P300 Evoked Response Potentials Patterns in Different Complex Concussion Phenotypes

ObjectiveDetermine the utility of P300 Evoked Response Potentials (ERP) voltage patterns in predicting phenotypical sequelae of patients with complex concussions or Persistent Post Concussive Symptoms (PPCS).BackgroundERPs have been used to aid in the diagnosis of multiple neurologic disorders. They have also been recently used in the evaluation of concussions.Design/MethodsA retrospective study of 54 patients, 10–71 year (mean age 29.6 yrs), with PPCS were tested between 6 and 12 weeks post-injury using the standard oddball audio P300 ERP protocol with measures extracted including best central parietal P300 ERP. PPCS Phenotyping was completed in each patient using a standardized post-concussive questionnaire and Rivermead method for 5 primary phenotypes and mixed type.ResultsP300 average Voltage for the entire group was 11.6 mV. Overall, these were significantly lower than age-matched non concussed controls whose average voltage was 16.3 mV (p < 0.0001). Average P300 voltages for each phenotype: Cognition- 14.1 mV, Vestibular- 8.6 mV, Headache- 11.1 mV, Mood- 13.6 mV, Neck Pain- 9.6 mV, Visual- 9.8 mV, Mixed- 6.9 mV, Mixed and Vestibular phenotypes demonstrated the lowest average voltage potentials (6.9 mV and 8.6 mV respectively) which coincided with higher average symptom scores (70.5 and 54.5 respectively). Cognition and Mood demonstrated the highest average voltage potentials (14.1 mV and 13.6 nV respectively), which coincided with lower average symptom scores (40.3 and 48.7, respectively). Mood (13.6 mV) was the lowest average symptom score in the group at 40.3 and Mixed (6.9 mV) was highest at 70.5. Comparing phenotypes against one other, mixed vs mood (p = 0.03), cognition vs vestibular (p = 0.02), and cognition vs mixed (p = 0.009) showed statistical significance.ConclusionsP300 ERPs may help identify persistent abnormal complex concussion neurophysiology. ERPs can also potentially exhibit phenotype specific patterns and be a useful tool in helping differentiate more somatic/physiologic vs mood-based phenotypes. This can ultimately lead in the aid in diagnosis, prognosis, subtyping, and targeted phenotype management.

Modal-Polar Representation of Evoked Response Potentials in Multiple Arousal States

An expansion of the corticothalamic transfer function into eigenmodes and resonant poles is used to derive a simple formula for evoked response potentials (ERPs) in various states of arousal. The transfer function corresponds to the cortical response to an external stimulus, which encodes all the information and properties of the linear system. This approach links experimental observations of resonances and characteristic timescales in brain activity with physically based neural field theory (NFT). The present work greatly simplifies the formula of the analytical ERP, and separates its spatial part (eigenmodes) from the temporal part (poles). Within this framework, calculations involve contour integrations that yield an explicit expression for ERPs. The dominant global mode is considered explicitly in more detail to study how the ERP varies with time in this mode and to illustrate the method. For each arousal state in sleep and wake, the resonances of the system are determined and it is found that five poles are sufficient to study the main dynamics of the system in waking eyes-open and eyes-closed states. Similarly, it is shown that six poles suffice to reproduce ERPs in rapid-eye movement sleep, sleep state 1, and sleep state 2 states, whereas just four poles suffice to reproduce the dynamics in slow wave sleep. Thus, six poles are sufficient to preserve the main global ERP dynamics of the system for all states of arousal. These six poles correspond to the dominant resonances of the system at slow-wave, alpha, and beta frequencies. These results provide the basis for simplified analytic treatment of brain dynamics and link observations more closely to theory.

On the necessity of recurrent processing during object recognition: it depends on the need for scene segmentation

While feed-forward activity may suffice for recognizing objects in isolation, additional visual operations that aid object recognition might be needed for real-world scenes. One such additional operation is figure-ground segmentation; extracting the relevant features and locations of the target object while ignoring irrelevant features. In this study of 60 participants, we show objects on backgrounds of increasing complexity to investigate whether recurrent computations are increasingly important for segmenting objects from more complex backgrounds. Three lines of evidence show that recurrent processing is critical for recognition of objects embedded in complex scenes. First, behavioral results indicated a greater reduction in performance after masking objects presented on more complex backgrounds; with the degree of impairment increasing with increasing background complexity. Second, electroencephalography (EEG) measurements showed clear differences in the evoked response potentials (ERPs) between conditions around 200ms - a time point beyond feed-forward activity and object decoding based on the EEG signal indicated later decoding onsets for objects embedded in more complex backgrounds. Third, Deep Convolutional Neural Network performance confirmed this interpretation; feed-forward and less deep networks showed a higher degree of impairment in recognition for objects in complex backgrounds compared to recurrent and deeper networks. Together, these results support the notion that recurrent computations drive figure-ground segmentation of objects in complex scenes.

Innovative Methodology

Knowing the depth and laminar location of the microelectrodes used to record neuronal activity from the cerebral cortex is crucial to properly interpret the recorded patterns of neuronal responses. Here, we present an innovative approach that allows inferring such properties with high accuracy and in an automated way (i.e., without the need of visual inspection and manual annotation) from the evoked response potentials elicited by sensory (e.g., visual) stimuli.

