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.

Computational ensemble expert system classification for the recognition of bruxism using physiological signals.

This study aimed to develop an automatic diagnostic scheme for bruxism, a sleep-related disorder characterized by teeth grinding and clenching. The aim was to improve on existing methods, which have been proven to be inefficient and challenging. We utilized a novel hybrid machine learning classifier, facilitated by the Weka tool, to diagnose bruxism from biological signals. The study processed and examined these biological signals by calculating the power spectral density. Data were categorized into normal or bruxism categories based on the EEG channel (C4-A1), and the sleeping phases were classified into wake (w) and rapid eye movement (REM) stages using the ECG channel (ECG1-ECG2). The classification resulted in a maximum specificity of 93% and an accuracy of 95% for the EEG-based diagnosis. The ECG-based classification yielded a supreme specificity of 87% and an accuracy of 96%. Furthermore, combining these phases using the EMG channel (EMG1-EMG2) achieved the highest specificity of 95% and accuracy of 98%. The ensemble Weka tool combined all three physiological signals EMG, ECG, and EEG, to classify the sleep stages and subjects. This integration increased the specificity and accuracy to 97% and 99%, respectively. This indicates that a more precise bruxism diagnosis can be obtained by including all three biological signals. The proposed method significantly improves bruxism diagnosis accuracy, potentially enhancing automatic home monitoring systems for this disorder. Future studies may expand this work by applying it to patients for practical use.

Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task.

The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to medium age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of 89.4% and 88.4% are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method.

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.

A Framework for the Continuous Evaluation of 3D Motion Perception in Virtual Reality.

OBJECTIVE: We present a novel framework for the detection and continuous evaluation of 3D motion perception by deploying a virtual reality environment with built-in eye tracking. METHODS: We created a biologically-motivated virtual scene that involved a ball moving in a restricted Gaussian random walk against a background of 1/f noise. Sixteen visually healthy participants were asked to follow the moving ball while their eye movements were monitored binocularly using the eye tracker. We calculated the convergence positions of their gaze in 3D using their fronto-parallel coordinates and linear least-squares optimization. Subsequently, to quantify 3D pursuit performance, we employed a first-order linear kernel analysis known as the Eye Movement Correlogram technique to separately analyze the horizontal, vertical and depth components of the eye movements. Finally, we checked the robustness of our method by adding systematic and variable noise to the gaze directions and re-evaluating 3D pursuit performance. RESULTS: We found that the pursuit performance in the motion-through depth component was reduced significantly compared to that for fronto-parallel motion components. We found that our technique was robust in evaluating 3D motion perception, even when systematic and variable noise was added to the gaze directions. CONCLUSION: The proposed framework enables the assessment of 3D Motion perception by evaluating continuous pursuit performance through eye-tracking. SIGNIFICANCE: Our framework paves the way for a rapid, standardized and intuitive assessment of 3D motion perception in patients with various eye disorders.

Vulnerability of facial attractiveness perception to early and multi-year visual deprivation.

Judgments of facial attractiveness invariably accompany our perception of faces. Even neonates appear to be capable of making such judgments in a manner consistent with adults. This suggests that the processes supporting facial attractiveness require little, if any, visual experience to manifest. Here we investigate the resilience of these processes to several years of early-onset visual deprivation. Specifically, we study whether congenitally blind children treated several years after birth possess the ability to rate facial attractiveness in a manner congruent to normally sighted individuals. The data reveal significant individual variability in the way each newly sighted child perceives attractiveness. This is in marked contrast to data from normally sighted controls who exhibit strong across-subject agreement in facial attractiveness ratings. This variability may be attributable, in part, to atypical facial encoding strategies used by the newly sighted children. Overall, our results suggest that the development of facial attractiveness perception is likely to be vulnerable to early visual deprivation, pointing to the existence of a possible sensitive period early in the developmental trajectory.

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients.

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearman's ρ = -0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders.

Reorganization of resting-state brain network functional connectivity across human brain developmental stages.

