A 3D ray traced biological neural network learning model.

Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another dataset. However, current choices of transfer learning algorithms are limited because the transferred models always have to adhere to the dimensions of the base model and can not easily modify the neural architecture to solve other datasets. On the other hand, biological neural networks (BNNs) are adept at rearranging themselves to tackle completely different problems using transfer learning. Taking advantage of BNNs, we design a dynamic neural network that is transferable to any other network architecture and can accommodate many datasets. Our approach uses raytracing to connect neurons in a three-dimensional space, allowing the network to grow into any shape or size. In the Alcala dataset, our transfer learning algorithm trains the fastest across changing environments and input sizes. In addition, we show that our algorithm also outperformance the state of the art in EEG dataset. In the future, this network may be considered for implementation on real biological neural networks to decrease power consumption.

A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition.

Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher information transfer rate (ITR) by selecting an appropriate window length. These methods dynamically evaluate the credibility of the result by linear discriminant analysis (LDA) or Bayesian estimation and extend the window length until credible results are obtained. However, the hypotheses introduced by LDA and Bayesian estimation may not align with the collected real-world SSVEPs, which leads to an inappropriate window length. To address the issue, we propose a novel dynamic window method based on reinforcement learning (RL). The proposed method optimizes the decision of whether to extend the window length based on the impact of decisions on the ITR, without additional hypotheses. The decision model can automatically learn a strategy that maximizes the ITR through trial and error. In addition, compared with traditional methods that manually extract features, the proposed method uses neural networks to automatically extract features for the dynamic selection of window length. Therefore, the proposed method can more accurately decide whether to extend the window length and select an appropriate window length. To verify the performance, we compared the novel method with other dynamic window methods on two public SSVEP datasets. The experimental results demonstrate that the novel method achieves the highest performance by using RL.

Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder.

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorders.

Auditory gamma-band entrainment enhances default mode network connectivity in dementia patients.

Dementia, and in particular Alzheimer's disease (AD), can be characterized by disrupted functional connectivity in the brain caused by beta-amyloid deposition in neural links. Non-pharmaceutical treatments for dementia have recently explored interventions involving the stimulation of neuronal populations in the gamma band. These interventions aim to restore brain network functionality by synchronizing rhythmic energy through various stimulation modalities. Entrainment, a newly proposed non-invasive sensory stimulation method, has shown promise in improving cognitive functions in dementia patients. This study investigates the effectiveness of entrainment in terms of promoting neural synchrony and spatial connectivity across the cortex. EEG signals were recorded during a 40 Hz auditory entrainment session conducted with a group of elderly participants with dementia. Phase locking value (PLV) between different intraregional and interregional sites was examined as an attribute of network synchronization, and connectivity of local and distant links were compared during the stimulation and rest trials. Our findings demonstrate enhanced neural synchrony between the frontal and parietal regions, which are key components of the brain's default mode network (DMN). The DMN operation is known to be impacted by dementia's progression, leading to reduced functional connectivity across the parieto-frontal pathways. Notably, entrainment alone significantly improves synchrony between these DMN components, suggesting its potential for restoring functional connectivity.

Validity of central pain processing biomarkers for predicting the occurrence of oncological chronic pain: a study protocol.

BACKGROUND: Despite recent improvements in cancer detection and survival rates, managing cancer-related pain remains a significant challenge. Compared to neuropathic and inflammatory pain conditions, cancer pain mechanisms are poorly understood, despite pain being one of the most feared symptoms by cancer patients and significantly impairing their quality of life, daily activities, and social interactions. The objective of this work was to select a panel of biomarkers of central pain processing and modulation and assess their ability to predict chronic pain in patients with cancer using predictive artificial intelligence (AI) algorithms. METHODS: We will perform a prospective longitudinal cohort, multicentric study involving 450 patients with a recent cancer diagnosis. These patients will undergo an in-person assessment at three different time points: pretreatment, 6 months, and 12 months after the first visit. All patients will be assessed through demographic and clinical questionnaires and self-report measures, quantitative sensory testing (QST), and electroencephalography (EEG) evaluations. We will select the variables that best predict the future occurrence of pain using a comprehensive approach that includes clinical, psychosocial, and neurophysiological variables. DISCUSSION: This study aimed to provide evidence regarding the links between poor pain modulation mechanisms at precancer treatment in patients who will later develop chronic pain and to clarify the role of treatment modality (modulated by age, sex and type of cancer) on pain. As a final output, we expect to develop a predictive tool based on AI that can contribute to the anticipation of the future occurrence of pain and help in therapeutic decision making.

