Architecture of the brain's visual system enhances network stability and performance through layers, delays, and feedback.

In the visual system of primates, image information propagates across successive cortical areas, and there is also local feedback within an area and long-range feedback across areas. Recent findings suggest that the resulting temporal dynamics of neural activity are crucial in several vision tasks. In contrast, artificial neural network models of vision are typically feedforward and do not capitalize on the benefits of temporal dynamics, partly due to concerns about stability and computational costs. In this study, we focus on recurrent networks with feedback connections for visual tasks with static input corresponding to a single fixation. We demonstrate mathematically that a network's dynamics can be stabilized by four key features of biological networks: layer-ordered structure, temporal delays between layers, longer distance feedback across layers, and nonlinear neuronal responses. Conversely, when feedback has a fixed distance, one can omit delays in feedforward connections to achieve more efficient artificial implementations. We also evaluated the effect of feedback connections on object detection and classification performance using standard benchmarks, specifically the COCO and CIFAR10 datasets. Our findings indicate that feedback connections improved the detection of small objects, and classification performance became more robust to noise. We found that performance increased with the temporal dynamics, not unlike what is observed in core vision of primates. These results suggest that delays and layered organization are crucial features for stability and performance in both biological and artificial recurrent neural networks.

Synchronized eye movements predict test scores in online video education.

Experienced teachers pay close attention to their students, adjusting their teaching when students seem lost. This dynamic interaction is missing in online education. We hypothesized that attentive students follow videos similarly with their eyes. Thus, attention to instructional videos could be assessed remotely by tracking eye movements. Here we show that intersubject correlation of eye movements during video presentation is substantially higher for attentive students and that synchronized eye movements are predictive of individual test scores on the material presented in the video. These findings replicate for videos in a variety of production styles, for incidental and intentional learning and for recall and comprehension questions alike. We reproduce the result using standard web cameras to capture eye movements in a classroom setting and with over 1,000 participants at home without the need to transmit user data. Our results suggest that online education could be made adaptive to a student's level of attention in real time.

Crowd wisdom enhanced by costly signaling in a virtual rating system.

Costly signaling theory was developed in both economics and biology and has been used to explain a wide range of phenomena. However, the theory's prediction that signal cost can enforce information quality in the design of new communication systems has never been put to an empirical test. Here we show that imposing time costs on reporting extreme scores can improve crowd wisdom in a previously cost-free rating system. We developed an online game where individuals interacted repeatedly with simulated services and rated them for satisfaction. We associated ratings with differential time costs by endowing the graphical user interface that solicited ratings from the users with "physics," including an initial (default) slider position and friction. When ratings were not associated with differential cost (all scores from 0 to 100 could be given by an equally low-cost click on the screen), scores correlated only weakly with objective service quality. However, introducing differential time costs, proportional to the deviation from the mean score, improved correlations between subjective rating scores and objective service performance and lowered the sample size required for obtaining reliable, averaged crowd estimates. Boosting time costs for reporting extreme scores further facilitated the detection of top performances. Thus, human collective online behavior, which is typically cost-free, can be made more informative by applying costly signaling via the virtual physics of rating devices.

Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.

In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason. This diagnostic dataset was enriched to have 259 hydrocephalus cases. A 3D convolutional neural network was trained on 16 manually segmented exams (ten hydrocephalus) and subsequently used to automatically segment the remaining 480 exams and extract volumetric anatomical features. A linear classifier of these features was trained on 240 exams to detect cases of hydrocephalus that required treatment with surgical intervention. Performance was compared to four neuroradiologists on the remaining 240 exams. Performance was also evaluated on a separate screening dataset of 451 exams collected from a routine clinical population to predict the consensus reading from four neuroradiologists using images alone. The pipeline was also tested on an external dataset of 31 exams from a 2nd clinical site. The most discriminant features were the Magnetic Resonance Hydrocephalic Index (MRHI), ventricle volume, and the ratio between ventricle and brain volume. At matching sensitivity, the specificity of the machine and the neuroradiologists did not show significant differences for detection of hydrocephalus on either dataset (proportions test, p > 0.05). ROC performance compared favorably with the state-of-the-art (AUC 0.90-0.96), and replicated in the external validation. Hydrocephalus cases requiring treatment can be detected automatically from MRI in a heterogeneous patient population based on quantitative characterization of brain anatomy with performance comparable to that of neuroradiologists.

