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Key human anatomy and physiology principles as they relate to rehabilitation engineering
Published in Alex Mihailidis, Roger Smith, Rehabilitation Engineering, 2023
Qussai Obiedat, Bhagwant S. Sindhu, Ying-Chih Wang
The nervous system is the control center of the human body. Any disruption in its function will manifest in several functional limitations. The severity of the limitations will generally depend on the location of the disorder/injury and the size of the damage. CNS disorders can be caused by trauma, which is commonly referred to as traumatic brain injury (TBI), and symptoms can vary widely from paralysis to mood disorders, depending on the site of the injury. Several diagnostic technologies have been developed to assess the nervous system non-invasively. A magnetoencephalography (MEG) scan is an imaging technique that identifies brain activity and measures weak magnetic fields produced in the brain, while electroencephalography (EEG) measures the neuronal electrical currents outside the human head (Hari et al. 2018). Functional magnetic resonance imaging (fMRI) is another imaging technique that uses blood flow differences in the brain to provide in vivo images of neuronal activity (Dutta, Woo, and Krummel 2012). All of these techniques can be used to provide millisecond-accurate information about neuronal currents supporting human brain function.
Processing Techniques and Analysis of Brain Sensor Data Using Electroencephalography
Published in Mridu Sahu, G. R. Sinha, Brain and Behavior Computing, 2021
This is an optical noninvasive brain signal visualization technique that passes laser beams with light wavelengths close to infrared from about 700–2500 nm through the skull. With this technique, brain functionality is analyzed through hemodynamic responses associated with neuron behavior [11,12]. As fMRI, it measures the oxygen level increase in active regions of the brain. The downside of this technique is that it does not have a good temporal resolution, as EEG and also spatial resolution is not as good as fMRI.
Brain Imaging Data
Published in Atsushi Kawaguchi, Multivariate Analysis for Neuroimaging Data, 2021
fMRI has been widely applied in the fields of neurosurgery, neurology, internal medicine, cognitive psychology, child development, rehabilitation medicine, and reproduction science among others. One of the fields using fMRI is Parkinson’s disease research. Parkinson’s disease is caused by dopamine deficiency in the brain, causing symptoms such as trembling and difficulty in movement. Mergers with dementia also occur. Many books and reviews on statistical analysis of fMRI data have been published. Friston et al. (2007) is a representative example that also discusses analysis using SPM. In addition, Lazar (2010), Poldrack et al. (2011), Ashby (2019), Polzehl and Tabelow (2019) treats the statistical analysis of fMRI data. Lindquist (2008) and Chen and Glover (2015) are review articles.
Classification of autism spectrum disorders individuals and controls using phase and envelope features from resting-state fMRI data
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Mahshid Naghashzadeh, Mehran Yazdi, Alireza Zolghadrasli
The traditional procedure for diagnosing Autism is primarily based on narrative interactions between individuals and clinical professionals. These methods, lacking biological evidence, are prone to generate a high disagreement during the diagnosis and require a long period to recognise abnormalities. Functional magnetic resonance imaging (fMRI) has been widely employed to investigate the brain’s functional characteristics to complement the current behaviour-based diagnoses (Guo et al. 2017). fMRI is a neuroimaging procedure utilising magnetic resonance imaging (MRI) technology that measures brain activity by detecting changes associated with a blood oxygen level-dependent (BOLD) signal. The fMRI study’s main approaches are task-related fMRI and resting-state fMRI (Seraj et al. 2019). Resting-state fMRI studies are focused on measuring the correlation between spontaneous activation patterns of brain areas. During a resting-state experiment, subjects are placed into the scanner and asked to close their eyes and to think about nothing in particular, without falling asleep (Van Den Heuvel and Pol 2010). Resting-state fMRI has provided a convenient tool to examine the changes in the intrinsic connectivity of specific regions and networks in Autism and control subjects (Hull et al. 2017). There remains substantial controversy regarding the nature of connectivity impairment in Autism, with researchers arguing in favour of under-connectivity, over-connectivity, or unique patterns of both under-connectivity and over-connectivity depending on the brain region (Ecker et al. 2015).
The effects of Alzheimer's disease related striatal pathologic changes on the fractional amplitude of low-frequency fluctuations
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2020
Resting-state functional Magnetic Resonance Imaging (rs-fMRI), a non-invasive functional imaging technique, has been used extensively in brain mapping for evaluating the regional interactions which occur in a resting state when a task is not being performed. fMRI is used to measure spontaneous brain activities in vivo and is most commonly performed using blood oxygenation level dependent (BOLD) contrast to study the local changes in deoxyhemoglobin concentration in the brain. Under normal and pathological conditions such as AD which is a progressive neurodegenerative disorder, it helps to detect the intrinsic brain functional architecture (Agosta et al. 2012; Liu et al. 2014; Lindquist and Wager 2016; Ren et al. 2016; Yang et al. 2018).
Deep Learning Techniques for EEG Signal Applications – A Review
Published in IETE Journal of Research, 2022
D. Merlin Praveena, D. Angelin Sarah, S. Thomas George
FMRI measures brain activity by detecting changes associated with blood flow. EEG-fMRI allows measuring both neuronal and haemodynamic activity which comprises two important components of the neurovascular coupling mechanism. Using fMRI images and the marked interictal epileptic form discharges, General Linear Model (GLM) analysis is applied to reveal the haemodynamic changes linked to IED and seizures, and it has been established as a valid scientific tool. The manual marking burden can be reduced and reproducibility can be improved in this deep learning-based automatic seizure detector for EEG-fMRI [9].