An EEG channel selection method for motor imagery based on Fisher score and local optimization.

Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

Unreported Source of Interference with the Bispectral Index During Liposuction: A Case Report.

Incorrect bispectral index (BIS) values have been reported due to interference with this monitoring system. We report a case of a 46-year-old woman who underwent liposuction and breast lipofilling, where we observed a misinterpretation by the BIS algorithm that has not yet been reported. Concurrently with abdominal and thigh liposuction, an increase in the BIS value was observed. The importance of examining electroencephalogram (EEG) and density spectral array (DSA) readings during liposuction procedures is highlighted in this case report, extending our observations beyond just the numerical BIS value, which is not always reliable.

Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study.

Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person's face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. METHOD: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. RESULTS: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. CONCLUSION: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

Differences in visuospatial cognition among table tennis players of different skill levels: an event-related potential study.

This study aimed to examine the influence of sport skill levels on behavioural and neuroelectric performance in visuospatial attention and memory visuospatial tasks were administered to 54 participants, including 18 elite and 18 amateur table tennis players and 18 nonathletes, while event-related potentials were recorded. In all the visuospatial attention and memory conditions, table tennis players displayed faster reaction times than nonathletes, regardless of skill level, although there was no difference in accuracy between groups. In addition, regardless of task conditions, both player groups had a greater P3 amplitude than nonathletes, and elite players exhibited a greater P3 amplitude than amateurs players. The results of this study indicate that table tennis players, irrespective of their skill level, exhibit enhanced visuospatial capabilities. Notably, athletes at the elite level appear to benefit from an augmented allocation of attentional resources when engaging in visuospatial tasks.

Peri-ictal Water Drinking in an Omani Patient with Bilateral Mesial Temporal Sclerosis.

Peri-ictal water drinking (PIWD) is a rare vegetative manifestation of temporal lobe epilepsy without a definite lateralisation value. We report a case of PIWD in a 22-year-old Omani male patient with post-concussion syndrome and epilepsy presented to a tertiary care hospital in Muscat, Oman, in 2021 for evaluation of paroxysmal events. His behaviour of PIWD was misinterpreted by his family until characterised in the epilepsy-monitoring unit as a manifestation of epilepsy that was treated medically. To the best of the authors' knowledge, this is the second reported case in the region.

[Guidelines for uniform management of breath-holding spells]. / Nya handläggningsrutiner för affektanfall hos barn föreslås.

Witnessing breath-holding spells (BHS) can be distressing and patients with BHS disproportionately consume a substantial amount of health care resources. Common among preschool children, BHS follow a distinct sequence of events. A comprehensive patient history is the primary diagnostic tool. BHS lacked standardized diagnostic criteria and guidelines until our recent Acta Paediatrica publication. Studying 519 BHS cases in Skåne (years 2004-2018), we found overuse of electrocardiograms (ECGs) and electroencephalograms (EEGs), and underuse of blood tests for treatable iron deficiency and anemia, both known BHS contributors. Building upon our cohort analysis, we refined the definition of BHS and introduced a clinical management algorithm. Simulations showed reduced EEG and ECG use and an increase in blood tests. Our guideline not only streamlines diagnostic processes, but also optimizes the allocation of healthcare resources for more effective and targeted interventions.

Multi-response deconvolution of auditory evoked potentials in a reduced representation space.

