The effect of seed location on functional connectivity: evidence from an image-based meta-analysis.

Introduction: Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA). Methods: We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps. Results: We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection. Discussion: Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.

Regional abnormalities of spontaneous brain activity in migraine: A coordinate-based meta-analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have explored abnormal regional spontaneous brain activity in migraine. However, these results are inconsistent. To identify the consistent regions with abnormal neural activity, we meta-analyzed these studies. We gathered whole-brain rs-fMRI studies measuring differences in the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), or regional homogeneity (ReHo) methods. Then, we performed a voxel-wise meta-analysis to identify consistent abnormal neural activity in migraine by anisotropic effect size seed-based d mapping (AES-SDM). To confirm the AES-SDM meta-analysis results, we conducted two meta-analyses: activation likelihood estimation (ALE) and multi-level kernel density analysis (MKDA). We found that migraine showed increased regional neural activities in the bilateral postcentral gyrus (PoCG), left hippocampus (HIP.L), right pons, left superior frontal gyrus (SFG.L), triangular part of right inferior frontal gyrus (IFGtriang.R), right middle frontal gyrus (MFG.R), and left precentral gyrus (PreCG.L) and decreased regional intrinsic brain activities were exhibited in the right angular gyrus (ANG.R), left superior occipital gyrus (SOG.L), right lingual gyrus (LING.R). Moreover, the meta-analysis of ALE further validated the abnormal neural activities in the PoCG, right pons, ANG.R, and HIP. Meta-regression demonstrated that headache intensity was positively associated with the abnormal activities in the HIP.L, ANG.R, and LING.R. These findings suggest that migraine is associated with abnormal spontaneous brain activities of some pain-related regions, which may contribute to a deeper understanding of the neural mechanism of migraine.

Echo Time Dependency of Local Activity Metrics of Resting-State Functional MRI.

Local activity metrics of resting-state functional MRI (RS-fMRI), such as the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC), are widely used to detect brain abnormalities based on signal fluctuations. Although signal changes with echo time (TE) have been widely studied, the effect of TE on local activity metrics has not been investigated. RS-fMRI datasets from 12 healthy subjects with eyes open (EO) and eyes closed (EC) were obtained with a four-echo gradient-echo-planar imaging pulse sequence with the following parameters: repetition time/TE1/TE2/TE3/TE4 = 2,000/13/30.93/48.86/66.79 ms. Six representative regions were selected for simulating the spatial feature of TE dependency of local activity metrics. Moreover, whole-brain local activity metrics were calculated from each echo dataset and compared between EO and EC conditions. Dice overlap coefficient (DOC) was then employed to calculate the overlap between the T maps. We found that all the local activity metrics displayed different TE dependency characteristics, while their overall change patterns were similar: an initial large change followed by a slow variation. The T maps for local activity metrics also varied greatly with TE. For ALFF, fALFF, ReHo, and DC, the DOCs for voxels in four TE datasets were 6.87, 0.73, 5.08, and 0.93%, respectively. Collectively, these findings demonstrate that local metrics are greatly dependent on TE. Therefore, TE should be carefully considered for the optimization of data acquisition and multi-center data analysis in RS-fMRI.

Resting-state abnormalities in functional connectivity of the default mode network in autism spectrum disorder: a meta-analysis.

Increasing evidence has shown that the resting state brain connectivity of default mode network (DMN) which are important for social cognition are disrupted in autism spectrum disorder (ASD). However, previous neuroimaging studies did not present consistent results. Therefore, we performed a meta-analysis of resting-state functional connectivity (rsFC) studies of DMN in the individuals with ASD and healthy controls (HCs) to provide a new perspective for investigating the pathophysiology of ASD. We carried out a search using the terms: ("ASD" OR "Autism") AND ("resting state" OR "rest") AND ("DMN" OR "default mode network") in PubMed, Web of Science and Embase to identify the researches published before January 2020. Ten resting state datasets including 203 patients and 208 HCs were included. Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) method was applied to identify group differences. In comparison with the HCs, the patients with ASD showed increased connectivity in cerebellum, right middle temporal gyrus, superior occipital gyrus, right supramarginal gyrus, supplementary motor area and putamen. Decreased connectivity was discovered in some nodes of DMN, such as medial prefrontal cortex, precuneus and angular gyrus. These results may help us to further clarify the neurobiological mechanisms in patients with ASD.

Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level.

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ≈ mPerAF ≈ mALFF) > (fALFF ≈ mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.

Gray matter density and white matter integrity in pianists' brain: a combined structural and diffusion tensor MRI study.

The current study combined structural magnetic resonance imaging (sMRI) and diffusion tensor MRI (DT-MRI) to investigate both gray matter density (GMD) and white matter integrity (WMI) in 18 pianists and 21 age-matched non-musicians. The pianists began their piano training at a mean age of 12. Voxel-based morphometry of the sMRI data showed that the pianists had higher GMD in the left primary sensorimotor cortex and right cerebellum. Voxel-based analysis of the DT-MRI data showed that pianists had higher fractional anisotropy (FA) (indicating higher WMI) in the right posterior limb of the internal capsule. The sMRI and DT-MRI results indicate that both the GMD and WMI of pianists may exhibit movement-related increases during adolescence or even early adulthood compared with non-musicians.

Correlations in spontaneous activity and gray matter density between left and right sensoritmotor areas of pianists.

Resting-state functional MRI and structural MRI were used to study correlations of spontaneous activity and gray matter density between the left and right primary sensorimotor areas in pianists and nonmusicians. Functional MRI analysis showed significant correlation of spontaneous activity between the left and right primary sensorimotor area in both groups; however, there was no between-group difference. Structural MRI analysis showed significant correlation in gray matter density between the left and right sensorimotor areas in nonmusicians (r=0.65, P=0.001), but not in pianists (r=0.07, P=0.78), with a significant between-group difference (P=0.035). The lack of correlation of gray matter density between the left and right sensorimotor areas might be the basis of bimanual coordination of the pianists.