Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data.

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

SpoT-mediated NapA upregulation promotes oxidative stress-induced Helicobacter pylori biofilm formation and confers multidrug resistance.

Recently, there is increased incidence of drug-resistant Helicobacter pylori infection. Biofilm formation confers multidrug resistance to bacteria. Moreover, it has been found that the formation of biofilm on the surface of gastric mucosa is an important reason for the difficulty of eradication of H. pylori The mechanisms underlying H. pylori biofilm formation in vivo have not been elucidated. Reactive oxygen species (ROS) released by the host immune cells in response to H. pylori infection cannot effectively clear the pathogen. Moreover, the extracellular matrix of the biofilm protects the bacteria against ROS-mediated toxicity. This study hypothesized that ROS can promote H. pylori biofilm formation and treatment with low concentrations of hydrogen peroxide (H2O2) promoted this process in vitro The comparative transcriptome analysis of planktonic and biofilm-forming cells revealed that the expression of SpoT, a (p)ppGpp (guanosine 3'-diphosphate 5'-triphosphate and guanosine 3',5'-bispyrophosphate) synthetase/hydrolase, is upregulated in H2O2-induced biofilms and that knockout of spoT inhibited H. pylori biofilm formation. Additionally, this study examined the key target molecules involved in SpoT regulation using weighted gene co-expression network analysis. The analysis revealed that neutrophil-activating protein (NapA; HP0243) promoted H2O2-induced biofilm formation and conferred multidrug resistance. Furthermore, vitamin C exhibited anti-H. pylori biofilm activity and downregulated the expression of napA in vitro These findings provide novel insight into the clearance of H. pylori biofilms.

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data.

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.

High dimensionality data have become common in neuroimaging fields, especially group-level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data-driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE-based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group-level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task-evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets.

Helicobacter pylori infection is correlated with the incidence of erosive oral lichen planus and the alteration of the oral microbiome composition.

BACKGROUND: Oral lichen planus (OLP), a common clinical oral disease, is associated with an increased risk of malignant transformation. The mechanism underlying the pathogenesis of OLP is unknown. Oral dysbacteriosis is reported to be one of the aetiological factors of OLP. Although Helicobacter pylori infection is associated with various oral diseases, the correlation between H. pylori infection and OLP is unclear. This study aimed to investigate the effect of H. pylori infection on OLP pathogenesis and oral microbiome composition in the Chinese population, which has a high incidence of H. pylori infection. RESULT: In this study, saliva samples of 30 patients with OLP (OLP group) and 21 negative controls (NC group) were collected. H. pylori infection was detected using the carbon-13-labeled urea breath test (UBT). The saliva samples were divided into the following four groups based on the H. pylori status: H. pylori-positive OLP (OLP+), H. pylori-positive NC (NC+), H. pylori-negative OLP (OLP-), and H. pylori-negative NC (NC-). Oral microbiome compositions were significantly different between the OLP and NC groups and between the OLP- and OLP+ groups. Compared with those in the OLP- group, those in the OLP+ group had a higher incidence of erosive OLP and higher levels of salivary cytokines. In contrast, the oral microbiome composition and cytokine levels were not significantly different between the NC- and NC+ groups. CONCLUSIONS: This is the first report to demonstrate that H. pylori infection is significantly correlated with the pathogenesis of erosive OLP.

Denoising scanner effects from multimodal MRI data using linked independent component analysis.

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.

Transporters HP0939, HP0497, and HP0471 participate in intrinsic multidrug resistance and biofilm formation in Helicobacter pylori by enhancing drug efflux.

