facial redness and surround redness enhanced perceived anger slightly, by 3–4 morph-%. Other colors did not affect perceived anger. As the magnitude of the enhancement is generally small and the effect is robust only in a small subset of the participants, we question the practical significance of red in anger recognition.

Vision-Based Robot Navigation through Combining Unsupervised Learning and Hierarchical Reinforcement Learning.

Extensive studies have shown that many animals’ capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus. Although there are numerous hippocampal modeling approaches, only a few span the wide functionalities ranging from processing raw sensory signals to planning and action generation. This paper presents a vision-based navigation system that involves generating place and HD cells through learning from visual images, building topological maps based on learned cell representations and performing navigation using hierarchical reinforcement learning. First, place and HD cells are trained from sequences of visual stimuli in an unsupervised learning fashion. A modified Slow Feature Analysis (SFA) algorithm is proposed to learn different cell types in an intentional way by restricting their learning to separate phases of the spatial exploration. Then, to extract the encoded metric information from these unsupervised learning representations, a self-organized learning algorithm is adopted to learn over the emerged cell activities and to generate topological maps that reveal the topology of the environment and information about a robot’s head direction, respectively. This enables the robot to perform self-localization and orientation detection based on the generated maps. Finally, goal-directed navigation is performed using reinforcement learning in continuous state spaces which are represented by the population activities of place cells. In particular, considering that the topological map provides a natural hierarchical representation of the environment, hierarchical reinforcement learning (HRL) is used to exploit this hierarchy to accelerate learning. The HRL works on different spatial scales, where a high-level policy learns to select subgoals and a low-level policy learns over primitive actions to specialize on the selected subgoals. Experimental results demonstrate that our system is able to navigate a robot to the desired position effectively, and the HRL shows a much better learning performance than the standard RL in solving our navigation tasks.

Temporal signals underlying a cognitive process in the dorsal premotor cortex

During discrimination between two sequential vibrotactile stimulus patterns, the primate dorsal premotor cortex (DPC) neurons exhibit a complex repertoire of coding dynamics associated with the working memory, comparison, and decision components of this task. In addition, these neurons and neurons with no coding responses show complex strong fluctuations in their firing rate associated with the temporal sequence of task events. Here, to make sense of this temporal complexity, we extracted the temporal signals that were latent in the population. We found a strong link between the individual and population response, suggesting a common neural substrate. Notably, in contrast to coding dynamics, these time-dependent responses were unaffected during error trials. However, in a nondemanding task in which monkeys did not require discrimination for reward, these time-dependent signals were largely reduced and changed. These results suggest that temporal dynamics in DPC reflect the underlying cognitive processes of this task.

An Event-Driven AR-Process Model for EEG-Based BCIs With Rapid Trial Sequences.

Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however, these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models' ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal model for event-related responses in the EEG evoked with a rapid sequence of stimuli in BCI applications. The model describes the EEG as a superposition of impulse responses time-locked to stimuli corrupted with an autoregressive noise process. The performance of the signal model is assessed in the context of RSVP keyboard, a language-model-assisted EEG-based BCI for typing. EEG data obtained for model calibration from 10 healthy participants are used to fit and compare two models: the proposed sequence-based EEG model and the trial-based feature-class-conditional distribution model that ignores temporal dependencies, which has been used in the previous work. The simulation studies indicate that the earlier model that ignores temporal dependencies may be causing drastic reductions in achievable information transfer rate (ITR). Furthermore, the proposed model, with better regularization, may achieve improved accuracy with fewer calibration data samples, potentially helping to reduce calibration time. Specifically, results show an average 8.6% increase in (cross-validated) calibration AUC for a single channel of EEG, and 54% increase in the ITR in a typing task.

A Bayesian race model for response times under cyclic stimulus discriminability

Response time (RT) data from psychology experiments are often used to validate theories of how the brain processes information and how long it takes a person to make a decision. When an RT results from a task involving two or more possible responses, the cognitive process that determines the RT may be modeled as the first-passage time of underlying competing (racing) processes with each process describing accumulation of information in favor of one of the responses. In one popular model the racers are assumed to be Gaussian diffusions. Their first-passage times are inverse Gaussian random variables and the resulting RT has a min-inverse Gaussian distribution. The RT data analyzed in this paper were collected in an experiment requiring people to perform a two-choice task in response to a regularly repeating sequence of stimuli. Starting from a min-inverse Gaussian likelihood for the RTs we build a Bayesian hierarchy for the rates and thresholds of the racing diffusions. The analysis allows us to characterize patterns in a person’s sequence of responses on the basis of features of the person’s diffusion rates (the “footprint” of the stimuli) and a person’s gradual changes in speed as trends in the diffusion thresholds. Last, we propose that a small fraction of RTs arise from distinct, noncognitive processes that are included as components of a mixture model. In the absence of sharp prior information, the inclusion of these mixture components is accomplished via a two-stage, empirical Bayes approach. The resulting framework may be generalized readily to RTs collected under a variety of experimental designs.

