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Background and aim: To investigate the effect of the light touch, vision and dual task interference on the surface electromyography (sEMG) activity of ankle muscles Participants: Thirty persons (12 male, 18 Female), right hand without any neuro-musculo-skeletal disorder and in healthy condition, participated in this study. Methods and Materials: Participants stood in upright and semi tandem position. There was a high sensitivity sensor on the right side in direction of femur greater trochanter. They should press the sensor less than 50 gr. Also we examined the rate of activity in 3 muscles (Tibialis Anterior, Proneus Longuse, Soleuse) on both lower limbs by Biometrix sEMG device. 4 positions were experimented in semi tandem standing: No Touch, No Counting; No Touch, Counting; Light Touch, No Counting; Light Touch, Counting. Each of the above positions was tested in 2 conditions: Eyes Open, Eyes Closed. Results: Our findings detect that light touch contact especially during vision, resulted in reducing the rate of sEMG activity in the leg postural muscles. No touch and closed eyes, raise the rate of sEMG activity to maximum. conclusion: On basis of findings, it seems that following skin receptors stimulation, some complicated mechanisms will be begun and in order to the orders of Central Nervous System (CNS), these muscles will make the hole body, stable. So it can reduce the entered pressure on leg postural muscles. Therefore we can see the lesser rate in sEMG activity of leg postural muscles. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) is a powerful neuroimaging technique that provides valuable insights into the microstructure and connectivity of the brain. By measuring the diffusion of water molecules along neuronal fibers, DTI allows the visualization and study of intricate networks of neural pathways. DTI is a noise-sensitive method, where a low signal-to-noise ratio (SNR) results in significant errors in the estimated tensor field. Tensor field regularization is an effective solution for noise reduction. Diffusion tensors are represented by symmetric positive-definite (SPD) matrices. The space of SPD matrices may be viewed as a Riemannian manifold after defining a suitable metric on its tangent bundle. The Log-Cholesky metric is a recently developed concept with advantages over previously defined Riemannian metrics, such as the affine-invariant and Log-Euclidean metrics. The utility of the Log-Cholesky metric for tensor field regularization and noise reduction has not been investigated in detail. This manuscript provides a quantitative investigation of the impact of Log-Cholesky filtering on noise reduction in DTI. It also provides sufficient details of the linear algebra and abstract differential geometry concepts necessary to implement this technique as a simple and effective solution to filtering diffusion tensor fields., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

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 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., (© 2023. The Author(s).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Khodaei, Sadati, Doost and Lashgari.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Marefat, Marefat, Hassannataj Joloudari, Nematollahi and Lashgari.)

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.

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.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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.

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., Competing Interests: The authors have declared that no competing interests exist.

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework's diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona's assistance., Competing Interests: The authors have declared that no competing interests exist.

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.

A central hypothesis in research on executive function is that controlled information processing is costly and is allocated according to the behavioral benefits it brings. However, while computational theories predict that the benefits of new information depend on prior uncertainty, the cellular effects of uncertainty on the executive network are incompletely understood. Using simultaneous recordings in monkeys, we describe several mechanisms by which the fronto-parietal network reacts to uncertainty. We show that the variance of expected rewards, independently of the value of the rewards, was encoded in single neuron and population spiking activity and local field potential (LFP) oscillations, and, importantly, asymmetrically affected fronto-parietal information transmission (measured through the coherence between spikes and LFPs). Higher uncertainty selectively enhanced information transmission from the parietal to the frontal lobe and suppressed it in the opposite direction, consistent with Bayesian principles that prioritize sensory information according to a decision maker's prior uncertainty.

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., Competing Interests: AB is an employee of HCL America and this does not alter our adherence to all PLOS policies on sharing data and materials. The authors have declared that no competing interests exist.

