Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study.

Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1-3 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-21 Hz), and high beta (22-30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = -0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.

Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network.

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging.

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche-Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.

Pulse waveform analysis on temporal changes in ocular blood flow due to caffeine intake: a comparative study between habitual and non-habitual groups.

PURPOSE: To evaluate and compare the temporal changes in pulse waveform parameters of ocular blood flow (OBF) between non-habitual and habitual groups due to caffeine intake. METHOD: This study was conducted on 19 healthy subjects (non-habitual 8; habitual 11), non-smoking and between 21 and 30 years of age. Using laser speckle flowgraphy (LSFG), three areas of optical nerve head were analyzed which are vessel, tissue, and overall, each with ten pulse waveform parameters, namely mean blur rate (MBR), fluctuation, skew, blowout score (BOS), blowout time (BOT), rising rate, falling rate, flow acceleration index (FAI), acceleration time index (ATI), and resistive index (RI). Two-way mixed ANOVA was used to determine the difference between every two groups where p < 0.05 is considered significant. RESULT: There were significant differences between the two groups in several ocular pulse waveform parameters, namely MBR (overall, vessel, tissue), BOT (overall), rising rate (overall), and falling rate (vessel), all with p < 0.05. In addition, the ocular pulse waveform parameters, i.e., MBR (overall), skew (tissue), and BOT (tissue) showed significant temporal changes within the non-habitual group, but not within the habitual group. The temporal changes in parameters MBR (vessel, tissue), skew (overall, vessel), BOT (overall, vessel), rising rate (overall), falling rate (overall, vessel), and FAI (tissue) were significant for both groups (habitual and non-habitual) in response to caffeine intake. CONCLUSION: The experiment results demonstrated caffeine does modulate OBF significantly and response differently in non-habitual and habitual groups. Among all ten parameters, MBR and BOT were identified as the suitable biomarkers to differentiate between the two groups.

Blood vessel segmentation in color fundus images based on regional and Hessian features.

PURPOSE: To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis. METHODS: Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel. RESULTS: The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy. CONCLUSIONS: Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method.

BACKGROUND: Disorders of rotator cuff tendons results in acute pain limiting the normal range of motion for shoulder. Of all the tendons in rotator cuff, supraspinatus (SSP) tendon is affected first of any pathological changes. Diagnosis of SSP tendon using ultrasound is considered to be operator dependent with its accuracy being related to operator's level of experience. METHODS: The automatic segmentation of SSP tendon ultrasound image was performed to provide focused and more accurate diagnosis. The image processing techniques were employed for automatic segmentation of SSP tendon. The image processing techniques combines curvelet transform and mathematical concepts of logical and morphological operators along with area filtering. The segmentation assessment was performed using true positives rate, false positives rate and also accuracy of segmentation. The specificity and sensitivity of the algorithm was tested for diagnosis of partial thickness tears (PTTs) and full thickness tears (FTTs). The ultrasound images of SSP tendon were taken from medical center with the help of experienced radiologists. The algorithm was tested on 116 images taken from 51 different patients. RESULTS: The accuracy of segmentation of SSP tendon was calculated to be 95.61% in accordance with the segmentation performed by radiologists, with true positives rate of 91.37% and false positives rate of 8.62%. The specificity and sensitivity was found to be 93.6%, 94% and 95%, 95.6% for partial thickness tears and full thickness tears respectively. The proposed methodology was successfully tested over a database of more than 116 US images, for which radiologist assessment and validation was performed. CONCLUSIONS: The segmentation of SSP tendon from ultrasound images helps in focused, accurate and more reliable diagnosis which has been verified with the help of two experienced radiologists. The specificity and sensitivity for accurate detection of partial and full thickness tears has been considerably increased after segmentation when compared with existing literature.

Efficient feature selection and classification of protein sequence data in bioinformatics.

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth.

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen with 34 healthy subjects to measure their EEG signals during resting states (eyes open and eyes closed) and while performing learning tasks. The subjects displayed no prior knowledge of the animated educational content presented in video format. The paper presents an analysis of EEG signals measured during a resting state with closed eyes using three deep learning techniques: Long-term, short-term memory (LSTM), Long-term, short-term memory-convolutional neural network (LSTM-CNN), and Long-term, short-term memory-Fully convolutional neural network (LSTM-FCNN). The chosen techniques were based on their suitability for real-time applications with varying data lengths and the need for less computational time. The optimization of hypertuning parameters has enabled the identification of visual learners through the implementation of three techniques. LSTM-CNN technique has the highest average accuracy of 94%, a sensitivity of 80%, a specificity of 92%, and an F1 score of 94% when identifying the visual learning style of the student out of all three techniques. This research has shown that the most effective method is the deep learning-based LSTM-CNN technique, which accurately identifies a student's visual learning style.

