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.

Increased functional connectivity of white-matter in myotonic dystrophy type 1.

Background: Myotonic dystrophy type 1 (DM1) is the most common and dominant inherited neuromuscular dystrophy disease in adults, involving multiple organs, including the brain. Although structural measurements showed that DM1 is predominantly associated with white-matter damage, they failed to reveal the dysfunction of the white-matter. Recent studies have demonstrated that the functional activity of white-matter is of great significance and has given us insights into revealing the mechanisms of brain disorders. Materials and methods: Using resting-state fMRI data, we adopted a clustering analysis to identify the white-matter functional networks and calculated functional connectivity between these networks in 16 DM1 patients and 18 healthy controls (HCs). A two-sample t-test was conducted between the two groups. Partial correlation analyzes were performed between the altered white-matter FC and clinical MMSE or HAMD scores. Results: We identified 13 white-matter functional networks by clustering analysis. These white-matter functional networks can be divided into a three-layer network (superficial, middle, and deep) according to their spatial distribution. Compared to HCs, DM1 patients showed increased FC within intra-layer white-matter and inter-layer white-matter networks. For intra-layer networks, the increased FC was mainly located in the inferior longitudinal fasciculus, prefrontal cortex, and corpus callosum networks. For inter-layer networks, the increased FC of DM1 patients is mainly located in the superior corona radiata and deep networks. Conclusion: Results demonstrated the abnormalities of white-matter functional connectivity in DM1 located in both intra-layer and inter-layer white-matter networks and suggested that the pathophysiology mechanism of DM1 may be related to the white-matter functional dysconnectivity. Furthermore, it may facilitate the treatment development of DM1.

Altered Local Brain Amplitude of Fluctuations in Patients With Myotonic Dystrophy Type 1.

This study is aimed at investigating the characteristics of the spontaneous brain activity in patients with myotonic dystrophy type 1 (DM1). A total of 18 patients with DM1 and 18 healthy controls (HCs) were examined by resting-state functional MRI. Combined methods include amplitude of low-frequency fluctuations (ALFFs), the fractional amplitude of low-frequency fluctuations (fALFFs), and Wavelet transform-based ALFFs (Wavelet-ALFFs) with standardization, percent amplitude of fluctuation (PerAF) with/without standardization were applied to evaluate the spontaneous brain activity of patients with DM1. Compared with HCs, patients with DM1 showed decreased ALFFs and Wavelet-ALFFs in the bilateral precuneus (PCUN), angular gyrus (ANG), inferior parietal, but supramarginal and angular gyri (IPL), posterior cingulate gyrus (PCG), superior frontal gyrus, medial (SFGmed), middle occipital gyrus (MOG), which were mainly distributed in the brain regions of default mode network (DMN). Decreased ALFFs and Wavelet-ALFFs were also seen in bilateral middle frontal gyrus (MFG), inferior frontal gyrus, opercular part (IFGoperc), which were the main components of the executive control network (ECN). Patients with DM1 also showed decreased fALFFs in SFGmed.R, the right anterior cingulate and paracingulate gyri (ACGR), bilateral MFG. Reduced PerAF in bilateral PCUN, ANG, PCG, MOG, and IPLL as well as decreased PerAF without standardization in PCUNR and bilateral PCG also existed in patients with DM1. In conclusion, patients with DM1 had decreased activity in DMN and ECN with increased fluctuations in the temporal cortex and cerebellum. Decreased brain activity in DMN was the most repeatable and reliable with PCUN and PCG being the most specific imaging biomarker of brain dysfunction in patients with DM1.

Amplitude of Low-Frequency Fluctuation in Multiple Frequency Bands in Tension-Type Headache Patients: A Resting-State Functional Magnetic Resonance Imaging Study.

