Abstract
Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.
Highlights
Low-Frequency Fluctuations in Human Brain ActivityLow-frequency fluctuation is an intrinsic property of human brain activity. Biswal et al (1995) used functional magnetic resonance imaging technology to initially demonstrate that spontaneous low-frequency fluctuation was synchronized in the bilateral motor cortices of the brain at rest
Another notable example is the method of amplitude of low-frequency fluctuation (ALFF) (Zang et al, 2007), and study has demonstrated that ALFF can be used as a metric of brain diseases (Han et al, 2011) and for decoding brain activity (Yang et al, 2018)
The proposed method was used in resting-state experiments for extracting brain networks in the eyes closed (EC) and eyes open (EO) conditions among 45 subjects, and the corresponding spectrum contrast mapping (SCM) maps for EC and EO states were generated
Summary
Low-Frequency Fluctuations in Human Brain ActivityLow-frequency fluctuation is an intrinsic property of human brain activity. Biswal et al (1995) used functional magnetic resonance imaging (fMRI) technology to initially demonstrate that spontaneous low-frequency fluctuation was synchronized in the bilateral motor cortices of the brain at rest. Yang et al (2016) found that the amplitude of low-frequency fluctuations and functional connectivity based on resting-state data exhibited consistent alterations in the bilateral anterior insula of subjects with major depressive disorder. Another notable example is the method of amplitude of low-frequency fluctuation (ALFF) (Zang et al, 2007), and study has demonstrated that ALFF can be used as a metric of brain diseases (Han et al, 2011) and for decoding brain activity (Yang et al, 2018). Lowfrequency fluctuation has been extensively applied in research into mental illness (Cui et al, 2020), neurological disease (Dutta et al, 2014), cognition (Fransson, 2005; Welsh et al, 2010), and neuroplasticity (Lewis et al, 2009; Di et al, 2012; Wu et al, 2020)
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