Impact of early visual experience on later usage of color cues.

Human visual recognition is remarkably robust to chromatic changes. In this work, we provide a potential account of the roots of this resilience based on observations with 10 congenitally blind children who gained sight late in life. Several months or years following their sight-restoring surgeries, the removal of color cues markedly reduced their recognition performance, whereas age-matched normally sighted children showed no such decrement. This finding may be explained by the greater-than-neonatal maturity of the late-sighted children's color system at sight onset, inducing overly strong reliance on chromatic cues. Simulations with deep neural networks corroborate this hypothesis. These findings highlight the adaptive significance of typical developmental trajectories and provide guidelines for enhancing machine vision systems.

Tracts in the limbic system show microstructural alterations post COVID-19 recovery.

Delirium, memory loss, attention deficit and fatigue are frequently reported by COVID survivors, yet the neurological pathways underlying these symptoms are not well understood. To study the possible mechanisms for these long-term sequelae after COVID-19 recovery, we investigated the microstructural properties of white matter in Indian cohorts of COVID-recovered patients and healthy controls. For the cross-sectional study presented here, we recruited 44 COVID-recovered patients and 29 healthy controls in New Delhi, India. Using deterministic whole-brain tractography on the acquired diffusion MRI scans, we traced 20 white matter tracts and compared fractional anisotropy, axial, mean and radial diffusivity between the cohorts. Our results revealed statistically significant differences (PFWE < 0.01) in the uncinate fasciculus, cingulum cingulate, cingulum hippocampus and arcuate fasciculus in COVID survivors, suggesting the presence of microstructural abnormalities. Additionally, in a subsequent subgroup analysis based on infection severity (healthy control, non-hospitalized patients and hospitalized patients), we observed a correlation between tract diffusion measures and COVID-19 infection severity. Although there were significant differences between healthy controls and infected groups, we found no significant differences between hospitalized and non-hospitalized COVID patients. Notably, the identified tracts are part of the limbic system and orbitofrontal cortex, indicating microstructural differences in neural circuits associated with memory and emotion. The observed white matter alterations in the limbic system resonate strongly with the functional deficits reported in Long COVID. Overall, our study provides additional evidence that damage to the limbic system could be a neuroimaging signature of Long COVID. The findings identify targets for follow-up studies investigating the long-term physiological and psychological impact of COVID-19.

Distinct rich and diverse clubs regulate coarse and fine binocular disparity processing: Evidence from stereoscopic task-based fMRI.

While cortical regions involved in processing binocular disparities have been studied extensively, little is known on how the human visual system adapts to changing disparity magnitudes. In this paper, we investigate causal mechanisms of coarse and fine binocular disparity processing using fMRI with a clinically validated, custom anaglyph-based stimulus. We make use of Granger causality and graph measures to reveal the existence of distinct rich and diverse clubs across different disparity magnitudes. We demonstrate that Middle Temporal area (MT) plays a specialized role with overlapping rich and diverse characteristics. Next, we show that subtle interhemispheric differences exist across various brain regions, despite an overall right hemisphere dominance. Finally, we pass the graph measures through the decision tree and found that the diverse clubs outperform rich clubs in decoding disparity magnitudes. Our study sets the stage for conducting further investigations on binocular disparity processing, particularly in the context of neuro-ophthalmic disorders with binocular impairments.

Assistive Completion of Agrammatic Aphasic Sentences: Amalgamation of NLP and Neurolinguistics-based Synthetic Dataset.

Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic aphasia wherein patients, although able to comprehend, lack the ability to form complete sentences. This inability leads to communication gaps which cause difficulties in their daily lives. The usage of assistive devices can help in mitigating these issues and enable the patients to communicate effectively. However, due to lack of large scale studies of linguistic deficits in aphasia, research on such assistive technology is relatively limited. In this work, we present two contributions that aim to re-initiate research and development in this field. Firstly, we propose a model that uses linguistic features from small scale studies on aphasia patients and generates large scale datasets of synthetic aphasic utterances from grammatically correct datasets. We show that the mean length of utterance, the noun/verb ratio, and the simple/complex sentence ratio of our synthetic datasets correspond to the reported features of aphasic speech. Further, we demonstrate how the synthetic datasets may be utilized to develop assistive devices for aphasia patients. The pre-trained T5 transformer is fine-tuned using the generated dataset to suggest 5 corrected sentences given an aphasic utterance as input. We evaluate the efficacy of the T5 model using the BLEU and cosine semantic similarity scores. Affirming results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00 were obtained. These results provide a strong foundation for the concept that a synthetic dataset based on small scale studies on aphasia can be used to develop effective assistive technology.Clinical relevance- We demonstrate the utilization of Natural Language Processing (NLP) for developing assistive technology for Aphasia patients. While disorders like Broca's aphasia offer a small sample size of patients and data, synthetic linguistic models like ours offer extensive scope for developing assistive technology and rehabilitation monitoring.

