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Brain Imaging Data
Published in Atsushi Kawaguchi, Multivariate Analysis for Neuroimaging Data, 2021
Figure 2.2 is called task-related research, which is conducted with various stimuli and tasks, and conversely, resting state research, which is conducted in a resting state without any stimuli. In recent years, network analysis has been widely used to evaluate the functional connectivity between resting sites. Please refer to Chapter 5 for the analysis. The medial prefrontal cortex and the anterior cuneiform and posterior cingulate gyrus are a network of brain regions called the default mode network (DMN), and they are more active when the subject is awake but not doing anything (Raichle et al., 2001). This neural activity consumes 60–80% of the energy in the brain. However, on the other hand, the energy expenditure of neural activity is only 0.5–1.0% in the brain while performing some kind of task. The DMN has been studied in various psychiatric disorders as it is believed to be involved in internal thought processes and the control of external stimuli (Joo et al., 2016; Liu et al., 2017). In addition to the DMN, several other networks showing activity at rest have been identified and resting-state fMRI is thought to contain a lot of information about individual brain function. Tavor et al. (2016) showed that an individual’s functional connectivity pattern of resting fMRI can predict the pattern of fMRI activity while performing a task.
Functional Magnetic Resonance Imaging (fMRI)
Published in Ioannis Tsougos, Advanced MR Neuroimaging, 2018
The human brain cannot completely shut-down, fortunately! A basic level of activity is present even in the absence of any external prompted task or stimulus, and a network of spatially distributed regions that continuously communicate with each other and share information is always active. In fMRI, this activity appears as low-frequency-fluctuations of the BOLD signal, and has been named resting-state fMRI (RS fMRI). Interestingly, as with many scientific findings, the discovery of RS fMRI, was actually made by accident by Biswal et al. (1995), when trying to isolate physiological noise in fMRI data from subjects at rest. They observed that there was a remaining low-frequency signal (<0.1 Hz) after the removal of noise, and in their investigation, they concluded that a high level of correlation existed between the right primary sensorimotor cortex and other motor areas. They thus hypothesized that this was the result of functional connections between these brain regions. More studies followed in order to validate the initial hypothesis and indeed proved that these spontaneous low-frequency fluctuations were blood-oxygenation dependent, like the BOLD signal (Biswal et al., 1997), implying the existence of a network of functional connectivity among different regions of the brain. Moving forward, several other studies provided evidence that RS fMRI has a physiological basis, demonstrating a link between physiological and hemodynamic related BOLD processes (Kenet et al., 2003; Lowe et al., 2000; Mantini et al., 2007).
Altered brain functional connectivity in the frontoparietal network following an ice hockey season
Published in European Journal of Sport Science, 2023
Melissa S. DiFabio, Daniel R. Smith, Katherine M. Breedlove, Ryan T. Pohlig, Thomas A. Buckley, Curtis L. Johnson
Resting-state fMRI analyses were performed using Statistical Parametric Mapping version 12 and the Functional Connectivity (CONN) toolbox (Whitfield-Gabrieli and Nieto-Castanon, 2012) in MATLAB version R2018b (Mathworks; Natick, MA). Functional and anatomical image pre-processing was performed first following the default pre-processing steps in CONN and included realignment, slice-timing correction, outlier identification, co-registration of each subject’s functional to anatomical image, segmentation of the anatomical image into white matter, gray matter, and cerebrospinal fluid, normalization to the Montreal Neurological Institute template standard space, scrubbing, and spatial smoothing with an 8 mm Gaussian kernel. Denoising included linear regression of white matter (five components) and cerebrospinal fluid (five components) BOLD timeseries using a CompCor strategy (Behzadi, Restom, Liau, & Liu, 2007), motion-correction, linear detrending, and scrubbing from the functional BOLD timeseries. Temporal band-pass filtering was performed after regression at 0.008–0.09 Hz to identify low-frequency BOLD fluctuations and decrease the influence of physiological noise and motion artefact.
Discriminant subgraph learning from functional brain sensory data
Published in IISE Transactions, 2022
Lujia Wang, Todd J. Schwedt, Catherine D. Chong, Teresa Wu, Jing Li
Imaging was conducted on 3-Tesla Siemens scanners using FDA-approached sequences. Prior to the imaging session, each subject was instructed to stay awake with eyes-closed, known as the resting state. Ten minutes of resting-state fMRI data were collected for each subject. Each fMRI dataset is 4-D, denoted by where are coordinates for each basic unit (called a voxel) of the 3-D brain image and is time. In our study, there were a total of voxels in the 3-D brain image and the time series of each voxel contains over 200 time points with some slight difference between subjects. Standard steps of fMRI pre-processing were followed (Chong et al., 2017). We selected 33 ROIs based on findings in the pain and migraine literature. These regions are those consistently shown to participate in pain processing. The 33 ROIs include 16 on each hemisphere and one midline region. Table 2 lists names of the ROIs. The coordinates for the center of each ROI were also reported. Each ROI was an 8-mm sphere drawn around the center coordinates. The average time series over the voxels included in each ROI was computed. The 33 ROI-level time series were used as input data to DSL and competing algorithms.
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).