The estimation of auditory evoked potentials requires deconvolution when the duration of the responses to be recovered exceeds the inter-stimulus interval. Based on least squares deconvolution, in this article we extend the procedure to the case of a multi-response convolutional model, that is, a model in which different categories of stimulus are expected to evoke different responses. The computational cost of the multi-response deconvolution significantly increases with the number of responses to be deconvolved, which restricts its applicability in practical situations. In order to alleviate this restriction, we propose to perform the multi-response deconvolution in a reduced representation space associated with a latency-dependent filtering of auditory responses, which provides a significant dimensionality reduction. We demonstrate the practical viability of the multi-response deconvolution with auditory responses evoked by clicks presented at different levels and categorized according to their stimulation level. The multi-response deconvolution applied in a reduced representation space provides the least squares estimation of the responses with a reasonable computational load. matlab/Octave code implementing the proposed procedure is included as supplementary material.

GABAergic neurons of anterior thalamic reticular nucleus regulate states of consciousness in propofol- and isoflurane-mediated general anesthesia.

BACKGROUND: The thalamus system plays critical roles in the regulation of reversible unconsciousness induced by general anesthetics, especially the arousal stage of general anesthesia (GA). But the function of thalamus in GA-induced loss of consciousness (LOC) is little known. The thalamic reticular nucleus (TRN) is the only GABAergic neurons-composed nucleus in the thalamus, which is composed of parvalbumin (PV) and somatostatin (SST)-expressing GABAergic neurons. The anterior sector of TRN (aTRN) is indicated to participate in the induction of anesthesia, but the roles remain unclear. This study aimed to reveal the role of the aTRN in propofol and isoflurane anesthesia. METHODS: We first set up c-Fos straining to monitor the activity variation of aTRNPV and aTRNSST neurons during propofol and isoflurane anesthesia. Subsequently, optogenetic tools were utilized to activate aTRNPV and aTRNSST neurons to elucidate the roles of aTRNPV and aTRNSST neurons in propofol and isoflurane anesthesia. Electroencephalogram (EEG) recordings and behavioral tests were recorded and analyzed. Lastly, chemogenetic activation of the aTRNPV neurons was applied to confirm the function of the aTRN neurons in propofol and isoflurane anesthesia. RESULTS: c-Fos straining showed that both aTRNPV and aTRNSST neurons are activated during the LOC period of propofol and isoflurane anesthesia. Optogenetic activation of aTRNPV and aTRNSST neurons promoted isoflurane induction and delayed the recovery of consciousness (ROC) after propofol and isoflurane anesthesia, meanwhile chemogenetic activation of the aTRNPV neurons displayed the similar effects. Moreover, optogenetic and chemogenetic activation of the aTRN neurons resulted in the accumulated burst suppression ratio (BSR) during propofol and isoflurane GA, although they represented different effects on the power distribution of EEG frequency. CONCLUSION: Our findings reveal that the aTRN GABAergic neurons play a critical role in promoting the induction of propofol- and isoflurane-mediated GA.

Emergent Management of Status Epilepticus.

OBJECTIVE: Status epilepticus is a neurologic emergency that can be life- threatening. The key to effective management is recognition and prompt initiation of treatment. Management of status epilepticus requires a patient-specific-approach framework, consisting of four axes: (1) semiology, (2) etiology, (3) EEG correlate, and (4) age. This article provides a comprehensive overview of status epilepticus, highlighting the current treatment approaches and strategies for management and control. LATEST DEVELOPMENTS: Administering appropriate doses of antiseizure medication in a timely manner is vital for halting seizure activity. Benzodiazepines are the first-line treatment, as demonstrated by three randomized controlled trials in the hospital and prehospital settings. Benzodiazepines can be administered through IV, intramuscular, rectal, or intranasal routes. If seizures persist, second-line treatments such as phenytoin and fosphenytoin, valproate, or levetiracetam are warranted. The recently published Established Status Epilepticus Treatment Trial found that all three of these drugs are similarly effective in achieving seizure cessation in approximately half of patients. For cases of refractory and super-refractory status epilepticus, IV anesthetics, including ketamine and γ-aminobutyric acid-mediated (GABA-ergic) medications, are necessary. There is an increasing body of evidence supporting the use of ketamine, not only in the early phases of stage 3 status epilepticus but also as a second-line treatment option. ESSENTIAL POINTS: As with other neurologic emergencies, "time is brain" when treating status epilepticus. Antiseizure medication should be initiated quickly to achieve seizure cessation. There is a need to explore newer generations of antiseizure medications and nonpharmacologic modalities to treat status epilepticus.