Structural Connectomes of COVID-Survivors Show Disruption in Global Integration and Small-Worldness.

Even after recovery from the COVID-19 infection, there have been a multitude of cases reporting post-COVID neurological symptoms including memory loss, brain fog, and attention deficit. Many studies have observed localized microstructural damages in the white matter regions of COVID survivors, indicating potential damage to the axonal pathways in the brain. Therefore, in this study, we have investigated the global impact of localized damage to white matter tracts using graph theoretical analysis of the structural connectome of 45 COVID-recovered subjects and 30 Healthy Controls (HCs). We have implemented Diffusion Tensor Imaging based reconstruction followed by deterministic tractography to extract structural connections among different regions of the brain. Interpreting this structural connectivity as weighted undirected graphs, we have used graph theoretical measures like global efficiency, characteristic path length (CPL), clustering coefficient (CC), modularity, Fiedler value, and assortativity coefficient to quantify the global integration, segregation, and robustness of the brain networks. We statistically compare the cohorts based on these graph measures by employing permutation testing for 100,000 permutations. Post multiple comparisons error correction, we find that the COVID-recovered cohort shows a reduction in global efficiency and CC while they exhibit higher modularity and CPL. This disruption of the balance between global integration and segregation indicates the loss of small-world property in COVID survivors' connectomes which has been linked with other disorders such as cognitive impairment and Alzheimer's. Overall, our study sheds light on the alterations in structural connectivity and its role in post-COVID symptoms.

Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD.

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children and is characterised by inattention, impulsiveness and hyperactivity. While several studies have analysed the static functional connectivity in the resting-state functional MRI (rs-fMRI) of ADHD patients, detailed investigations are required to characterize the connectivity dynamics in the brain. In an attempt to establish a link between attention instability and the dynamic properties of Functional Connectivity (FC), we investigated the differences in temporal variability of FC between 40 children with ADHD and 40 Typically Developing (TD) children. Using a sliding-window method to segment the rs-fMRI scans in time, we employed seed-to-voxel correlation analysis for each window to obtain time-evolving seed connectivity maps for seeds placed in the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC). For each subject, the standard deviation of the voxel connectivity time series was used as a measure of the temporal variability of FC. Results showed that ADHD patients exhibited significantly higher variability in dFC than TD children in the cingulo-temporal, cingulo-parietal, fronto-temporal, and fronto-parietal networks ( pFW E < 0.05). Atypical temporal variability was observed in the left and right temporal gyri, the anterior cingulate cortex, and lateral regions of the right parietal cortex. The observations are consistent with visual attention issues, executive control deficit, and rightward parietal dysfunction reported in ADHD, respectively. These results help in understanding the disorder with a fresh perspective linking behavioural inattention with instability in FC in the brain.

A Portable Low-Cost Respiration Rate Measurement System for Sleep Apnea Detection.

Continuous monitoring of breathing activity is vital in detecting respiratory-based diseases such as obstructive sleep apnea (OSA) and hypopnea. Sleep apnea (SA) is a potentially dangerous sleep problem that occurs when a person's breathing stops and begins periodically while they sleep. In addition, SA interrupts sleep, causing significant daytime sleepiness, weirdness, and irritability. This study aims to design a single inertial measurement unit (IMU) sensor-based system to analyze the respiratory rate of humans. The results of the developed system is validated with the Equivital Wireless Physiological Systems for different activities. Further, the experiment has been designed to identify the optimal sensor placement location for efficient respiration rate estimation during different activities. The performance of the developed model has been quantified using breathing rate estimation accuracy (% BREA) and mean absolute error (MAE). Among all sensor placement locations and activities combinations, a window size of 30sec resulted in the worst performance, whereas a window size ≥ 60sec resulted in a better performance (p-value<0.05). In addition, the performance of the model has been found consistent for window size ≥ 60sec (p-value>0.05). For activity 3 (lying straight), comparably similar performance, 0.52±0.24 and 0.52±0.12 (p-value>0.05) have been depicted by the sensor placement position 3 (Abdomen) and position 1 (chest), respectively. Further, for the other two activities, activity 1 (sitting and working) and activity 2 (sitting straight), the best performance has been depicted as 0.32±0.18, 0.49±0.21 respectively (p-value<0.05), by the sensor placement position 2 (left ribs). This research presents a reliable, cost-effective, portable respiration monitoring system that could detect SA during sleep.

