Electrocortical responses in anticipation of avoidable and inevitable threats: a multisite study.

When faced with danger, human beings respond with a repertoire of defensive behaviors, including freezing and active avoidance. Previous research has revealed a pattern of physiological responses, characterized by heart rate bradycardia, reduced visual exploration, and heightened sympathetic arousal in reaction to avoidable threats, suggesting a state of attentive immobility in humans. However, the electrocortical underpinnings of these behaviors remain largely unexplored. To investigate the visuocortical components of attentive immobility, we recorded parieto-occipital alpha activity, along with eye-movements and autonomic responses, while participants awaited either an avoidable, inevitable, or no threat. To test the robustness and generalizability of our findings, we collected data from a total of 101 participants (76 females, 35 males) at two laboratories. Across sites, we observed an enhanced suppression of parieto-occipital alpha activity during avoidable threats, in contrast to inevitable or no threat trials, particularly towards the end of the trial that prompted avoidance responses. This response pattern coincided with heart rate bradycardia, centralization of gaze and increased sympathetic arousal. Furthermore, our findings expand on previous research by revealing that the amount of alpha suppression, along with centralization of gaze, and heart rate changes, predict the speed of motor responses. Collectively, these findings indicate that when individuals encounter avoidable threats, they enter a state of attentive immobility, which enhances perceptual processing and facilitates action preparation. This state appears to reflect freezing-like behavior in humans.Significance Statement In response to avoidable danger, organisms often exhibit freezing-like behavior. Recent research suggests that freezing is not merely a passive response but involves a state of attentive immobility aimed at enhancing threat avoidance and perception. However, the attentional mechanisms involved in response to avoidable threats at the level of the brain remain poorly understood. To address this gap, we employed EEG, eye-tracking, and measurements of autonomic activity. Our findings revealed a suppression of EEG alpha power, along with cardiac deceleration, reduced eye-movements, and heightened sympathetic activity during the anticipation of avoidable threats. Moreover, this response pattern was predictive of motor response times. These results underscore the significance of heightened perceptual processing during freezing-like states in humans.

Stimulus repetition induces a two-stage learning process in primary visual cortex.

Repeated stimulus exposure alters the brain's response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level (∼60% to ∼70%) at the beginning of the stage to chance level at the end of the stage (∼50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the ∼35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.

How labels shape visuocortical processing in infants.

The current study examined the extent to which labels shape visuocortical processing during the first year of life during a brief (~6-min) associative learning task. Images of computer-generated artificial objects were paired with either individual-level (e.g., Jimmy, Boris) or category-level labels (e.g., Hitchel) while event-related potentials were recorded in response to the onset of the visual stimulus in 6- (n = 41), 9- (n = 27), and 12-month-old (n = 28) infants. Analyses examined experience-dependent visuocortical changes within and across trials, label conditions, and ages. Overall, results demonstrate that infants deploy greater visuocortical resources during the first half of associative learning trials and to stimuli paired with category-level relative to individual-level labels. Waveform morphologies also differed between stimuli paired with individual- and category-level labels and across the age groups, with more complex deflections and amplitude differences between label type at 9- and 12-month-olds, but not 6-month-old infants. The present results highlight the importance of associative learning during infancy and suggest that category- versus individual-level labels differentially direct infant attention and visuocortical processing.

Stimulus-related oscillatory brain activity discriminates hoarding disorder from OCD and healthy controls.