BACKGROUND: The multidrug resistance of Helicobacter pylori is becoming an increasingly serious issue. It is therefore necessary to study the mechanism of multidrug resistance of H pylori. We have previously identified that the HP0939, HP0497, and HP0471 transporters affect the efflux of drugs from H pylori. As efflux pumps participate in bacterial multidrug resistance and biofilm formation, we hypothesized that these transporters could be involved in the multidrug resistance and biofilm formation of H pylori. MATERIALS AND METHODS: We therefore constructed three knockout strains, Δhp0939, Δhp0497, and Δhp0471, and three high-expression strains, Hp0939he , Hp0497he , and Hp0471he , using the wild-type (WT) 26 695 strain of H pylori as the template. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of wild strains, knockout strains, and high-expression strains to amoxicillin, metronidazole, and other antibiotics were measured. The efflux capacity of high-expression strains and wild strains was compared by Hoechst 33 342 accumulation assay. RESULTS: Determination of the MIC and MBC of the antibiotics revealed that the knockout strains were more sensitive to antibiotics, while the high-expression strains were less sensitive to antibiotics, compared to the WT. The ability of the high-expression strains to efflux drugs was significantly higher than that of the WT. We also induced H pylori to form biofilms, and observed that the knockout strains could barely form biofilms and were more sensitive to several antibiotics, compared to the WT. The mRNA expression of hp0939, hp0497, and hp0471 in the clinically sensitive and multidrug-resistant strains was determined, and it was found that these genes were highly expressed in the multidrug-resistant strains that were isolated from the clinics. CONCLUSIONS: In this study, we found three transporters involved in intrinsic multidrug resistance of H pylori.

Developing Polysulfide-Sensitive GFPs for Real-Time Analysis of Polysulfides in Live Cells and Subcellular Organelles.

Polysulfides are newly discovered cellular contents, and they are involved in multiple intracellular processes, including redox homeostasis and protein sulfhydration. The dynamic changes of polysulfides inside the cell are directly related to these processes. To monitor the intracellular dynamics and subcellular levels of polysulfides, we developed green-fluorescent-protein (GFP)-based probes that are polysulfide-specific. A pair of cysteine residues was introduced near the GFP chromophore with the spatial distance between the cysteine residues designed to allow the formation of internal -S n- ( n ≥ 3) bonds but not -S2- (disulfide) bonds. We tested these probes in model microorganisms and found that they displayed ratiometric changes to intracellular polysulfides that had clear variations associated with the growth phases. The distribution of polysulfides in subcellular organelles is heterogeneous, suggesting that polysulfides have multiple origins and functions in cells. These probes provided long-desired tools for polysulfide in vivo studies.

FisR activates σ54 -dependent transcription of sulfide-oxidizing genes in Cupriavidus pinatubonensis JMP134.

Some heterotrophic bacteria are able to oxidize sulfide (H2 S, HS- and S2- ) to sulfite and thiosulfate via polysulfide. The genes coding for the oxidation enzymes in Cupriavidus pinatubonensis JMP134 have recently been identified; however, their regulation is unknown. A regulator gene is adjacent to the operon of the sulfide-oxidizing genes, encoding a σ54 -dependent transcription factor (FisR) with three domains: an R domain, an AAA+ domain and a DNA-binding domain. Here it is reported that the regulator responds to the presence of sulfide and activates the sulfide-oxidizing genes. FisR binds to its cognate operator at -114 to -135 bp of the transcription start of the operon. When polysulfide reacts with the R domain of FisR through the three conserved cysteine residues (C53, C64 and C71), FisR activates the expression of the operon. FisR is highly sensitive to polysulfide, activating σ54 -dependent transcription of sulfide-oxidizing genes for sulfide removal. Further, sequence analysis indicates that FisR-type regulators are relatively common for controlling sulfide-oxidizing genes under sulfide stress in the Proteobacteria.

The Complete Pathway for Thiosulfate Utilization in Saccharomyces cerevisiae.

Saccharomyces cerevisiae is known to grow with thiosulfate as a sulfur source, and it produces more ethanol when using thiosulfate than using sulfate. Here, we report how it assimilates thiosulfate. S. cerevisiae absorbed thiosulfate into the cell through two sulfate permeases, Sul1 and Sul2. Two rhodaneses, Rdl1 and Rdl2, converted thiosulfate to a persulfide and sulfite. The persulfide was reduced by cellular thiols to H2S, and sulfite was reduced by sulfite reductase to H2S. Cysteine synthase incorporated H2S into O-acetyl-l-homoserine to produce l-homocysteine, which is the precursor for cysteine and methionine in S. cerevisiae Several other rhodaneses replaced Rdl1 and Rdl2 for thiosulfate utilization in the yeast. Thus, any organisms with the sulfate assimilation system potentially could use thiosulfate as a sulfur source, since rhodaneses are common in most organisms.IMPORTANCE The complete pathway of thiosulfate assimilation in baker's yeast is determined. The finding reveals the extensive overlap between sulfate and thiosulfate assimilation. Rhodanese is the only additional enzyme for thiosulfate utilization. The common presence of rhodanese in most organisms, including Bacteria, Archaea, and Eukarya, suggests that most organisms with the sulfate assimilation system also use thiosulfate. Since it takes less energy to reduce thiosulfate than sulfate for assimilation, thiosulfate has the potential to become a choice of sulfur in optimized media for industrial fermentation.

