Alcohol for seizure induction in the epilepsy monitoring unit.

RATIONALE: Seizure induction techniques are used in the epilepsy monitoring unit (EMU) to increase diagnostic yield and reduce length of stay. There are insufficient data on the efficacy of alcohol as an induction technique. METHODS: We performed a retrospective cohort study using six years of EMU data at our institution. We compared cases who received alcohol for seizure induction to matched controls who did not. The groups were matched on the following variables: age, reason for admission, length of stay, number of antiseizure medications (ASM) at admission, whether ASMs were tapered during admission, and presence of interictal epileptiform discharges. We used both propensity score and exact matching strategies. We compared the likelihood of epileptic seizures and nonepileptic events in cases versus controls using Kaplan-Meier time-to-event analysis, as well as odds ratios for these outcomes occurring at any time during the admission. RESULTS: We analyzed 256 cases who received alcohol (median dose 2.5 standard drinks) and 256 propensity score-matched controls. Cases who received alcohol were no more likely than controls to have an epileptic seizure (X2(1) = 0.01, p = 0.93) or nonepileptic event (X2(1) = 2.1, p = 0.14) in the first 48 h after alcohol administration. For the admission overall, cases were no more likely to have an epileptic seizure (OR 0.89, 95 % CI 0.61-1.28, p = 0.58), nonepileptic event (OR 0.97, CI 0.62-1.53, p = 1.00), nor require rescue benzodiazepine (OR 0.63, CI 0.35-1.12, p = 0.15). Stratified analyses revealed no increased risk of epileptic seizure in any subgroups. Sensitivity analysis using exact matching showed that results were robust to matching strategy. CONCLUSIONS: Alcohol was not an effective induction technique in the EMU. This finding has implications for counseling patients with epilepsy about the risks of drinking alcohol in moderation in their daily lives.

Clinical characteristics and surgical outcomes of epilepsy associated with temporal encephalocele: A systematic review.

Temporal encephaloceles (TE) are an under-identified, potentially intervenable cause of epilepsy. This systematic review consolidates the current data to identify the major clinical, neuroimaging, and EEG features and surgical outcomes of epilepsy associated with TE. Literature searches were carried out using MEDLINE, Embase, PsycINFO, Scopus, and Cochrane Library databases from inception to December 7, 2023. Studies were included if they described clinical, neuroimaging, EEG, or surgical data in ≥5 patients with TE and epilepsy. Of 562 studies identified in the search, 24 met the eligibility criteria, reporting 423 unique patients with both epilepsy and TE. Compared to epilepsy patients without TE, those with TE had a higher mean age of seizure onset and were less likely to have a history of febrile seizures. Seizure semiologies were variable, but primarily mirrored temporal lobe onset patterns. Epilepsy patients with TE had a higher likelihood of having clinical or radiographic features of idiopathic intracranial hypertension (IIH) than those without. Brain MRI may show ipsilateral mesial temporal sclerosis (16 %). CT scans of the skull base usually revealed bony defects near the TE (90 %). Brain PET scans primarily showed ipsilateral temporal lobe hypometabolism (80 %), mostly in the anterior temporal lobe (67 %). Scalp EEG mostly lateralized ipsilateral to the implicated TE (92 % seizure onset) and localized to the temporal lobe (96 %). Intracranial EEG revealed seizure onset near the TE (11 of 12 cases including TE-adjacent electrodes) with variable timing of spread to the ipsilateral hippocampus. After surgical treatment of the TE, the rate of Engel I or ILAE 1 outcomes at one year was 75 % for lesionectomy, 85 % for anterior temporal lobectomy (ATL), and 80 % for ATL with amygdalohippocampectomy. Further studies are needed to better elucidate the relationship between IIH, TE, and epilepsy, improve the identification of TE, and optimize surgical interventions.

Spontaneous alpha-band amplitude predicts subjective visibility but not discrimination accuracy during high-level perception.

Near-threshold perception is a paradigm case of awareness diverging from reality - the perception of an unchanging stimulus can vacillate from undetected to clearly perceived. The amplitude of low-frequency brain oscillations - particularly in the alpha-band (8-13 Hz) - has emerged as a reliable predictor of trial-to-trial variability in perceptual decisions based on simple, low-level stimuli. Here, we addressed the question of how spontaneous oscillatory amplitude impacts subjective and objective aspects of perception using high-level visual stimuli. Human observers completed a near-threshold face/house discrimination task with subjective visibility ratings while electroencephalograms (EEG) were recorded. Using single-trial multiple regression analysis, we found that spontaneous fluctuations in prestimulus alpha-band amplitude were negatively related to visibility judgments but did not predict trial-by-trial accuracy. These results extend previous findings that indicate that strong prestimulus alpha diminishes subjective perception without affecting the accuracy or sensitivity (d') of perceptual decisions into the domain of high-level perception.