The evaluation of building facades is one of the most important elements in built environments for helping architects and professionals to develop future designs. The form or shape of windows in building facades has direct impacts on perceivers' affective state and emotions. To understand the impacts of geometric windows on the subject's feedback and cortical activity, psychophysics experiments and electroencephalogram (EEG) recordings were measured from the participants. Our behavioral results show a distinguished categorization of the window shapes as pleasant and unpleasant stimuli. The rectangular, square, circular and semi-circular arch were determined as the pleasant window shapes, while the triangular and triangular arch window shapes were distinguished as unpleasant. Furthermore, event-related potential (ERP) components (N1, P2 and P3) were investigated to determine the influence of window shapes on the local brain activity. To measure reliable cortical responses, a Butterworth notch filter (50 Hz), band pass filter (0.1-60 Hz) and ADJUST filter were employed to remove the artifacts. The electrophysiological results show increased activity for the unpleasant in comparison to the pleasant windows ( p < 0.05, Rank-Sum test) in both frontal (for P2 component) and posterio-occipital (ERP amplitudes; the N1 through to the P3 peak) channels. The ERP amplitudes of the right hemisphere were significantly larger than in the left hemisphere, not only in response to the unpleasant ( p < 0.001) but also to the pleasant window stimuli ( p < 0.001, Signed-Rank test). However, the unpleasant stimuli evoked significantly larger ERP amplitude than the pleasant stimuli. Moreover, the significant ERP P2 amplitude was more distinguished for unpleasant ( p = 0.01, Signed-Rank test) than pleasant windows ( p = 0.01, Rank-Sum test) between frontal and central cortical lobes. Overall, our behavioral and electrophysiological studies demonstrate a distinguished categorization of pleasant and unpleasant window shapes and more significant ERP modulations in the right than left hemisphere for unpleasant windows compared to pleasant ones.

Background: Local field potential (LFP) recordings have become an important tool to study the activity of populations of neurons. The functional activity of LFPs is usually compared with the activity of neighboring single spike neurons with sampling rates much higher than those of the continuous field potential channel (5 kHz). However, comparison of these signals generated with the lower sampling rate technique is important., New Method: In this study, we provide an analysis of extracellular field potential time series using the sophisticated nonlinear multifractal detrended fluctuation analysis (MF-DFA). Using the MF-DFA, we demonstrate that the integral of the singularity spectrum is a powerful new method to measure the response tuning of spikes in the continuous field potential channel., Results: Results show that the spikes in the continuous channel at frequency ranges above the LFP component signals were consistently tuned similar to those in the spike channel. Our results also show that using a low-pass filter (<250 Hz), which is commonly applied as a preprocessing step to insulate LFPs from spikes, significantly influences the nonlinearity of the multifractal time series., Comparison With Existing Methods and Conclusions: Our approach for inferring the tuning curve of spiking activity from the continuous channel has some advantages compared to conventional methods such as spike trains. The MF-DFA does not require any preprocessing of the raw signal data and makes no assumptions about the time series characteristics. This method is robust and can be applied to short time series of continuous raw signals., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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.

The visual system is constantly bombarded with information originating from the outside world, but it is unable to process all the received information at any given time. In fact, the most salient parts of the visual scene are chosen to be processed involuntarily and immediately after the first glance along with endogenous signals in the brain. Vision scientists have shown that the early visual system, from retina to lateral geniculate nucleus (LGN) and then primary visual cortex, selectively processes the low-level features of the visual scene. Everything we perceive from the visual scene is based on these feature properties and their subsequent combination in higher visual areas. Different experiments have been designed to investigate the impact of these features on saliency and understand the relative visual mechanisms. In this paper, we review the psychophysical experiments which have been published in the last decades to indicate how the low-level salient features are processed in the early visual cortex and extract the most important and basic information of the visual scene. Important and open questions are discussed in this review as well and one might pursue these questions to investigate the impact of higher level features on saliency in complex scenes or natural images.

Purpose: We have shown previously that normal observers detect dark targets faster and more accurately than light targets, when presented in noisy backgrounds. We investigated how these differences in detection time and accuracy are affected by age and ganglion cell pathology associated with glaucoma., Methods: We asked 21 glaucoma patients, 21 age-similar controls, and 5 young control observers to report as fast as possible the number of 1 to 3 light or dark targets. The targets were positioned at random in a binary noise background, within the central 30° of the visual field., Results: We replicate previous findings that darks are detected faster and more accurately than lights. We extend these findings by demonstrating that differences in detection of darks and lights are found reliably across different ages and in observers with glaucoma. We show that differences in detection time increase at a rate of approximately 55 msec/dB at early stages of glaucoma and then remain constant at later stages at approximately 800 msec. In normal subjects, differences in detection time increase with age at a rate of approximately 8 msec/y. We also demonstrate that the accuracy to detect lights and darks is significantly correlated with the severity of glaucoma and that the mean detection time is significantly longer for subjects with glaucoma than age-similar controls., Conclusions: We conclude that differences in detection of darks and lights can be demonstrated over a wide range of ages, and asymmetries in dark/light detection increase with age and early stages of glaucoma.