Comparison of a non-stationary voxelation-corrected cluster-size test with TFCE for group-Level MRI inference.

Two powerful methods for statistical inference on MRI brain images have been proposed recently, a non-stationary voxelation-corrected cluster-size test (CST) based on random field theory and threshold-free cluster enhancement (TFCE) based on calculating the level of local support for a cluster, then using permutation testing for inference. Unlike other statistical approaches, these two methods do not rest on the assumptions of a uniform and high degree of spatial smoothness of the statistic image. Thus, they are strongly recommended for group-level fMRI analysis compared to other statistical methods. In this work, the non-stationary voxelation-corrected CST and TFCE methods for group-level analysis were evaluated for both stationary and non-stationary images under varying smoothness levels, degrees of freedom and signal to noise ratios. Our results suggest that, both methods provide adequate control for the number of voxel-wise statistical tests being performed during inference on fMRI data and they are both superior to current CSTs implemented in popular MRI data analysis software packages. However, TFCE is more sensitive and stable for group-level analysis of VBM data. Thus, the voxelation-corrected CST approach may confer some advantages by being computationally less demanding for fMRI data analysis than TFCE with permutation testing and by also being applicable for single-subject fMRI analyses, while the TFCE approach is advantageous for VBM data. Hum Brain Mapp 38:1269-1280, 2017. © 2016 Wiley Periodicals, Inc.

Irisin suppresses the migration, proliferation, and invasion of lung cancer cells via inhibition of epithelial-to-mesenchymal transition.

Irisin is involved in promoting metabolism, immune regulation, and affects chronic inflammation in many systemic diseases, including gastric cancer. However, the role of irisin in lung cancer is not well characterized. To determine whether irisin has a protective effect against lung cancer, we cultured A549 and NCI-H446 lung cancer cells and treated them with irisin. We detected the proliferation by MTT assay, and assessed the migration and invasion of the cells by scratch wound healing assay and Tran-swell assay. The expression levels of epithelial-to-mesenchymal transition (EMT) markers and the related signaling pathways were detected by western blot analysis. Meanwhile, an inhibitor of PI3K was used to investigate the effect of irsin. Finally, the expression of Snail was detected. We demonstrated that irisin inhibits the proliferation, migration, and invasion of lung cancer cells, and has a novel role in mediating the PI3K/AKT pathway in the cells. Irisin can reverse the activity of EMT and inhibit the expression of Snail via mediating the PI3K/AKT pathway, which is a key regulator of Snail. These results revealed that irisin inhibited EMT and reduced the invasion of lung cancer cells via the PI3K/AKT/Snail pathway.

Irisin reverses platelet derived growth factor-BB-induced vascular smooth muscle cells phenotype modulation through STAT3 signaling pathway.

Vascular smooth muscle cells (VSMCs) phenotype modulation toward a synthetic phenotype is the main cause of cardiovascular disease. As a newly discovered myokine, Irisin is thought to be a promising candidate for the treatment of metabolic disturbances, as well as cardiovascular disease. However, no evidence has been shown for the direct effect of Irisin on VSMCs phenotype modulation and its underling mechanisms. The aim of this study was to explore the effect of Irisin on VSMCs phenotype modulation and the mechanisms involved. In the present study, it was found that Irisin restored the PDGF-BB-induced VSMCs phenotype modulation which exhibited down-regulation of smooth muscle cells (SMC) expression and up-regulation of matrix synthesis related marker expression, as well as proliferative phenotype. Moreover, our research demonstrated that Irisin further activated STAT3 signaling pathways. Finally, by applying an STAT3 inhibitor, WP1066, we revealed the roles of STAT3 in the PDGF-BB-induced VSMCs phenotype modulation when they were treated with Irisin. Taken together, these results demonstrated that Irisin may play a crucial role in regulating VSMCs phenotype modulation via the STAT3 signaling pathway.