Hoarding disorder (HD) and obsessive-compulsive disorder (OCD) are highly comorbid and genetically related, but their similarities and differences at the neural level are not well characterized. The present study examined the time-frequency information contained in stimulus-related EEG data as participants worked on a visual flanker task. Three groups were included: participants diagnosed with HD (N = 33), OCD (N = 26), and healthy controls (N = 35). Permutation-controlled mass-univariate analyses found no differences between groups in terms of the magnitude of the oscillatory responses. Differences between groups were found selectively for phase-based measures (phase-locking across trials and across sensors) in time ranges well after those consistent with initial visuocortical processes, in the alpha (10 Hz) as well as theta and beta frequency bands, centered around 6 Hz and 15 Hz, respectively. Specifically, HD showed attenuated phase locking in theta and alpha compared to OCD and HC, while OCD showed heightened inter-site phase locking in alpha/beta. Including age as a covariate attenuated, but did not eliminate, the group differences. These findings point to signatures of cortical dynamics and cortical communication task processing that are unique to HD, and which are specifically present during higher-order visual cognition such as stimulus-response mapping, response selection, and action monitoring.

Quantifying Population-level Neural Tuning Functions Using Ricker Wavelets and the Bayesian Bootstrap.

Experience changes the tuning of sensory neurons, including neurons in retinotopic visual cortex, as evident from work in humans and non-human animals. In human observers, visuo-cortical re-tuning has been studied during aversive generalization learning paradigms, in which the similarity of generalization stimuli (GSs) with a conditioned threat cue (CS+) is used to quantify tuning functions. This work utilized pre-defined tuning shapes reflecting prototypical generalization (Gaussian) and sharpening (Difference-of-Gaussians) patterns. This approach may constrain the ways in which re-tuning can be characterized, for example if tuning patterns do not match the prototypical functions or represent a mixture of functions. The present study proposes a flexible and data-driven method for precisely quantifying changes in neural tuning based on the Ricker wavelet function and the Bayesian bootstrap. The method is illustrated using data from a study in which university students (n = 31) performed an aversive generalization learning task. Oriented gray-scale gratings served as CS+ and GSs and a white noise served as the unconditioned stimulus (US). Acquisition and extinction of the aversive contingencies were examined, while steady-state visual event potentials (ssVEP) and alpha-band (8-13 Hz) power were measured from scalp EEG. Results showed that the Ricker wavelet model fitted the ssVEP and alpha-band data well. The pattern of re-tuning in ssVEP amplitude across the stimulus gradient resembled a generalization (Gaussian) shape in acquisition and a sharpening (Difference-of-Gaussian) shape in an extinction phase. As expected, the pattern of re-tuning in alpha-power took the form of a generalization shape in both phases. The Ricker-based approach led to greater Bayes factors and more interpretable results compared to prototypical tuning models. The results highlight the promise of the current method for capturing the precise nature of visuo-cortical tuning functions, unconstrained by the exact implementation of prototypical a-priori models.

Influence of Heart Rate and Change in Wavefront Direction through Pacing on Conduction Velocity and Voltage Amplitude in a Porcine Model: A High-Density Mapping Study.

BACKGROUND: Understanding the dynamics of conduction velocity (CV) and voltage amplitude (VA) is crucial in cardiac electrophysiology, particularly for substrate-based catheter ablations targeting slow conduction zones and low voltage areas. This study utilizes ultra-high-density mapping to investigate the impact of heart rate and pacing location on changes in the wavefront direction, CV, and VA of healthy pig hearts. METHODS: We conducted in vivo electrophysiological studies on four healthy juvenile pigs, involving various pacing locations and heart rates. High-resolution electroanatomic mapping was performed during intrinsic normal sinus rhythm (NSR) and electrical pacing. The study encompassed detailed analyses at three levels: entire heart cavities, subregions, and localized 5-mm-diameter circular areas. Linear mixed-effects models were used to analyze the influence of heart rate and pacing location on CV and VA in different regions. RESULTS: An increase in heart rate correlated with an increase in conduction velocity and a decrease in voltage amplitude. Pacing influenced conduction velocity and voltage amplitude. Pacing also influenced conduction velocity and voltage amplitude, with varying effects observed based on the pacing location within different heart cavities. Pacing from the right atrium (RA) decreased CV in all heart cavities. The overall CV and VA changes in the whole heart cavities were not uniformly reflected in all subregions and subregional CV and VA changes were not always reflected in the overall analysis. Overall, there was a notable variability in absolute CV and VA changes attributed to pacing. CONCLUSIONS: Heart rate and pacing location influence CV and VA within healthy juvenile pig hearts. Subregion analysis suggests that specific regions of the heart cavities are more susceptible to pacing. High-resolution mapping aids in detecting regional changes, emphasizing the substantial physiological variations in CV and VA.

