Role of Entorhinal and Parahippocampal in Diagnosis of Alzheimer's Disease from Resting State EEG.

Alzheimer's disease (AD) poses a significant public health problem. An early diagnosis during the initial development stage of this neurodegenerative brain disorder is a requisite. Initial symptoms of AD involve dysfunctioning in brain memory which later progresses toward language, comprehension, reasoning, and social behavior. The degree of dysfunctionality in AD is accompanied by neuronal damage thereby leading to alteration in functional connectivity of brain regions with AD progression. In literature, substantial studies have focused on topological brain function characteristics, scalp EEG temporal features, and functional MRI or PET scan to diagnose AD. However, source domain based study using EEG is limited. This work establishes the significance of EEG based source domain connectivity in AD diagnosis. In particular, the cortical sources, Parahippocampal and Entorhinal, are particularly studied for cognitive processes and memory in AD and healthy control (HC). A publicly available AD and HC resting state EGG dataset is utilized for this purpose. The dipole imaging method converts surface EEG information into source space. Functional connectivity (FC), supervised classification, frequency analysis, and clustering are then utilized to establish the importance of the entorhinal and parahippocampal in AD diagnosis. The entorhinal source involved in memory is found to be a potential biomarker for AD diagnosis. This memory-associated source dynamics-based approach can further lead to early diagnosis of AD.

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.

EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network.

Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the feasibility of hand kinematics decoding using EEG cortical source signals has been explored for grasp and lift task. In particular, pre-movement EEG segment is utilized. A residual convolutional neural network (CNN) - long short-term memory (LSTM) based kinematics decoding model is proposed that utilizes motor neural information present in pre-movement brain activity. Various EEG windows at 50 ms prior to movement onset, are utilized for hand kinematics decoding. Correlation value (CV) between actual and predicted hand kinematics is utilized as performance metric for source and sensor domain. The performance of the proposed deep learning model is compared in sensor and source domain. The results demonstrate the viability of hand kinematics decoding using pre-movement EEG cortical source data.

Computation of spherical sector harmonics norm with application to blind source separation.

Spherical microphone arrays (SMAs) are widely being used for source localization and separation. However, it is uneconomical to build a full SMA when sources are present in restricted regions of environment. Hence, a spherical sector microphone array is utilized for blind source separation for the first time. In particular, the norm of the spherical sector harmonics basis function is computed for mixing matrix estimation. The estimated steering vectors are clustered using mean-shift algorithm. The number of sources is estimated automatically from the number of clusters. The developed mathematical framework is verified using various simulations and experiments on real data.

Development of FE modeling for hybrid greenhouse dryer for potato chips drying.

In this communication, a combination of heat and mass transfer model was developed using finite element (FE) model to explain the drying performance of the hybrid greenhouse dryer for potato chips. The hybrid greenhouse dryer is integrated with a single-pass solar air heater (SAH). A partial differential equation for a combined set of heat and mass transfer was numerically solved by the FE method. In order to see the spatial moisture distribution within the potato sample, a 3-dimensional FE model was created, and moisture removal takes place from the surface during drying of the products. Lagrange triangle FEs of extremely small size and second-order geometry shape were employed for meshing the geometry of model. Time-dependent study was express the fluctuation in time interval of 0-5 h. The developed model showed the maximum crop and ground temperature are 67.1 and 79.1°C, respectively. Moisture ratio in dry basis is reduced from 1 to 0.005 in 03 h and remains constant at 0.005. Thus, average moisture ratio in dry basis was found as 0.18902. Drying efficiency for the hybrid greenhouse dryer found to be 20.52%, whereas thermal efficiency for SAH was found 54.53%. Relative humidity inside the drying chamber found to be 26.50% in hybrid greenhouse dryer. The predicted versus the experimental results observed that hybrid greenhouse dryer having moderate inside temperature is suitable for crop drying as well as ith sustaining the environmental balance, hybrid greenhouse proves to be most effective.

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multivariable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving a maximum correlation of up to 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) for CNN-LSTM are presented. In addition, novelty in the work includes the utilization of brain source localization (BSL) [using standardized low-resolution brain electromagnetic tomography (sLORETA)] for the reliable decoding of motor intention. The information is utilized for channel selection and accurate EEG time segment selection. The performance of the proposed models is compared with the traditionally utilized mLR technique on the reach, grasp, and lift (GAL) dataset. The effectiveness of the proposed framework is established using the Pearson correlation coefficient (PCC) and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with the state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real-time BCI implementation.

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.

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.

Adaptive area-preserving parameterization of open and closed anatomical surfaces.

