Generalisability of epileptiform patterns across time and patients.

The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.

Evidence for Multiscale Multiplexed Representation of Visual Features in EEG.

Distinct neural processes such as sensory and memory processes are often encoded over distinct timescales of neural activations. Animal studies have shown that this multiscale coding strategy is also implemented for individual components of a single process, such as individual features of a multifeature stimulus in sensory coding. However, the generalizability of this encoding strategy to the human brain has remained unclear. We asked if individual features of visual stimuli were encoded over distinct timescales. We applied a multiscale time-resolved decoding method to electroencephalography (EEG) collected from human subjects presented with grating visual stimuli to estimate the timescale of individual stimulus features. We observed that the orientation and color of the stimuli were encoded in shorter timescales, whereas spatial frequency and the contrast of the same stimuli were encoded in longer timescales. The stimulus features appeared in temporally overlapping windows along the trial supporting a multiplexed coding strategy. These results provide evidence for a multiplexed, multiscale coding strategy in the human visual system.

Deeper neural network models better reflect how humans cope with contrast variation in object recognition.

Visual inputs are far from ideal in everyday situations such as in the fog where the contrasts of input stimuli are low. However, human perception remains relatively robust to contrast variations. To provide insights about the underlying mechanisms of contrast invariance, we addressed two questions. Do contrast effects disappear along the visual hierarchy? Do later stages of the visual hierarchy contribute to contrast invariance? We ran a behavioral experiment where we manipulated the level of stimulus contrast and the involvement of higher-level visual areas through immediate and delayed backward masking of the stimulus. Backward masking led to significant drop in performance in our visual categorization task, supporting the role of higher-level visual areas in contrast invariance. To obtain mechanistic insights, we ran the same categorization task on three state-of the-art computational models of human vision each with a different depth in visual hierarchy. We found contrast effects all along the visual hierarchy, no matter how far into the hierarchy. Moreover, that final layers of deeper hierarchical models, which had been shown to be best models of final stages of the visual system, coped with contrast effects more effectively. These results suggest that, while contrast effects reach the final stages of the hierarchy, those stages play a significant role in compensating for contrast variations in the visual system.

Caveats and Nuances of Model-Based and Model-Free Representational Connectivity Analysis.

Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarizing activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions, and transformation of representational structure across brain regions. The article is accompanied by scripts (https://osf.io/3nxfa/) that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG.

Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features.

How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, amplitude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

Dissociable Contribution of Extrastriate Responses to Representational Enhancement of Gaze Targets.

Before saccadic eye movements, our perception of the saccade targets is enhanced. Changes in the visual representation of saccade targets, which presumably underlie this perceptual benefit, emerge even before the eye begins to move. This perisaccadic enhancement has been shown to involve changes in the response magnitude, selectivity, and reliability of visual neurons. In this study, we quantified multiple aspects of perisaccadic changes in the neural response, including gain, feature tuning, contrast response function, reliability, and correlated activity between neurons. We then assessed the contributions of these various perisaccadic modulations to the population's enhanced perisaccadic representation of saccade targets. We found a partial dissociation between the motor information, carried entirely by gain changes, and visual information, which depended on all three types of modulation. These findings expand our understanding of the perisaccadic enhancement of visual representations and further support the existence of multiple sources of motor modulation and visual enhancement within extrastriate visual cortex.

#EEGManyLabs: Investigating the replicability of influential EEG experiments.

There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.

Neural signatures of vigilance decrements predict behavioural errors before they occur.

There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these 'vigilance decrements'? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. This was mirrored in neural decoding using magnetoencephalography: coding of critical information declined more during monitoring versus active conditions along the experiment. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This facilitates pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.

Perceptual difficulty modulates the direction of information flow in familiar face recognition.

Humans are fast and accurate when they recognize familiar faces. Previous neurophysiological studies have shown enhanced representations for the dichotomy of familiar vs. unfamiliar faces. As familiarity is a spectrum, however, any neural correlate should reflect graded representations for more vs. less familiar faces along the spectrum. By systematically varying familiarity across stimuli, we show a neural familiarity spectrum using electroencephalography. We then evaluated the spatiotemporal dynamics of familiar face recognition across the brain. Specifically, we developed a novel informational connectivity method to test whether peri-frontal brain areas contribute to familiar face recognition. Results showed that feed-forward flow dominates for the most familiar faces and top-down flow was only dominant when sensory evidence was insufficient to support face recognition. These results demonstrate that perceptual difficulty and the level of familiarity influence the neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.