Perceptual Tuning Influences Rule Generalization: Testing Humans With Monkey-Tailored Stimuli.

Comparative research investigating how nonhuman animals generalize patterns of auditory stimuli often uses sequences of human speech syllables and reports limited generalization abilities in animals. Here, we reverse this logic, testing humans with stimulus sequences tailored to squirrel monkeys. When test stimuli are familiar (human voices), humans succeed in two types of generalization. However, when the same structural rule is instantiated over unfamiliar but perceivable sounds within squirrel monkeys’ optimal hearing frequency range, human participants master only one type of generalization. These findings have methodological implications for the design of comparative experiments, which should be fair towards all tested species’ proclivities and limitations.

Binding identity and orientation in object recognition.

We tested whether an object's orientation is inherently bound to its identity in a holistic view-based representation at the early stages of visual identification, or whether identity and orientation are represented separately. Observers saw brief and masked stimulus sequences containing two rotated objects. They had to detect if a previously cued object was present in the sequence and report its orientation. In Experiments 1 and 2, the objects were presented sequentially in the same spatial location for 70 ms each, whereas in Experiments 3 and 4 they were presented simultaneously in different spatial locations for 70 ms and 140 ms, respectively. Across all experiments, observers reported the correct orientation for approximately 70% of the positively identified objects, and were at chance in reporting the orientation when they had not recognized the object. This finding suggests that orientation information is accessed after an object has been identified. In addition, when the two objects were presented sequentially in the same spatial location, orientation errors were not random-observers tended to report the orientation of the alternative object in the sequence, indicating misbindings between the identities and orientations of objects that share spatial location. This susceptibility to binding errors was not observed when the objects were in different spatial locations. These results suggest that identity and orientation may be prone to misbinding, and that spatial location may serve to protect their joint integrity.

Pupillary responses and reaction times index different cognitive processes in a combined Go/Switch incidental learning task

In previous studies we have provided evidence that performance in speeded response tasks with infrequent target stimuli reflects both automatic and controlled cognitive processes, based on differences in reaction time (RT) and task-related brain responses (Cheyne et al. 2012, Isabella et al. 2015). Here we test the hypothesis that such shifts in cognitive control may be influenced by changes in cognitive load related to stimulus predictability, and that these changes can be indexed by task-evoked pupillary responses (TEPR). We manipulated stimulus predictability using fixed stimulus sequences that were unknown to the participants in a Go/Switch task (requiring a switch response on 25% of trials) while monitoring TEPR as a measure of cognitive load in 12 healthy adults. Results showed significant improvement in performance (reduced RT, increased efficiency) for repeated sequences compared to occasional deviant sequences (10% probability) indicating that incidental learning of the predictable sequences facilitated performance. All behavioral measures varied between Switch and Go trials (RT, efficiency), however mean TEPR amplitude (mTEPR) and latency to maximum pupil dilation were particularly sensitive to Go/Switch. Results were consistent with the hypothesis that mTEPR indexes cognitive load, whereas TEPR latency indexes time to response selection, independent from response execution. The present study provides evidence that incidental pattern learning during response inhibition tasks may modulate several cognitive processes including cognitive load, effort, response selection and execution, which can in turn have differential effects on measures of performance. In particular, we demonstrate that reaction time may not be indicative of underlying cognitive load.

Encoding information into autonomously bursting neural network with pairs of time-delayed pulses

Biological neural networks with many plastic synaptic connections can store external input information in the map of synaptic weights as a form of unsupervised learning. However, the same neural network often produces dramatic reverberating events in which many neurons fire almost simultaneously – a phenomenon coined as ‘population burst.’ The autonomous bursting activity is a consequence of the delicate balance between recurrent excitation and self-inhibition; as such, any periodic sequences of burst-generating stimuli delivered even at a low frequency (~1 Hz) can easily suppress the entire network connectivity. Here we demonstrate that ‘Δt paired-pulse stimulation’, can be a novel way for encoding spatially-distributed high-frequency (~10 Hz) information into such a system without causing a complete suppression. The encoded memory can be probed simply by delivering multiple probing pulses and then estimating the precision of the arrival times of the subsequent evoked recurrent bursts.

