Functional imaging analyzes a person's brain ability using data evaluation. Information is obtained through functional imaging modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography MEG), Electroencephalography (EEG), and Positron Emission Tomography (PET). The study aims to study how the brain works, its functional structure, and its dimensions. The framework of conducting all these evaluations includes classical neuroanatomic methods, cognitive neuroscience, neurophysiology, and experimental psychology. Unlike traditional functional imaging, the current functional imaging has some pros. The first is it is typically non-invasive. Human beings can be used during experimental studies. It enables researchers to evaluate distinct personal traits such as language and motor abilities. The second advantage is that it can offer a broad field of view. Instead of recording data about one of the smallest numbers of neuronal cells, an image may be collected, enabling the simultaneous summary of the whole brain. Additionally, it offers a unique yet complementary dimension on neural coding (Amaro & Barker, 2006). The current studies aim to bridge the gap of limited evidence by combining experimental studies and mathematical modeling techniques. The paper will analyze multiple functional imaging modalities, analyze data, and an example of an experimental study of the current functional imaging.
Description of Functional Imaging Studies
The fMRI measures the least variation in blood flows and evaluates brain functionality. It is also utilized to assess the brain's functional anatomy, which recognizes which region of the brain handles fundamental roles. Additionally, it assesses the impact of stroke and other illnesses to assist in guiding brain medication. Numerous analysis has found that fMRI detects abnormal brain activities that other imaging methods cannot detect. fMRI is non-invasive, and thus it can be used in human beings. The main technique that enables fMRI to be used in the human body rests on the fact that the brain role is spatially segregated, which implies that particular roles are localized at distinct regions. EEG measures the oscillation of electric brain signals obtained from around 20 to 256 electrodes (Peng & May, 2020). The recorded data are distributed to an EEG system which contains amplifiers, filters, computer monitors, or paper charts. The brain cells communicate through an electrical impulse that works even if they are asleep. These activities demonstrate wavy lines on an EEG recording. EEG is mainly used to testing epilepsy and other brain disorders.
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PET is a functional imaging modality that reveals how human beings' tissues and organs function. It utilizes a radioactive drug to demonstrate brain activities. This test can detect illness before it is detected on other imaging techniques. The radioactive drug can be inhaled, swallowed, or injected but depend on organs and tissues being evaluated. When reaching the body, the substance has the maximum level of chemical activities related to regions of the disease because these parts will change into bright spots (Caplan, 2009). The scan helps detect and analyze many conditions such as brain disorders, heart disorders, and cancer types. MEG is another imaging technique that identifies brain activities and evaluates minute magnetic fields that the brain produces. The scanner uses in detecting and amplifying magnetic signals obtained in pain does not produce radiation or magnetic fields (Gross, 2019). The scan is mainly beneficial for detecting seizures.
Role of Statistics and Data processing
The procedures of gathering, organizing, and evaluating clinical practice data play a fundamental role in comparing groups such as task versus control and clinical versus non-clinical. For example, task versus control is mainly utilized when recognizing whether studies work. A task implies intervention that works toward improving population behaviors for the better. It entails aspects such as medications of psychotherapies. Also, it is used to measure the efficacy of medication because the population will be randomly assigned to a particular task condition or control condition. The task conditions are where the population gets medication while there is no medication received in the control condition. Leschak et al. (2020) argue that treatment works if the task state's population ends up being better than those in the control condition. In that case, clinical studies have utilized data processing and statistics to provide official accounting evidence for differentiation in response to treatment. Also, medical studies case engages in treating patients or offering relative patient management of any form. Non-clinical cases can involve supporting patient management without offering direct testing or medications for the patients. The utilization of statistics and data processing in both scenarios allows medical scholars to draw precise and valuation inferences for the data gathered, allowing for accurate decisions in cases where there is a high uncertainty level. Hence, the utilization of statistics and data processes can help prevent many biases and mistakes in clinical studies.
Designing an Experiment on the Portion of the Brain
The portion of the brain in this week's discussion is the cerebellum. It is a structure found at the back of the brain between the cerebral context's occipital and temporal lobes. It contains about 10% of the brain's quantity and has numerous neurons in the brain (Caplan, 2009). The cerebellum is taken as a motor structure because slight damage can lead to motor control impairment and posture. Functional imaging techniques have provided numerous understanding of the cerebellum's role in individuals. However, there has been slow development because there are still distinct problems the cerebellar imaging techniques have on fMRI (Gifuni et al., 2016). My understanding of functional imaging design is a way that fMRI is utilized to assess individual cerebellum for excellent comprehension of how it works.
The study experiment will utilize the high-quality 3D technique. It will need an insertion that precisely fits the shape of the subject top and head coil. The model will be a Next Engine 3D scanner of high quality to obtain the 3D model of the brain. Also, to design the coil's internal part, the experiment will utilize the Blender animation software. Also, obtaining the T1 weighted anatomical MRI would help get a 3D image of the brain. Quadric decimation and Laplacian smoothing will simplify and smoothen the surface reconstruction. The surface design will expand 2mm in each vertex normal to obtain the signal of hair and expansion of pulsatile in the brain. There will also be filtering and smoothening the anatomical picture to decrease the noise issues. The picture in 2D will also be useful while doing a canny edge filtering. Binary dilation will be utilized to seal holes in the edges and be accompanied by binary erosion. The experiment will use a flood fill algorithm to match cube algorithms to generate a surface construction.
Lastly, to evaluate whether the insertion would be effective, I will evaluate my experiment's outcomes to a general audience by creating a comparison of the approximate parameter with and without insertion. Insertion will act similar to task condition while without insertion, which will act in the same principle as the control condition. If parameters with insertion do not lead to head motion, then it implies that a custom 3D model insertion experiment could help eradicate numerous problems with the current fMRI studies hence the development of concrete knowledge on how cerebellum works.
References
Amaro, E., & Barker, G. J. (2006). Study design in fMRI: Basic principles. Brain and Cognition , 60 (3), 220-232. https://doi.org/10.1016/j.bandc.2005.11.009
Caplan, D. (2009). Experimental design and interpretation of functional neuroimaging studies of cognitive processes. Human Brain Mapping , 30 (1), 59-77. https://doi.org/10.1002/hbm.20489
Gifuni, A. J., Kendal, A., & Jollant, F. (2016). Neural mapping of guilt: A quantitative meta-analysis of functional imaging studies. Brain Imaging and Behavior , 11 (4), 1164-1178. https://doi.org/10.1007/s11682-016-9606-6
Gross, J. (2019). Magnetoencephalography in cognitive neuroscience: A primer. Neuron , 104 (2), 189-204. https://doi.org/10.1016/j.neuron.2019.07.001
Leschak, C. J., Dutcher, J. M., Haltom, K. E., Breen, E. C., Bower, J. E., & Eisenberger, N. I. (2020). Associations between amygdala reactivity to social threat, perceived stress and C-reactive protein in breast cancer survivors. Social Cognitive and Affective Neuroscience , 15 (10), 1056-1063. https://doi.org/10.1093/scan/nsz103
Peng, K., & May, A. (2020). Redefining migraine phases – a suggestion based on clinical, physiological, and functional imaging evidence. Cephalalgia , 40 (8), 866-870. https://doi.org/10.1177/0333102419898868
