About this Research Topic
Resting-state functional connectivity measures the temporal coherence of the spontaneous neural activity of spatially distinct regions. It is commonly measured using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI). However, the BOLD signal measured by fMRI is influenced not only by neuronal activity but also by the effects of physiological processes such as changes in cardiac rate, spontaneous fluctuations in end-tidal carbon dioxide, and changes in breath depth/rate patterns. Such physiological noise can obscure neuronal signals because the noise and signals can overlap in time, be anatomically co-localized, and have (or alias into) expected temporal frequencies. Therefore, it is essential to understand how physiological processes—which may be unrelated to neural activation—influence fMRI time series.
Better understanding and quantification of physiological processes' effects on fMRI studies can pave the way for improved characterization of normal and pathological brains and accelerate the discovery of functional connectivity-based biomarkers for diagnosing neurological disorders, as it will contribute towards disentangling the neural vs. vascular sources of BOLD signal fluctuations. For example, researchers have found that hippocampal functional connectivity strength was associated with Alzheimer's disease severity as subjects transitioned from early mild cognitive impairment to late-stage Alzheimer's disease. Developing a new method to remove BOLD components induced by physiological processes leads to a more accurate construction of brain networks, which can eventually lead to a more precise characterization of neurodegenerative diseases such as Alzheimer's and Parkinson's.
This Research Topic aims to investigate new machine learning algorithms' performance in modeling, reconstructing, and to remove different physiological processes from BOLD fMRI data.
Potential topics include but are not limited to the following.
• Using deep learning to reconstruct the physiological signals like the respiratory signal from fMRI data alone;
• MRI brain image de-noising using deep/machine learning;
• Calculating accurate brain functional connectivity;
• Impact of precise functional connectivity on the diagnosis of neurodegenerative diseases or mental disorders;
• Investigation of new indices for brain aging, dementia;
• Brain imaging in brain disorders, mental illness, and healthy brain.
Better understanding and quantification of physiological processes' effects on fMRI studies can pave the way for improved characterization of normal and pathological brains and accelerate the discovery of functional connectivity-based biomarkers for diagnosing neurological disorders, as it will contribute towards disentangling the neural vs. vascular sources of BOLD signal fluctuations. For example, researchers have found that hippocampal functional connectivity strength was associated with Alzheimer's disease severity as subjects transitioned from early mild cognitive impairment to late-stage Alzheimer's disease. Developing a new method to remove BOLD components induced by physiological processes leads to a more accurate construction of brain networks, which can eventually lead to a more precise characterization of neurodegenerative diseases such as Alzheimer's and Parkinson's.
This Research Topic aims to investigate new machine learning algorithms' performance in modeling, reconstructing, and to remove different physiological processes from BOLD fMRI data.
Potential topics include but are not limited to the following.
• Using deep learning to reconstruct the physiological signals like the respiratory signal from fMRI data alone;
• MRI brain image de-noising using deep/machine learning;
• Calculating accurate brain functional connectivity;
• Impact of precise functional connectivity on the diagnosis of neurodegenerative diseases or mental disorders;
• Investigation of new indices for brain aging, dementia;
• Brain imaging in brain disorders, mental illness, and healthy brain.
Keywords: fMRI, physiological noise, neurological disorders, Alzheimer's disease, Parkinson's disease, image de-noising
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