Neural responses to natural visual motion are spatially selective across the visual field, with selectivity differing across brain areas and task.

It is well established that neural responses to visual stimuli are enhanced at select locations in the visual field. Although spatial selectivity and the effects of spatial attention are well understood for discrete tasks (e.g. visual cueing), little is known for naturalistic experience that involves continuous dynamic visual stimuli (e.g. driving). Here, we assess the strength of neural responses across the visual space during a kart-race game. Given the varying relevance of visual location in this task, we hypothesized that the strength of neural responses to movement will vary across the visual field, and it would differ between active play and passive viewing. To test this, we measure the correlation strength of scalp-evoked potentials with optical flow magnitude at individual locations on the screen. We find that neural responses are strongly correlated at task-relevant locations in visual space, extending beyond the focus of overt attention. Although the driver's gaze is directed upon the heading direction at the centre of the screen, neural responses were robust at the peripheral areas (e.g. roads and surrounding buildings). Importantly, neural responses to visual movement are broadly distributed across the scalp, with visual spatial selectivity differing across electrode locations. Moreover, during active gameplay, neural responses are enhanced at select locations in the visual space. Conventionally, spatial selectivity of neural response has been interpreted as an attentional gain mechanism. In the present study, the data suggest that different brain areas focus attention on different portions of the visual field that are task-relevant, beyond the focus of overt attention.

A double-blind sham-controlled phase 1 clinical trial of tDCS of the dorsolateral prefrontal cortex in cocaine inpatients: Craving, sleepiness, and contemplation to change.

Impaired inhibitory control accompanied by enhanced salience attributed to drug-related cues, both associated with function of the dorsolateral prefrontal cortex (dlPFC), are hallmarks of drug addiction, contributing to worse symptomatology including craving. dlPFC modulation with transcranial direct current stimulation (tDCS) previously showed craving reduction in inpatients with cocaine use disorder (CUD). Our study aimed at assessing feasibility of a longer tDCS protocol in CUD (15 versus the common five/10 sessions) and replicability of previous results. In a randomized double-blind sham-controlled protocol, 17 inpatients with CUD were assigned to either a real-tDCS (right anodal/left cathodal) or a sham-tDCS condition for 15 sessions. Following the previous report, primary outcome measures were self-reported craving, anxiety, depression, and quality of life. Secondary measures included sleepiness, readiness to change drug use, and affect. We also assessed cognitive function including impulsivity. An 88% retention rate demonstrated feasibility. Partially supporting the previous results, there was a trend for self-reported craving to decrease in the real-tDCS group more than the sham-group, an effect that would reach significance with 15 subjects per group. Quality of life and impulsivity improved over time in treatment in both groups. Daytime sleepiness and readiness to change drug use showed significant Group × Time interactions whereby improvements were noted only in the real-tDCS group. One-month follow-up suggested transient effects of tDCS on sleepiness and craving. These preliminary results suggest the need for including more subjects to show a unique effect of real-tDCS on craving and examine the duration of this effect. After replication in larger sample sizes, increased vigilance and motivation to change drug use in the real-tDCS group may suggest fortification of dlPFC-supported executive functions.

During natural viewing, neural processing of visual targets continues throughout saccades.

Relatively little is known about visual processing during free-viewing visual search in realistic dynamic environments. Free-viewing is characterized by frequent saccades. During saccades, visual processing is thought to be suppressed, yet we know that the presaccadic visual content can modulate postsaccadic processing. To better understand these processes in a realistic setting, we study here saccades and neural responses elicited by the appearance of visual targets in a realistic virtual environment. While subjects were being driven through a 3D virtual town, they were asked to discriminate between targets that appear on the road. Using a system identification approach, we separated overlapping and correlated activity evoked by visual targets, saccades, and button presses. We found that the presence of a target enhances early occipital as well as late frontocentral saccade-related responses. The earlier potential, shortly after 125 ms post-saccade onset, was enhanced for targets that appeared in the peripheral vision as compared to the central vision, suggesting that fast peripheral processing initiated before saccade onset. The later potential, at 195 ms post-saccade onset, was strongly modulated by the visibility of the target. Together these results suggest that, during natural viewing, neural processing of the presaccadic visual stimulus continues throughout the saccade, apparently unencumbered by saccadic suppression.