EEG correlates of perceived food product similarity in a cross-modal taste-visual task

The most common tools to understand perception of food products are hall-tests, surveys, observations, etc. However to get reliable results these approaches require large samples, making them costly and time-consuming. Furthermore, they are also highly expert-dependent and rely on the assumption that study participants can express their preferences consciously and explicitly. Here we suggested an EEG-based approach to evaluate perceived product similarity in a cross-modal taste-visual task. Two candidate neurometrics measured from Fz electrode were tested: the amplitude of N430-620 from evoked response potentials (ERP) and the power of induced gamma oscillations during 400-600 ms period after visual stimulus presentation. Both suggested metrics showed a strong correlation with the perceived similarity scores at both individual and group levels, however N430-630 had greater inter-subject variability making it less suitable for practical applications. The results based on the power of induced gamma oscillations (N=18) not only reflected the results from traditional hall-tests (N=200) but also allowed better discrimination between different food products.

A template-matching algorithm for laminar identification of cortical recording sites from evoked response potentials

In recent years, the advent of the so-called silicon probes has made it possible to homogeneously sample spikes and local field potentials (LFPs) from a regular grid of cortical recording sites. In principle, this allows inferring the laminar location of the sites based on the spatiotemporal pattern of LFPs recorded along the probe, as in the well-known current source-density (CSD) analysis. This approach, however, has several limitations, since it relies on visual identification of landmark features (i.e., current sinks and sources) by human operators – features that can be absent from the CSD pattern if the probe does not span the whole cortical thickness, thus making manual labelling harder. Furthermore, as any manual annotation procedure, the typical CSD-based workflow for laminar identification of recording sites is affected by subjective judgment undermining the consistency and reproducibility of results. To overcome these limitations, we developed an alternative approach, based on finding the optimal match between the LFPs recorded along a probe in a given experiment and a template LFP profile that was computed using 18 recording sessions, in which the depth of the recording sites had been recovered through histology. We show that this method can achieve an accuracy of 79 µm in recovering the cortical depth of recording sites and a 76% accuracy in inferring their laminar location. As such, our approach provides an alternative to CSD that, being fully automated, is less prone to the idiosyncrasies of subjective judgment and works reliably also for recordings spanning a limited cortical stretch.New and noteworthyKnowing the depth and laminar location of the microelectrodes used to record neuronal activity from the cerebral cortex is crucial to properly interpret the recorded patterns of neuronal responses. Here we present an innovative approach that allows inferring such properties with high accuracy and in an automated way (i.e., without the need of visual inspection and manual annotation) from the evoked response potentials (ERPs) elicited by sensory (e.g., visual) stimuli.

Distinct neural mechanisms and temporal constraints govern a cascade of audiotactile interactions

Asynchrony is a critical cue informing the brain whether sensory signals are caused by a common source and should be integrated or segregated. It is unclear how the brain binds audiotactile signals into behavioural benefits depending on their asynchrony. Participants actively responded (psychophysics) or passively attended (electroencephalogrpahy) to noise bursts, ‘taps-to-the-face’, and their audiotactile (AT) combinations at seven audiotactile asynchronies: 0, ±20, ±70, and ±500ms. Observers were faster at detecting AT than unisensory stimuli, maximally for synchronous stimulation and declining within a ≤70ms temporal integration window. We observed AT interactions for (1) near-synchronous stimuli within a ≤20ms temporal integration window for evoked response potentials (ERPs) at 110ms and ∼400ms, (2) specifically ±70ms asynchronies, across the P200 ERP and theta-band inter-trial coherence (ITC) and power at ∼200ms, with a frontocentral topography, and (3) beta-band power across several asynchronies. Our results suggest that early AT interactions for ERP and theta-band ITC and power mediate behavioural response facilitation within a ≤70ms temporal integration window, but beta-band power reflects AT interactions that are less relevant for behaviour. This diversity of temporal profiles and constraints demonstrates how audiotactile integration unfolds in a cascade of interactions to generate behavioural benefits.

EEG Event-Related Desynchronization and Event-Related Synchronization

Event-related desynchronization (ERD) reflects a decrease of oscillatory activity related to internally or externally paced events. The increase of rhythmic activity is called event-related synchronization (ERS). They represent dynamical states of thalamocortical networks associated with cortical information-processing changes. This chapter discusses differences between ERD/ERS and evoked response potentials and methodologies for quantifying ERD/ERS and selecting frequency bands. It covers the interpretation of ERD/ERS in the alpha and beta bands and theta ERS and alpha ERD in behavioral tasks. ERD/ERS in scalp and subdural recordings, in various frequency bands, is discussed. Also presented is the modulation of alpha and beta rhythms by 0.1-Hz oscillations in the resting state and phase-coupling of the latter with slow changes of prefrontal hemodynamic signals (HbO2), blood pressure oscillations, and heart rate interval variations in the resting state and in relation to behavioral motor tasks. Potential uses of ERD-based strategies in stroke patients are discussed.