The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI–EEG study

Abstract

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI–ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI–ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG–amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG–ALFF). We observed that the MEG–ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG–amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG–ALFF in the sensorimotor cortex overlapped with the most reliable EC–EO differences observed in fMRI at slow-3 (0.073–0.198 Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI–ALFF may relate to the ALFF in neural activity.

Maternal psychological distress associates with alterations in resting‐state low‐frequency fluctuations and distal functional connectivity of the neonate medial prefrontal cortex

“…Prior multimodal studies performed on adults have shown that fMRI and EEG signal amplitudes are modulated by effects of aging and sex (Zhong & Chen, 2020), with the hypothesis that as vasculature reactivity declines with age, so does BOLD‐signal amplitude (Zhong & Chen, 2020). Contrarily, frequency bands of MEG‐fMRI measurements follow similar power‐law distributions with electrophysiological signals, suggesting that intensity of low‐frequency modulations stem from neural activity rather than neurovascular coupling effects (Zhang et al, 2022). Finally, a recent multimodal study enrolling a small sample of male adults to be simultaneously scanned with PET, rs‐fMRI and EEG with focus on DMN demonstrated no correlations between rs‐fMRI ReHo and fALFF metrics, FDG‐PET and EEG microstates, but revealed positive correlations between rs‐fMRI degree‐centrality metric and EEG microstates (Rajkumar et al, 2021).…”

Macrophage Migration Inhibitory Factor as a Potential Plasma Biomarker of Cognitive Impairment in Cerebral Small Vessel Disease

“…Data preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSFA 2.3) toolbox . Based on blood oxygenation level-dependent signals, the ALFF was obtained to estimate local spontaneous neuronal activity . REST software was used to calculate the mALFF values and help perform statistical analysis (ANCOVA test) among the three groups (Alphasim multiple comparison correction p < 0.001 and cluster size > 100 voxels), with age, sex, and education years as covariates.…”

“… 35 Based on blood oxygenation level-dependent signals, the ALFF was obtained to estimate local spontaneous neuronal activity. 36 REST software was used to calculate the mALFF values and help perform statistical analysis (ANCOVA test) among the three groups (Alphasim multiple comparison correction p < 0.001 and cluster size > 100 voxels), with age, sex, and education years as covariates. Brain regions with significant differences in mALFF values were visualized using BrainNet Viewer software.…”

Explainable Deep-Learning Prediction for Brain–Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion

“…In these cases, brain signals measured during restingstate become essential and could help with prediction. Recent studies have shown that the eyes-closed and eyes-open resting states have distinct features based on founding from EEG and MRI measurements [16][17][18]. Moreover, the EEG features in those two states show different relationships in predicting post-stroke patient motor recovery [19][20][21].…”