Attention Biases Competition for Visual Representation via Dissociable Influences from Frontal and Parietal Cortex.

What mechanisms underlie the prioritization of neural representations of visually perceived information to guide behavior? We assessed the dynamics whereby attention biases competition for representation of visual stimuli by enhancing representations of relevant information and suppressing the irrelevant. Multivariate pattern analysis (MVPA) classifiers were trained to discriminate patterns of fMRI activity associated with each of three stimuli, within several predefined ROIs. Participants performed a change-detection task wherein two of three presented items flashed at 1 Hz, one to each side of central fixation. Both flashing stimuli would unpredictably change state, but participants covertly counted the number of changes only for the cued item. In the ventral occipito-temporal ROI, MVPA evidence (a proxy for representational fidelity) was dynamically enhanced for attended stimuli and suppressed for unattended stimuli, consistent with a mechanism of biased competition between stimulus representations. Frontal and parietal ROIs displayed a qualitatively distinct, more "source-like" profile, wherein MVPA evidence for only the attended stimulus could be observed above baseline levels. To assess how attentional modulation of ventral occipito-temporal representations might relate to signals originating in the frontal and/or parietal ROIs, we analyzed informational connectivity (IC), which indexes time-varying covariation between regional levels of MVPA evidence. Parietal-posterior IC was elevated during the task, but did not differ for cued versus uncued items. Frontal-posterior IC, in contrast, was sensitive to an item's priority status. Thus, although regions of frontal and parietal cortex act as sources of top-down attentional control, their precise functions likely differ.

Parietal-Occipital Interactions Underlying Control- and Representation-Related Processes in Working Memory for Nonspatial Visual Features.

Although the manipulation of load is popular in visual working memory research, many studies confound general attentional demands with context binding by drawing memoranda from the same stimulus category. In this fMRI study of human observers (both sexes), we created high- versus low-binding conditions, while holding load constant, by comparing trials requiring memory for the direction of motion of one random dot kinematogram (RDK; 1M trials) versus for three RDKs (3M), or versus one RDK and two color patches (1M2C). Memory precision was highest for 1M trials and comparable for 3M and 1M2C trials. And although delay-period activity in occipital cortex did not differ between the three conditions, returning to baseline for all three, multivariate pattern analysis decoding of a remembered RDK from occipital cortex was also highest for 1M trials and comparable for 3M and 1M2C trials. Delay-period activity in intraparietal sulcus (IPS), although elevated for all three conditions, displayed more sensitivity to demands on context binding than to load per se. The 1M-to-3M increase in IPS signal predicted the 1M-to-3M declines in both behavioral and neural estimates of working memory precision. These effects strengthened along a caudal-to-rostral gradient, from IPS0 to IPS5. Context binding-independent load sensitivity was observed when analyses were lateralized and extended into PFC, with trend-level effects evident in left IPS and strong effects in left lateral PFC. These findings illustrate how visual working memory capacity limitations arise from multiple factors that each recruit dissociable brain systems.SIGNIFICANCE STATEMENT Visual working memory capacity predicts performance on a wide array of cognitive and real-world outcomes. At least two theoretically distinct factors are proposed to influence visual working memory capacity limitations: an amodal attentional resource that must be shared across remembered items; and the demands on context binding. We unconfounded these two factors by varying load with items drawn from the same stimulus category ("high demands on context binding") versus items drawn from different stimulus categories ("low demands on context binding"). The results provide evidence for the dissociability, and the neural bases, of these two theorized factors, and they specify that the functions of intraparietal sulcus may relate more strongly to the control of representations than to the general allocation of attention.

Within-Category Decoding of Information in Different Attentional States in Short-Term Memory.

A long-standing assumption of cognitive neuroscience has been that working memory (WM) is accomplished by sustained, elevated neural activity. More recently, theories of WM have expanded this view by describing different attentional states in WM with differing activation levels. Several studies have used multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to study neural activity corresponding to these WM states. Intriguingly, no evidence was found for active neural representations for information held in WM outside the focus of attention ("unattended memory items," UMIs), suggesting that only attended memory items (AMIs) are accompanied by an active trace. However, these results depended on category-level decoding, which lacks sensitivity to neural representations of individual items. Therefore, we employed a WM task in which subjects remembered the directions of motion of two dot arrays, with a retrocue indicating which was relevant for an imminent memory probe (the AMI). This design allowed MVPA decoding of delay-period fMRI signal at the stimulus-item level, affording a more sensitive test of the neural representation of UMIs. Whereas evidence for the AMI was reliably high, evidence for the UMI dropped to baseline, consistent with the notion that different WM attentional states may have qualitatively different mechanisms of retention.