Purpose: Tension-type headache (TTH), the most prevalent primary headache disorder, imposes an enormous burden on the people of the world. The quest to ease suffering from this neurological disorder has sustained research interest. The present study aimed at evaluating the amplitude of low-frequency oscillations (LFOs) of the brain in multiple frequency bands in patients with TTH. Methods: To address this question, 63 participants were enrolled in the study, including 32 TTH patients and 31 healthy controls (HCs). For all the participants, amplitude of low-frequency fluctuation (ALFF) was measured in six frequency bands (conventional frequency bands, 0.01-0.08 Hz; slow-2, 0.198-0.25 Hz; slow-3, 0.073-0.198 Hz; slow-4, 0.027-0.073 Hz; slow-5, 0.01-0.027 Hz; and slow-6, 0-0.01 Hz), and the differences between TTH patients and HCs were examined. To explore the relationship between the altered ALFF brain regions in the six frequency bands and the Visual Analog Scale (VAS) score in the TTH patients, Pearson's correlation analysis was performed. Results: In all the six frequency bands, a decreased ALFF value was detected, and regions showing reduced ALFF values were mostly located in the middle frontal gyrus and superior gyrus. A frequency-dependent alternating characterization of intrinsic brain activity was found in the left caudate nucleus in the slow-2 band of 0.198-0.25 Hz and in the right inferior frontal orbital gyrus in the slow-5 band of 0.01-0.027 Hz. For the correlation results, both the left anterior cingulate and paracingulate gyri and right superior parietal gyrus showed a positive correlation with the VAS score in the slow-4 frequency band of 0.027-0.073 Hz. Conclusion: The ALFF alterations in the brain regions of TTH patients are involved in pain processing. The altered LFOs in the multiple regions may help promote the understanding of the pathophysiology of TTH. These observations could also allow the future treatment of TTH to be more directional and targeted and could promote the development of TTH treatment.

Abnormal Brain Glucose Metabolism in Papillary Thyroid Cancer Patients 4 Weeks After Withdrawal of Levothyroxine: A Cross-Sectional Study Using 18F-FDG PET/CT.

Background: There is no doubt that thyroid dysfunction is associated with psychiatric disorders. A large amount of thyroid carcinoma patients displayed mood disorders after the withdrawal of levothyroxine (LT4). However, it is unclear whether the disorders are related to the transient withdrawal of LT4, and if yes, what the possible underlying mechanism is. This study aims to investigate the abnormal regional cerebral glucose metabolism (rCMRglu) in a group of papillary thyroid cancer (PTC) patients without LT4 for 4 weeks and prove the relationship between the abnormal rCMRglu with depression and anxiety. Methods: Brain 18F-FDG PET/CT data of 38 consecutive PTC patients with high/intermediate-risk from June 2016 to December 2017 have been analyzed. Of the 38 patients, 23 are in the LT4 withdrawal group (WG) and 15 in the LT4 replacement group (RG). These patients were also evaluated for depressive and anxiety symptoms within 24 h after the scans based on the Hamilton Depression Rating Scale (17 items, HRDS-17) and the Hamilton Anxiety Rating Scale (HAMA) respectively. Results: Thirty-eight patients (12 men, 26 women; age range, 25-69 years; mean age, 45.8 years) were selected in the study. Compared with the RG, patients in WG showed depression and anxiety with higher total scores of HRDS-17 and HAMA (14.7 ± 5.8 vs 3.8 ± 5.5, t = -5.74, p = 0.00; 9.3 ± 4.3 vs 2.7 ± 4.1, t = -4.74, p = 0.00, respectively). In the brain glucose metabolism analysis, the WG patients showed lower rCMRglu in Occipital_Mid_R and Postcentral_L. On the other hand, data illustrated significant rCMRglu increases in the Frontal_Sup_Orb_L. Compared with the healthy group (HG), the rCMRglu of the Postcentral_L and Precuneus_L showed hypoactivity, but the Hippocampus_R and the Temporal_Inf_L showed hyperactivity. This analysis yielded a significant correlation between abnormal rCMRglu with the free thyroxine level, the serum thyroid-stimulating hormone level, HRDS-17, and HAMA scores. Conclusions: The findings showed that more PTC patients exhibited depression and anxiety after LT4 withdrawal for 4 weeks. More attention should be paid to these hypothyroid patients while they were in the hospital. Such a short-term LT4 withdrawal also likely induced abnormal rCMRglu. Our study attempts to explain the possible mechanism of mood disorders related to transient hypothyroidism.

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.

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification.

Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In this study, we first investigated statistical differences in large-scale resting-state networks (RSNs) on 192 healthy controls (HCs) and 103 ASD patients by using independent component analysis (ICA). Second, an image-based meta-analysis (IBMA) was applied to discover the consistency of spatial patterns from different sites. Last, utilizing these patterns as features, we used Support Vector Machine (SVM) classifier to identify whether a subject was suffering from ASD or not. As a result, six RSNs were obtained with ICA. In each RSN, we identified altered functional connectivity between ASD and HC across the multi-site data. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine the classification performance. The AUC value of classification reaches 0.988. In conclusion, the present study indicates that intrinsic connectivity patterns produced from rsfMRI data could yield a possible biomarker of ASD and contributed to the neurobiology of ASD.

Gut microbiota interacts with intrinsic brain activity of patients with amnestic mild cognitive impairment.

