Abstract
The nervous system has enormous scale to its production. From the large-scale ganglia to the microscopic neuron, there is tremendous communication that serves a synergistic role. Our ability to identify deficits, or absences, in such communication is critical toward detecting and treating pathological conditions and states of injury, as well as optimizing human performance. Research to date has focused on very large-scale observations of the human nervous system, providing insight through computed tomography, positron emission tomography, and magnetic resonance imaging of the brain. More recent developments have allowed us to make significant advances in our ability to monitor neurological health in finer neuronal structures of the peripheral nervous system and identifying pathology through molecular and genetic markers. As we proceed from the more granular to fine-grained observations, our ability to detect pathology grows, but so does the level of noise. Techniques such as machine learning and artificial intelligence will help reduce this noise and provide a more accurate picture of a healthy neuron and clinical decision points for treatment.
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Abbreviations
- AI:
-
Artificial intelligence
- CHO:
-
Choline
- EEG:
-
Electroencephalography
- fMRI:
-
Functional magnetic resonance imaging
- ML:
-
Machine learning
- MRI:
-
Magnetic resonance imaging
- MRS:
-
Magnetic resonance spectroscopy
- mTBI:
-
Mild traumatic brain injury
- NAA:
-
N-acetyl aspartate
- PET:
-
Positron emission tomography
- TBI:
-
Traumatic brain injury
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Holmes, S.A. (2023). Nerve Injury and Biomarkers. In: Rajendram, R., Preedy, V.R., Patel, V.B. (eds) Biomarkers in Trauma, Injury and Critical Care. Biomarkers in Disease: Methods, Discoveries and Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-07395-3_4
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