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., (© The Author 2014. Published by Oxford University Press.)

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., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Images are processed in the primary visual cortex by neurons that encode different stimulus orientations and spatial phases. In primates and carnivores, neighboring cortical neurons share similar orientation preferences, but spatial phases were thought to be randomly distributed. We discovered a columnar organization for spatial phase in cats that shares similarities with the columnar organization for orientation. For both orientation and phase, the mean difference across vertically aligned neurons was less than one-fourth of a cycle. Cortical neurons showed threefold more diversity in phase than orientation preference; however, the average phase of local neuronal populations was similar through the depth of layer 4. We conclude that columnar organization for visual space is not only defined by the spatial location of the stimulus, but also by absolute phase. Taken together with previous findings, our results suggest that this phase-visuotopy is responsible for the emergence of orientation maps.

Visual information is mediated by two major thalamic pathways that signal light decrements (OFF) and increments (ON) in visual scenes, the OFF pathway being faster than the ON. Here, we demonstrate that this OFF temporal advantage is transferred to visual cortex and has a correlate in human perception. OFF-dominated cortical neurons in cats responded ∼3 ms faster to visual stimuli than ON-dominated cortical neurons, and dark-mediated suppression in ON-dominated neurons peaked ∼14 ms faster than light-mediated suppression in OFF-dominated neurons. Consistent with the neuronal differences, human observers were 6-14 ms faster at detecting darks than lights and better at discriminating dark than light flickers. Neuronal and perceptual differences both vanished if backgrounds were biased toward darks. Our results suggest that the cortical OFF pathway is faster than the ON pathway at increasing and suppressing visual responses, and these differences have parallels in the human visual perception of lights and darks., (Copyright © 2014 Elsevier Inc. All rights reserved.)

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.

Introduction: The primary somatosensory cortex has an important role in nociceptive sensory-discriminative processing. Altered peripheral inputs produced by deafferentation or by long-term changes in levels of afferent stimulation can result in plasticity of cortex. Capsaicin-induced depletion of C-fiber afferents results in plasticity of the somatosensory system. Plasticity includes short-term and long-term changes in synaptic strength. We studied the interaction between paired-pulse facilitation, as one form of short-term plasticity, with long-term potentiation (LTP) in the neocortex of normal and C-fiber depleted freely moving rat., Methods: Neonatally capsaicin-treated rats and their controls were allowed to mature until they reached a weight between 250 and 300g. Then animals were anesthetized with ketamine and xylazine. For recording and stimulation, twisted teflon-coated stainless steel wires were implanted into somatosensory cortex or corpus callusom. In experiments for LTP induction, after two weeks of recovery period, 30 high frequency pulse trains were delivered once per day for 12 days. Paired-pulse ratio (PPR) was monitored before and after the induction of LTP in capsaicin-treated and control rats., Results: Paired-pulse stimulation affected all field potential components at intervals < 200 ms. The largest changes occurred at intervals between 20-30 ms. C-fiber depletion postponed the development of LTP, whereas it had no effect on PPR., Discussion: This finding provides further evidence that the expression of this form of LTP is postsynaptic. Furthermore, these results suggest that the effect of C-fiber depletion on cortical LTP is also postsynaptic and, therefore, is not caused by a decrease in neurotransmitter release.

Objectives: It has been demonstrated that exercise has neuroprotective effects in the central nervous system (CNS), especially in hippocampus. Previous studies have indicated that diabetes mellitus affects synaptic plasticity in the hippocampus leading to impairments in learning and memory. The aim of this study was to evaluate the effects of treadmill running on synaptic plasticity at dentate gyrus (DG) of streptozotocin-induced diabetic rats., Study Design: Experimental groups were the control, the diabetes and the diabetes-exercise groups. Long-term potentiation (LTP) in perforant path-DG synapses was assessed (by 400Hz tetanization) in order to investigate the effect of exercise on synaptic plasticity. Field excitatory post-synaptic potential (fEPSP) slope and population spike (PS) amplitude were measured., Results: With respect to the control group, fEPSP were significantly decreased in the diabetes group. However, there were no differences between responses of the diabetes-exercise group and the control., Conclusion: The present results suggest that LTP induction in the dentate gyrus is affected under diabetic conditions and that treadmill running prevents these effects. The data suggest that treadmill running protect against diabetes-induced decrease of learning ability and memory function of the hippocampus., (Copyright © 2009 Elsevier Ireland Ltd. All rights reserved.)

