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Shankar Beriha, Siba. "Computer Aided Diagnosis System To Distinguish Adhd From Similar Behavioral Disorders." Biomedical and Pharmacology Journal 11, no. 2 (June 12, 2018): 1135–41. http://dx.doi.org/10.13005/bpj/1474.
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ADHD is one of the most prevalent psychiatric disorder of childhood, characterized by inattention and distractibility, with or without accompanying hyperactivity. The main aim of this research work is to develop a Computer Aided Diagnosis (CAD) technique with minimal steps that can differentiate the ADHD children from the other similar children behavioral disorders such as anxiety, depression and conduct disorder based on the Electroencephalogram (EEG) signal features and symptoms. The proposed technique is based on soft computing and bio inspired computing algorithms. Four non-linear features are extracted from the EEG such as Higuchi fractal dimension, Katz fractal dimension, Sevick fractal dimension and Lyapunov exponent and 14 symptoms which are most important in differentiation are extracted by experts in the field of psychiatry. Particle Swarm Optimization (PSO) tuned Back Propagation Neural Network (BPNN) and PSO tuned Radial Basis Function (RBF) employed as a classifier. By investigating these integrated features, we obtained good classification accuracy. Simulation results suggest that the proposed technique offer high potential in the diagnosis of ADHD and may be a good preliminary assistant for psychiatrists in diagnosing high risk behavioral disorders of children.
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Kawakubo, Hideko, Yusuke Matsui, Itaru Kushima, Norio Ozaki, and Teppei Shimamura. "A network of networks approach for modeling interconnected brain tissue-specific networks." Bioinformatics 35, no. 17 (January 15, 2019): 3092–101. http://dx.doi.org/10.1093/bioinformatics/btz032.
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Abstract Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes. Results We introduce a novel and efficient framework, called a ‘Network of Networks’ approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the ‘Network of Networks’ model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. Availability and implementation Our software is available from https://github.com/infinite-point/GOSPEL. Supplementary information Supplementary data are available at Bioinformatics online.
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Verma, Archana, Shweta Singh Chauhan, Vaishali Pankaj, Neha Srivastva, and Prachi Srivastava. "Network Biology Approaches to Identify the Drug Lead Molecule for Neurodevelopmental Disorders in Human." Open Bioinformatics Journal 13, no. 1 (March 20, 2020): 15–24. http://dx.doi.org/10.2174/1875036202013010015.
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Aims: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches Background: Neurodevelopmental disorders (NNDs) are disabilities associated chiefly with the functioning of the neurological system and brain. Children with neurodevelopmental disorders have difficulties with speech, behaviour, learning and other neurological functions. Systems biology is a holistic approach to enciphering the complexity of biological systems and their interactions. It opens the way to a more successful discovery of novel therapeutics. Objective: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches. Methods: A list of genes was collected from NCBI database for Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases. STRING database and Cytoscape software was used for construction and interpreting molecular interaction in the network. 3D structure of target protein, was build and validated.The phytochemicals were identified through various research articles and filtered out by virtual screening through Molinspiration. Molecular docking analyses of known phytochemical with target proteins were performed usingAutoDock tool. Result: AKT1 for Autism, SNAP25 for Intellectual Disability (ID) and DRD4 for Attention Deficit Hyperactivity Disorder (ADHD) were identified as most potential drug target through network study. further the modelled structure of obtained target were undergo molecular docking study with kown phytochemicals. Based on lowest binding energy, Huperzine A for Autism and ID, Valerenic acid for ADHD found to be the most potential therapeutic molecules. Conclusion: Huperzine A against Autism and ID, Valerenic acid against ADHD found to be the most potential therapeutic molecules and expected to be effective in the treatment of NNDs. Phytochemicals do not have side effects so extract of these can be taken in preventive form too as these disorders occur during developmental stages of the child. Further the obtained molecule if experimentally validated would play promising role for the treatment of NDDs in human.
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Basha, Omer, Chanan M. Argov, Raviv Artzy, Yazeed Zoabi, Idan Hekselman, Liad Alfandari, Vered Chalifa-Caspi, and Esti Yeger-Lotem. "Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes." Bioinformatics 36, no. 9 (January 21, 2020): 2821–28. http://dx.doi.org/10.1093/bioinformatics/btaa034.
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Abstract Motivation Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. Results Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases. Summary Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. Availability and implementation Datasets are available as part of the Supplementary data. Supplementary information Supplementary data are available at Bioinformatics online.
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Chien, Chung-Yao, Szu-Wei Hsu, Tsung-Lin Lee, Pi-Shan Sung, and Chou-Ching Lin. "Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images." Biomedicines 9, no. 1 (December 24, 2020): 12. http://dx.doi.org/10.3390/biomedicines9010012.
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Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.
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De Silva, Senuri, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, and Sampath Jayarathna. "fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network." International Journal of E-Health and Medical Communications 12, no. 1 (January 2021): 81–105. http://dx.doi.org/10.4018/ijehmc.2021010106.
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Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to obtain the specificity and sensitivity. This helps to determine the best features for ADHD classification using convolutional neural networks (CNN). The methodology using fALFF and ReHo resulted in an accuracy of 67%, while SBC gained an accuracy between 84% and 86% and sensitivity between 65% and 75%.
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Morabito, Francesco Carlo, Domenico Ursino, Nadia Mammone, Francesco Cauteruccio, Paolo Lo Giudice, and Giorgio Terracina. "A new network-based approach to investigating neurological disorders." International Journal of Data Mining, Modelling and Management 11, no. 4 (2019): 315. http://dx.doi.org/10.1504/ijdmmm.2019.10023732.
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Cauteruccio, Francesco, Paolo Lo Giudice, Giorgio Terracina, Domenico Ursino, Nadia Mammone, and Francesco Carlo Morabito. "A new network-based approach to investigating neurological disorders." International Journal of Data Mining, Modelling and Management 11, no. 4 (2019): 315. http://dx.doi.org/10.1504/ijdmmm.2019.102730.
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Gavrilescu, Mihai, and Nicolae Vizireanu. "Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech." Data 4, no. 3 (July 15, 2019): 101. http://dx.doi.org/10.3390/data4030101.
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We propose a novel feedforward neural network (FFNN)-based speech emotion recognition system built on three layers: A base layer where a set of speech features are evaluated and classified; a middle layer where a speech matrix is built based on the classification scores computed in the base layer; a top layer where an FFNN- and a rule-based classifier are used to analyze the speech matrix and output the predicted emotion. The system offers 80.75% accuracy for predicting the six basic emotions and surpasses other state-of-the-art methods when tested on emotion-stimulated utterances. The method is robust and the fastest in the literature, computing a stable prediction in less than 78 s and proving attractive for replacing questionnaire-based methods and for real-time use. A set of correlations between several speech features (intensity contour, speech rate, pause rate, and short-time energy) and the evaluated emotions is determined, which enhances previous similar studies that have not analyzed these speech features. Using these correlations to improve the system leads to a 6% increase in accuracy. The proposed system can be used to improve human–computer interfaces, in computer-mediated education systems, for accident prevention, and for predicting mental disorders and physical diseases.