Neurophysiological and Autonomic Dynamics of Threat Processing During Sustained Social Fear Generalization.

Survival in dynamic environments requires that organisms learn to predict danger from situational cues. One key facet of threat prediction is generalization from a predictive cue to similar cues, ensuring that a cue-outcome contingency is applied beyond the original learning environment. Generalization has been observed in laboratory studies of aversive conditioning: behavioral and physiological processes generalize responses from a stimulus paired with threat (the CS+) to unpaired stimuli, with response magnitudes varying with CS+ similarity. In contrast, work focusing on sensory responses in visual cortex has found a sharpening pattern, in which responses to stimuli closely resembling the CS+ are maximally suppressed, potentially reflecting lateral inhibitory interactions with the CS+ representation. Originally demonstrated with simple visual cues, changes in visuocortical tuning have also been observed in threat generalization learning across facial identities. It is unclear to what extent these visuocortical changes represent transient or sustained effects and if generalization learning requires prior conditioning to the CS+. The present study addressed these questions using EEG and pupillometry in an aversive generalization paradigm involving hundreds of trials using a gradient of facial identities. Visuocortical ssVEP sharpening occurred after dozens of trials of generalization learning without prior differential conditioning, but diminished as learning continued. By contrast, generalization of alpha power suppression, pupil dilation, and self-reported valence and arousal was seen throughout the experiment. Findings are consistent with threat processing models emphasizing the role of changing visucocortical and attentional dynamics when forming, curating, and shaping fear memories as observers continue learning about stimulus-outcome contingencies.

Auditory aversive generalization learning prompts threat-specific changes in alpha-band activity.

Pairing a neutral stimulus with aversive outcomes prompts neurophysiological and autonomic changes in response to the conditioned stimulus (CS+), compared to cues that signal safety (CS-). One of these changes-selective amplitude reduction of parietal alpha-band oscillations-has been reliably linked to processing of visual CS+. It is, however, unclear to what extent auditory conditioned cues prompt similar changes, how these changes evolve as learning progresses, and how alpha reduction in the auditory domain generalizes to similar stimuli. To address these questions, 55 participants listened to three sine wave tones, with either the highest or lowest pitch (CS+) being associated with a noxious white noise burst. A threat-specific (CS+) reduction in occipital-parietal alpha-band power was observed similar to changes expected for visual stimuli. No evidence for aversive generalization to the tone most similar to the CS+ was observed in terms of alpha-band power changes, aversiveness ratings, or pupil dilation. By-trial analyses found that selective alpha-band changes continued to increase as aversive conditioning continued, beyond when participants reported awareness of the contingencies. The results support a theoretical model in which selective alpha power represents a cross-modal index of continuous aversive learning, accompanied by sustained sensory discrimination of conditioned threat from safety cues.

Coherent Off-Axis Terahertz Tomography with a Multi-Channel Array and f-theta Optics.

Terahertz tomography is a promising method among non-destructive inspection techniques to detect faults and defects in dielectric samples. Recently, image quality was improved significantly through the incorporation of a priori information and off-axis data. However, this improvement has come at the cost of increased measurement time. To aim toward industrial applications, it is therefore necessary to speed up the measurement by parallelizing the data acquisition employing multi-channel setups. In this work, we present two tomographic frequency-modulated continuous wave (FMCW) systems working at a bandwidth of 230-320 GHz, equipped with an eight-channel detector array, and we compare their imaging results with those of a single-pixel setup. While in the first system the additional channels are used exclusively to detect radiation refracted by the sample, the second system features an f-θ lens, focusing the beam at different positions on its flat focal plane, and thus utilizing the whole detector array directly. The usage of the f-θ lens in combination with a scanning mirror eliminates the necessity of the formerly used slow translation of a single-pixel transmitter. This opens up the potential for a significant increase in acquisition speed, in our case by a factor of four to five, respectively.