Unlabelled: The midbrain neural basis underlying each phase of behavior in the formalin test has not been clarified. The present study was designed to investigate neuronal responses to formalin-induced 2-phase pain and morphine-induced antinociception in the nucleus cuneiformis (CnF) that is part of the descending pain modulatory system. Formalin-induced neuronal activities were recorded from the CnF during first and second phases of the formalin test, using an extracellular single-unit recording technique. Our results showed that: 1) the majority of neurons in the CnF displayed monophasic excitatory responses in the first or second phase after formalin injection, except a small portion of neurons which did not exhibit any responses; 2) unit activity of CnF neurons was suppressed after subcutaneous (sc) morphine administration and resumed by naloxone; 3) the increased neuronal firing induced by sc formalin could be suppressed by a single dose of sc morphine; and 4) the response patterns of many CnF neurons changed by preinjection of morphine during 2 phases of the formalin test. Our findings suggest that the diverse activity pattern in the spontaneous background of CnF neurons may have different roles in the transmission of nociceptive information induced by the peripheral noxious stimuli (eg, formalin)., Perspective: Growing evidence shows the involvement of nucleus cuneiformis in the descending pain modulatory system. Further elucidation of the pain modulatory system could potentially lead to better understanding of pain modulation as well as development of new clinical treatments and/or strategies., (Copyright 2010 American Pain Society. Published by Elsevier Inc. All rights reserved.)

The locus coeruleus (LC) is the main source of noradrenergic innervations to the forebrain and the hippocampal formation but does not receive noradrenergic projections itself. Previous studies have suggested that hippocampal neural response is modulated by the noradrenergic pathway and that the experimental activation of the LC can potentiate hippocampal responses. Most studies have suggested that the noradrenergic system has controversial effects on long-term potentiation (LTP) in hippocampal neurons, because its influence on synaptic plasticity in perforant path-DG synapses is ambiguous. The aim of this article was to study the LC's role in baseline activity and LTP in perforant path-DG cells of hippocampus by in vivo LC inactivation. Rats were anesthetized with urethane, and LC was temporarily suppressed by intra-LC injection of lidocaine. Population spike (PS) amplitude and excitatory postsynaptic potential (EPSP) slope in DG were recorded 10 min before and 5, 10, 20, 40, 60, and 120 min after tetanization (400 Hz). Saline or lidocaine was injected during the baseline recording (experiment 1), 5 min before tetanus (experiment 2), and 5 min after tetanus (experiment 3). The results from this study indicated that the LC inactivation has no effect on baseline activity of granular cells or maintenance of LTP after tetanization. Moreover, LC inactivation before tetanus had no effect on LTP induction but decreased PS-LTP amplitude 60 and 120 min after tetanization. Taken together, the LC noradrenergic system likely influences LTP induction in later time periods while it has no effect on LTP in earlier time periods.

The locus coeruleus (LC) located at the level of the pons, is involved in cognitive functions such as learning and memory. The bilateral lidocaine-induced reversible inactivation of this nucleus has been considered in order to study its role in the phases of memory processing (acquisition, consolidation and retention) without any interference with the function of the same structure either during earlier and/or later phases of the same process. In this study, inhibitory avoidance (IA) learning task used to find the LC function in acquisition, consolidation and retrieval. Saline or lidocaine 4% (0.5 microl/side) microinjected into the LC, for assessing the acquisition (5 min before training), consolidation (5, 90 and 360 min after training) and memory retrieval, 5 min before testing. The retention test was done 24h after learning. Our results indicated that: (1) The bilateral functional inactivation of LC before training did not affect acquisition, but affected subsequent memory retention 24h later in IA task. (2) The lidocaine-induced inactivation of LC only 5 min after training impaired consolidation but did not affect it after 90 or 360 min. (3) Inactivation of the LC, 5 min before pre-retrieval test, impaired memory retrieval in IA task. In conclusion, it seems that the nucleus locus coeruleus does not affect acquisition while it involves in the memory consolidation and retrieval of inhibitory avoidance learning task.

