Ripples in macaque V1 and V4 are modulated by top-down visual attention.

Sharp-wave ripples (SWRs) are highly synchronous neuronal activity events. They have been predominantly observed in the hippocampus during offline states such as pause in exploration, slow-wave sleep, and quiescent wakefulness. SWRs have been linked to memory consolidation, spatial navigation, and spatial decision-making. Recently, SWRs have been reported during visual search, a form of remote spatial exploration, in macaque hippocampus. However, the association between SWRs and multiple forms of awake conscious and goal-directed behavior is unknown. We report that ripple activity occurs in macaque visual areas V1 and V4 during focused spatial attention. The occurrence of ripples is modulated by stimulus characteristics, increased by attention toward the receptive field, and by the size of the attentional focus. During attention cued to the receptive field, the monkey's reaction time in detecting behaviorally relevant events was reduced by ripples. These results show that ripple activity is not limited to hippocampal activity during offline states, rather they occur in the neocortex during active attentive states and vigilance behaviors.

Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis.

Breast cancer is the second most common cancer among women worldwide, and the diagnosis by pathologists is a time-consuming procedure and subjective. Computer-aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from the activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to higher accuracy in the classification task and dimension reduction plays an important role. We have proposed reduced DeCAF (R-DeCAF) for this purpose, and different dimension reduction methods are applied to achieve an effective combination of features by capturing the essence of DeCAF features. This framework uses pre-trained CNNs such as AlexNet, VGG-16, and VGG-19 as feature extractors in transfer learning mode. The DeCAF features are extracted from the first fully connected layer of the mentioned CNNs, and a support vector machine is used for classification. Among linear and nonlinear dimensionality reduction algorithms, linear approaches such as principal component analysis (PCA) represent a better combination among deep features and lead to higher accuracy in the classification task using a small number of features considering a specific amount of cumulative explained variance (CEV) of features. The proposed method is validated using experimental BreakHis and ICIAR datasets. Comprehensive results show improvement in the classification accuracy up to 4.3% with a feature vector size (FVS) of 23 and CEV equal to 0.15.

Multiple-target tracking in human and machine vision.

Humans are able to track multiple objects at any given time in their daily activities-for example, we can drive a car while monitoring obstacles, pedestrians, and other vehicles. Several past studies have examined how humans track targets simultaneously and what underlying behavioral and neural mechanisms they use. At the same time, computer-vision researchers have proposed different algorithms to track multiple targets automatically. These algorithms are useful for video surveillance, team-sport analysis, video analysis, video summarization, and human-computer interaction. Although there are several efficient biologically inspired algorithms in artificial intelligence, the human multiple-target tracking (MTT) ability is rarely imitated in computer-vision algorithms. In this paper, we review MTT studies in neuroscience and biologically inspired MTT methods in computer vision and discuss the ways in which they can be seen as complementary.

Cortical Balance Between ON and OFF Visual Responses Is Modulated by the Spatial Properties of the Visual Stimulus.

The primary visual cortex of carnivores and primates is dominated by the OFF visual pathway and responds more strongly to dark than light stimuli. Here, we demonstrate that this cortical OFF dominance is modulated by the size and spatial frequency of the stimulus in awake primates and we uncover a main neuronal mechanism underlying this modulation. We show that large grating patterns with low spatial frequencies drive five times more OFF-dominated than ON-dominated neurons, but this pronounced cortical OFF dominance is strongly reduced when the grating size decreases and the spatial frequency increases, as when the stimulus moves away from the observer. We demonstrate that the reduction in cortical OFF dominance is not caused by a selective reduction of visual responses in OFF-dominated neurons but by a change in the ON/OFF response balance of neurons with diverse receptive field properties that can be ON or OFF dominated, simple, or complex. We conclude that cortical OFF dominance is continuously adjusted by a neuronal mechanism that modulates ON/OFF response balance in multiple cortical neurons when the spatial properties of the visual stimulus change with viewing distance and/or optical blur.

Effectiveness of "rescue saccades" on the accuracy of tracking multiple moving targets: An eye-tracking study on the effects of target occlusions.