The short- and long-term fates of memory items retained outside the focus of attention.

When a test of working memory (WM) requires the retention of multiple items, a subset of them can be prioritized. Recent studies have shown that, although prioritized (i.e., attended) items are associated with active neural representations, unprioritized (i.e., unattended) memory items can be retained in WM despite the absence of such active representations, and with no decrement in their recognition if they are cued later in the trial. These findings raise two intriguing questions about the nature of the short-term retention of information outside the focus of attention. First, when the focus of attention shifts from items in WM, is there a loss of fidelity for those unattended memory items? Second, could the retention of unattended memory items be accomplished by long-term memory mechanisms? We addressed the first question by comparing the precision of recall of attended versus unattended memory items, and found a significant decrease in precision for unattended memory items, reflecting a degradation in the quality of those representations. We addressed the second question by asking subjects to perform a WM task, followed by a surprise memory test for the items that they had seen in the WM task. Long-term memory for unattended memory items from the WM task was not better than memory for items that had remained selected by the focus of attention in the WM task. These results show that unattended WM representations are degraded in quality and are not preferentially represented in long-term memory, as compared to attended memory items.

Distributed patterns of activity in sensory cortex reflect the precision of multiple items maintained in visual short-term memory.

Traditionally, load sensitivity of sustained, elevated activity has been taken as an index of storage for a limited number of items in visual short-term memory (VSTM). Recently, studies have demonstrated that the contents of a single item held in VSTM can be decoded from early visual cortex, despite the fact that these areas do not exhibit elevated, sustained activity. It is unknown, however, whether the patterns of neural activity decoded from sensory cortex change as a function of load, as one would expect from a region storing multiple representations. Here, we use multivoxel pattern analysis to examine the neural representations of VSTM in humans across multiple memory loads. In an important extension of previous findings, our results demonstrate that the contents of VSTM can be decoded from areas that exhibit a transient response to visual stimuli, but not from regions that exhibit elevated, sustained load-sensitive delay-period activity. Moreover, the neural information present in these transiently activated areas decreases significantly with increasing load, indicating load sensitivity of the patterns of activity that support VSTM maintenance. Importantly, the decrease in classification performance as a function of load is correlated with within-subject changes in mnemonic resolution. These findings indicate that distributed patterns of neural activity in putatively sensory visual cortex support the representation and precision of information in VSTM.

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data are captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-centre study for model development; two-centre study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using several interictal EEG features derived from recent publications. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. Forty-seven patients (30 female; ages 20-69; 20 left-sided, 10 right-sided and 17 bilateral seizure onsets) were analysed for model development and internal validation. Nineteen patients (10 female; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analysed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. We replicated the finding that right temporal lobe epilepsy was harder to distinguish in a separate modality of resting-state functional MRI. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome. A potential clinical application of these findings could be to either support or oppose a hypothesis of unilateral temporal lobe epilepsy when deciding to pursue surgical resection or ablation as opposed to device implantation.

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study.

BACKGROUND AND OBJECTIVES: Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG. METHODS: A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study. RESULTS: A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, p = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, p = 0.31); in false negative rate for the 2HELPS2B = 0 group (p = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses. DISCUSSION: 2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

Decoding attended information in short-term memory: an EEG study.

For decades it has been assumed that sustained, elevated neural activity--the so-called active trace--is the neural correlate of the short-term retention of information. However, a recent fMRI study has suggested that this activity may be more related to attention than to retention. Specifically, a multivariate pattern analysis failed to find evidence that information that was outside the focus of attention, but nonetheless in STM, was retained in an active state. Here, we replicate and extend this finding by querying the neural signatures of attended versus unattended information within STM with electroencephalograpy (EEG), a method sensitive to oscillatory neural activity to which the previous fMRI study was insensitive. We demonstrate that in the delay-period EEG activity, there is information only about memory items that are also in the focus of attention. Information about items outside the focus of attention is not detectable. This result converges with the fMRI findings to suggest that, contrary to conventional wisdom, an active memory trace may be unnecessary for the short-term retention of information.

Quantifying interictal intracranial EEG to predict focal epilepsy.