Probing a neural unreliability account of auditory sensory processing atypicalities in Rett Syndrome.

BACKGROUND: In the search for objective tools to quantify neural function in Rett Syndrome (RTT), which are crucial in the evaluation of therapeutic efficacy in clinical trials, recordings of sensory-perceptual functioning using event-related potential (ERP) approaches have emerged as potentially powerful tools. Considerable work points to highly anomalous auditory evoked potentials (AEPs) in RTT. However, an assumption of the typical signal-averaging method used to derive these measures is "stationarity" of the underlying responses - i.e. neural responses to each input are highly stereotyped. An alternate possibility is that responses to repeated stimuli are highly variable in RTT. If so, this will significantly impact the validity of assumptions about underlying neural dysfunction, and likely lead to overestimation of underlying neuropathology. To assess this possibility, analyses at the single-trial level assessing signal-to-noise ratios (SNR), inter-trial variability (ITV) and inter-trial phase coherence (ITPC) are necessary. METHODS: AEPs were recorded to simple 100 Hz tones from 18 RTT and 27 age-matched controls (Ages: 6-22 years). We applied standard AEP averaging, as well as measures of neuronal reliability at the single-trial level (i.e. SNR, ITV, ITPC). To separate signal-carrying components from non-neural noise sources, we also applied a denoising source separation (DSS) algorithm and then repeated the reliability measures. RESULTS: Substantially increased ITV, lower SNRs, and reduced ITPC were observed in auditory responses of RTT participants, supporting a "neural unreliability" account. Application of the DSS technique made it clear that non-neural noise sources contribute to overestimation of the extent of processing deficits in RTT. Post-DSS, ITV measures were substantially reduced, so much so that pre-DSS ITV differences between RTT and TD populations were no longer detected. In the case of SNR and ITPC, DSS substantially improved these estimates in the RTT population, but robust differences between RTT and TD were still fully evident. CONCLUSIONS: To accurately represent the degree of neural dysfunction in RTT using the ERP technique, a consideration of response reliability at the single-trial level is highly advised. Non-neural sources of noise lead to overestimation of the degree of pathological processing in RTT, and denoising source separation techniques during signal processing substantially ameliorate this issue.

Discrimination sensitivity of visual shapes sharpens in autistic adults but only after explicit category learning.

BACKGROUND: Categorization and its influence on perceptual discrimination are essential processes to organize information efficiently. Individuals with Autism Spectrum Condition (ASC) are suggested to display enhanced discrimination on the one hand, but also to experience difficulties with generalization and ignoring irrelevant differences on the other, which underlie categorization. Studies on categorization and discrimination in ASC have mainly focused on one process at a time, however, and typically only used either behavioral or neural measures in isolation. Here, we aim to investigate the interrelationships between these perceptual processes using novel stimuli sampled from a well-controlled artificial stimulus space. In addition, we complement standard behavioral psychophysical tasks with frequency-tagging EEG (FT-EEG) to obtain a direct, non-task related neural index of discrimination and categorization. METHODS: The study was completed by 38 adults with ASC and 38 matched neurotypical (NT) individuals. First, we assessed baseline discrimination sensitivity by administering FT-EEG measures and a complementary behavioral task. Second, participants were trained to categorize the stimuli into two groups. Finally, participants again completed the neural and behavioral discrimination sensitivity measures. RESULTS: Before training, NT participants immediately revealed a categorical tuning of discrimination, unlike ASC participants who showed largely similar discrimination sensitivity across the stimuli. During training, both autistic and non-autistic participants were able to categorize the stimuli into two groups. However, in the initial training phase, ASC participants were less accurate and showed more variability, as compared to their non-autistic peers. After training, ASC participants showed significantly enhanced neural and behavioral discrimination sensitivity across the category boundary. Behavioral indices of a reduced categorical processing and perception were related to the presence of more severe autistic traits. Bayesian analyses confirmed overall results. LIMITATIONS: Data-collection occurred during the COVID-19 pandemic. CONCLUSIONS: Our behavioral and neural findings indicate that adults with and without ASC are able to categorize highly similar stimuli. However, while categorical tuning of discrimination sensitivity was spontaneously present in the NT group, it only emerged in the autistic group after explicit categorization training. Additionally, during training, adults with autism were slower at category learning. Finally, this multi-level approach sheds light on the mechanisms underlying sensory and information processing issues in ASC.