Occlusion is one of the main challenges in tracking multiple moving objects. In almost all real-world scenarios, a moving object or a stationary obstacle occludes targets partially or completely for a short or long time during their movement. A previous study (Zelinsky & Todor, 2010) reported that subjects make timely saccades toward the object in danger of being occluded. Observers make these so-called "rescue saccades" to prevent target swapping. In this study, we examined whether these saccades are helpful. To this aim, we used as the stimuli recorded videos from natural movement of zebrafish larvae swimming freely in a circular container. We considered two main types of occlusion: object-object occlusions that naturally exist in the videos, and object-occluder occlusions created by adding a stationary doughnut-shape occluder in some videos. Four different scenarios were studied: (1) no occlusions, (2) only object-object occlusions, (3) only object-occluder occlusion, or (4) both object-object and object-occluder occlusions. For each condition, two set sizes (two and four) were applied. Participants' eye movements were recorded during tracking, and rescue saccades were extracted afterward. The results showed that rescue saccades are helpful in handling object-object occlusions but had no reliable effect on tracking through object-occluder occlusions. The presence of occlusions generally increased visual sampling of the scenes; nevertheless, tracking accuracy declined due to occlusion.

Neuronal nonlinearity explains greater visual spatial resolution for darks than lights.

Astronomers and physicists noticed centuries ago that visual spatial resolution is higher for dark than light stimuli, but the neuronal mechanisms for this perceptual asymmetry remain unknown. Here we demonstrate that the asymmetry is caused by a neuronal nonlinearity in the early visual pathway. We show that neurons driven by darks (OFF neurons) increase their responses roughly linearly with luminance decrements, independent of the background luminance. However, neurons driven by lights (ON neurons) saturate their responses with small increases in luminance and need bright backgrounds to approach the linearity of OFF neurons. We show that, as a consequence of this difference in linearity, receptive fields are larger in ON than OFF thalamic neurons, and cortical neurons are more strongly driven by darks than lights at low spatial frequencies. This ON/OFF asymmetry in linearity could be demonstrated in the visual cortex of cats, monkeys, and humans and in the cat visual thalamus. Furthermore, in the cat visual thalamus, we show that the neuronal nonlinearity is present at the ON receptive field center of ON-center neurons and ON receptive field surround of OFF-center neurons, suggesting an origin at the level of the photoreceptor. These results demonstrate a fundamental difference in visual processing between ON and OFF channels and reveal a competitive advantage for OFF neurons over ON neurons at low spatial frequencies, which could be important during cortical development when retinal images are blurred by immature optics in infant eyes.

Chromatic and Achromatic Spatial Resolution of Local Field Potentials in Awake Cortex.

Local field potentials (LFPs) have become an important measure of neuronal population activity in the brain and could provide robust signals to guide the implant of visual cortical prosthesis in the future. However, it remains unclear whether LFPs can detect weak cortical responses (e.g., cortical responses to equiluminant color) and whether they have enough visual spatial resolution to distinguish different chromatic and achromatic stimulus patterns. By recording from awake behaving macaques in primary visual cortex, here we demonstrate that LFPs respond robustly to pure chromatic stimuli and exhibit ∼2.5 times lower spatial resolution for chromatic than achromatic stimulus patterns, a value that resembles the ratio of achromatic/chromatic resolution measured with psychophysical experiments in humans. We also show that, although the spatial resolution of LFP decays with visual eccentricity as is also the case for single neurons, LFPs have higher spatial resolution and show weaker response suppression to low spatial frequencies than spiking multiunit activity. These results indicate that LFP recordings are an excellent approach to measure spatial resolution from local populations of neurons in visual cortex including those responsive to color.

Mixing of Chromatic and Luminance Retinal Signals in Primate Area V1.

Vision emerges from activation of chromatic and achromatic retinal channels whose interaction in visual cortex is still poorly understood. To investigate this interaction, we recorded neuronal activity from retinal ganglion cells and V1 cortical cells in macaques and measured their visual responses to grating stimuli that had either luminance contrast (luminance grating), chromatic contrast (chromatic grating), or a combination of the two (compound grating). As with parvocellular or koniocellular retinal ganglion cells, some V1 cells responded mostly to the chromatic contrast of the compound grating. As with magnocellular retinal ganglion cells, other V1 cells responded mostly to the luminance contrast and generated a frequency-doubled response to equiluminant chromatic gratings. Unlike magnocellular and parvocellular retinal ganglion cells, V1 cells formed a unimodal distribution for luminance/color preference with a 2- to 4-fold bias toward luminance. V1 cells associated with positive local field potentials in deep layers showed the strongest combined responses to color and luminance and, as a population, V1 cells encoded a diverse combination of luminance/color edges that matched edge distributions of natural scenes. Taken together, these results suggest that the primary visual cortex combines magnocellular and parvocellular retinal inputs to increase cortical receptive field diversity and to optimize visual processing of our natural environment.

Response properties of local field potentials and neighboring single neurons in awake primary visual cortex.