Gender classification using mesh networks on multiresolution multitask fMRI data.

Brain connectivity networks have been shown to represent gender differences under a number of cognitive tasks. Recently, it has been conjectured that fMRI signals decomposed into different resolutions embed different types of cognitive information. In this paper, we combine multiresolution analysis and connectivity networks to study gender differences under a variety of cognitive tasks, and propose a machine learning framework to discriminate individuals according to their gender. For this purpose, we estimate a set of brain networks, formed at different resolutions while the subjects perform different cognitive tasks. First, we decompose fMRI signals recorded under a sequence of cognitive stimuli into its frequency subbands using Discrete Wavelet Transform (DWT). Next, we represent the fMRI signals by mesh networks formed among the anatomic regions for each task experiment at each subband. The mesh networks are constructed by ensembling a set of local meshes, each of which represents the relationship of an anatomical region as a weighted linear combination of its neighbors. Then, we estimate the edge weights of each mesh by ridge regression. The proposed approach yields 2CL functional mesh networks for each subject, where C is the number of cognitive tasks and L is the number of subband signals obtained after wavelet decomposition. This approach enables one to classify gender under different cognitive tasks and different frequency subbands. The final step of the suggested framework is to fuse the complementary information of the mesh networks for each subject to discriminate the gender. We fuse the information embedded in mesh networks formed for different tasks and resolutions under a three-level fuzzy stacked generalization (FSG) architecture. In this architecture, different layers are responsible for fusion of diverse information obtained from different cognitive tasks and resolutions. In the experimental analyses, we use Human Connectome Project task fMRI dataset. Results reflect that fusing the mesh network representations computed at multiple resolutions for multiple tasks provides the best gender classification accuracy compared to the single subband task mesh networks or fusion of representations obtained using only multitask or only multiresolution data. Besides, mesh edge weights slightly outperform pairwise correlations between regions, and significantly outperform raw fMRI signals. In addition, we analyze the gender discriminative power of mesh edge weights for different tasks and resolutions.

Action scheduling in multitasking: A multi-phase framework of response-order control.

Temporal organization of human behavior is particularly important when several action requirements must be processed around the same time. A crucial challenge in such multitasking situations is to control the temporal response order. However, multitasking studies usually focus on temporal processing dynamics after a specific response order - which is usually triggered by stimulus sequence and instructions - has been determined, whereas a comprehensive study of response-order scheduling mechanisms is still lacking. Across three psychological refractory period (PRP) experiments, we examined the impact of stimulus order, response characteristics, and several other factors on response order. Crucially, we utilized a combination of effector systems (oculomotor and manual) that are known to ensure reasonable response order variability in the first place. The results suggest that - contrary to previous assumptions - bottom-up factors (e.g., stimulus order) are not the primary determinant of temporal action scheduling. Instead, we found a major influence of effector-based characteristics (i.e., oculomotor task prioritization) that could be attenuated by both instructions and changes in the task environment (providing temporally predictable input). Effects of between-task compatibility suggest that a dedicated stimulus-code comparison process precedes and affects response-order scheduling. Based on the present results and previous findings, we propose a multi-phase framework of temporal response-order control that emphasizes the extent to which cognitive control of action scheduling is dynamically adaptive to particular task characteristics.

An Online Interactive Paradigm for P300 Brain-Computer Interface Speller.

For each brain-computer interface system, efficiency is a key issue that considers both accuracy and speed. The P300 spellers built upon oddball paradigm are usually less efficient due to the repetitive stimulation of multiple characters for reliable detection. In this paper, based on the online EEG signal, we propose an interactive paradigm for P300 speller to improve its efficiency, primarily focusing within the single characterP300 paradigm. Specifically, after each stimulation, we first evaluate the posterior probability of each character in the stimuli set to be the target. The lowprobability characters are then removed fromthe stimuli set in the subsequent round(s), as character flash continues until the probability of any character surpasses a predefined threshold. Then, the character is selected as the target and data collection for the trial terminates. By reducing stimulus sequence characters, the system efficiency can be substantially improved. The spelling accuracy is insignificantly affected as the characters being removed have low probability to be the target. The online experimental results from a total of eight subjects show that an average practical information transfer rate of 50.26 bits/min (i.e. 9.07 characters/min) has been achieved, with 91% average spelling accuracy rate.