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Jalata, Ibsa K., Thanh-Dat Truong, Jessica L. Allen, Han-Seok Seo, and Khoa Luu. "Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network." Future Internet 13, no. 8 (July 28, 2021): 194. http://dx.doi.org/10.3390/fi13080194.
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Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders. However, using optical motion sensors and wearable sensors is expensive and often requires highly trained professionals to identify specific impairments. In this work, we proposed a graph convolutional neural network that mimics the intuition of physical therapists to identify patient-specific impairments based on video of a patient. In addition, two modeling approaches are compared: a graph convolutional network applied solely on skeleton input data and a graph convolutional network accompanied with a 1-dimensional convolutional neural network (1D-CNN). Experiments on the dataset showed that the proposed method not only improves the correlation of the predicted gait measure with the ground truth value (speed = 0.791, gait deviation index (GDI) = 0.792) but also enables faster training with fewer parameters. In conclusion, the proposed method shows that the possibility of using video-based data to treat neurological and musculoskeletal disorders with acceptable accuracy instead of depending on the expensive and labor-intensive optical motion capture systems.
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Stacey, William C., Abba Krieger, and Brian Litt. "Network recruitment to coherent oscillations in a hippocampal computer model." Journal of Neurophysiology 105, no. 4 (April 2011): 1464–81. http://dx.doi.org/10.1152/jn.00643.2010.
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Coherent neural oscillations represent transient synchronization of local neuronal populations in both normal and pathological brain activity. These oscillations occur at or above gamma frequencies (>30 Hz) and often are propagated to neighboring tissue under circumstances that are both normal and abnormal, such as gamma binding or seizures. The mechanisms that generate and propagate these oscillations are poorly understood. In the present study we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of stochastic resonance and coherence resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. “Epileptic” high-frequency oscillations (HFOs), produced by pathologies such as increased synaptic activity and recurrent connections, were superior at recruiting neighboring tissue. “Normal” HFOs, associated with fast firing of inhibitory cells and sparse pyramidal cell firing, tended to suppress surrounding cells and showed very limited ability to recruit. These findings point to synaptic noise and physiological coupling as important targets for understanding the generation and propagation of both normal and pathological HFOs, suggesting potential new diagnostic and therapeutic approaches to human disorders such as epilepsy.
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Chen, Bing Mei, Han Wen Zhou, Xiao Ping Fan, Xue Rong Li, and Zhi Ming Zhou. "The Research on Neural Network Diagnosis and Treatment System of Child Mental Health Disorders." Applied Mechanics and Materials 707 (December 2014): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amm.707.188.
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The research of diagnosis and treatment system of child mental health disorders is based on artificial neural network and expert system. It combines the diagnosis standard of ICD 10, DSM IV with 40 years clinical experiences and knowledge of senior child psychiatrists. It also combines computer science with child psychiatry, child psychology, psychological estimate, psychological therapy and so on. The learning samples come from the epidemiological data in more than a dozen nationwide hospitals. The correct rate of system diagnosis is 99%. The system can diagnose 61 kinds of child mental health disorders and give a treatment method suggestion.
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Jiang, Hao, Peng Cao, MingYi Xu, Jinzhu Yang, and Osmar Zaiane. "Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction." Computers in Biology and Medicine 127 (December 2020): 104096. http://dx.doi.org/10.1016/j.compbiomed.2020.104096.
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Mohammed, Mazin Abed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mohd Khanapi Abd Ghani, Mashael S. Maashi, Begonya Garcia-Zapirain, Ibon Oleagordia, Hosam Alhakami, and Fahad Taha AL-Dhief. "Voice Pathology Detection and Classification Using Convolutional Neural Network Model." Applied Sciences 10, no. 11 (May 27, 2020): 3723. http://dx.doi.org/10.3390/app10113723.
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Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.
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Verde, Laura, and Giuseppe De Pietro. "A neural network approach to classify carotid disorders from Heart Rate Variability analysis." Computers in Biology and Medicine 109 (June 2019): 226–34. http://dx.doi.org/10.1016/j.compbiomed.2019.04.036.
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Lévesque, Maxime, Giuseppe Biagini, and Massimo Avoli. "Neurosteroids and Focal Epileptic Disorders." International Journal of Molecular Sciences 21, no. 24 (December 10, 2020): 9391. http://dx.doi.org/10.3390/ijms21249391.
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Neurosteroids are a family of compounds that are synthesized in principal excitatory neurons and glial cells, and derive from the transformation of cholesterol into pregnenolone. The most studied neurosteroids—allopregnanolone and allotetrahydrodeoxycorticosterone (THDOC)—are known to modulate GABAA receptor-mediated transmission, thus playing a role in controlling neuronal network excitability. Given the role of GABAA signaling in epileptic disorders, neurosteroids have profound effects on seizure generation and play a role in the development of chronic epileptic conditions (i.e., epileptogenesis). We review here studies showing the effects induced by neurosteroids on epileptiform synchronization in in vitro brain slices, on epileptic activity in in vivo models, i.e., in animals that were made epileptic with chemoconvulsant treatment, and in epileptic patients. These studies reveal that neurosteroids can modulate ictogenesis and the occurrence of pathological network activity such as interictal spikes and high-frequency oscillations (80–500 Hz). Moreover, they can delay the onset of spontaneous seizures in animal models of mesial temporal lobe epilepsy. Overall, this evidence suggests that neurosteroids represent a new target for the treatment of focal epileptic disorders.
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Mellal, Idir, Mourad Laghrouche, and Hung Tien Bui. "Field Programmable Gate Array (FPGA) Respiratory Monitoring System Using a Flow Microsensor and an Accelerometer." Measurement Science Review 17, no. 2 (April 1, 2017): 61–67. http://dx.doi.org/10.1515/msr-2017-0008.
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AbstractThis paper describes a non-invasive system for respiratory monitoring using a Micro Electro Mechanical Systems (MEMS) flow sensor and an IMU (Inertial Measurement Unit) accelerometer. The designed system is intended to be wearable and used in a hospital or at home to assist people with respiratory disorders. To ensure the accuracy of our system, we proposed a calibration method based on ANN (Artificial Neural Network) to compensate the temperature drift of the silicon flow sensor. The sigmoid activation functions used in the ANN model were computed with the CORDIC (COordinate Rotation DIgital Computer) algorithm. This algorithm was also used to estimate the tilt angle in body position. The design was implemented on reconfigurable platform FPGA.
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Starodubtseva, Yu A. "Comprehensive approach to therapy of adaptation disorders associated with computer addiction." Archives of psychiatry 25, no. 3 (September 18, 2019): 155–59. http://dx.doi.org/10.37822/2410-7484.2019.25.3.155-159.