Recordings from local field potentials (LFPs) are becoming increasingly common in research and clinical applications, but we still have a poor understanding of how LFP stimulus selectivity originates from the combined activity of single neurons. Here, we systematically compared the stimulus selectivity of LFP and neighboring single-unit activity (SUA) recorded in area primary visual cortex (V1) of awake primates. We demonstrate that LFP and SUA have similar stimulus preferences for orientation, direction of motion, contrast, size, temporal frequency, and even spatial phase. However, the average SUA had 50 times better signal-to-noise, 20% higher contrast sensitivity, 45% higher direction selectivity, and 15% more tuning depth than the average LFP. Low LFP frequencies (<30 Hz) were most strongly correlated with the spiking frequencies of neurons with nonlinear spatial summation and poor orientation/direction selectivity that were located near cortical current sinks (negative LFPs). In contrast, LFP gamma frequencies (>30 Hz) were correlated with a more diverse group of neurons located near cortical sources (positive LFPs). In summary, our results indicate that low- and high-frequency LFP pool signals from V1 neurons with similar stimulus preferences but different response properties and cortical depths.

LFP polarity changes across cortical and eccentricity in primary visual cortex.

Local field potentials (LFPs) can evaluate neural population activity in the cortex and their interaction with other cortical areas. Analyzing current source density (CSD) rather than LFPs is very significant due to the reduction of volume conduction effects. Current sinks are construed as net inward transmembrane currents, while current sources are net outward ones. Despite extensive studies of LFPs and CSDs, their morphology in different cortical layers and eccentricities are still largely unknown. Because LFP polarity changes provide a measure of neural activity, they can be useful in implanting brain-computer interface (BCI) chips and effectively communicating the BCI devices to the brain. We hypothesize that sinks and sources analyses could be a way to quantitatively achieve their characteristics in response to changes in stimulus size and layer-dependent differences with increasing eccentricities. In this study, we show that stimulus properties play a crucial role in determining the flow. The present work focusses on the primary visual cortex (V1). In this study, we investigate a map of the LFP-CSD in V1 area by presenting different stimulus properties (e.g., size and type) in the visual field area of Macaque monkeys. Our aim is to use the morphology of sinks and sources to measure the input and output information in different layers as well as different eccentricities. According to the value of CSDs, the results show that the stimuli smaller than RF's size had lower strength than the others and the larger RF's stimulus size showed smaller strength than the optimized stimulus size, which indicated the suppression phenomenon. Additionally, with the increased eccentricity, CSD's strengths were increased across cortical layers.

Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier.

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.

CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.

Exploring Methodological Approaches of Experimental Studies in the Field of Neuroarchitecture: A Systematic Review.

OBJECTIVES: This systematic review aims to strengthen the relationship between architecture and neuroscience by classifying data measurement techniques in the field of neuroarchitecture with a focus on the most practical and common methodological approaches. It classifies data recording techniques in different architectural categories (e.g., interior, urban, built environment). BACKGROUNDS: With regard to urban life developments and technological breakthroughs, studies of human interactions with environments have been expanding in recent years. Additionally, recent advances in neuroscience have allowed architects to find out more about human experiences in built environments, but there are few valid frameworks about what methodologies and instruments are more common to conduct experimental tasks in this interdisciplinary field. METHODS: Twenty-eight experimental studies were selected based on the preferred reporting items for systematic reviews and meta-analyses literature search extension (PRISMA) systematic review protocol and a comprehensive analysis. The task-space of selected articles was categorized into three subfields, namely, "interior design," "urban design," and "building design" based on environments and their stimuli. As for this context-based categorization, recording techniques and methodology were distinguished for each subfield division. RESULTS: More than 50% of the studies were incorporated in the first two categories, and the EEG recording was the most frequently employed neuroimaging technique thanks to the technical efficacy of its setup and the high temporal resolution of its electrophysiological signals. CONCLUSION: In this study, a summary of techniques and methodological approaches applied in the field is provided in a nut shell, and a general framework of instruments is presented to help scholars to carry out more practical research in the future leading to designing built environments more efficiently.

Neural oscillatory characteristics of feedback-associated activity in globus pallidus interna.

