Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data.

BACKGROUND: In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism. NEW METHOD: The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism. RESULTS: The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas. COMPARISON WITH EXISTING METHOD (S): The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%. CONCLUSIONS: This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism.

Mental workload level assessment based on compounded hysteresis effect.

In the domain of neuroergonomics, cognitive workload estimation has taken a significant concern among the researchers. This is because the knowledge gathered from its estimation is useful for distributing tasks among the operators, understanding human capability and intervening operators at times of havoc. Brain signals give a promising prospective for understanding cognitive workload. For this, electroencephalography (EEG) is by far the most efficient modality in interpreting the covert information arising in the brain. The present work explores the feasibility of EEG rhythms for monitoring continuous change occurring in a person's cognitive workload. This continuous monitoring is achieved by graphicallyinterpreting the cumulative effect of changes in EEG rhythms observed in the current instance and the former instance based on the hysteresis effect. In this work, classification is done to predict the data class label using an artificial neural network (ANN) architecture. The proposed model gives a classification accuracy of 98.66%.

ALERT: Atlas-Based Low Estimation Rank Tensor Approach to Detect Autism Spectrum Disorder.

In response to a stimulus, distinct areas of the human brain are activated. Also, it is known that the regions interact with one another. This functional connectivity is helpful to diagnose any neurological abnormality, such as autism spectrum disorder (ASD). This work proposes an approach to construct a functional connectivity network from fMRI image data. For obtaining a functional connectivity network, the time series component of fMRI data is used and from it correlation matrix is calculated showing the degree of interaction among the brain regions. To map the different regions of a brain, the brain atlas is considered. This essentially yields a low-rank tensor approximation of the functional connectivity matrix. A 2D convolutional deep neural network model is built to categorize topological similarity in the functional connectivity matrices related to ASD and typically developing control. The proposed approach has been tested with ABIDE dataset of fMRI data for autism spectrum disorder. Several brain atlases have been considered in the experiment. With a majority voting concept on the results from the atlases, the proposed technique reveals an ASD detection accuracy of 84.79%, which is significantly comparable to the state of the art techniques.Clinical Relevance- ASD is one of the least understood neurological disorders that has been recently recognized to have major sociological consequences on an affected individual's life. A symptom-based diagnosis is in practice. However, this requires prolonged behavioural examinations under the supervision of a highly skilled multidisciplinary team. An early and cost-effective detection using an fMRI image is considered an appropriate, comprehensive, and advanced treatment plan.

EEG-Based Mental Workload Estimation.

Knowledge of the level of mental workload induced by any task is essential for optimizing load share among the operators. This helps in assessing their capability; besides, helping in task allocation. Since a persistently high workload experienced by operators such as aircraft pilots and automobile drivers many times compromises their performance and safety. Despite the availability of various mental workload evaluation techniques such as heart rate variability, pupil dilation, sac-cades, etc., assessment of mental workload is still a challenging task. In this work, we aim to evaluate the workload of the operator involved in long duration tasks. For this, experiments have been carried out in a working environment which provides tasks to be done simultaneously, tasks with a pause or break in activity and cross-functional tasks. The experiment data is recorded continuously in different modes and analyzed in segments to show the change in mental workload. The artificial neural network (ANN) architecture classified the workload data with an accuracy of 96.6%. The brain connectivity analysis shows the efficacy of the proposed approach.

Weighted sparse representation for classification of motor imagery EEG signals.

Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in demand for many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual reality, movement of wheelchairs, etc. Traditional sparse representation based classification (SRC) is a thriving technique in recent years and has been a successful approach for classifying MI EEG signals. To further improve the capability of SRC, in this paper, a weighted SRC (WSRC) has been proposed for classifying two-class MI tasks (right-hand, right-foot). WSRC constructs a weighted dictionary according to the dissimilarity information between the test data and the training samples. Then for the given test data the sparse coefficients are computed over the weighted dictionary using l0-minimization problem. The sparse solution obtained using WSRC gives better discriminative information than SRC and as a consequence, WSRC proves to be superior for MI EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.

Correlation of P300 ERPs with visual stimuli and its application to vigilance detection.

Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. It is an important aspect in industries such as aerospace and nuclear power, which involve tremendous man-machine interaction and where safety of any component/system or environment as a whole is extremely crucial. Many methods for vigilance detection, based on biological and behavioral characteristics, have been proposed in the literature. Nevertheless, the existing methods are associated with high time complexity, unhandy devices and incur huge equipment overhead. This paper aims to pave an alternative solution to the existing techniques using brain computing interface (BCI). EEG device being a non-invasive BCI technique is popular in many applications. In this work, we have utilized P300 component of ERPs of EEG signal for vigilance detection task as it can be detected fast and accurately. Through this work, we aim to establish the correlation between P300 ERP and vigilance. We have performed a number of experiments to substantiate the correctness of our proposal and have also proposed an approach to measure the vigilance level.

Data in support of in vivo studies of silk based gold nano-composite conduits for functional peripheral nerve regeneration.

In the present data article we report the in vitro and in vivo biocompatibility of fabricated nerve conduits described in Das et al. [1]. Green synthesised gold nanoparticles (GNPs) were evaluated for their cytotoxicity in rat Schwann cells (SCTM41). We also describe herein the adhesion and proliferation of Schwann cells over the nanofibrous scaffolds. Methods describing surgical implantation of conduits in a rat sciatic nerve injury model, confirming its accurate implantation as well as the porosity and swelling tendency of the nerve conduits are illustrated in the various figures and graphs.

In vivo studies of silk based gold nano-composite conduits for functional peripheral nerve regeneration.

We report a novel silk-gold nanocomposite based nerve conduit successfully tested in a neurotmesis grade sciatic nerve injury model in rats over a period of eighteen months. The conduit was fabricated by adsorbing gold nanoparticles onto silk fibres and transforming them into a nanocomposite sheet by electrospinning which is finally given a tubular structure by rolling on a stainless steel mandrel of chosen diameter. The conduits were found to promote adhesion and proliferation of Schwann cells in vitro and did not elicit any toxic or immunogenic responses in vivo. We also report for the first time, the monitoring of muscular regeneration post nerve conduit implantation by recording motor unit potentials (MUPs) through needle electromyogram. Pre-seeding the conduits with Schwann cells enhanced myelination of the regenerated tissue. Histo-morphometric and electrophysiological studies proved that the nanocomposite based conduits pre-seeded with Schwann cells performed best in terms of structural and functional regeneration of severed sciatic nerves. The near normal values of nerve conduction velocity (50 m/sec), compound muscle action potential (29.7 mV) and motor unit potential (133 µV) exhibited by the animals implanted with Schwann cell loaded nerve conduits in the present study are superior to those observed in previous reports with synthetic materials as well as collagen based nerve conduits. Animals in this group were also able to perform complex locomotory activities like stretching and jumping with excellent sciatic function index (SFI) and led a normal life.