The parameterization of open and closed anatomical surfaces is of fundamental importance in many biomedical applications. Spherical harmonics, a set of basis functions defined on the unit sphere, are widely used for anatomical shape description. However, establishing a one-to-one correspondence between the object surface and the entire unit sphere may induce a large geometric distortion in case the shape of the surface is too different from a perfect sphere. In this work, we propose adaptive area-preserving parameterization methods for simply-connected open and closed surfaces with the target of the parameterization being a spherical cap. Our methods optimize the shape of the parameter domain along with the mapping from the object surface to the parameter domain. The object surface will be globally mapped to an optimal spherical cap region of the unit sphere in an area-preserving manner while also exhibiting low conformal distortion. We further develop a set of spherical harmonics-like basis functions defined over the adaptive spherical cap domain, which we call the adaptive harmonics. Experimental results show that the proposed parameterization methods outperform the existing methods for both open and closed anatomical surfaces in terms of area and angle distortion. Surface description of the object surfaces can be effectively achieved using a novel combination of the adaptive parameterization and the adaptive harmonics. Our work provides a novel way of mapping anatomical surfaces with improved accuracy and greater flexibility. More broadly, the idea of using an adaptive parameter domain allows easy handling of a wide range of biomedical shapes.

An Integrated Dynamic Closed Loop Simulation Platform for Elbow Flexion Augmentation Using an Upper Limb Exosuit Model.

Wearable robotic devices are designed to assist, enhance or restore human muscle performance. Understanding how a wearable robotic device changes human biomechanics through complex interaction is important to guide its proper design, parametric optimization and functional success. The present work develops a human-machine-interaction simulation platform for closed loop dynamic analysis with feedback control and to study the effect of soft-robotic wearables on human physiology. The proposed simulation platform incorporates Computed Muscle Control (CMC) algorithm and is implemented using the MATLAB -OpenSim interface. The framework is generic and will allow incorporation of any advanced control strategy for the wearable devices. As a demonstration, a Gravity Compensation (GC) controller has been implemented on the wearable device and the resulting decrease in the joint moments, muscle activations and metabolic costs during a simple repetitive load lifting task with two different speeds is investigated.

Synthesis and Assessment of Fused ß-Carboline Derivatives as Kappa Opioid Receptor Agonists.

The synthesis of 5-formyl-6-aryl-6H-indolo[3,2,1-de][1,5] naphthyridine-2-carboxylates by reaction between 1-formyl-9H-ß-carbolines and cinnamaldehydes in the presence of pyrrolidine in water with microwave irradiation is described. Pharmacophoric modification of the formyl group offered several new fused ß-carboline derivatives, which were investigated for their κ-opioid receptor (KOR) agonistic activity. Two compounds 4 a and 4 c produced appreciable agonist activity on KOR with EC50 values of 46±19 and 134±9 nM, respectively. Moreover, compound-induced KOR signaling studies suggested both compounds to be extremely G-protein-biased agonists. The analgesic effect of 4 a was validated by the increase in tail flick latency in mice in a time-dependent manner, which was completely blocked by the KOR-selective antagonist norBNI. Moreover, unlike U50488, an unbiased full KOR agonist, 4 a did not induce sedation. The docking of 4 a with the human KOR was studied to rationalize the result.

Spherical sector harmonics representation of sound fields using a microphone array over spherical sector.

Spherical microphone arrays (SMAs) are widely used for sound recording and analysis, with processing being done in the spherical harmonics (SH) domain. This is due to the ease of array processing in the SH domain without spatial ambiguity. However, it is uneconomical to construct a full SMA when sources are present in restricted regions of the environment. Additionally, the use of a full SMA comes at the cost of more microphone signals to process. Attempts have been made to use hemispherical microphone arrays on the basis of the acoustic image principle, enabling application of SH but with greater computational complexity. In this paper, the use of a spherical sector microphone array instead of a full SMA is proposed. An orthonormal spherical sector harmonics (S2H) basis function is developed for accurate representation of pressure over the sector. The orthonormality of the S2H function is established using orthogonality of shifted associated Legendre polynomials and a scaled exponential function. An addition theorem for S2H basis functions is derived. The S2H basis function is applied to the decomposition of a sound field over a sector array. The S2H basis function has potential applications to brain source localization and physiological shape description.

Chronic calcific pancreatitis presenting as an isolated left perinephric abscess: a case report and review of the literature.

CONTEXT: Acute and chronic pancreatitis may present with pseudocysts in atypical locations. Activated pancreatic enzymes track along anatomic fascial planes causing digestion of the surrounding tissues and resulting in distant pseudocysts. Pseudocysts at atypical locations pose significant diagnostic as well as therapeutic challenges. CASE REPORT: We report an unusual presentation of a pancreatic pseudocyst in a young male who presented with a left perinephric abscess. Percutaneous drainage was not successful in resolving the abscess and he was subsequently diagnosed as having chronic pancreatitis together with a left perinephric abscess. Needle knife sphincterotomy of the ampulla of Vater resulted in the gradual resolution of the abscess. CONCLUSION: We report a rare presentation of chronic pancreatitis with a perinephric abscess and its non-surgical management. This case report indicates that any patient presenting with a perinephric abscess of unknown etiology not responding to conventional treatment modalities should be investigated for underlying pancreatitis.