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI.

A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ''functional connectivity'' -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.

Visually evoked responses are enhanced when engaging in a video game.

While it is well known that vision guides movement, less appreciated is that the motor cortex also provides input to the visual system. Here, we asked whether neural processing of visual stimuli is acutely modulated during motor activity, hypothesizing that visual evoked responses are enhanced when engaged in a motor task that depends on the visual stimulus. To test this, we told participants that their brain activity was controlling a video game that was in fact the playback of a prerecorded game. The deception, which was effective in half of participants, aimed to engage the motor system while avoiding evoked responses related to actual movement or somatosensation. In other trials, subjects actively played the game with keyboard control or passively watched a playback. The strength of visually evoked responses was measured as the temporal correlation between the continuous stimulus and the evoked potentials on the scalp. We found reduced correlation during passive viewing, but no difference between active and sham play. Alpha-band (8-12 Hz) activity was reduced over central electrodes during sham play, indicating recruitment of motor cortex despite the absence of overt movement. To account for the potential increase of attention during gameplay, we conducted a second study with subjects counting screen items during viewing. We again found increased correlation during sham play, but no difference between counting and passive viewing. While we cannot fully rule out the involvement of attention, our findings do demonstrate an enhancement of visual evoked responses during active vision.

Inherent physiological artifacts in EEG during tDCS.

Online imaging and neuromodulation is invalid if stimulation distorts measurements beyond the point of accurate measurement. In theory, combining transcranial Direct Current Stimulation (tDCS) with electroencephalography (EEG) is compelling, as both use non-invasive electrodes and image-guided dose can be informed by the reciprocity principle. To distinguish real changes in EEG from stimulation artifacts, prior studies applied conventional signal processing techniques (e.g. high-pass filtering, ICA). Here, we address the assumptions underlying the suitability of these approaches. We distinguish physiological artifacts - defined as artifacts resulting from interactions between the stimulation induced voltage and the body and so inherent regardless of tDCS or EEG hardware performance - from methodology-related artifacts - arising from non-ideal experimental conditions or non-ideal stimulation and recording equipment performance. Critically, we identify inherent physiological artifacts which are present in all online EEG-tDCS: 1) cardiac distortion and 2) ocular motor distortion. In conjunction, non-inherent physiological artifacts which can be minimized in most experimental conditions include: 1) motion and 2) myogenic distortion. Artifact dynamics were analyzed for varying stimulation parameters (montage, polarity, current) and stimulation hardware. Together with concurrent physiological monitoring (ECG, respiration, ocular, EMG, head motion), and current flow modeling, each physiological artifact was explained by biological source-specific body impedance changes, leading to incremental changes in scalp DC voltage that are significantly larger than real neural signals. Because these artifacts modulate the DC voltage and scale with applied current, they are dose specific such that their contamination cannot be accounted for by conventional experimental controls (e.g. differing stimulation montage or current as a control). Moreover, because the EEG artifacts introduced by physiologic processes during tDCS are high dimensional (as indicated by Generalized Singular Value Decomposition- GSVD), non-stationary, and overlap highly with neurogenic frequencies, these artifacts cannot be easily removed with conventional signal processing techniques. Spatial filtering techniques (GSVD) suggest that the removal of physiological artifacts would significantly degrade signal integrity. Physiological artifacts, as defined here, would emerge only during tDCS, thus processing techniques typically applied to EEG in the absence of tDCS would not be suitable for artifact removal during tDCS. All concurrent EEG-tDCS must account for physiological artifacts that are a) present regardless of equipment used, and b) broadband and confound a broad range of experiments (e.g. oscillatory activity and event related potentials). Removal of these artifacts requires the recognition of their non-stationary, physiology-specific dynamics, and individualized nature. We present a broad taxonomy of artifacts (non/stimulation related), and suggest possible approaches and challenges to denoising online EEG-tDCS stimulation artifacts.

