Localized Task-Invariant Emotional Valence Encoding Revealed by Intracranial Recordings

The ability to distinguish between negative, positive and neutral valence is a key part of emotion perception. Emotional valence has conceptual meaning that supersedes any particular type of stimulus, although it is typically captured experimentally in association with particular tasks. We sought to identify neural encoding for task-invariant emotional valence. We evaluated whether high gamma responses (HGRs) to visually displayed words conveying emotions could be used to decode emotional valence from HGRs to facial expressions. Intracranial electroencephalography (iEEG) was recorded from fourteen individuals while they participated in two tasks, one involving reading words with positive, negative, and neutral valence, and the other involving viewing faces with positive, negative, and neutral facial expressions. Quadratic discriminant analysis was used to identify information in the HGR that differentiates the three emotion conditions. A classifier was trained on the emotional valence labels from one task and was cross-validated on data from the same task (within-task classifier) as well as the other task (between-task classifier). Emotional valence could be decoded in the left medial orbitofrontal cortex and middle temporal gyrus, both using within-task classifiers as well as between-task classifiers. These observations suggest the presence of task-independent emotional valence information in the signals from these regions.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy

In this study, we quantified the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes. We included 65 patients with strip electrodes (n = 12), strip and grid electrodes (n = 24), strip, grid, and depth electrodes (n = 7), or depth electrodes only (n = 22). Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. Gray and white matter coverage was quantified using spherical volumes centered on electrode centroids, with radii ranging from 1 to 15 mm, along with detailed finite element models of local electric fields. Gray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage and amygdala and hippocampal coverage was greatest for depth electrodes at all RoIs. This study provides the first probabilistic analysis to quantify coverage for different intracranial recording configurations. Depth electrodes offer increased coverage of gray matter over other recording strategies if the desired signals are local, while subdural grids and strips sample more gray matter if the desired signals are diffuse.

Temporal order of signal propagation within and across intrinsic brain networks

We studied the temporal dynamics of activity within and across functional MRI (fMRI)–derived nodes of intrinsic resting-state networks of the human brain using intracranial electroencephalography (iEEG) and repeated single-pulse electrical stimulation (SPES) in neurosurgical subjects implanted with intracranial electrodes. We stimulated and recorded from 2,133 and 2,372 sites, respectively, in 29 subjects. We found that N1 and N2 segments of the evoked responses are associated with intra- and internetwork communications, respectively. In a separate cognitive experiment, evoked electrophysiological responses to visual target stimuli occurred with less temporal separation across pairs of electrodes that were located within the same fMRI-defined resting-state networks compared with those located across different resting-state networks. Our results suggest intranetwork prior to internetwork information processing at the subsecond timescale.

P.129 Bi-insular Responsive Neurostimulation Artifact on Scalp Electroencephalogram

Background: Background: Responsive Neurostimulation (RNS) has proven efficacy in treating medically resistant epilepsy as an intracranial system detecting, recording and treating seizures automatically. No information exists pertaining to artifact characteristics of RNS findings in scalp EEG. Methods: A 30 year-old female was diagnosed using intracranial electroencephalography(iEEG), with bi-insular epilepsy, of unknown cause. She presented large number of focal unaware non-motor seizures and seizures with progression to bilateral tonic-clonic. She was implanted with bi-insular RNS. Results: During scalp EEG recordings, a prominent artifact was seen corresponding to an automatized discharge suspectedly evoked by the RNS trying to minimize the frequent epileptiform activity in her case. Figure 1 and 2 depict these findings. Conclusions: Artifact seen by the RNS in scalp EEG has not been previously described in scientific literature. These findings must be identified to better characterize the role of the RNS in EEG and treatment of seizure activity visible on scalp recordings.

Reiterating the role of corpus callosum in generalization of interictal and ictal epileptiform discharges: a case report with post-callosotomy intracranial electroencephalography in Lennox–Gastaut syndrome

Lennox–Gastaut syndrome (LGS) is an epileptic encephalopathy characterized by delayed mental development and intractable multiple seizure types, predominantly tonic. Drop attacks are the commonest and the most disabling type of seizures. Resective surgery is often not possible in LGS as the electroencephalogram (EEG) abnormalities are usually multifocal and generalized, and magnetic resonance image is often either normal or multilesional. We report a case of LGS with bilateral parieto-occipital gliosis where EEG before and after callosotomy demonstrated synchronized bilateral interictal epileptiform discharges and ictal discharges becoming desynchronized and running down. This phenomenon emphasizes the role of the corpus callosum in secondary bilateral synchrony.

Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation

Distinct brain systems are thought to support statistical learning over different timescales. Regularities encountered during online perceptual experience can be acquired rapidly by the hippocampus. Further processing during offline consolidation can establish these regularities gradually in cortical regions, including the medial prefrontal cortex (mPFC). These mechanisms of statistical learning may be critical during spatial navigation, for which knowledge of the structure of an environment can facilitate future behavior. Rapid acquisition and prolonged retention of regularities have been investigated in isolation, but how they interact in the context of spatial navigation is unknown. We had the rare opportunity to study the brain systems underlying both rapid and gradual timescales of statistical learning using intracranial electroencephalography (iEEG) longitudinally in the same patient over a period of three weeks. As hypothesized, spatial patterns were represented in the hippocampus but not mPFC for up to one week after statistical learning and then represented in the mPFC but not hippocampus two and three weeks after statistical learning. Taken together, these findings clarify that the hippocampus may do the initial work of extracting regularities and transfer these integrated memories to cortex, rather than only storing individual experiences and leaving it up to cortex to extract regularities.

Direct brain recordings reveal continuous encoding of structure in random stimuli

The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to the creation of sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where this modeling takes place is a core question in statistical learning. It is unknown how this modeling applies to random sensory signals. Here, we identify conditional relations, through transitional probabilities, as an implicit structure supporting the encoding of a random auditory stream. We evaluate this representation using intracranial electroencephalography recordings by applying information-theoretical principles to high-frequency activity (75-145 Hz). We demonstrate how the brain continuously encodes conditional relations between random stimuli in a network outside of the auditory system following a hierarchical organization including temporal, frontal and hippocampal regions. Our results highlight that hierarchically organized brain areas continuously attempt to order incoming information by maintaining a probabilistic representation of the sensory input, even under random stimuli presentation.

Insular Cortex Response to Static Visual Sexual Stimuli

According to previous research, the insula is important for processing salient and emotional stimuli, but its precise role remains elusive. By combining high spatial and temporal resolution, intracranial electroencephalography (iEEG) might contribute to filling this gap. Four drug-resistant epileptic patients with intracranial electrodes in the insula were instructed to watch and rate pictures with sexual content and neutral pictures. Event-related potentials (ERPs) were computed separately for both types of stimuli. Ninety-three percent of the anterior insula (AI) and 85% of the posterior insula (PI) contacts showed differences between ERPs. AI-positive deflections tended to have an earlier onset than PI-positive deflections. The results suggest that the AI generates a P300-like response and contributes to the early phase of the late positive potential, both components found enhanced while viewing emotional stimuli in the ERP literature. The present findings are interpreted as congruent with the role of the AI in maintaining attention to salient stimuli.