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Background. In modern conditions there is a quick increase in non-chemical addictions, primarily computer addiction. Unfortunately, patients with non-chemical addictions do not fall into the field of view of doctors at the early stages of the development of the disease. They seek specialized help when addiction becomes a chronic process; due to disability, and delinquent behavior, which contributes to a violation of social functioning and quality of life of the patient and his family.
Objective – to develop and evaluate the effectiveness of a comprehensive program for the treatment of adaptation disorders associated with computer addiction, based on the study of clinical, psychopathological and pathopsychological patterns of their formation.
Materials and methods. The study involved 117 patients with signs of computer addiction according to the results of AUDIT-like tests and with adaptation disorders. The main group consisted of 66 patients who took part in the complex therapy program using the methods of pharmacotherapy, psychotherapy and psycho-education; control group – 51 patients who received standard regulated therapy in a medical institution. We used such study methods: clinical-anamnestic; clinical-psychopathological, using AUDIT-like tests for a comprehensive assessment of addictive status, psychodiagnostic using a hospital scale of anxiety and depression, Hamilton anxiety rating scale, Hamilton depression rating scale, the questionnaire of neuro-psychic tension according to T. A. Niemchyn; statistical.
Results. The clinical picture of computer addiction noted: compulsive surfing in a computer network (45.8±1.6% of the examined), computer games (22.3±1.2%), virtual communication (5.8±0.4%), gambling on-line (14.1±1.1%), passion for porn sites (1.2±0.1%). All examined patients received pharmacotherapy – antidepressants (SSRI) and anxiolytic drugs. and anxiolytic drugs. The psychotherapeutic complex included the use of rational psychotherapy, personality-oriented psychotherapy, existential psychotherapy, art therapy (painting technique). Psycho-educational work included the use of information modules, motivational trainings, the formation of communicative skills, problem-oriented discussions and teaching coping skills. Psychotherapeutic and psycho-educational work was aimed at determining the patient’s resource in overcoming computer addiction and its occurrence. Due to effects of the developed comprehensive program for the treatment of adaptation disorders associated with computer addiction, positive dynamics of the emotional status of patients were achieved, reduction of manifestations of anxiety and depression by HADS, reduction of manifestations of severe depressive and anxious episodes according to the Hamilton scales as well as reduce neuropsychic stress on a scale of T. A. Niemchyn were indicated.
Conclusions. A comprehensive system for the treatment of adaptation disorders associated with computer addiction should include a combination of pharmacotherapy, psychotherapy and psycho-education. Positive dynamics of the emotional status of patients, the predominance of subclinical manifestations or the absence of anxiety and depression on the HADS scale; mild depressive and anxious episodes or their absence according to the HAM-D and HAM-A scales; reduction of neuropsychic stress on a scale of T. A. Niemchyn as well as stability of the therapeutic effect during a two-year follow-up study indicates the effectiveness of the developed comprehensive therapy program.
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Padmapriya K.C., Leelavathy V., and Angelin Gladston. "Automatic Multiface Expression Recognition Using Convolutional Neural Network." International Journal of Artificial Intelligence and Machine Learning 11, no. 2 (July 2021): 1–13. http://dx.doi.org/10.4018/ijaiml.20210701.oa8.
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The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.
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Di Nanni, Noemi, Matteo Bersanelli, Francesca Anna Cupaioli, Luciano Milanesi, Alessandra Mezzelani, and Ettore Mosca. "Network-Based Integrative Analysis of Genomics, Epigenomics and Transcriptomics in Autism Spectrum Disorders." International Journal of Molecular Sciences 20, no. 13 (July 9, 2019): 3363. http://dx.doi.org/10.3390/ijms20133363.
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Current studies suggest that autism spectrum disorders (ASDs) may be caused by many genetic factors. In fact, collectively considering multiple studies aimed at characterizing the basic pathophysiology of ASDs, a large number of genes has been proposed. Addressing the problem of molecular data interpretation using gene networks helps to explain genetic heterogeneity in terms of shared pathways. Besides, the integrative analysis of multiple omics has emerged as an approach to provide a more comprehensive view of a disease. In this work, we carry out a network-based meta-analysis of the genes reported as associated with ASDs by studies that involved genomics, epigenomics, and transcriptomics. Collectively, our analysis provides a prioritization of the large number of genes proposed to be associated with ASDs, based on genes’ relevance within the intracellular circuits, the strength of the supporting evidence of association with ASDs, and the number of different molecular alterations affecting genes. We discuss the presence of the prioritized genes in the SFARI (Simons Foundation Autism Research Initiative) database and in gene networks associated with ASDs by other investigations. Lastly, we provide the full results of our analyses to encourage further studies on common targets amenable to therapy.
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Liu, Xian, Cheng-Xia Sun, Jing Gao, and Shi-Yun Xu. "Controllability of Networks of Multiple Coupled Neural Populations: An Analytical Method for Neuromodulation’s Feasibility." International Journal of Neural Systems 30, no. 02 (January 23, 2020): 2050001. http://dx.doi.org/10.1142/s012906572050001x.
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Neuromodulation plays a vital role in the prevention and treatment of neurological and psychiatric disorders. Neuromodulation’s feasibility is a long-standing issue because it provides the necessity for neuromodulation to realize the desired purpose. A controllability analysis of neural dynamics is necessary to ensure neuromodulation’s feasibility. Here, we present such a theoretical method by using the concept of controllability from the control theory that neuromodulation’s feasibility can be studied smoothly. Firstly, networks of multiple coupled neural populations with different topologies are established to mathematically model complicated neural dynamics. Secondly, an analytical method composed of a linearization method, the Kalman controllable rank condition and a controllability index is applied to analyze the controllability of the established network models. Finally, the relationship between network dynamics or topological characteristic parameters and controllability is studied by using the analytical method. The proposed method provides a new idea for the study of neuromodulation’s feasibility, and the results are expected to guide us to better modulate neurodynamics by optimizing network dynamics and network topology.
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Yolcu, Gozde, Ismail Oztel, Serap Kazan, Cemil Oz, Kannappan Palaniappan, Teresa E. Lever, and Filiz Bunyak. "Facial expression recognition for monitoring neurological disorders based on convolutional neural network." Multimedia Tools and Applications 78, no. 22 (July 23, 2019): 31581–603. http://dx.doi.org/10.1007/s11042-019-07959-6.
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Al-Hiyali, Mohammed Isam, Norashikin Yahya, Ibrahima Faye, and Ahmed Faeq Hussein. "Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network." Sensors 21, no. 16 (August 4, 2021): 5256. http://dx.doi.org/10.3390/s21165256.
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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.