Neural oscillatory activities in basal ganglia have prominent roles in cognitive processes. However, the characteristics of oscillatory activities during cognitive tasks have not been extensively explored in human Globus Pallidus internus (GPi). This study aimed to compare oscillatory characteristics of GPi between dystonia and Parkinson's Disease (PD). A dystonia and a PD patient performed the Intra-Extra-Dimension shift (IED) task during both on and off-medication states. During the IED task, patients had to correctly choose between two visual stimuli containing shapes or lines based on a hidden rule via trial and error. Immediate auditory and visual feedback was provided upon the choice to inform participants if they chose correctly. Bilateral GPi Local Field Potentials (LFP) activity was recorded via externalized DBS leads. Transient high gamma activity (~ 100-150 Hz) was observed immediately after feedback in the dystonia patient. Moreover, these bursts were phase synchronous between left and right GPi with an antiphase clustering of phase differences. In contrast, no synchronous high gamma activity was detected in the PD patient with or without dopamine administration. The off-med PD patient also displayed enhanced low frequency clusters, which were ameliorated by medication. The current study provides a rare report of antiphase homotopic synchrony in human GPi, potentially related to incorporating and processing feedback information. The absence of these activities in off and on-med PD patient indicates the potential presence of impaired medication independent feedback processing circuits. Together, these findings suggest a potential role for GPi's synchronized activity in shaping feedback processing mechanisms required in cognitive tasks.

Faster thalamocortical processing for dark than light visual targets.

ON and OFF visual pathways originate in the retina at the synapse between photoreceptor and bipolar cells. OFF bipolar cells are shorter in length and use receptors with faster kinetics than ON bipolar cells and, therefore, process information faster. Here, we demonstrate that this temporal advantage is maintained through thalamocortical processing, with OFF visual responses reaching cortex ~3-6 ms before ON visual responses. Faster OFF visual responses could be demonstrated in recordings from large populations of cat thalamic neurons representing the center of vision (both X and Y) and from subpopulations making connection with the same cortical orientation column. While the OFF temporal advantage diminished as visual responses reached their peak, the integral of the impulse response was greater in OFF than ON neurons. Given the stimulus preferences from OFF and ON channels, our results indicate that darks are processed faster than lights in the thalamocortical pathway.

Brain-inspired multiple-target tracking using Dynamic Neural Fields.

Despite considerable progress in the field of automatic multi-target tracking, several problems such as data association remained challenging. On the other hand, cognitive studies have reported that humans can robustly track several objects simultaneously. Such circumstances happen regularly in daily life, and humans have evolved to handle the associated problems. Accordingly, using brain-inspired processing principles may contribute to significantly increase the performance of automatic systems able to follow the trajectories of multiple objects. In this paper, we propose a multiple-object tracking algorithm based on dynamic neural field theory which has been proven to provide neuro-plausible processing mechanisms for cognitive functions of the brain. We define several input neural fields responsible for representing previous location and orientation information as well as instantaneous linear and angular speed of the objects in successive video frames. Image processing techniques are applied to extract the critical object features including target location and orientation. Two prediction fields anticipate the objects' locations and orientations in the upcoming frame after receiving excitatory and inhibitory inputs from the input fields in a feed-forward architecture. This information is used in the data association and labeling process. We tested the proposed algorithm on a zebrafish larvae segmentation and tracking dataset and an ant-tracking dataset containing non-rigid objects with spiky movements and frequently occurring occlusions. The results showed a significant improvement in tracking metrics compared to state-of-the-art algorithms.

Pre-stimulus Alpha Activity Modulates Face and Object Processing in the Intra-Parietal Sulcus, a MEG Study.

Face perception is crucial in all social animals. Recent studies have shown that pre-stimulus oscillations of brain activity modulate the perceptual performance of face vs. non-face stimuli, specifically under challenging conditions. However, it is unclear if this effect also occurs during simple tasks, and if so in which brain regions. Here we used magnetoencephalography (MEG) and a 1-back task in which participants decided if the two sequentially presented stimuli were the same or not in each trial. The aim of the study was to explore the effect of pre-stimulus alpha oscillation on the perception of face (human and monkey) and non-face stimuli. Our results showed that pre-stimulus activity in the left occipital face area (OFA) modulated responses in the intra-parietal sulcus (IPS) at around 170 ms after the presentation of human face stimuli. This effect was also found after participants were shown images of motorcycles. In this case, the IPS was modulated by pre-stimulus activity in the right OFA and the right fusiform face area (FFA). We conclude that pre-stimulus modulation of post-stimulus response also occurs during simple tasks and is therefore independent of behavioral responses.

Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression.

BACKGROUND: Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. OBJECTIVE: The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. METHODS: We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. RESULTS: In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. CONCLUSION: Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.

A machine learning based exploration of COVID-19 mortality risk.

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients' day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.

Correction: ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans.

[This corrects the article DOI: 10.1371/journal.pone.0250952.].