Torsion of the spermatic cord revealing cystic dysplasia of the testicle.

Cystic dysplasia of the testis is characterized by the presence of multiple cysts within the testicular parenchyma. It is a rare benign tumor. It is often accompanied by kidney malformations. There is no consensus on treatment. We report here the case of testicular dysplasia revealed by a torsion of the spermatic cord in an adult. The diagnosis of cystic dysplasia of the testis was made intraoperatively and confirmed by pathology. An orchiectomy was performed. Serum testicular cancer markers were normal postoperatively.

Posterior reversible encephalopathy syndrome: A case report.

Posterior Reversible Encephalopathy Syndrome (PRES) is a radio-clinical entity associating reversible damage of the central nervous system and typical brain imaging. The clinical context is often suggestive with, in half of cases, the use of vasoactive substances (cannabis, antidepressants, nasal decongestants) and/or postpartum. The etiologies are dominated by hypertensive encephalopathy, preeclampsia, eclampsia, immunosuppressive therapies, and systemic diseases. We report a case of posterior encephalopathy syndrome occurring in a young female without hypertension. It was about a 40-year-old female without hypertension underlying condition, received at the emergency department for headaches and generalized tonic-clonic seizures. The physical examination was unremarkable, and her blood pressure was 130/70 mm Hg. CT scan revealed bilateral white matter hypodensity in the posterior occipital regions and a right frontal subarachnoid hemorrhage. There was no aneurysmal malformation of the polygon of Willis and no cerebral thrombophlebitis. Brain MRI showed T2 and FLAIR hypersignal areas in the occipital and frontal cortico-subcortical regions, with no diffusion signal abnormalities or contrast enhancement, and a right frontal subarachnoid hemorrhagic lesion with no other impairment. The diagnosis of reversible posterior encephalopathy syndrome was made up, and the outcome was favorable under treatment. Posterior reversible encephalopathy syndrome is an uncommon but probably underdiagnosed condition. Hypertensive encephalopathy is the most common etiology. However, there would be cases of PRES without hypertension as shown in this observation.

Anterior dislocation of the shoulder associated with a diaphyseal fracture of the ipsilateral humerus: a case report.

Background: Anterior dislocation of the shoulder associated with a diaphyseal fracture of the ipsilateral humerus is a rare and controversial occurrence, with very few cases reported in the literature. Case presentation: We present a case of a 39-year-old right-handed driver who presented with an anterior dislocation of the shoulder associated with a diaphyseal fracture of the ipsilateral humerus following a road traffic accident. The lateral approach to the fracture allowed us to use two forceps to gain a good grip on the proximal fragment and perform the maneuver to reduce the dislocation. The fracture was reduced and fixed with a molded Lecestre-type plate. Conclusion: In this case, we employed the approach of initially reducing the shoulder dislocation with forceps, followed by osteosynthesis of the humeral fracture. The functional results were excellent after 6 months.

A novel method for efficient estimation of brain effective connectivity in EEG.

BACKGROUND AND OBJECTIVE: Brain connectivity plays a pivotal role in understanding the brain's information processing functions by providing various details including magnitude, direction, and temporal dynamics of inter-neuron connections. While the connectivity may be classified as structural, functional and causal, a complete in-vivo directional analysis is guaranteed by the latter and is referred to as Effective Connectivity (EC). Two most widely used EC techniques are Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) which are based on multivariate autoregressive models. The drawbacks of these techniques include poor frequency resolution and the requirement for experimental approach to determine signal normalization and thresholding techniques in identifying significant connectivities between multivariate sources. METHODS: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source. RESULTS: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively. CONCLUSION: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.

Weber's syndrome revealing a Percheron artery infarction: A case report.

Key Clinical Message: Weber's syndrome revealing a Percheron artery infarction is a rare clinical occurrence. Its diagnosis requires careful clinical examination and brain MRI, which is the gold standard for diagnosis. If this is not available, combined cerebral CT scan with a CT angiography of supra-aortic arteries may be useful for the diagnosis. Abstract: Percheron's artery (PA) occlusion is an uncommon type of stroke involving paramedian thalamus and/or midbrain infarction. It accounts for 4%-18% of all thalamic infarcts and 0.1%-2% of all strokes. Its clinical manifestations are variable and its mode of presentation as Weber's syndrome is exceptional due to the unusual clinical presentation.

