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Music, Brain, and Rehabilitation: Emerging Therapeutic Applications and Potential Neural Mechanisms

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889198313 Year: Pages: 308 DOI: 10.3389/978-2-88919-831-3 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-01-19 14:05:46
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Music is an important source of enjoyment, learning, and well-being in life as well as a rich, powerful, and versatile stimulus for the brain. With the advance of modern neuroimaging techniques during the past decades, we are now beginning to understand better what goes on in the healthy brain when we hear, play, think, and feel music and how the structure and function of the brain can change as a result of musical training and expertise. For more than a century, music has also been studied in the field of neurology where the focus has mostly been on musical deficits and symptoms caused by neurological illness (e.g., amusia, musicogenic epilepsy) or on occupational diseases of professional musicians (e.g., focal dystonia, hearing loss). Recently, however, there has been increasing interest and progress also in adopting music as a therapeutic tool in neurological rehabilitation, and many novel music-based rehabilitation methods have been developed to facilitate motor, cognitive, emotional, and social functioning of infants, children and adults suffering from a debilitating neurological illness or disorder. Traditionally, the fields of music neuroscience and music therapy have progressed rather independently, but they are now beginning to integrate and merge in clinical neurology, providing novel and important information about how music is processed in the damaged or abnormal brain, how structural and functional recovery of the brain can be enhanced by music-based rehabilitation methods, and what neural mechanisms underlie the therapeutic effects of music. Ideally, this information can be used to better understand how and why music works in rehabilitation and to develop more effective music-based applications that can be targeted and tailored towards individual rehabilitation needs. The aim of this Research Topic is to bring together research across multiple disciplines with a special focus on music, brain, and neurological rehabilitation. We encourage researchers working in the field to submit a paper presenting either original empirical research, novel theoretical or conceptual perspectives, a review, or methodological advances related to following two core topics: 1) how are musical skills and attributes (e.g., perceiving music, experiencing music emotionally, playing or singing) affected by a developmental or acquired neurological illness or disorder (for example, stroke, aphasia, brain injury, Alzheimer’s disease, Parkinson’s disease, autism, ADHD, dyslexia, focal dystonia, or tinnitus) and 2) what is the applicability, effectiveness, and mechanisms of music-based rehabilitation methods for persons with a neurological illness or disorder? Research methodology can include behavioural, physiological and/or neuroimaging techniques, and studies can be either clinical group studies or case studies (studies of healthy subjects are applicable only if their findings have clear clinical implications).

Eyeblink Conditioning in Psychiatric Conditions - State of the Field and Future Directions

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889452750 Year: Pages: 96 DOI: 10.3389/978-2-88945-275-0 Language: English
Publisher: Frontiers Media SA
Subject: Medicine (General) --- Psychiatry
Added to DOAB on : 2018-02-27 16:16:44
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Eyeblink classical conditioning (EBC) is a model paradigm for associative (also termed Pavlovian) learning, one of the simplest and best understood forms of learning and memory. Because EBC paradigms are readily adapted across species, the neural substrates of EBC have been well characterized, and include but are not limited to the cerebellum and anterior interpositus nucleus, the hippocampus, and prefrontal cortices. The ability to collect EBC data across many different species (i.e. including but not limited to humans) also has the distinct advantage of facilitating translational research, and therefore may be of particular benefit to elucidate mechanistic changes associated with a wide variety of psychiatric disorders. In fact, EBC paradigms have been employed to assess individuals with a wide range of neurological deficits (including Korsakoff’s amnesia, Alzheimer's disease as well as normal aging, dyslexia, inflammatory tremor, dystonia, and multiple sclerosis) and psychiatric disorders (including major depressive disorder, anxiety disorders, schizophrenia, autism, and alcohol use/addiction disorders). Individuals with these disorders exhibit differential impairments across different EBC task types (e.g., delay vs. trace EBC), with some showing impairment in one but not the other task and some showing impairments in both; across learning stage (e.g., acquisition, discrimination, or extinction), and across response variables (e.g., magnitude and timing of the conditioned eyeblink motor response, modality of the conditioned stimulus). Evaluating specific individual differences in the context of variable brain pathology should aid characterization and refinement of our understanding of complex neuropsychiatric disorders. The field of psychiatry has seen a transition from more traditional use of symptom clusters to define psychiatric disorders with subsequent examination of associated behaviors and traits, to the use of physiological and behavioral indicators to characterize individuals with respect to various psychological domains [in line with the National Institute of Mental Health Research Domain Criteria (RDoC) initiative]. This approach employs a neuroscience-based framework to assess the pathophysiology of chronic mental illnesses. Behavioral and cognitive processes are critical domains of interest in evaluating potential maladaptive patterns that may be indicative of specific psychopathologies. Furthermore, the rapid development of technological advances that allow for more detailed examination (e.g., EEG, MEG, MRI, fMRI, infrared imaging) and manipulation (e.g. transcranial magnetic and direct current stimulation) of brain functions should enhance our ability to better characterize EBC performance and its utility in characterizing aspects of particular neuropathologies. Substantial research evidence exists for the value of EBC paradigms to inform our understanding of the pathophysiologies underlying a wide variety of neurological and psychiatric disorders. Despite these findings, this readily implemented classic cognitive-behavioral paradigm is relatively underutilized in clinical settings. This e-book highlights recent convergence of clinical and research efforts in this area and aims to promote a resurgent interest in eyeblink classical conditioning, and to emphasize the potential for future translational and diagnostic applications of EBC in combination with other techniques to strengthen our understanding of alterations in brain function manifested in behaviors characteristic of specific psychopathologies.