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Naqvi, Syed Faraz, Syed Saad Azhar Ali, Norashikin Yahya, Mohd Azhar Yasin, Yasir Hafeez, Ahmad Rauf Subhani, Syed Hasan Adil, Ubaid M. Al Saggaf, and Muhammad Moinuddin. "Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network." Sensors 20, no. 16 (August 7, 2020): 4400. http://dx.doi.org/10.3390/s20164400.
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Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.
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Wang, Zijian, Yaqin Zhu, Haibo Shi, Yanting Zhang, and Cairong Yan. "A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images." Mathematical Biosciences and Engineering 18, no. 5 (2021): 6978–3994. http://dx.doi.org/10.3934/mbe.2021347.
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<abstract> <p>Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.</p> </abstract>
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Kratz, Alexander, Laura Rubattu, Micheline Maier-Redelsperger, Giovanni Amendola, Paolo Danise, Giuseppe d’Onofrio, Gina Zini, et al. "Automated Pre-Classification of Anemia Based on the Results of a Routine Automated Hematology System." Blood 106, no. 11 (November 16, 2005): 2253. http://dx.doi.org/10.1182/blood.v106.11.2253.2253.
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Abstract Laboratory workup of the cause of anemia requires clinical staff to develop a differential diagnosis based on routine CBC parameters and to subsequently order confirmatory tests. The number of parameters to be reviewed and the complexity of calculations which are performed by individuals on a routine basis is necessarily limited. The wealth of information in the many novel parameters and the complex patterns provided by modern hematology analyzers are frequently not utilized in routine clinical care. The use of computers for pre-classification of common RBC disorders would provide immediate information to order reflex confirmatory tests on the first sample, thereby improving patient care and allowing significant cost savings. Eleven European sites collected 2,303 data files from hematologically normal patients and individuals diagnosed with at least one of 36 RBC disorders. Samples were run on the ADVIA 120 Hematology System (Bayer HealthCare LLC, Diagnostics Division, Tarrytown, NY), an automated cell counter used in routine clinical hematology laboratories worldwide. Based on their representation within this database, a subset of 5 diseases (β-thalassemia heterozygote, β-thalassemia homozygote, Hb S homozygote, Hb SC, and hereditary spherocytosis; n=779 samples) and 123 normal cases were selected and used to develop a neural-network based computer program, the Computer Assisted RBC Disorder (CARD) Classification tool. The CARD utilizes hundreds of routine and novel CBC, differential and reticulocyte parameters available from the ADVIA 120 and 2120 Hematology Systems to determine possible causes of a patient’s anemia. We evaluated the CARD by using it to classify 273 new cases from 9 worldwide centers. The program correctly identified 93% of the cases. The majority of misidentifications were due to normal cases being classified as abnormal. 2 Hb S homozygote and one β-thalassemia heterozygote samples were misidentified as Hb SC. Only 2 of 137 abnormal cases, which were β-thalassemia heterozygote, were misclassified as normal. The performance of the tool for the presence of any hemoglobinopathy/thalassemia investigated was: sensitivity: 99%; specificity: 90%; PPV: 90%, NPV: 98%. This neural network-based computer program has demonstrated excellent performance with a validation set of samples and demonstrates the potential for using information from automated hematology analyzers to screen for the presence of certain hemoglobinopathies and to provide real-time information to direct an anemia workup. CARD TOOL ACCURACY # Correct # Incorrect Normal 122 14 β-Thalassemia Heterozygote 99 3 β-Thalassemia Homozygote 18 0 Hb S Homozygote 11 2 Hb SC 2 0 Hereditary Spherocytosis 2 0 TOTAL 254 (93%) 19 (7%)
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Machado, P. "Intact: Individually Tailored Stepped Care for Women with Eating Disorders." European Psychiatry 24, S1 (January 2009): 1. http://dx.doi.org/10.1016/s0924-9338(09)70984-8.
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INTACT is a multi-disciplinary and inter-sectorial network of 9 European partners from 8 EU countries aiming at the development of new strategies for the prevention and treatment of eating disorders.Acknowledging that not all women at risk for an eating disorder actually develop the disorder and that not all of those who get ill need the same type and intensity of care, INTACT studies stepped care treatment and individually tailored interventions based on research into the process of getting ill, getting well, and staying well.The development of such innovative approaches in which treatments are provided sequentially according to patients’ needs promises to optimise health care for ED patients.Specifically, the INTACT studies focus on the development of:1.risk models,2.step-up interventions,3.therapy process-outcome models, and4.step-down interventions and apply methods from the fields of psychotherapy research, genetics, biology, linguistics and computer sciences.INTACT is funded by the European Commission (MRTN-CT-2006-035988).
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Shuai, Hong-Han, Chih-Ya Shen, De-Nian Yang, Yi-Feng Carol Lan, Wang-Chien Lee, Philip S. Yu, and Ming-Syan Chen. "A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining." IEEE Transactions on Knowledge and Data Engineering 30, no. 7 (July 1, 2018): 1212–25. http://dx.doi.org/10.1109/tkde.2017.2786695.
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Fu, Zhongjie, Ye Sun, Bertan Cakir, Yohei Tomita, Shuo Huang, Zhongxiao Wang, Chi-Hsiu Liu, et al. "Targeting Neurovascular Interaction in Retinal Disorders." International Journal of Molecular Sciences 21, no. 4 (February 22, 2020): 1503. http://dx.doi.org/10.3390/ijms21041503.
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The tightly structured neural retina has a unique vascular network comprised of three interconnected plexuses in the inner retina (and choroid for outer retina), which provide oxygen and nutrients to neurons to maintain normal function. Clinical and experimental evidence suggests that neuronal metabolic needs control both normal retinal vascular development and pathological aberrant vascular growth. Particularly, photoreceptors, with the highest density of mitochondria in the body, regulate retinal vascular development by modulating angiogenic and inflammatory factors. Photoreceptor metabolic dysfunction, oxidative stress, and inflammation may cause adaptive but ultimately pathological retinal vascular responses, leading to blindness. Here we focus on the factors involved in neurovascular interactions, which are potential therapeutic targets to decrease energy demand and/or to increase energy production for neovascular retinal disorders.
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SPITZER, MANFRED, and MANFRED NEUMANN. "NOISE IN MODELS OF NEUROLOGICAL AND PSYCHIATRIC DISORDERS." International Journal of Neural Systems 07, no. 04 (September 1996): 355–61. http://dx.doi.org/10.1142/s0129065796000312.
