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Cross-Recurrence Quantification Analysis for Distinguishing Emotions Induced by Indian Classical Music
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
M. Sushrutha Bharadwaj, V. G. Sangam, Shantala Hegde, Anand Prem Rajan
The human brain is the central part of the nervous system and is extremely complex in carrying out its functions. All the actions and reactions of the body are monitored and regulated by the brain. The brain controls the functions and different actions of the body by acquiring information from the sense organs, analyzing the data received and responding to the information suitably. Brain is a complex nonlinear system where all information is perceived, and nonlinear processing of signals obtained from the brain can be used to assess the behavior of the person. Signals from the brain can be recorded using modalities such as electroencephalography (EEG), computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI), which give valuable information about the structural and functional changes in the brain due to several activities (D’Elia and Madaffari 2012).
Deep Learning to Diagnose Diseases and Security in 5G Healthcare Informatics
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
The human brain is vulnerable to a wide range of disorders that can hit at any age. Autism spectrum disorder and dyslexia are two examples of developmental conditions that appear in early childhood. Psychiatric illnesses, like depression and schizophrenia, are often diagnosed in teenagers or early adulthood, but their causes can be found much earlier in life. Then, as people get older, they become more vulnerable to dementia disorders like Alzheimer's, Parkinson's, and others.
Significant Advancements in Cancer Diagnosis Using Machine Learning
Published in Meenu Gupta, Rachna Jain, Arun Solanki, Fadi Al-Turjman, Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective, 2021
Gurmanik Kaur, Ajat Shatru Arora
The human brain is a complex organ in the body. An abnormal mitosis mechanism affects the process of morphological cells in the human brain. Cancer cells with various morphological features, such as size and intensity, are formed during this process. There are two kinds of brain tumors: low grade (develops slowly) and high grade (grows quickly and disrupts blood-brain supply). As a result, the vast majority of malignant brain tumor cells have been referred to as neuroepithelial cancer cells. Glioblastoma is a common brain tumor, with 5% prevalence and a patient survival rate of less than 5 years [6]. When compared to neighboring cells, the majority of cancerous cells have low contrast. As a result, accurately detecting brain tumors is a crucial step. The most commonly utilized modality for detecting brain tumors is MRI, a pain-free method that aids in tumor analysis from various perspectives and viewpoints. As a result, MRI analysis is the most effective method for identifying brain tumors [7].
Dilation velocity is associated with Glasgow Coma Scale scores in patients with brain injury
Published in Brain Injury, 2021
Barsha Thakur, Hend Nadim, Folefac Atem, Sonja E. Stutzman, DaiWai M. Olson
The human nervous system is considered one of the most complex structures of the body (1). Understanding how it functions and various conditions related to the nervous system has been a matter of fascination for clinicians and researchers alike (2). Advances in knowledge about the human brain have revolutionized care for patients experiencing neurological dysfunction or disease. In spite of these advancements, the importance of early interventions for preventing long-term complications cannot be stressed enough. The neurological examination involves a thorough assessment of the subject’s nervous system, which identifies underlying pathology to expedite early intervention and appropriate routing of care in a clinic and hospital setting (3). The Glasgow Coma Scale (GCS) and the pupillary light reflex (PLR) are high-priority elements of the neurological assessment (4). Hand held automated infrared pupillometry (AIP) allows for accurate and reliable measurement of the PLR (5). There is currently little research data available regarding variables measured by the pupillometer and therefore, the purpose of this research is to advance the current knowledge about pupilometer output of dilation velocity (DV) and how it relates to the GCS.
Cellular and circuit mechanisms of olfactory associative learning in Drosophila
Published in Journal of Neurogenetics, 2020
Tamara Boto, Aaron Stahl, Seth M. Tomchik
The human brain is among the most complex organs in the body. Understanding its functionality is, as Emerson Pugh’s famous historical quip highlights, a longstanding challenge for biological inquiry (Pugh, 1977). Within the field of neuroscience, deciphering how memories are formed and maintained is a major area of focus. Memories of our past experiences interact with present sensory perceptions to influence our behaviors, but what is the neural substrate of these interactions? What molecular pathways drive the modifications of neural activity supporting the formation of memories, and how are these manifested at the circuit and systems levels? Our ability to distill these broad questions into meaningful, experimentally-tractable derivatives will be the one of the greatest determinants of the success of neuroscience research. Given the immense challenge of reverse engineering the human brain, with its 86 billion neurons and estimated ∼100 trillion synapses, neuroscience research heavily leverages model organisms with numerically-reduced nervous systems. Offering a good compromise between relative brain simplicity and behavioral sophistication, the fruit fly Drosophila melanogaster is a highly informative model organism for the study of behaviors ranging from circadian rhythms to learning and memory (Hardin, Hall, & Rosbash, 1992; Heisenberg, Borst, Wagner, & Byers, 1985; McBride et al., 1999; Nitabach & Taghert, 2008; Zars, 2010; Zars, Fischer, Schulz, & Heisenberg, 2000a).
There’s no cure for brain injury: work-related stress in brain injury rehabilitation professionals
Published in Brain Injury, 2019
Gillian Murray, Tessa Hart, Andrea Doyle, Casey Bohrman, Chelsea Toth
Unpredictability was a common link amongst our findings, at the micro, mezzo, and macro levels of practice. The unpredictability of how a brain injury presents in each person, the uncertainty of whether or not clients will be receptive to treatment approaches, unforeseeable events that occur when providing services in the home and community, and the general unknown about the human brain itself compounds the experience of not only brain injury professionals but survivors themselves. The unpredictability and arbitrary approval of funding impacts professionals and providers at the organizational level and ultimately the outcomes achieved by survivors. At the policy level, federal and state funding for the continuum of care is already sparse, and the existing political climate further threatens the availability of resources for this vulnerable population. Brain injury professionals have to live with feelings of ‘unfinished business’ and work with the unpredictability of what the future holds for brain injury rehabilitation funding throughout the United States.