Spatiotemporal analysis of category and target-related information processing in the brain during object detection.

To recognize a target object, the brain implements strategies which involve a combination of externally sensory-driven and internally task-driven mechanisms. While several studies have suggested a role for frontal brain areas in enhancing task-related representations in visual cortices, especially in the lateral-occipital cortex, they have remained silent about the type of information transferred to visual areas. However, the recently developed methods of representational connectivity analysis, allowed us to track the movement of different types of information in the brain. Accordingly, we designed an EEG object detection experiment and inspected the spatiotemporal dynamics of category- and target-related information across the brain. Results showed that the prefrontal area initiated the processing of target-related information. This information was then transferred to posterior brain areas during stimulus presentation probably to facilitate object detection and to direct the decision-making procedure. We also observed that, as compared to category-related information, the target-related information could predict the behavioral performance more accurately, suggesting the dominant representation of internal compared to external information in brain signals. These results provided new evidence about the role of prefrontal cortices in the processing of task-related information in the brain during object detection.

Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices.

Object recognition has been a central question in human vision research. The general consensus is that the ventral and dorsal visual streams are the major processing pathways undertaking objects' category and variation processing. This overlooks mounting evidence supporting the role of peri-frontal areas in category processing. Yet, many aspects of visual processing in peri-frontal areas have remained unattended including whether these areas play role only during active recognition and whether they interact with lower visual areas or process information independently. To address these questions, subjects were presented with a set of variation-controlled object images while their EEG were recorded. Considerable amounts of category and variation information were decodable from occipital, parietal, temporal and prefrontal electrodes. Using information-selectivity indices, phase and Granger causality analyses, three processing stages were identified showing distinct directions of information transaction between peri-frontal and peri-occipital areas suggesting their parallel yet interactive role in visual processing. A brain-plausible model supported the possibility of interactive mechanisms in peri-occipital and peri-frontal areas. These findings, while promoting the role of prefrontal areas in object recognition, extend their contributions from active recognition, in which peri-frontal to peri-occipital pathways are activated by higher cognitive processes, to the general sensory-driven object and variation processing.

Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models.

One key ability of human brain is invariant object recognition, which refers to rapid and accurate recognition of objects in the presence of variations such as size, rotation and position. Despite decades of research into the topic, it remains unknown how the brain constructs invariant representations of objects. Providing brain-plausible object representations and reaching human-level accuracy in recognition, hierarchical models of human vision have suggested that, human brain implements similar feed-forward operations to obtain invariant representations. However, conducting two psychophysical object recognition experiments on humans with systematically controlled variations of objects, we observed that humans relied on specific (diagnostic) object regions for accurate recognition which remained relatively consistent (invariant) across variations; but feed-forward feature-extraction models selected view-specific (non-invariant) features across variations. This suggests that models can develop different strategies, but reach human-level recognition performance. Moreover, human individuals largely disagreed on their diagnostic features and flexibly shifted their feature extraction strategy from view-invariant to view-specific when objects became more similar. This implies that, even in rapid object recognition, rather than a set of feed-forward mechanisms which extract diagnostic features from objects in a hard-wired fashion, the bottom-up visual pathways receive, through top-down connections, task-related information possibly processed in prefrontal cortex.

Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition.

Humans perform object recognition effortlessly and accurately. However, it is unknown how the visual system copes with variations in objects' appearance and the environmental conditions. Previous studies have suggested that affine variations such as size and position are compensated for in the feed-forward sweep of visual information processing while feedback signals are needed for precise recognition when encountering non-affine variations such as pose and lighting. Yet, no empirical data exist to support this suggestion. We systematically investigated the impact of the above-mentioned affine and non-affine variations on the categorization performance of the feed-forward mechanisms of the human brain. For that purpose, we designed a backward-masking behavioral categorization paradigm as well as a passive viewing EEG recording experiment. On a set of varying stimuli, we found that the feed-forward visual pathways contributed more dominantly to the compensation of variations in size and position compared to lighting and pose. This was reflected in both the amplitude and the latency of the category separability indices obtained from the EEG signals. Using a feed-forward computational model of the ventral visual stream, we also confirmed a more dominant role for the feed-forward visual mechanisms of the brain in the compensation of affine variations. Taken together, our experimental results support the theory that non-affine variations such as pose and lighting may need top-down feedback information from higher areas such as IT and PFC for precise object recognition.