Introduction: Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. Methods: We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the "5 Sense Score (5SS)," a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians' choice of therapeutic intervention and the patient outcome. Results: The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Conclusions: Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.

Resting-state background features demonstrate multidien cycles in long-term EEG device recordings.

Background: Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. Objective/Hypothesis: We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may disrupt these cycles. Methods: We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Results: Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). Conclusions: Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may disrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.

Resting-state background features demonstrate multidien cycles in long-term EEG device recordings.

BACKGROUND: Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. OBJECTIVE/HYPOTHESIS: We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. METHODS: We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. RESULTS: Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56-0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). CONCLUSIONS: Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may temporarily interrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data is captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal (between-seizure) intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-center study for model development; two-center study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using interictal EEG. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. 47 patients (30 women; ages 20-69; 20 left-sided, 10 right-sided, and 17 bilateral seizure onsets) were analyzed for model development and internal validation. 19 patients (10 women; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analyzed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome.

Training in Neurology: Objective Structured Clinical Examination Case to Teach and Model Feedback Skills in Neurology Residency.

We describe an educational intervention for neurology residents aimed at developing feedback skills. An objective structured clinical examination case was designed to simulate the provision of feedback to a medical student. After the simulated case session, residents received structured, individualized feedback on their performance and then participated in a group discussion about feedback methods. Survey data were collected from the standardized medical student regarding residents' performance and from residents for assessments of their performance and of the Objective Structured Clinical Examination case. This article aims to describe this educational intervention and to demonstrate the feasibility of this approach for feedback skills development.

The neural correlates of dreaming.

Consciousness never fades during waking. However, when awakened from sleep, we sometimes recall dreams and sometimes recall no experiences. Traditionally, dreaming has been identified with rapid eye-movement (REM) sleep, characterized by wake-like, globally 'activated', high-frequency electroencephalographic activity. However, dreaming also occurs in non-REM (NREM) sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density electroencephalography, we contrasted the presence and absence of dreaming in NREM and REM sleep. In both NREM and REM sleep, reports of dream experience were associated with local decreases in low-frequency activity in posterior cortical regions. High-frequency activity in these regions correlated with specific dream contents. Monitoring this posterior 'hot zone' in real time predicted whether an individual reported dreaming or the absence of dream experiences during NREM sleep, suggesting that it may constitute a core correlate of conscious experiences in sleep.

Dissociating Perceptual Confidence from Discrimination Accuracy Reveals No Influence of Metacognitive Awareness on Working Memory.

Visual awareness is hypothesized to be intimately related to visual working memory (WM), such that information present in WM is thought to have necessarily been represented consciously. Recent work has challenged this longstanding view by demonstrating that visual stimuli rated by observers as unseen can nevertheless be maintained over a delay period. These experiments have been criticized, however, on the basis that subjective awareness ratings may contain response bias (e.g., an observer may report no awareness when in fact they had partial awareness). We mitigated this issue by investigating WM for visual stimuli that were matched for perceptual discrimination capacity (d'), yet which varied in subjective confidence ratings (so-called relative blindsight). If the degree of initial subjective awareness of a stimulus facilitates later maintenance of that information, WM performance should improve for stimuli encoded with higher confidence. In contrast, we found that WM performance did not benefit from higher visual discrimination confidence. This relationship was observed regardless of WM load (1 or 3). Insofar as metacognitive ratings (e.g., confidence, visibility) reflect visual awareness, these results challenge a strong relationship between conscious perception and WM using a paradigm that controls for discrimination accuracy and is less subject to response bias (since confidence is manipulated within subjects). Methodologically, we replicate prior efforts to induce relative blindsight using similar stimulus displays, providing a general framework for isolating metacognitive awareness in order to examine the function of consciousness.

Reactivation of latent working memories with transcranial magnetic stimulation.

The ability to hold information in working memory is fundamental for cognition. Contrary to the long-standing view that working memory depends on sustained, elevated activity, we present evidence suggesting that humans can hold information in working memory via "activity-silent" synaptic mechanisms. Using multivariate pattern analyses to decode brain activity patterns, we found that the active representation of an item in working memory drops to baseline when attention shifts away. A targeted pulse of transcranial magnetic stimulation produced a brief reemergence of the item in concurrently measured brain activity. This reactivation effect occurred and influenced memory performance only when the item was potentially relevant later in the trial, which suggests that the representation is dynamic and modifiable via cognitive control. The results support a synaptic theory of working memory.

Multiple neural states of representation in short-term memory? It's a matter of attention.

Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.