Application of Bioinformatics in Cancers

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ISBN: 9783039217885 9783039217892 Year: Pages: 418 DOI: 10.3390/books978-3-03921-789-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-12-09 11:49:16
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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.

Keywords

comorbidity score --- mortality --- locoregionally advanced --- HNSCC --- curative surgery --- traditional Chinese medicine --- health strengthening herb --- cancer treatment --- network pharmacology --- network target --- high-throughput analysis --- brain metastases --- colorectal cancer --- KRAS mutation --- PD-L1 --- tumor infiltrating lymphocytes --- drug resistance --- gefitinib --- erlotinib --- biostatistics --- bioinformatics --- Bufadienolide-like chemicals --- molecular mechanism --- anti-cancer --- bioinformatics --- cancer --- brain --- pathophysiology --- imaging --- machine learning --- extreme learning --- deep learning --- neurological disorders --- pancreatic cancer --- TCGA --- curation --- DNA --- RNA --- protein --- single-biomarkers --- multiple-biomarkers --- cancer-related pathways --- colorectal cancer --- DNA sequence profile --- Monte Carlo --- mixture of normal distributions --- somatic mutation --- tumor --- mutable motif --- activation induced deaminase --- AID/APOBEC --- transcriptional signatures --- copy number variation --- copy number aberration --- TCGA mining --- cancer CRISPR --- firehose --- gene signature extraction --- gene loss biomarkers --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- microarray --- ovarian cancer --- predictive model --- machine learning --- overall survival --- observed survival interval --- skin cutaneous melanoma --- The Cancer Genome Atlas --- omics --- breast cancer prognosis --- artificial intelligence --- machine learning --- decision support systems --- cancer prognosis --- independent prognostic power --- omics profiles --- histopathological imaging features --- cancer --- intratumor heterogeneity --- genomic instability --- epigenetics --- mitochondrial metabolism --- miRNAs --- cancer biomarkers --- breast cancer detection --- machine learning --- feature selection --- classification --- denoising autoencoders --- breast cancer --- feature extraction and interpretation --- concatenated deep feature --- cancer modeling --- interaction --- histopathological imaging --- clinical/environmental factors --- oral cancer --- miRNA --- bioinformatics --- datasets --- biomarkers --- TCGA --- GEO DataSets --- hormone sensitive cancers --- breast cancer --- StAR --- estrogen --- steroidogenic enzymes --- hTERT --- telomerase --- telomeres --- alternative splicing --- network analysis --- hierarchical clustering analysis --- differential gene expression analysis --- cancer biomarker --- diseases genes --- variable selection --- false discovery rate --- knockoffs --- bioinformatics --- copy number variation --- cell-free DNA --- methylation --- mutation --- next generation sequencing --- self-organizing map --- head and neck cancer --- treatment de-escalation --- HP --- molecular subtypes --- tumor microenvironment --- Bioinformatics tool --- R package --- machine learning --- meta-analysis --- biomarker signature --- gene expression analysis --- survival analysis --- functional analysis --- bioinformatics --- machine learning --- artificial intelligence --- Network Analysis --- single-cell sequencing --- circulating tumor DNA (ctDNA) --- Neoantigen Prediction --- precision medicine --- Computational Immunology

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