Multiway canonical correlation analysis of brain data.

Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.

Extracting multidimensional stimulus-response correlations using hybrid encoding-decoding of neural activity.

In neuroscience, stimulus-response relationships have traditionally been analyzed using either encoding or decoding models. Here we propose a hybrid approach that decomposes neural activity into multiple components, each representing a portion of the stimulus. The technique is implemented via canonical correlation analysis (CCA) by temporally filtering the stimulus (encoding) and spatially filtering the neural responses (decoding) such that the resulting components are maximally correlated. In contrast to existing methods, this approach recovers multiple correlated stimulus-response pairs, and thus affords a richer, multidimensional analysis of neural representations. We first validated the technique's ability to recover multiple stimulus-driven components using electroencephalographic (EEG) data simulated with a finite element model of the head. We then applied the technique to real EEG responses to auditory and audiovisual narratives experienced identically across subjects, as well as uniquely experienced video game play. During narratives, both auditory and visual stimulus-response correlations (SRC) were modulated by attention and tracked inter-subject correlations. During video game play, SRC varied with game difficulty and the presence of a dual task. Interestingly, the strongest component extracted for visual and auditory features of film clips had nearly identical spatial distributions, suggesting that the predominant encephalographic response to naturalistic stimuli is supramodal. The diversity of these findings demonstrates the utility of measuring multidimensional SRC via hybrid encoding-decoding.

Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus.

Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (NEEG = 45, NfMRI = 11, NECoG = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing.

Neural engagement with online educational videos predicts learning performance for individual students.

Online educational materials are largely disseminated through videos, and yet there is little understanding of how these videos engage students and fuel academic success. We hypothesized that components of the electroencephalogram (EEG), previously shown to reflect video engagement, would be predictive of academic performance in the context of educational videos. Two groups of subjects watched educational videos in either an intentional learning paradigm, in which they were aware of an upcoming test, or in an incidental learning paradigm, in which they were unaware that they would be tested. "Neural engagement" was quantified by the inter-subject correlation (ISC) of the EEG that was evoked by the videos. In both groups, students with higher neural engagement retained more information. Neural engagement also discriminated between attentive and inattentive video viewing. These results suggest that this EEG metric is a marker of the stimulus-related attentional mechanisms necessary to retain information. In the future, EEG may be used as a tool to design and assess online educational content.

Attention Strongly Modulates Reliability of Neural Responses to Naturalistic Narrative Stimuli.

Attentional engagement is a major determinant of how effectively we gather information through our senses. Alongside the sheer growth in the amount and variety of information content that we are presented with through modern media, there is increased variability in the degree to which we "absorb" that information. Traditional research on attention has illuminated the basic principles of sensory selection to isolated features or locations, but it provides little insight into the neural underpinnings of our attentional engagement with modern naturalistic content. Here, we show in human subjects that the reliability of an individual's neural responses with respect to a larger group provides a highly robust index of the level of attentional engagement with a naturalistic narrative stimulus. Specifically, fast electroencephalographic evoked responses were more strongly correlated across subjects when naturally attending to auditory or audiovisual narratives than when attention was directed inward to a mental arithmetic task during stimulus presentation. This effect was strongest for audiovisual stimuli with a cohesive narrative and greatly reduced for speech stimuli lacking meaning. For compelling audiovisual narratives, the effect is remarkably strong, allowing perfect discrimination between attentional state across individuals. Control experiments rule out possible confounds related to altered eye movement trajectories or order of presentation. We conclude that reliability of evoked activity reproduced across subjects viewing the same movie is highly sensitive to the attentional state of the viewer and listener, which is aided by a cohesive narrative.

Direct current stimulation boosts synaptic gain and cooperativity in vitro.