AIMS: To explore the potential relationships among gut microbiota (GM), local brain spontaneous activity, and neuropsychological characteristics in amnestic mild cognitive impairment (aMCI) patients. METHODS: Twenty aMCI and 22 healthy control (HC) subjects were recruited. The GM composition was determined by 16S ribosomal RNA gene sequencing. Resting-state functional magnetic resonance imaging scans were performed, and fractional amplitude of low-frequency fluctuations (fALFF) was calculated across different frequencies. The Spearman or Pearson correlation analysis was used to analyze the relationship between spontaneous brain activity and cognitive function, and GM composition. RESULTS: aMCI patients had altered GM state and local spontaneous brain activity as compared with HC subjects. Correlation analysis showed that aMCI and HC groups had different "GM-intrinsic brain activity interaction" patterns. In aMCI group, at the typical band (0.01-0.08 Hz), the relative abundance (RA) of Bacteroides from phylum to genus level was negatively correlated with fALFF value of cerebellar vermis IV-V, and the Ruminococcaceae RA was negatively correlated with fALFF values of left lenticular nucleus and pallidum. The Clostridiaceae RA and Blautia RA were positively correlated with the left cerebellum lobules IV-V at the slow-4 band (0.027-0.073 Hz). The Veillonellaceae RA was positively correlated with fALFF values of left precentral gyrus at the slow-5 band (0.073-0.08 Hz). Correlation analysis showed that Clostridium members (Lachnospiraceae and Blautia) were positively, while Veillonellaceae was negatively, correlated with cognition test. Bacteroides was positively correlated with attention and computation, and negatively correlated with the three-stage command score. CONCLUSIONS: aMCI patients have a specific GM-intrinsic brain activity-cognitive function interaction pattern.

Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach.

PURPOSE: In recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning. PATIENTS AND METHODS: We measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests. RESULTS: The results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain-behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05). CONCLUSION: This study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.

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.

Intrinsic Resting-State Activity in Older Adults With Video Game Experience.

Playing video games is a prevalent leisure activity in current daily life, and studies have found that video game experience has positive effects in several cognitive domains. However, few studies have examined the effect of video game experience on the amplitude of low-frequency fluctuations (ALFF) among older adults. In the current study, we compared behavioral performance in the flanker task and ALFF activities of older adults, of whom 15 were video game players (VGPs) and 18 non-video game players (NVGPs). The results showed that VGPs outperformed NVGPs in the flanker task and that VGPs showed significantly increased ALFF relative to NVGPs in the left inferior occipital gyrus, left cerebellum and left lingual gyrus. Furthermore, the ALFF in the left inferior occipital gyrus and left lingual gyrus was positively correlated with cognitive performance as measured by Mini-Mental State Examination (MMSE) scores. These results revealed that playing video games might improve behavioral performance and change intrinsic brain activity in older adults. Future video game training studies in older adults are warranted to provide more evidence of the positive effects of video game experience on behavioral and brain function.

Intra- and Inter-Scanner Reliability of Voxel-Wise Whole-Brain Analytic Metrics for Resting State fMRI.

As the multi-center studies with resting-state functional magnetic resonance imaging (RS-fMRI) have been more and more applied to neuropsychiatric studies, both intra- and inter-scanner reliability of RS-fMRI are becoming increasingly important. The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) are 3 main RS-fMRI metrics in a way of voxel-wise whole-brain (VWWB) analysis. Although the intra-scanner reliability (i.e., test-retest reliability) of these metrics has been widely investigated, few studies has investigated their inter-scanner reliability. In the current study, 21 healthy young subjects were enrolled and scanned with blood oxygenation level dependent (BOLD) RS-fMRI in 3 visits (V1 - V3), with V1 and V2 scanned on a GE MR750 scanner and V3 on a Siemens Prisma. RS-fMRI data were collected under two conditions, eyes open (EO) and eyes closed (EC), each lasting 8 minutes. We firstly evaluated the intra- and inter-scanner reliability of ALFF, ReHo, and DC. Secondly, we measured systematic difference between two scanning visits of the same scanner as well as between two scanners. Thirdly, to account for the potential difference of intra- and inter-scanner local magnetic field inhomogeneity, we measured the difference of relative BOLD signal intensity to the mean BOLD signal intensity of the whole brain between each pair of visits. Last, we used percent amplitude of fluctuation (PerAF) to correct the difference induced by relative BOLD signal intensity. The inter-scanner reliability was much worse than intra-scanner reliability; Among the VWWB metrics, DC showed the worst (both for intra-scanner and inter-scanner comparisons). PerAF showed similar intra-scanner reliability with ALFF and the best reliability among all the 4 metrics. PerAF reduced the influence of BOLD signal intensity and hence increase the inter-scanner reliability of ALFF. For multi-center studies, inter-scanner reliability should be taken into account.