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The concept of noise has only recently been applied to modelling neuropsychiatric disorders. Two examples of such models are presented. 1. A phantom limb is a neurological condition after the amputation of an extremity. It consists of sensations of the presence of the lost limb and has been attributed to cortical as well as non-cortical mechanisms. A neural network model of phantom limbs is proposed which can parsimoniously account for a large number of clinical features and recent findings of cortical map plasticity after deafferentation. In trained self-organizing feature maps, deafferentation was simulated. Reorganization is shown to be driven by input noise. According to the model, the production of input noise by the deafferented primary sensory neuron drives cortical reorganization in amputees. No such noise is generated and/or conducted to the cortex in paraplegics. 2. Several clinical features of schizophrenia have been related to the ratio of signal to noise in neuronal information processing. In particular, dopamine — which has been implicated in the causation of schizophrenia for decades — has been proposed to modulate signal-to-noise ratio. Data are presented which suggest that schizophrenic thought disorder is the result of a hypodopaminergic state and concomitant increased effects of noise in semantic information processing. Possible functions of noise in the nervous systems are discussed.
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Trusculescu, Ana Adriana, Diana Manolescu, Emanuela Tudorache, and Cristian Oancea. "Deep learning in interstitial lung disease—how long until daily practice." European Radiology 30, no. 11 (June 14, 2020): 6285–92. http://dx.doi.org/10.1007/s00330-020-06986-4.
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Abstract Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease. Key Points • Deep learning algorithms are used in pattern recognition of different interstitial lung diseases. • High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease. • Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.
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Trifonova, Ekaterina A., Alexandra I. Klimenko, Zakhar S. Mustafin, Sergey A. Lashin, and Alex V. Kochetov. "Do Autism Spectrum and Autoimmune Disorders Share Predisposition Gene Signature Due to mTOR Signaling Pathway Controlling Expression?" International Journal of Molecular Sciences 22, no. 10 (May 16, 2021): 5248. http://dx.doi.org/10.3390/ijms22105248.
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Autism spectrum disorder (ASD) is characterized by uncommon genetic heterogeneity and a high heritability concurrently. Most autoimmune disorders (AID), similarly to ASD, are characterized by impressive genetic heterogeneity and heritability. We conducted gene-set analyses and revealed that 584 out of 992 genes (59%) included in a new release of the SFARI Gene database and 439 out of 871 AID-associated genes (50%) could be attributed to one of four groups: 1. FMRP (fragile X mental retardation protein) target genes, 2. mTOR signaling network genes, 3. mTOR-modulated genes, and 4. vitamin D3-sensitive genes. With the exception of FMRP targets, which are obviously associated with the direct involvement of local translation disturbance in the pathological mechanisms of ASD, the remaining categories are represented among AID genes in a very similar percentage as among ASD predisposition genes. Thus, mTOR signaling pathway genes make up 4% of ASD and 3% of AID genes, mTOR-modulated genes—31% of both ASD and AID genes, and vitamin D-sensitive genes—20% of ASD and 23% of AID genes. The network analysis revealed 3124 interactions between 528 out of 729 AID genes for the 0.7 cutoff, so the great majority (up to 67%) of AID genes are related to the mTOR signaling pathway directly or indirectly. Our present research and available published data allow us to hypothesize that both a certain part of ASD and AID comprise a connected set of disorders sharing a common aberrant pathway (mTOR signaling) rather than a vast set of different disorders. Furthermore, an immune subtype of the autism spectrum might be a specific type of autoimmune disorder with an early manifestation of a unique set of predominantly behavioral symptoms.
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Figoń, Piotr. "Mathematical models of single-phase long lines." Bulletin of the Military University of Technology 68, no. 4 (February 28, 2020): 119–37. http://dx.doi.org/10.5604/01.3001.0013.9735.
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Most simulation packages provide long line models containing only two input terminals and two output terminals. These models allow testing transient states initiated by the occurrence of interference at the ends of the line. For this reason, it is not possible to study disorders occurring at any point of the electrical network. The article describes in detail the mathematical models of the long line, their implementation in the Matlab environment and exemplary results of computer simulations. Keywords: power line, state variables, differential scheme
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Vuksanović, Vesna, Roger T. Staff, Trevor Ahearn, Alison D. Murray, and Claude M. Wischik. "Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer’s Disease and Behavioral Variant Fronto-Temporal Dementia." International Journal of Neural Systems 29, no. 06 (July 29, 2019): 1850055. http://dx.doi.org/10.1142/s0129065718500557.
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Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer’s disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.
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KUNHIMANGALAM, REEDA, SUJITH OVALLATH, and PAUL K. JOSEPH. "ARTIFICIAL NEURAL NETWORKS IN THE IDENTIFICATION OF PERIPHERAL NERVE DISORDERS." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240018. http://dx.doi.org/10.1142/s0219519412400180.
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The recent years have witnessed an increase in the use of newer analytical tools in the field of medicine to assist in diagnostic procedure. Among the new tools, artificial neural networks (ANNs) have received particular attention because of their ability to analyze complex nonlinear data sets. This study suggests that ANNs can be used for the diagnosis of peripheral nerve disorders particularly the carpal tunnel syndrome (CTS) and neuropathy. This paper aims at building a classifier using a feed forward neural network that can distinguish between CTS, neuropathy, and normal controls using a reduced set of measurements or features from nerve conduction study (NCS) data. Three different ANN training algorithms, viz. Levenberg–Marquardt (LM), Conjugate gradient (CGB), and resilient back-propagation (RP) are used to see which algorithm produces better results and has faster training for the application under consideration. The data used were obtained from the Neurology Department, Kannur Medical College, Kerala, India. The obtained resultant confusion matrix indicated only a few misclassifications in all the three cases. The analysis showed that the CGB and RB algorithms provide faster convergence on pattern recognition problems, but the best performance in terms of accuracy is given by the LM algorithm. The accuracy obtained for the LM, CGB, and RB were 98.3%, 97.8%, and 97.2%, respectively. The respective sensitivities were 96.1%, 94.1%, and 94.1%, while the specificities were found to be equal to 99.4%, 98.8%, and 97.5%, respectively. The study aims at showing that ANNs may prove useful in combination with other systems in providing diagnostic and predictive medical opinions. However, it must always be kept in mind that ANNs represent only one form of computer-aided diagnosis, and the clinician's responsibility and overall control of patient care should never be underestimated.
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Santiago, Jose A., Virginie Bottero, and Judith A. Potashkin. "Transcriptomic and Network Analysis Identifies Shared and Unique Pathways across Dementia Spectrum Disorders." International Journal of Molecular Sciences 21, no. 6 (March 17, 2020): 2050. http://dx.doi.org/10.3390/ijms21062050.