KEY POINTS: Direct current stimulation (DCS) polarity specifically modulates synaptic efficacy during a continuous train of presynaptic inputs, despite synaptic depression. DCS polarizes afferent axons and postsynaptic neurons, boosting cooperativity between synaptic inputs. Polarization of afferent neurons in upstream brain regions may modulate activity in the target brain region during transcranial DCS (tDCS). A statistical theory of coincident activity predicts that the diffuse and weak profile of current flow can be advantageous in enhancing connectivity between co-active brain regions. ABSTRACT: Transcranial direct current stimulation (tDCS) produces sustained and diffuse current flow in the brain with effects that are state dependent and outlast stimulation. A mechanistic explanation for tDCS should capture these spatiotemporal features. It remains unclear how sustained DCS affects ongoing synaptic dynamics and how modulation of afferent inputs by diffuse stimulation changes synaptic activity at the target brain region. We tested the effect of acute DCS (10-20 V m-1 for 3-5 s) on synaptic dynamics with constant rate (5-40 Hz) and Poisson-distributed (4 Hz mean) trains of presynaptic inputs. Across tested frequencies, sustained synaptic activity was modulated by DCS with polarity-specific effects. Synaptic depression attenuates the sensitivity to DCS from 1.1% per V m-1 to 0.55%. DCS applied during synaptic activity facilitates cumulative neuromodulation, potentially reversing endogenous synaptic depression. We establish these effects are mediated by both postsynaptic membrane polarization and afferent axon fibre polarization, which boosts cooperativity between synaptic inputs. This potentially extends the locus of neuromodulation from the nominal target to afferent brain regions. Based on these results we hypothesized the polarization of afferent neurons in upstream brain regions may modulate activity in the target brain region during tDCS. A multiscale model of transcranial electrical stimulation including a finite element model of brain current flow, numerical simulations of neuronal activity, and a statistical theory of coincident activity predicts that the diffuse and weak profile of current flow can be advantageous. Thus, we propose that specifically because tDCS is diffuse, weak and sustained it can boost connectivity between co-active brain regions.

Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation.

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4-7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation.

The New York Head-A precise standardized volume conductor model for EEG source localization and tES targeting.

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semi-automated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an 'arbitrary' individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck-two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5mm(3) resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the 'ground truth') is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an 'individualized' BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption.

Neural correlates of perceived confidence in a partial report paradigm.

Confidence judgments are often severely distorted: People may feel underconfident when responding correctly or, conversely, overconfident in erred responses. Our aim here was to identify the timing of brain processes that lead to variations in objective performance and subjective judgments of confidence. We capitalized on the Partial Report Paradigm [Sperling, G. The information available in brief visual presentations. Psychological Monographs: General and Applied, 74, 1, 1960], which allowed us to separate experimentally the moment of encoding of information from that of its retrieval [Zylberberg, A., Dehaene, S., Mindlin, G. B., & Sigman, M. Neurophysiological bases of exponential sensory decay and top-down memory retrieval: A model. Frontiers in Computational Neuroscience, 3, 2009]. We observed that the level of subjective confidence is indexed by two very specific evoked potentials at latencies of about 400 and 600 msec during the retrieval stage and by a stationary measure of intensity of the alpha band during the encoding period. When factoring out the effect of confidence, objective performance shows a weak effect during the encoding and retrieval periods. These results have relevant implications for theories of decision-making and confidence, suggesting that confidence is not constructed online as evidence is accumulated toward a decision. Instead, confidence attributions are more consistent with a retrospective mechanism that monitors the entire decision process.

Lasting modulation of in vitro oscillatory activity with weak direct current stimulation.

Transcranial direct current stimulation (tDCS) is emerging as a versatile tool to affect brain function. While the acute neurophysiological effects of stimulation are well understood, little is know about the long-term effects. One hypothesis is that stimulation modulates ongoing neural activity, which then translates into lasting effects via physiological plasticity. Here we used carbachol-induced gamma oscillations in hippocampal rat slices to establish whether prolonged constant current stimulation has a lasting effect on endogenous neural activity. During 10 min of stimulation, the power and frequency of gamma oscillations, as well as multiunit activity, were modulated in a polarity specific manner. Remarkably, the effects on power and multiunit activity persisted for more than 10 min after stimulation terminated. Using a computational model we propose that altered synaptic efficacy in excitatory and inhibitory pathways could be the source of these lasting effects. Future experimental studies using this novel in vitro preparation may be able to confirm or refute the proposed hypothesis.