Alzheimer's Disease Is Associated with Increased Network Assortativity: Evidence from Metabolic Connectivity.

Introduction: Unraveling the network pathobiology in neurodegenerative disorders is a popular and promising field in research. We use a relatively newer network measure of assortativity in metabolic connectivity to understand network differences in patients with Alzheimer's Disease (AD), compared with those with mild cognitive impairment (MCI). Methods: Eighty-three demographically matched patients with dementia (56 AD and 27 MCI) who underwent positron emission tomography-magnetic resonance imaging (PET-MRI) study were recruited for this exploratory study. Global and nodal network measures obtained using the BRain Analysis using graPH theory toolbox were used to derive group-level differences (corrected p < 0.05). The methods were validated in age, and gender-matched 23 cognitively normal, 25 MCI, and 53 AD patients from the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Regions that revealed significant differences were correlated with the Addenbrooke's Cognitive Examination-III (ACE-III) scores. Results: Patients with AD revealed significantly increased global assortativity compared with the MCI group. In addition, they also revealed increased modularity and decreased participation coefficient. These findings were validated in the ADNI data. We also found that the regional standard uptake values of the right superior parietal and left superior temporal lobes were proportional to the ACE-III memory subdomain scores. Conclusion: Global errors associated with network assortativity are found in patients with AD, making the networks more regular and less resilient. Since the regional measures of these network errors were proportional to memory deficits, these measures could be useful in understanding the network pathobiology in AD.

White matter plasticity following cataract surgery in congenitally blind patients.

The visual system develops abnormally when visual input is absent or degraded during a critical period early in life. Restoration of the visual input later in life is generally thought to have limited benefit because the visual system will lack sufficient plasticity to adapt to and utilize the information from the eyes. Recent evidence, however, shows that congenitally blind adolescents can recover both low-level and higher-level visual function following surgery. In this study, we assessed behavioral performance in both a visual acuity and a face perception task alongside longitudinal structural white matter changes in terms of fractional anisotropy (FA) and mean diffusivity (MD). We studied congenitally blind patients with dense bilateral cataracts, who received cataract surgery at different stages of adolescence. Our goal was to differentiate between age- and surgery-related changes in both behavioral performance and structural measures to identify neural correlates which might contribute to recovery of visual function. We observed surgery-related long-term increases of structural integrity of late-visual pathways connecting the occipital regions with ipsilateral fronto-parieto-temporal regions or homotopic contralateral areas. Comparison to a group of age-matched healthy participants indicated that these improvements went beyond the expected changes in FA and MD based on maturation alone. Finally, we found that the extent of behavioral improvement in face perception was mediated by changes in structural integrity in late visual pathways. Our results suggest that sufficient plasticity remains in adolescence to partially overcome abnormal visual development and help localize the sites of neural change underlying sight recovery.

3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images.

This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

Analysing the brain networks corresponding to the facial contrast-chimeras.

How humans recognise faces and objects effortlessly, has become a great point of interest. To understand the underlying process, one of the approaches is to study the facial features, in particular ordinal contrast relations around the eye region, which plays a crucial role in face recognition and perception. Recently the graph-theoretic approaches to electroencephalogram (EEG) analysis are found to be effective in understating the underlying process of human brain while performing various tasks. We have explored this approach in face recognition and perception to know the importance of contrast features around the eye region. We studied functional brain networks, formed using EEG responses, corresponding to four types of visual stimuli with varying contrast relationships: Positive faces, chimeric faces (photo-negated faces, preserving the polarity of contrast relationships around eyes), photo-negated faces and only eyes. We observed the variations in brain networks of each type of stimuli by finding the distribution of graph distances across brain networks of all subjects. Moreover, our statistical analysis shows that positive and chimeric faces are equally easy to recognise in contrast to difficult recognition of negative faces and only eyes.