Numerical Solution of Inverse Problem in Functional Near Infrared Spectroscopy using L1-Norm Method.

It has been more than three decades since researchers began investigating functional near-infrared spectroscopy (fNIRs) and its applications with near-infrared light for use in both clinical and pre-clinical settings. In order to increase the accuracy of fNIRs of complex tissue structures, it is necessary to create more advanced image reconstruction methods. Real fNIRs data have been used to develop an implementation of the L1-Norm approach for tackling the inverse problem in this work. The Monte Carlo (MC) simulation is used to construct the sensitivity matrix for this research. Finally, a numerical algorithm for the L1-Norm approach of image reconstruction is developed and implemented in MATLAB to aid in the process. The results showed good agreement with the actual fNIRs data.

Numerical investigation of treated brain glioma model using a two-stage successive over-relaxation method.

A brain tumor is a dynamic system in which cells develop rapidly and abnormally, as is the case with most cancers. Cancer develops in the brain or inside the skull when aberrant and odd cells proliferate in the brain. By depriving the healthy cells of leisure, nutrition, and oxygen, these aberrant cells eventually cause the healthy cells to perish. This article investigated the development of glioma cells in treating brain tumors. Mathematically, reaction-diffusion models have been developed for brain glioma growth to quantify the diffusion and proliferation of the tumor cells within brain tissues. This study presents the formulation the two-stage successive over-relaxation (TSSOR) algorithm based on the finite difference approximation for solving the treated brain glioma model to predict glioma cells in treating the brain tumor. Also, the performance of TSSOR method is compared to the Gauss-Seidel (GS) and two-stage Gauss-Seidel (TSGS) methods in terms of the number of iterations, the amount of time it takes to process the data, and the rate at which glioma cells grow the fastest. The implementation of the TSSOR, TSGS, and GS methods predicts the growth of tumor cells under the treatment protocol. The results show that the number of glioma cells decreased initially and then increased gradually by the next day. The computational complexity analysis is also used and concludes that the TSSOR method is faster compared to the TSGS and GS methods. According to the results of the treated glioma development model, the TSSOR approach reduced the number of iterations by between 8.0 and 71.95%. In terms of computational time, the TSSOR approach is around 1.18-76.34% faster than the TSGS and GS methods.

Early stage of cerebral amyloid angiopathy revealed by follow-up of a minimal head injury.

Cerebral amyloid angiopathy (CAA) is an age-related cerebral microangiopathy characterized by the accumulation of amyloid-beta peptide in the wall of leptomeningeal arteries and cortical vessels. Diagnosis of sporadic amyloid angiopathy is most often made in elderly patient with lobar hematoma. We report a case of a 68-year-old female who had minimal head injury. Cerebral CT showed a right cerebellar hematoma. Follow-up MRI after 4 months showed signs of cerebral amyloid angiopathy. Through this observation, we describe the MRI semiology that helps make the diagnosis of cerebral amyloid angiopathy.

Pylephlebitis secondary to inflammatory colitis: A case report.

Pylephlebitis is a thrombotic occlusion of the portal vein or its branches secondary to infection in regions that drain to the portal venous system. Clinical presentation is often atypical, and patients may initially present with non-specific abdominal symptoms. We report a case of pylephlebitis secondary to inflammatory colitis depicted by CT scan in a 35-year-old female admitted for acute abdominal pain associated with vomiting and fever.

Atypical fibromuscular dysplasia or carotid web revealed by cerebral infarction: A review of 2 cases.

Atypical fibromuscular dysplasia of the bulb or carotid web is a nonatheromatous pathology more common in African and African-American populations. It is implicated in the occurrence of cerebral infarcts of unknown causes. Its diagnosis is made by angio-CT of the supra-aortic trunks and is characterized by a defect in the posterior wall of the bulb. Treatment with antiplatelet agents prevents the occurrence of stroke, but radical treatment remains surgical and endovascular. We report 2 observations of carotid web diagnosed and medically managed at the regional hospital of Saint Louis.

Pylephlebitis complicating acute calculous cholecystitis: A case report.

Pylephlebitis is a complication of intra-abdominal infections. Its occurrence during cholecystitis is a rare situation. We report the case of a 43-year-old female patient who presented with septic thrombosis of the right portal branch following acute calculous cholecystitis diagnosed on abdominal CT. The clinical evolution was favorable under antibiotic therapy and a cholecystectomy was scheduled.

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives.

Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.