Previous studies indicated that diabetes mellitus leads to impairments in hippocampal synaptic plasticity and defects in learning and memory. Although diabetes affects synaptic transmission in the hippocampus through both pre- and post-synaptic influences, it is not clear if the defects are pre- or post-synaptic or both; and whether these are prevented by running. The aim of this study was to evaluate the effects of treadmill running on short-term plasticity in inhibitory interneurons in the dentate gyrus of STZ-induced diabetic rats. Experimental groups were the control-rest group, the control-exercise group, the diabetes-rest group and the diabetes-exercise group (n=6 for each experimental group). The exercise program was moderate exercise consisting of treadmill running at 17 m/min and 0-degree inclination for 40 min/day, 7 days/week, for 12 weeks. The paired pulse paradigm was used to stimulate the perforant pathway and field excitatory post-synaptic potentials (fEPSP) were recorded in dentate gyrus (DG). In the diabetic-rest group paired pulse facilitation was significantly increased comparing to the control-rest group. However, there were no differences between responses of the control-exercise and diabetes-exercise groups compared to the control-rest group. The present results suggest that the pre-synaptic component of synaptic plasticity in the dentate gyrus is affected under diabetic conditions and that treadmill running prevents this effect. The data support the possibility that alterations in transmission may account, in part, for learning and memory deficits induced in diabetes, and that treadmill running is helpful in alleviating the neural complications of diabetes mellitus.

The hippocampal GABAergic interneurons are responsible for controlling the input of large principal cell populations, and they thereby determine the oscillatory discharge patterns and synaptic plasticity in the hippocampus. Such oscillations within neuronal systems serve various complex functions, such as perception, cognition, plasticity and memory. The aim of this study is to define the function of GABAergic synaptic transmission in the dentate gyrus (DG) of the hippocampus in the different stages of inhibitory avoidance (IA) learning and memory in the rat. Two cannulae were implanted above the hippocampal DG. Then the rats were trained on a step-through IA learning task. Each rat received intra-DG injection of picrotoxin (PTX) or saline before training, after training or before the retrieval test. The results show that post-training injection of PTX impaired the IA memory. On the other hand, pre-training and pre-retrieval injection of PTX had no significant effect on the IA activity. Therefore, it seems that GABAergic transmission in the DG is involved in the consolidation step (but not in the acquisition and retrieval steps) of the IA task by controlling the input to the principal cells.

Herbal therapies are commonly used to enhance memory and learning. Ginkgo biloba has shown to be one of the most popular herbs that is used to treat amnesia and retard age related memory deficits. Although, there have been several reports on the memory enhancing effects of Ginkgo, involvement of glutamatergic system that plays pivotal role in learning and memory has not been precisely assessed so far. The current study intended to investigate the effect of Ginkgo intake on amnesia while NMDA (N-methyl D-aspartic acid) receptors blocked by the administration of MK-801. The study used passive avoidance (PA) task to investigate the effect of chronic administration of Ginkgo extract (40 and 90 mg/kg; oral) on the memory span in male Wistar rats, suffering from MK-801-induced forgetfulness (0.06 and 0.1 mg/kg; i.p.). The results indicate that Ginkgo was able to remove MK-801-induced forgetfulness, indicating that Ginkgo can affect memory retention but not effect on passive avoidance acquisition, using pathways other than glutamatergic system as well. The results might indicate that Ginkgo extract can be effective in removing forgetfulness caused by inhibiting NMDA receptors from performing their activities.

To induce morphine dependence, different programs have been used and a wide range of behaviors evaluated. A quantitative method based on mice withdrawal jumping behavior has been suggested and later described by Marshall and Grahame-Smith. The purpose of this work was to evaluate different aspects of this method using variations in doses and duration of the protocol. All experiments were done in three parallel methods according to the Marshall protocol: (i) except the dose of morphine (n=20) where we divided all morphine doses by two in first 3 days, (ii) 2-day (M100%-2D, n=14) and (iii) 1-day morphine application (M100%-1D; n=13). The results showed that only 50% doses of morphine that are used according to Marshall method is sufficient to induce morphine dependence whereas they are not able to induce tolerance. On the other hand, M100%-2D protocol could induce morphine tolerance but not dependence whereas M100%-1D group could induce neither morphine tolerance nor dependence in mice. Therefore, these simple modifications in dose and period of morphine application in Marshall method resulted in distinct protocols for induction of morphine tolerance and dependence along with saving of drug and time.