The emergence of the EEG dominant rhythm across the first year of life.

The spectral composition of EEG provides important information on the function of the developing brain. For example, the frequency of the dominant rhythm, a salient features of EEG data, increases from infancy to adulthood. Changes of the dominant rhythm during infancy are yet to be fully characterized, in terms of their developmental trajectory and spectral characteristics. In this study, the development of dominant rhythm frequency was examined during a novel sustained attention task across 6-month-old (n = 39), 9-month-old (n = 30), and 12-month-old (n = 28) infants. During this task, computer-generated objects and faces floated down a computer screen for 10 s after a 5-second fixation cross. The peak frequency in the range between 5 and 9 Hz was calculated using center of gravity (CoG) and examined in response to faces and objects. Results indicated that peak frequency increased from 6 to 9 to 12 months of age in face and object conditions. We replicated the same result for the baseline. There was high reliability between the CoGs in the face, object, and baseline conditions across all channels. The developmental increase in CoG was more reliable than measures of mode frequency across different conditions. These findings suggest that CoG is a robust index of brain development across infancy.

Groupwise structural sparsity for discriminative voxels identification.

This paper investigates the selection of voxels for functional Magnetic Resonance Imaging (fMRI) brain data. We aim to identify a comprehensive set of discriminative voxels associated with human learning when exposed to a neutral visual stimulus that predicts an aversive outcome. However, due to the nature of the unconditioned stimuli (typically a noxious stimulus), it is challenging to obtain sufficient sample sizes for psychological experiments, given the tolerability of the subjects and ethical considerations. We propose a stable hierarchical voting (SHV) mechanism based on stability selection to address this challenge. This mechanism enables us to evaluate the quality of spatial random sampling and minimizes the risk of false and missed detections. We assess the performance of the proposed algorithm using simulated and publicly available datasets. The experiments demonstrate that the regularization strategy choice significantly affects the results' interpretability. When applying our algorithm to our collected fMRI dataset, it successfully identifies sparse and closely related patterns across subjects and displays stable weight maps for three experimental phases under the fear conditioning paradigm. These findings strongly support the causal role of aversive conditioning in altering visual-cortical activity.

Sustained selective attention to chromatic information enhances visuocortical gain at the population level.

Prior work in selective attention research has shown that colour-selective attention enhances neural activity in visuocortical areas sensitive to the attended colour while suppressing activity in areas sensitive to ignored colours. However, it is currently unclear whether this effect is limited to attending to specific colour hues or extends to chromatic information more broadly. To investigate this question, we used steady-state visual evoked potentials (ssVEPs) frequency tagging to quantify participants' visuocortical responses to specific elements embedded in arrays of flickering, randomly moving mid-complex patterns. Participants were instructed to attend to either coloured or greyscale patterns while ignoring the others. We found that attending to either coloured or greyscale patterns produced robust increases in ssVEP amplitudes both compared to ignored stimuli and to baseline. There was however no evidence of suppressed responses to ignored patterns. These findings demonstrate that attentional selection based on the presence or absence of chromatic information prompts selectively enhanced visuocortical processing but this selective amplification is not accompanied by suppression of unattended stimuli. Findings are consistent with theoretical notions that predict strong competition between specific exemplars within a given feature dimension, such as red or green, but weak competition between broadly defined stimulus categories, such as chromatic versus non-chromatic.

CNN stability training improves robustness to scanner and IHC-based image variability for epithelium segmentation in cervical histology.

