Integrated neural technologies: Solutions beyond tomorrow
Keywords:neurotechnology, prosthetics, neuroimaging, brain-computer interface, nanotechnology
Neuroscience is an exciting area in which, at a fast rate, revolutionary advances materialise. Neurotechnology is interesting and contentious at the same time, as one of its aims is to "wire" human brains directly into computers. Neurotechnology is defined as the assembly of methods and instruments which allow a direct connection to the nervous system of technical components. These instruments are electrodes, machines or smart prostheses. They are designed to record and/or "translate" impulses from the brain into control instructions, or to modify brain function through the application of electrical or optical stimulation. The emergence of neuro-technologies is interdisciplinary. It supports the amalgamation of neurobiology with atomic, nano- and micro-sciences, as a fascinating path for significant development in the neuroscience domain. It poses a scientific foundation for potential therapeutic strategies.
Chatterjee, I. (2018). Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients. F1000Research, 7, 1615. https://doi.org/10.12688/f1000research.16405.2
Chatterjee, I., Agarwal, M., Rana, B., Lakhyani, N., & Kumar, N. (2018). Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data. Multimedia Tools and Applications, 77(20), 26991–27015. https://doi.org/10.1007/s11042-018-5901-0
Chatterjee, I., Kumar, V., Rana, B., Agarwal, M., & Kumar, N. (2020). Identification of changes in grey matter volume using an evolutionary approach: an MRI study of schizophrenia. Multimedia Systems, 26(4), 383–396. https://doi.org/10.1007/s00530-020-00649-6
Chatterjee, I., Kumar, V., Sharma, S., Dhingra, D., Rana, B., Agarwal, M., & Kumar, N. (2019). Identification of brain regions associated with working memory deficit in schizophrenia. F1000Research, 8, 124. https://doi.org/10.12688/f1000research.17731.1
Liotti, M., & Mayberg, H. S. (2001). The role of functional neuroimaging in the neuropsychology of depression. Journal of Clinical and Experimental Neuropsychology, 23(1), 121–136. https://doi.org/10.1076/jcen.126.96.36.1993
Müller, O., & Rotter, S. (2017). Neurotechnology: Current Developments and Ethical Issues. Frontiers in Systems Neuroscience, 11, 93. https://doi.org/10.3389/fnsys.2017.00093
Nasrallah, I., & Dubroff, J. (2013). An overview of PET neuroimaging. Seminars in Nuclear Medicine, 43(6), 449–461. https://doi.org/10.1053/j.semnuclmed.2013.06.003
Sabatini, U., Boulanouar, K., Fabre, N., Martin, F., Carel, C., Colonnese, C., Bozzao, L., Berry, I., Montastruc, J. L., Chollet, F., & Rascol, O. (2000). Cortical motor reorganisation in akinetic patients with Parkinson's disease. A functional MRI study. Brain, 123(2), 394–403. https://doi.org/10.1093/brain/123.2.394
Stephan, K. E. (2004). On the role of general system theory for functional neuroimaging. Journal of Anatomy, 205(6), 443–470. https://doi.org/10.1111/j.0021-8782.2004.00359.x
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Copyright (c) 2020 Wael MY Mohamed, Indranath Chatterjee, Mohammad A Kamal
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