Cognitive brain aging can either be healthy or degenerative in nature. Multiple alterations occur in brain networks with healthy aging. Much of this has yet to be investigated. This study seeks to understand the typical healthy human brain's developmental stages using a publicly available dataset from the human connectome project. As the human brain's developmental stage varies, we also intend to identify a pattern of reorganization in the resting state functional connectivity of several brain networks. The results are specifically presented based on the resting state BOLD signals of 1096 healthy volunteers between the age group of 7-89 years. The k-means clustering method has been used to determine the various human brain developmental stages. Using the t-SNE technique, the clusters are visually separated. BrainNet Viewer is used to study the changes in resting state functional connectivity of the entire brain as the human brain developmental stages vary. The age-related pattern of change in the resting state functional connectivity of six Dosenbasch brain networks that were grouped using the k-means elbow approach has been additionally presented. For performance evaluation, three metrics of brain network connection including network segregation, between network connectivity, and within-network connectivity are used. As the age cohort changes, a consistent pattern in the variance of these connection indices is seen for different Dosenbasch brain networks. Thus, the study's findings suggest that healthy aging causes a functional reorganization of the resting state brain network connections.

Stereopsis following surgery in children with congenital and developmental cataracts: A systematic review and meta-analysis.

We estimated the proportion of children with stereopsis following surgery in congenital and developmental cataracts by systematic review and meta-analysis and also considered the factors influencing stereopsis, such as intervention age and presence of strabismus. Stereopsis is directly related to quality of life, and investigating its levels following cataract surgery in children may help decide the right time to intervene, particularly in the context of brain plasticity. We conducted a systematic literature search using Scopus, PubMed, and Web of Science and found 25 case series, 3 cohorts, and 3 clinical trial studies from 1/1/1995 to 31/12/2020. Study-specific proportions of stereopsis from 923 children were pooled using a random-effects model, and stratified analyses were conducted based on intervention age and pre-existing strabismus as a confounder. We appraised the risk of bias using tools published by National Institutes of Health and evaluated publication bias with funnel plots and the Egger test. The pooled proportions of stereopsis based on 8 unilateral and 6 bilateral congenital cataract studies were 0.37 (95% CIs: [0.24, 0.53]) and 0.45 (95% CIs: [0.24,0.68]) when patients with preexisting strabismus were excluded as a confounder. When the intervention age was ≤6 months, proportions in unilateral congenital cataract group significantly increased to 0.52 (95% CIs: [0.37, 0.66]; P = 0.49) compared to 0.26 (95% CIs: [0.14, 0.44]; P = 0.16) otherwise. A similar increase in proportions was found when intervention age ≤4 months. In both unilateral and bilateral congenital cataract groups, proportions increased significantly when the confounder was excluded. Overall, proportions in bilateral congenital cataracts were significantly greater than unilateral cases (irrespective of confounder). Eight unilateral and 5 bilateral developmental cataract studies resulted in pooled proportions of 0.62 (95% CIs: [0.27, 0.88] and 0.82 (95% CIs: [0.4, 0.97]), respectively. Although proportions for bilateral developmental cataracts were greater than unilateral cataracts (irrespective of confounder), results were not statistically significant. Finally, proportions in unilateral developmental cataracts were significantly greater than unilateral congenital cataracts (Z = 7.413, P = 6.173694e-14). We conclude that surgical intervention within first 4-6 months can significantly affect postoperative outcomes in unilateral congenital cataracts. Analysis of existing data does not show a significant effect of intervention age on stereopsis outcomes for developmental cataracts.

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.

Worldwide COVID-19 is a highly infectious and rapidly spreading disease in almost all age groups. The Computed Tomography (CT) scans of lungs are found to be accurate for the timely diagnosis of COVID-19 infection. In the proposed work, a deep learning-based P-shot N-ways Siamese network along with prototypical nearest neighbor classifiers is implemented for the classification of COVID-19 infection from lung CT scan slices. For this, a Siamese network with an identical sub-network (weight sharing) is used for image classification with a limited dataset for each class. The feature vectors are obtained from the pre-trained sub-networks having weight sharing. The performance of the proposed methodology is evaluated on the benchmark MosMed dataset having categories zero (healthy control) and numerous COVID-19 infections. The proposed methodology is evaluated on (a) chest CT scans provided by medical hospitals in Moscow, Russia for 1110 patients, and (b) case study of low-dose CT scans of 42 patients provided by Avtaran healthcare in India. The deep learning-based Siamese network (15-shot 5-ways) obtained an accuracy of 98.07%, the sensitivity of 95.66%, specificity of 98.83%, and F1-score of 95.10%. The proposed work outperforms the COVID-19 infection severity classification with limited scans availability for numerous infection categories.

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.