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Background: Dementia is a growing public health concern with an estimated prevalence of 50 million people worldwide. Alzheimer’s disease (AD) and vascular and frontotemporal dementias (VaD, FTD), share many clinical, genetical, and pathological features making the diagnosis difficult. Methods: In this study, we compared the transcriptome from the frontal cortex of patients with AD, VaD, and FTD to identify dysregulated pathways. Results: Upregulated genes in AD were enriched in adherens and tight junctions, mitogen-activated protein kinase, and phosphatidylinositol 3-kinase and protein kinase B/Akt signaling pathways, whereas downregulated genes associated with calcium signaling. Upregulated genes in VaD were centered on infectious diseases and nuclear factor kappa beta signaling, whereas downregulated genes are involved in biosynthesis of amino acids and the pentose phosphate pathway. Upregulated genes in FTD were associated with ECM receptor interactions and the lysosome, whereas downregulated genes were involved in glutamatergic synapse and MAPK signaling. The transcription factor KFL4 was shared among the 3 types of dementia. Conclusions: Collectively, we identified similarities and differences in dysregulated pathways and transcription factors among the dementias. The shared pathways and transcription factors may indicate a potential common etiology, whereas the differences may be useful for distinguishing dementias.
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Gomes, Ana Rita, Nasim Bahram Sangani, Tiago G. Fernandes, M. Margarida Diogo, Leopold M. G. Curfs, and Chris P. Reutelingsperger. "Extracellular Vesicles in CNS Developmental Disorders." International Journal of Molecular Sciences 21, no. 24 (December 11, 2020): 9428. http://dx.doi.org/10.3390/ijms21249428.
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The central nervous system (CNS) is the most complex structure in the body, consisting of multiple cell types with distinct morphology and function. Development of the neuronal circuit and its function rely on a continuous crosstalk between neurons and non-neural cells. It has been widely accepted that extracellular vesicles (EVs), mainly exosomes, are effective entities responsible for intercellular CNS communication. They contain membrane and cytoplasmic proteins, lipids, non-coding RNAs, microRNAs and mRNAs. Their cargo modulates gene and protein expression in recipient cells. Several lines of evidence indicate that EVs play a role in modifying signal transduction with subsequent physiological changes in neurogenesis, gliogenesis, synaptogenesis and network circuit formation and activity, as well as synaptic pruning and myelination. Several studies demonstrate that neural and non-neural EVs play an important role in physiological and pathological neurodevelopment. The present review discusses the role of EVs in various neurodevelopmental disorders and the prospects of using EVs as disease biomarkers and therapeutics.
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Samuel, Pearl Mary, and Thanikaiselvan Veeramalai. "Multilevel and Multiscale Deep Neural Network for Retinal Blood Vessel Segmentation." Symmetry 11, no. 7 (July 22, 2019): 946. http://dx.doi.org/10.3390/sym11070946.
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Retinal blood vessel segmentation influences a lot of blood vessel-related disorders such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular disorders, etc. It is found that vessel segmentation using a convolutional neural network (CNN) showed increased accuracy in feature extraction and vessel segmentation compared to the classical segmentation algorithms. CNN does not need any artificial handcrafted features to train the network. In the proposed deep neural network (DNN), a better pre-processing technique and multilevel/multiscale deep supervision (DS) layers are being incorporated for proper segmentation of retinal blood vessels. From the first four layers of the VGG-16 model, multilevel/multiscale deep supervision layers are formed by convolving vessel-specific Gaussian convolutions with two different scale initializations. These layers output the activation maps that are capable to learn vessel-specific features at multiple scales, levels, and depth. Furthermore, the receptive field of these maps is increased to obtain the symmetric feature maps that provide the refined blood vessel probability map. This map is completely free from the optic disc, boundaries, and non-vessel background. The segmented results are tested on Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), High-Resolution Fundus (HRF), and real-world retinal datasets to evaluate its performance. This proposed model achieves better sensitivity values of 0.8282, 0.8979 and 0.8655 in DRIVE, STARE and HRF datasets with acceptable specificity and accuracy performance metrics.
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Babaei, Sepideh, and Amir Geranmayeh. "Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals." Computers in Biology and Medicine 39, no. 1 (January 2009): 8–15. http://dx.doi.org/10.1016/j.compbiomed.2008.10.004.
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Leys, Mark. "Innovations and networks: the case of mental health care reforms in Belgium." Journal on Chain and Network Science 10, no. 2 (January 1, 2010): 135–44. http://dx.doi.org/10.3920/jcns2010.x116.
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This article discusses mental health care reforms in Belgium from an interorganisational perspective. In 2007 a three year experiential government programme was launched seeking for alternative organisation models in mental health care, labeled as 'care circuits' and 'networks'. The target population is 'persons with chronic and complex mental disorders'. This article reports some of the observations based of an ongoing evaluation process. The research evaluates the plan and implementation processes of these collaboration and networking models (the TP). The qualitative approach aims at developing, testing and refining insights in the dynamics of developing collaboration and networks by exploring the complex and dynamic interaction among context, mechanism, and outcome. The article uses insights form health care innovation literature, interorganisational network theories and literature on organisational fields. Health care innovations take place in a complex multi-agent environment. The implementation (adopting and sustaining) of health services innovations is influenced both by 'external' and 'internal' processes and barriers. The public sector context of mental health care includes 'external' influences by multiple stakeholder through values, power plays, regulations and normative frameworks. The development of interprofessional collaboration and interorganisational networks develops thus in an organisational field. An organisational field is complex, heterogeneous, multi-layered and dynamic. Organisations and actors in the field act on the basis of their interests and respond strategically to institutional pressures. Fields also shape the discourse, norms and structures in ways that match their individual interests and objectives. We found plenty of indications in the mental health reform programme. But the research also urges to develop that further insights into the question whether and under what conditions networks and collaborations between different types of organisations actually are effective. The issue of network governance should be elaborated upon.
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Yoo, Minjoo, Sangwon Lee, and Taehyun Ha. "Semantic network analysis for understanding user experiences of bipolar and depressive disorders on Reddit." Information Processing & Management 56, no. 4 (July 2019): 1565–75. http://dx.doi.org/10.1016/j.ipm.2018.10.001.
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An, Ran, Yuncheng Man, Shamreen Iram, Erdem Kucukal, Muhammad Noman Hasan, Ambar Solis-Fuentes, Allison Bode, et al. "Computer Vision and Deep Learning Assisted Microchip Electrophoresis for Integrated Anemia and Sickle Cell Disease Screening." Blood 136, Supplement 1 (November 5, 2020): 46–47. http://dx.doi.org/10.1182/blood-2020-142548.