Background: In digital pathology, image properties such as color, brightness, contrast and blurriness may vary based on the scanner and sample preparation. Convolutional Neural Networks (CNNs) are sensitive to these variations and may underperform on images from a different domain than the one used for training. Robustness to these image property variations is required to enable the use of deep learning in clinical practice and large scale clinical research. Aims: CNN Stability Training (CST) is proposed and evaluated as a method to increase CNN robustness to scanner and Immunohistochemistry (IHC)-based image variability. Methods: CST was applied to segment epithelium in immunohistological cervical Whole Slide Images (WSIs). CST randomly distorts input tiles and factors the difference between the CNN prediction for the original and distorted inputs within the loss function. CNNs were trained using 114 p16-stained WSIs from the same scanner, and evaluated on 6 WSI test sets, each with 23 to 24 WSIs of the same tissue but different scanner/IHC combinations. Relative robustness (rAUC) was measured as the difference between the AUC on the training domain test set (i.e., baseline test set) and the remaining test sets. Results: Across all test sets, The AUC of CST models outperformed "No CST" models (AUC: 0.940-0.989 vs. 0.905-0.986, p < 1e - 8), and obtained an improved robustness (rAUC: [-0.038, -0.003] vs. [-0.081, -0.002]). At a WSI level, CST models showed an increase in performance in 124 of the 142 WSIs. CST models also outperformed models trained with random on-the-fly data augmentation (DA) in all test sets ([0.002, 0.021], p < 1e-6). Conclusion: CST offers a path to improve CNN performance without the need for more data and allows customizing distortions to specific use cases. A python implementation of CST is publicly available at https://github.com/TIGACenter/CST_v1.

Quantum range-migration-algorithm for synthetic aperture radar applications.

The 3D range-migration algorithm (RMA) and its 2D equivalent, the omega-k algorithm, are employed in a wide range of applications where reconstruction of synthetic aperture data is required, from satellite radar imaging of planets over seismic imaging of the earth crust, down to phased-array ultrasound and ultrasonic application, and recently in-line synthetic aperture radar for non-destructive testing. These algorithms are based on Fourier transforms and share their time-complexity. This limits highly-resolved measurement data to be processed at high speeds which would be advantageous for modern production feed lines. In this publication, we present the development and implementation of the RMA on a quantum computer that scales favourably compared to the time complexity of the classical RMA. We compare reconstruction results of simulated and measured data of the classical and quantum RMA. Hereby, the quantum RMA is run on a quantum simulator backend as well as on IBM's Q System One quantum computer. The results show that real world applications and testing tasks may benefit from future quantum computers.

Feature-based Attentional Amplitude Modulations of the Steady-state Visual Evoked Potentials Reflect Blood Oxygen Level Dependent Changes in Feature-sensitive Visual Areas.

Recent EEG studies have investigated basic principles of feature-based attention by means of frequency-tagged random dot kinematograms in which different colors are simultaneously presented at different temporal frequencies to elicit steady-state visual evoked potentials (SSVEPs). These experiments consistently showed global facilitation of the to-be-attended random dot kinematogram-a basic principle of feature-based attention. SSVEP source estimation suggested that posterior visual cortex from V1 to area hMT+/V5 is broadly activated by frequency-tagged stimuli. What is presently unknown is whether the feature-based attentional facilitation of SSVEPs is a rather unspecific neural response including all visual areas that follow the "on/off," or whether SSVEP feature-based amplitude enhancements are driven by activity in visual areas most sensitive to a specific feature, such as V4v in the case of color. Here, we leverage multimodal SSVEP-fMRI recordings in human participants and a multidimensional feature-based attention paradigm to investigate this question. Attending to shape produced significantly greater SSVEP-BOLD covariation in primary visual cortex compared with color. SSVEP-BOLD covariation during color selection increased along the visual hierarchy, with greatest values in areas V3 and V4. Importantly, in area hMT+/V5, we found no differences between shape and color selection. Results suggest that SSVEP amplitude enhancements in feature-based attention is not an unspecific enhancement of neural activity in all visual areas following the "on/off." These findings open new avenues to investigating neural dynamics of competitive interactions in specific visual areas sensitive to a certain feature in a more economical way and better temporal resolution compared with fMRI.