Anatomical harmonics basis based brain source localization with application to epilepsy.

Brain Source Localization (BSL) using Electroencephalogram (EEG) has been a useful noninvasive modality for the diagnosis of epileptogenic zones, study of evoked related potentials, and brain disorders. The inverse solution of BSL is limited by high computational cost and localization error. The performance is additionally limited by head shape assumption and the corresponding harmonics basis function. In this work, an anatomical harmonics basis (Spherical Harmonics (SH), and more particularly Head Harmonics (H2)) based BSL is presented. The spatio-temporal four shell head model is formulated in SH and H2 domain. The anatomical harmonics domain formulation leads to dimensionality reduction and increased contribution of source eigenvalues, resulting in decreased computation and increased accuracy respectively. The performance of spatial subspace based Multiple Signal Classification (MUSIC) and Recursively Applied and Projected (RAP)-MUSIC method is compared with the proposed SH and H2 counterparts on simulated data. SH and H2 domain processing effectively resolves the problem of high computational cost without sacrificing the inverse source localization accuracy. The proposed H2 MUSIC was additionally validated for epileptogenic zone localization on clinical EEG data. The proposed framework offers an effective solution to clinicians in automated and time efficient seizure localization.

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization.

Background: Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by employing computational intelligent techniques on radiological images like CT-Scans (Computed Tomography) of lungs. Extensive research has been reported using deep learning models to diagnose the severity of COVID-19 from CT images. This has undoubtedly minimized the manual involvement in abnormality identification but reported detection accuracy is limited. Methods: The present work proposes an expert model based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier to diagnose novel coronavirus. In this proposed model, features are extracted from the fully connected layer of transfer learned MobileNetv2 followed by FKNN training. The hyperparameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-art methods proposed in past. Conclusion: The proposed model will help in timely and accurate identification of the coronavirus at the various phases. Such kind of rapid diagnosis will assist clinicians to manage the healthcare condition of patients well and will help in speedy recovery from the diseases. Clinical and Translational Impact Statement - The proposed automated system can provide accurate and fast detection of COVID-19 signature from lung radiographs. Also, the usage of lighter MobileNetv2 architecture makes it practical for deployment in real-time.

Artificial Intelligence in Epilepsy.

BACKGROUND: The study of seizure patterns in electroencephalography (EEG) requires several years of intensive training. In addition, inadequate training and human error may lead to misinterpretation and incorrect diagnosis. Artificial intelligence (AI)-based automated seizure detection systems hold an exciting potential to create paradigms for proper diagnosis and interpretation. AI holds the promise to transform healthcare into a system where machines and humans can work together to provide an accurate, timely diagnosis, and treatment to the patients. OBJECTIVE: This article presents a brief overview of research on the use of AI systems for pattern recognition in EEG for clinical diagnosis. MATERIAL AND METHODS: The article begins with the need for understanding nonstationary signals such as EEG and simplifying their complexity for accurate pattern recognition in medical diagnosis. It also explains the core concepts of AI, machine learning (ML), and deep learning (DL) methods. RESULTS AND CONCLUSIONS: In this present context of epilepsy diagnosis, AI may work in two ways; first by creating visual representations (e.g., color-coded paradigms), which allow persons with limited training to make a diagnosis. The second is by directly explaining a complete automated analysis, which of course requires more complex paradigms than the previous one. We also clarify that AI is not about replacing doctors and strongly emphasize the need for domain knowledge in building robust AI models that can work in real-time scenarios rendering good detection accuracy in a minimum amount of time.

Autonomic and Electrophysiological Evidence for Reduced Auditory Habituation in Autism.