Bifurcation-based embodied logic and autonomous actuation

Many plants autonomously change morphology and function in response to environmental stimuli or sequences of stimuli. In contrast with the electronically-integrated sensors, actuators, and microprocessors in traditional mechatronic systems, natural systems embody these sensing, actuation, and control functions within their compositional and structural features. Inspired by nature, we embody logic in autonomous systems to enable them to respond to multiple stimuli. Using 3D printable fibrous composites, we fabricate structures with geometries near bifurcation points associated with a transition between bistability and monostability. When suitable stimuli are present, the materials swell anisotropically. This forces a key geometric parameter to pass through a bifurcation, triggering rapid and large-amplitude self-actuation. The actuation time can be programmed by varying structural parameters (from 0.6 to 108 s for millimeter-scale structures). We demonstrate this bioinspired control strategy with examples that respond to their environment according to their embodied logic, without electronics, external control, or tethering.

Different Contexts in the Oddball Paradigm Induce Distinct Brain Networks in Generating the P300.

Despite the P300 event-related potential (ERP) differences between distinct stimulus sequences, the effect of stimulus sequence on the brain network is still left unveiled. To uncover the corresponding effect of stimulus sequence, we thus investigated the differences of functional brain networks, when a target (T) or standard (S) stimulus was presented preceding another T as background context. Results of this study demonstrated that, when an S was first presented preceding a T (i.e., ST sequence), the P300 experiencing large amplitude was evoked by the T, along with strong network architecture. In contrast, if a T was presented in advance [i.e., target-to-target (TT) sequence], decreased P300 amplitude and attenuated network efficiency were demonstrated. Additionally, decreased activations in regions, such as inferior frontal gyrus and superior frontal gyrus were also revealed in TT sequence. Particularly, the effect of stimulus sequence on P300 network could be quantitatively measured by brain network properties, the increase in network efficiency corresponded to large P300 amplitude evoked in P300 task.

Human Midbrain Participates In Mismatch Detection Without Cognitive Activity Of The Cerebral Cortex: Case Report

Mismatch detection is the process of identification of rare stimuli from a sequence of stimuli. This important cognitive function is usually attributed to the cerebral cortex. To test the human midbrain involvement in sound mismatch detection, we recorded local field potentials in the states of deep anesthesia and clear consciousness from the drainage-electrode implanted in the cerebral aqueduct of an adult patient with an obstructive hydrocephaly who had undergone pineal region tumor removal through anterior interhemispheric transcallosal approach. We found a significant difference in the state of deep anesthesia at 256-364 ms after hearing a rarely presented sounds compared with frequently presented sounds and equally probable sounds. This difference was not found in the same experiment in the state of clear consciousness. The results suggest that human midbrain participates in mismatch detection and can do it even without cognitive activity of the cerebral cortex. Amplitude-frequency analysis of the midbrain records revealed that propofol affects the electrical activity of both human midbrain and cortex but the level of inhibition of the cortex is 6 times higher than the level of inhibition of the midbrain. We suppose that the human cortex is more susceptible to propofol than the human midbrain.

The Parallel Auditory Brainstem Response.

The frequency-specific tone-evoked auditory brainstem response (ABR) is an indispensable tool in both the audiology clinic and research laboratory. Most frequently, the toneburst ABR is used to estimate hearing thresholds in infants, toddlers, and other patients for whom behavioral testing is not feasible. Therefore, results of the ABR exam form the basis for decisions regarding interventions and hearing habilitation with implications extending far into the child’s future. Currently, responses are elicited by periodic sequences of toneburst stimuli presented serially to one ear at a time, which take a long time to measure multiple frequencies and intensities, and provide incomplete information if the infant wakes up early. Here, we describe a new method, the parallel ABR (pABR), which uses randomly timed toneburst stimuli to simultaneously acquire ABR waveforms to five frequencies in both ears. Here, we describe the pABR and quantify its effectiveness in addressing the greatest drawback of current methods: test duration. We show that in adults with normal hearing the pABR yields high-quality waveforms over a range of intensities, with similar morphology to the standard ABR in a fraction of the recording time. Furthermore, longer latencies and smaller amplitudes for low frequencies at a high intensity evoked by the pABR versus serial ABR suggest that responses may have better place specificity due to the masking provided by the other simultaneous toneburst sequences. Thus, the pABR has substantial potential for facilitating faster accumulation of more diagnostic information that is important for timely identification and treatment of hearing loss.