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Introduction: Anemia affects a third of the world's population with the heaviest burden borne by women and children. Anemia leads to preventable impaired development in children, as well as high morbidity and early mortality among sufferers. Inherited hemoglobin (Hb) disorders, such as sickle cell disease (SCD), are associated with chronic hemolytic anemia causing high morbidity and mortality. Anemia and SCD are inherently associated and are both prevalent in the same regions of the world including sub-Saharan Africa, India, and south-east Asia. Anemia and SCD-related complications can be mitigated by screening, early diagnosis followed by timely intervention. Anemia treatment depends on the accurate characterization of the cause, such as inherited Hb disorders. Meanwhile, Hb disorders or SCD treatments, such as hydroxyurea therapy, requires close monitoring of blood Hb level and the patient's anemia status over time. As a result, it is crucially important to perform integrated detection and monitoring of blood Hb level, anemia status, and Hb variants, especially in areas where anemia and inherited Hb disorders are the most prevalent. Blood Hb level (in g/dL) is used as the main indicator of anemia, while the presence of Hb variants (e.g., sickle Hb or HbS) in blood is the primary indicator of an inherited disorder. The current clinical standards for anemia testing and Hb variant identification are complete blood count (CBC) and High-Performance Liquid Chromatography (HPLC), respectively. State-of-the-art laboratory infrastructure and trained personnel are required for these laboratory tests. However, these resources are typically scarce in low- and middle-income countries, where anemia and Hb disorders are the most prevalent. As a result, there is a dire need for high accuracy portable point-of-care (POC) devices to perform integrated anemia and Hb variant tests with affordable cost and high throughput. Methods: In 2019, the World Health Organization (WHO) listed Hb electrophoresis as an essential in vitro diagnostic (IVD) technology for diagnosing SCD and sickle cell trait. We have leveraged the common Hb electrophoresis method and developed a POC microchip electrophoresis test, Hemoglobin Variant/Anemia (HbVA). This technology is being commercialized under the product name "Gazelle" by Hemex Health Inc. for Hb variant identification with integrated anemia detection (Fig. 1A&B). We hypothesized that computer vision and deep learning will enhance the accuracy and reproducibility of blood Hb level prediction and anemia detection in cellulose acetate based Hb electrophoresis, which is a clinical standard test for Hb variant screening and diagnosis worldwide (Fig. 1C). To test this hypothesis, we integrated, for the first time, a new, computer vision and artificial neural network (ANN) based deep learning imaging and data analysis algorithm, to Hb electrophoresis. Here, we show the feasibility of this new, computer vision and deep learning enabled diagnostic approach via testing of 46 subjects, including individuals with anemia and homozygous (HbSS) or heterozygous (HbSC or Sβ-thalassemia) SCD. Results and Discussion: HbVA computer vision tracked the electrophoresis process real-time and the deep learning neural network algorithm determined Hb levels which demonstrated significant correlation with a Pearson Correlation Coefficient of 0.95 compared to the results of reference standard CBC (Fig.1D). Furthermore, HbVA demonstrated high reproducibly with a mean absolute error of 0.55 g/dL and a bias of -0.10 g/dL (95% limits of agreement: 1.5 g/dL) according to Bland-Altman analysis (Fig. 1E). Anemia determination was achieved with 100% sensitivity and 92.3% specificity with a receiver operating characteristic area under the curve (AUC) of 0.99 (Fig. 1F). Within the same test, subjects with SCD were identified with 100% sensitivity and specificity (Fig. 1G). Overall, the results suggested that computer vision and deep learning methods can be used to extract new information from Hb electrophoresis, enabling, for the first time, reproducible, accurate, and integrated blood Hb level prediction, anemia detection, and Hb variant identification in a single affordable test at the POC. Disclosures An: Hemex Health, Inc.: Patents & Royalties. Hasan:Hemex Health, Inc.: Patents & Royalties. Ahuja:Genentech: Consultancy; Sanofi-Genzyme: Consultancy; XaTec Inc.: Consultancy; XaTec Inc.: Research Funding; XaTec Inc.: Divested equity in a private or publicly-traded company in the past 24 months; Genentech: Honoraria; Sanofi-Genzyme: Honoraria. Little:GBT: Research Funding; Bluebird Bio: Research Funding; BioChip Labs: Patents & Royalties: SCD Biochip (patent, no royalties); Hemex Health, Inc.: Patents & Royalties: Microfluidic electropheresis (patent, no royalties); NHLBI: Research Funding; GBT: Membership on an entity's Board of Directors or advisory committees. Gurkan:Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding; BioChip Labs: Patents & Royalties; Xatek Inc.: Patents & Royalties; Dx Now Inc.: Patents & Royalties.
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Mahmood, Usman, Zening Fu, Vince D. Calhoun, and Sergey Plis. "A Deep Learning Model for Data-Driven Discovery of Functional Connectivity." Algorithms 14, no. 3 (February 26, 2021): 75. http://dx.doi.org/10.3390/a14030075.
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Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of functional magnetic resonance imaging (fMRI) correlation matrix. However, most of the work with the FC depends on the way the connectivity is computed, and it further depends on the manual post-hoc analysis of the FC matrices. In this work, we propose a deep learning architecture BrainGNN that learns the connectivity structure as part of learning to classify subjects. It simultaneously applies a graphical neural network to this learned graph and learns to select a sparse subset of brain regions important to the prediction task. We demonstrate that the model’s state-of-the-art classification performance on a schizophrenia fMRI dataset and demonstrate how introspection leads to disorder relevant findings. The graphs that are learned by the model exhibit strong class discrimination and the sparse subset of relevant regions are consistent with the schizophrenia literature.
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Shushliapina, N. O., and O. Ye Cherniakova. "FEATURES OF CAPILLARY BLOOD FLOW IN PATIENTS WITH PATHOLOGY OF INTRANASAL STRUCTURES AND NASAL BREATHING DISORDERS." International Medical Journal, no. 3 (September 16, 2020): 53–59. http://dx.doi.org/10.37436/2308-5274-2020-3-11.
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The investigation of the vascular microcirculation system is important for diagnosis, assessment of the severity and nature of pathological processes in human body, monitoring the effectiveness of treatment. Monitoring the state of microcirculation in impaired respiratory function of the nose helps to study the subtle mechanisms of regulation of vascular−tissue relations. To do this, there were used the biomicroscopic methods to study capillary blood flow, one of the most relevant and promising is optical capillaroscopy of the nail bed. This method makes it possible to identify at the evidence level the peculiarities of the functioning of the peripheral circulatory system by the state of the capillary system and to evaluate the effectiveness of treatment by the rheological properties of blood in hematological practice. There were examined 145 patients by means of computer capillaroscopy to study the rate of capillary circulation in the patients with pathology of intranasal structures and nasal breathing disorders. All patients underwent a complete clinical examination, routine instrumental examinations, and computer capillaroscopy using a video capillaroscope with a visual magnification of up to 550 times. The obtained images were stored and processed according to a special software. During the characterization of the capillaroscopic picture there were evaluated: pathological tortuosity, change in the caliber of arterioles and venules, disorganization of the capillary network, the number of functioning capillaries. Changes in the speed and nature of capillary blood flow (accelerated, slow, stasis) were observed. The optical capillaroscopy method allows not only to visually assess the condition of microvessels, but also to determine such an important parameter as blood circulation, actually, it can replace the study of laser Doppler. Such data will be important in the diagnosis of respiratory and olfactory disorders and the formation of adequate tactics for their treatment. Key words: microcirculation, microcirculatory tract, capillary circulation, nasal obstruction, nasal breathing disorders, pathology of intranasal structures, computer capillaroscopy.