It is estimated that nearly 90% of children on the autism spectrum exhibit sensory atypicalities. What aspects of sensory processing are affected in autism? Although sensory processing can be studied along multiple dimensions, two of the most basic ones involve examining instantaneous sensory responses and how the responses change over time. These correspond to the dimensions of 'sensitivity' and 'habituation'. Results thus far have indicated that autistic individuals do not differ systematically from controls in sensory acuity/sensitivity. However, data from studies of habituation have been equivocal. We have studied habituation in autism using two measures: galvanic skin response (GSR) and magneto-encephalography (MEG). We report data from two independent studies. The first study, was conducted with 13 autistic and 13 age-matched neurotypical young adults and used GSR to assess response to an extended metronomic sequence. The second study involved 24 participants (12 with an ASD diagnosis), different from those in study 1, spanning the pre-adolescent to young adult age range, and used MEG. Both studies reveal consistent patterns of reduced habituation in autistic participants. These results suggest that autism, through mechanisms that are yet to be elucidated, compromises a fundamental aspect of sensory processing, at least in the auditory domain. We discuss the implications for understanding sensory hypersensitivities, a hallmark phenotypic feature of autism, recently proposed theoretical accounts, and potential relevance for early detection of risk for autism.

Tracking the Transitions of Brain States: An Analytical Approach Using EEG.

OBJECTIVE: Classification of the neural activity of the brain is a well known problem in the field of brain computer interface. Machine learning based approaches for classification of brain activities do not reveal the underlying dynamics of the human brain. METHODS: Since eigen decomposition has been found useful in a variety of applications, we conjecture that change of brain states would manifest in terms of changes in the invariant spaces spanned by eigen vectors as well as amount of variance along them. Based on this, our first approach is to track the brain state transitions by analysing invariant space variations over time. Whereas, our second approach analyses sub-band characteristic response vector formed using eigen values along with the eigen vectors to capture the dynamics. RESULT: We have taken two real time EEG datasets to demonstrate the efficacy of proposed approaches. It has been observed that in case of unimodal experiment, invariant spaces explicitly show the transitions of brain states. Whereas sub-band characteristic response vector approach gives better performance in the case of cross-modal conditions. CONCLUSIONS: Evolution of invariant spaces along with the eigen values may help in understanding and tracking the brain state transitions. SIGNIFICANCE: The proposed approaches can track the activity transitions in real time. They do not require any training dataset.

Selection of optimum frequency bands for detection of epileptiform patterns.

The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.

Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.

OBJECTIVES: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. METHODS: Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain "rsfMRI epilepsy networks." RESULTS: SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. CONCLUSIONS: IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these "rsfMRI epilepsy networks" could reflect epileptogenesis in TLE. KEY POINTS: • ICA of resting-state fMRI carries disease-specific information about epilepsy. • Machine learning can classify these components with 97.5% accuracy. • "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo.

Emergence of categorical face perception after extended early-onset blindness.

It is unknown whether the ability to visually distinguish between faces and nonfaces is subject to a critical period during development. Would a congenitally blind child who gains sight several years after birth be able to acquire this skill? This question has remained unanswered because of the rarity of cases of late sight onset. We had the opportunity to work with five early-blind individuals who gained sight late in childhood after treatment for dense bilateral cataracts. We tested their ability to categorize patterns as faces, using natural images that spanned a spectrum of face semblance. The results show that newly sighted individuals are unable to distinguish between faces and nonfaces immediately after sight onset, but improve markedly in the following months. These results demonstrate preserved plasticity for acquiring face/nonface categorization ability even late in life, and set the stage for investigating the informational and neural basis of this skill acquisition.

Neural correlates of the food/non-food visual distinction.

An evolutionarily ancient skill we possess is the ability to distinguish between food and non-food. Our goal here is to identify the neural correlates of visually driven 'edible-inedible' perceptual distinction. We also investigate correlates of the finer-grained likability assessment. Our stimuli depicted food or non-food items with sub-classes of appealing or unappealing exemplars. Using data-classification techniques drawn from machine-learning, as well as evoked-response analyses, we sought to determine whether these four classes of stimuli could be distinguished based on the patterns of brain activity they elicited. Subjects viewed 200 images while in a MEG scanner. Our analyses yielded two successes and a surprising failure. The food/non-food distinction had a robust neural counterpart and emerged as early as 85 ms post-stimulus onset. The likable/non-likable distinction too was evident in the neural signals when food and non-food stimuli were grouped together, or when only the non-food stimuli were included in the analyses. However, we were unable to identify any neural correlates of this distinction when limiting the analyses only to food stimuli. Taken together, these positive and negative results further our understanding of the substrates of a set of ecologically important judgments and have clinical implications for conditions like eating-disorders and anhedonia.