Average activity, but not variability, is the dominant factor in the representation of object categories in the brain.

To categorize the perceived objects, brain utilizes a broad set of its resources and encoding strategies. Yet, it remains elusive how the category information is encoded in the brain. While many classical studies have sought the category information in the across-trial-averaged activity of neurons/neural populations, several recent studies have observed category information also in the within-trial correlated variability of activities between neural populations (i.e. dependent variability). Moreover, other studies have observed that independent variability of activity, which is the variability of the measured neural activity without any influence from correlated variability with other neurons/populations, could also be modulated for improved categorization. However, it was unknown how important each of the three factors (i.e. average activity, dependent and independent variability of activities) was in category encoding. Therefore, we designed an EEG experiment in which human subjects viewed a set of object exemplars from four categories. Using a computational model, we evaluated the contribution of each factor separately in category encoding. Results showed that the average activity played a significant role while the independent variability, although effective, contributed moderately to the category encoding. The inter-channel dependent variability showed an ignorable effect on the encoding. We also investigated the role of those factors in the encoding of variations which showed similar effects. These results imply that the brain, rather than variability, seems to use the average activity to convey information on the category of the perceived objects.

Skin Temperature Over the Carotid Artery, an Accurate Non-invasive Estimation of Near Core Temperature.

BACKGROUND: During anesthesia, continuous body temperature monitoring is essential, especially in children. Anesthesia can increase the risk of loss of body temperature by three to four times. Hypothermia in children results in increased morbidity and mortality. Since the measurement points of the core body temperature are not easily accessible, near core sites, like rectum, are used. OBJECTIVES: The purpose of this study was to measure skin temperature over the carotid artery and compare it with the rectum temperature, in order to propose a model for accurate estimation of near core body temperature. PATIENTS AND METHODS: Totally, 124 patients within the age range of 2 - 6 years, undergoing elective surgery, were selected. Temperature of rectum and skin over the carotid artery was measured. Then, the patients were randomly divided into two groups (each including 62 subjects), namely modeling (MG) and validation groups (VG). First, in the modeling group, the average temperature of the rectum and skin over the carotid artery were measured separately. The appropriate model was determined, according to the significance of the model's coefficients. The obtained model was used to predict the rectum temperature in the second group (VG group). Correlation of the predicted values with the real values (the measured rectum temperature) in the second group was investigated. Also, the difference in the average values of these two groups was examined in terms of significance. RESULTS: In the modeling group, the average rectum and carotid temperatures were 36.47 ± 0.54°C and 35.45 ± 0.62°C, respectively. The final model was obtained, as follows: Carotid temperature × 0.561 + 16.583 = Rectum temperature. The predicted value was calculated based on the regression model and then compared with the measured rectum value, which showed no significant difference (P = 0.361). CONCLUSIONS: The present study was the first research, in which rectum temperature was compared with that of skin over carotid artery, to find a safe location with easier access and higher accuracy for estimating near core body temperature. Results obtained in this study showed that, using a model, it is possible to evaluate near core body temperature in children, by measuring skin temperature over carotid artery.

Seroprevalence of Human Fasciolosis in Pirabad, Lorestan Province, Western Iran.

BACKGROUND: The purpose of this study was to perform seroepidemiological investigation for determining the status of human fasciolosis in Pirabad Village, Lorestan Province, western Iran. METHODS: Blood samples were taken from residents of the village including 801 individuals. Sera were separated and stored at -20°C until used. The samples were analyzed using ELISA. RESULTS: Anti-Fasciola antibodies were detected in 6 (0.7%) individuals. Difference between age, sex and drinking or swimming in the surface water with seropositivity to fasciolosis was not significant. Out of 7 shepherds, 1 (14.3%) was seropositive. Due to the small number of shepherds, comprehensive statistical inference in this regard cannot be done. Significant difference was detected between seropositivity to fasciolosis and consuming local freshwater vegetables during the last 6 months (P=0.001). CONCLUSION: Metacercariae carrying local freshwater plants might be the main source of contamination because consumption of these kinds of vegetables was confirmed by all participants. Awareness of local communities regarding the danger of freshwater plant consumption, through health education programs, will decrease the risk of infection.