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Mehraei, Mani, Rza Bashirov, and Şükrü Tüzmen. "Target-based drug discovery for β-globin disorders: drug target prediction using quantitative modeling with hybrid functional Petri nets." Journal of Bioinformatics and Computational Biology 14, no. 05 (October 2016): 1650026. http://dx.doi.org/10.1142/s0219720016500268.
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Recent molecular studies provide important clues into treatment of [Formula: see text]-thalassemia, sickle-cell anaemia and other [Formula: see text]-globin disorders revealing that increased production of fetal hemoglobin, that is normally suppressed in adulthood, can ameliorate the severity of these diseases. In this paper, we present a novel approach for drug prediction for [Formula: see text]-globin disorders. Our approach is centered upon quantitative modeling of interactions in human fetal-to-adult hemoglobin switch network using hybrid functional Petri nets. In accordance with the reverse pharmacology approach, we pose a hypothesis regarding modulation of specific protein targets that induce [Formula: see text]-globin and consequently fetal hemoglobin. Comparison of simulation results for the proposed strategy with the ones obtained for already existing drugs shows that our strategy is the optimal as it leads to highest level of [Formula: see text]-globin induction and thereby has potential beneficial therapeutic effects on [Formula: see text]-globin disorders. Simulation results enable verification of model coherence demonstrating that it is consistent with qPCR data available for known strategies and/or drugs.
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NAYAK, JAGADISH, P. SUBBANNA BHAT, U. RAJENDRA ACHARYA, OLIVER FAUST, and LIM CHOO MIN. "COMPUTER-BASED IDENTIFICATION OF CATARACT AND CATARACT SURGERY EFFICACY USING OPTICAL IMAGES." Journal of Mechanics in Medicine and Biology 09, no. 04 (December 2009): 589–607. http://dx.doi.org/10.1142/s0219519409003140.
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The eyes are complex sensory organs, they are designed to capture images under varying light conditions. Eye disorders, such as cataract, among the elderly are a major health problem. Cataract is a painless clouding of the eye lens which develops over a long period of time. During this time, the eyesight gradually worsens. It can eventually lead to blindness and, is common in older people. In fact, about a third of people over 65 have cataracts in one or both eyes. In this paper, we made use of two types of classifiers for identification of normal, cataract (early and developed stage), and post-cataract eyes using features extracted from optical images. These classifiers are artificial neural network and support vector machine. A database of 174 subjects, using the cross-validation strategy, is used to test the effectiveness of both classifiers. We demonstrate a sensitivity of more than 90% for both of these classifiers. Furthermore, they have a specificity of 100% and, as such, the results obtained are very promising. The proposed feature extraction and classification systems are ready clinically to run on a large amount of data sets.
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Miranda, Priscilla Ingrid Gomes, Jackeline Vieira Amaral, Jaqueline Carvalho e. Silva Sales, Fernando José Guedes da Silva Júnior, and Ana Paula Cardoso Costa. "Actions carried out in primary health care towards people with mental disorders: an integrative review." Rev Rene 22 (December 10, 2020): e60496. http://dx.doi.org/10.15253/2175-6783.20212260496.
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Objective: to synthesize the types of actions developed by the multiprofessional team in the primary health care to people with mental disorders. Methods: integrative review, carried out in the databases Medical Literature Analysis and Retrieval System Online, via PubMed, Web Of Science, Literatura Latino-Americana e do Caribe em Ciências da Saúde, Base de Dados de Enfermagem, and Índice Bibliográfico Espanhol de Ciências de Saúde. Results: six studies were selected and grouped into the following axes: actions with the use of digital technologies (PyDeSalud.com platform; Partnering to Achieve School Success; Audio computer-assisted self-interview version of the Alcohol, Smoking and Substance Involvement Screening Test) and traditional actions (questionnaires during consultation; intervention in stages; support network to stop smoking). Conclusion: evidences show that traditional or digital actions in mental health are necessary to offer an integral care. The multiprofessional team has the tendency to incorporate digital technologies to care for these people.
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Tanriver, Gizem, Merva Soluk Tekkesin, and Onur Ergen. "Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders." Cancers 13, no. 11 (June 2, 2021): 2766. http://dx.doi.org/10.3390/cancers13112766.
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Oral cancer is the most common type of head and neck cancer worldwide, leading to approximately 177,757 deaths every year. When identified at early stages, oral cancers can achieve survival rates of up to 75–90%. However, the majority of the cases are diagnosed at an advanced stage mainly due to the lack of public awareness about oral cancer signs and the delays in referrals to oral cancer specialists. As early detection and treatment remain to be the most effective measures in improving oral cancer outcomes, the development of vision-based adjunctive technologies that can detect oral potentially malignant disorders (OPMDs), which carry a risk of cancer development, present significant opportunities for the oral cancer screening process. In this study, we explored the potential applications of computer vision techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for detecting OPMD. Exploiting the advancements in deep learning, a two-stage model was proposed to detect oral lesions with a detector network and classify the detected region into three categories (benign, OPMD, carcinoma) with a second-stage classifier network. Our preliminary results demonstrate the feasibility of deep learning-based approaches for the automated detection and classification of oral lesions in real time. The proposed model offers great potential as a low-cost and non-invasive tool that can support screening processes and improve detection of OPMD.
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Messa, Gian Marco, Francesco Napolitano, Sarah H. Elsea, Diego di Bernardo, and Xin Gao. "A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data." Bioinformatics 36, Supplement_2 (December 2020): i787—i794. http://dx.doi.org/10.1093/bioinformatics/btaa841.
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Abstract Motivation Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN). Results The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future. Availability and implementation Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).
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Casilli, Antonio A., Paola Tubaro, and Pedro Araya. "Ten years of Ana: Lessons from a transdisciplinary body of literature on online pro-eating disorder websites." Social Science Information 51, no. 1 (March 2012): 120–39. http://dx.doi.org/10.1177/0539018411425880.
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Resume This paper offers a methodical review of the scientific literature of the last decade that concerns itself with online services offering supportive advocacy for anorexia nervosa and bulimia nervosa (‘pro-ana’ and ‘pro-mia’). The main question is whether these studies reproduce the traditional divide in the study of eating disorders, between clinical and social science perspectives, with limited mutual exchanges. Having first identified a specific body of literature, the authors investigate its content, methods and approaches, and analyse the network of cross-citations the components generate and share. On this basis, the authors argue that the scientific literature touching on pro-ana websites can be regarded as a single transdisciplinary body of knowledge. What’s more, they show that the literature on computer-mediated sociabilities centred on eating disorders displays different structural characteristics with respect to the traditional, non-Web-related research on eating disorders. In the latter, the social sciences have usually provided a critical counterpoint to the development of a health sciences mainstream. In the case of Web-related research, however, the social sciences have taken the lead role in defining the field, with the health sciences following suit.