Association between gait speed deterioration and EEG abnormalities.

Physical and cognitive decline at an older age is preceded by changes that accumulate over time until they become clinically evident difficulties. These changes, frequently overlooked by patients and health professionals, may respond better than fully established conditions to strategies designed to prevent disabilities and dependence in later life. The objective of this study was twofold; to provide further support for the need to screen for early functional changes in older adults and to look for an early association between decline in mobility and cognition. A cross-sectional cohort study was conducted on 95 active functionally independent community-dwelling older adults in Havana, Cuba. We measured their gait speed at the usual pace and the cognitive status using the MMSE. A value of 0.8 m/s was used as the cut-off point to decide whether they presented a decline in gait speed. A quantitative analysis of their EEG at rest was also performed to look for an associated subclinical decline in brain function. Results show that 70% of the sample had a gait speed deterioration (i.e., lower than 0.8 m/s), of which 80% also had an abnormal EEG frequency composition for their age. While there was no statistically significant difference in the MMSE score between participants with a gait speed above and below the selected cut-off, individuals with MMSE scores below 25 also had a gait speed<0.8 m/s and an abnormal EEG frequency composition. Our results provide further evidence of early decline in older adults-even if still independent and active-and point to the need for clinical pathways that incorporate screening and early intervention targeted at early deterioration to prolong the years of functional life in older age.

Representations of imaginary scenes and their properties in cortical alpha activity.

Imagining natural scenes enables us to engage with a myriad of simulated environments. How do our brains generate such complex mental images? Recent research suggests that cortical alpha activity carries information about individual objects during visual imagery. However, it remains unclear if more complex imagined contents such as natural scenes are similarly represented in alpha activity. Here, we answer this question by decoding the contents of imagined scenes from rhythmic cortical activity patterns. In an EEG experiment, participants imagined natural scenes based on detailed written descriptions, which conveyed four complementary scene properties: openness, naturalness, clutter level and brightness. By conducting classification analyses on EEG power patterns across neural frequencies, we were able to decode both individual imagined scenes as well as their properties from the alpha band, showing that also the contents of complex visual images are represented in alpha rhythms. A cross-classification analysis between alpha power patterns during the imagery task and during a perception task, in which participants were presented images of the described scenes, showed that scene representations in the alpha band are partly shared between imagery and late stages of perception. This suggests that alpha activity mediates the top-down re-activation of scene-related visual contents during imagery.

Attention deployment in natural scenes: Higher-order scene statistics rather than semantics modulate the N2pc component.

Which properties of a natural scene affect visual search? We consider the alternative hypotheses that low-level statistics, higher-level statistics, semantics, or layout affect search difficulty in natural scenes. Across three experiments (n = 20 each), we used four different backgrounds that preserve distinct scene properties: (a) natural scenes (all experiments); (b) 1/f noise (pink noise, which preserves only low-level statistics and was used in Experiments 1 and 2); (c) textures that preserve low-level and higher-level statistics but not semantics or layout (Experiments 2 and 3); and (d) inverted (upside-down) scenes that preserve statistics and semantics but not layout (Experiment 2). We included "split scenes" that contained different backgrounds left and right of the midline (Experiment 1, natural/noise; Experiment 3, natural/texture). Participants searched for a Gabor patch that occurred at one of six locations (all experiments). Reaction times were faster for targets on noise and slower on inverted images, compared to natural scenes and textures. The N2pc component of the event-related potential, a marker of attentional selection, had a shorter latency and a higher amplitude for targets in noise than for all other backgrounds. The background contralateral to the target had an effect similar to that on the target side: noise led to faster reactions and shorter N2pc latencies than natural scenes, although we observed no difference in N2pc amplitude. There were no interactions between the target side and the non-target side. Together, this shows that-at least when searching simple targets without own semantic content-natural scenes are more effective distractors than noise and that this results from higher-order statistics rather than from semantics or layout.