Detection of subnanotesla oscillatory magnetic fields using MRI.

PURPOSE: Direct mapping of neuronal currents using MRI would have fundamental impacts on brain functional imaging. Previous reports indicated that the stimulus-induced rotary saturation (SIRS) mechanism had the best potential of direct detection of neural oscillations; however, it lacked the high-sensitivity level needed. In this study, a novel strategy is proposed in an effort to improve the detection sensitivity. METHODS: In our modified SIRS sequence, an external oscillatory magnetic field is used as the excitation pulse in place of the standard 90-degree excitation pulse. This approach could potentially lead to tens or even hundreds times of enhancement in the detection sensitivity for low field signals. It also helps to lower the physiological noise, allows for shorter pulse repetition time, and is less affected by the blood oxygen level. RESULTS: We demonstrate that a 100-Hz oscillatory magnetic field with magnitude as low as 0.25 nanotesla generated in a current loop can be robustly detected using a 3-Tesla MRI scanner. CONCLUSION: The modified SIRS sequence offers higher detection sensitivity as well as several additional advantages. These promising results suggest that the direct detection of neural oscillation might be within the grasp of the current MRI technology.

3D interslab echo-shifted FLASH sequence for susceptibility weighted imaging.

PURPOSE: To develop a novel three-dimensional (3D) sequence for susceptibility weighted imaging that is able to reduce scan time substantially while maintaining high image signal-to-noise ratio (SNR). METHODS: The proposed fast T2 *-weighted sequence was based on a 3D full-balanced gradient frame and a pair of crusher gradients. The pair of crusher gradients were used to shift MR signal from the repetition time where the MR signal was originated to a later repetition time to enhance T2 * weighting. To avoid image SNR reduction due to the repeated signal excitations by later RF pulses, as it would occur for typical echo-shifted (ES) FLASH, an interslab scan mode for the fast T2 *-weighted sequence was introduced for signal acquisition. The effectiveness of this novel sequence was evaluated by comparing it with 3D FLASH and ES-FLASH sequences. RESULTS: The proposed interslab ES T2 *-weighted sequence was able to reduce the scan time by half with a SNR comparable to the typical multislab FLASH. Besides, it yielded a higher image SNR than the traditional multislab ES-FLASH and was more flexible than the whole-volume ES-FLASH. CONCLUSION: An interslab ES sequence was developed with high time efficiency and relatively high image SNR compared with the conventional acquisition sequences. Magn Reson Med 76:222-228, 2016. © 2015 Wiley Periodicals, Inc.

A voxelation-corrected non-stationary 3D cluster-size test based on random field theory.

Cluster-size tests (CSTs) based on random field theory (RFT) are commonly adopted to identify significant differences in brain images. However, the use of RFT in CSTs rests on the assumption of uniform smoothness (stationarity). When images are non-stationary, CSTs based on RFT will likely lead to increased false positives in smooth regions and reduced power in rough regions. An adjustment to the cluster size according to the local smoothness at each voxel has been proposed for the standard test based on RFT to address non-stationarity, however, this technique requires images with a large degree of spatial smoothing, large degrees of freedom and high intensity thresholding. Recently, we proposed a voxelation-corrected 3D CST based on Gaussian random field theory that does not place constraints on the degree of spatial smoothness. However, this approach is only applicable to stationary images, requiring further modification to enable use for non-stationary images. In this study, we present modifications of this method to develop a voxelation-corrected non-stationary 3D CST based on RFT. Both simulated and real data were used to compare the voxelation-corrected non-stationary CST to the standard cluster-size adjusted non-stationary CST based on RFT and the voxelation-corrected stationary CST. We found that voxelation-corrected stationary CST is liberal for non-stationary images and the voxelation-corrected non-stationary CST performs better than cluster-size adjusted non-stationary CST based on RFT under low smoothness, low intensity threshold and low degrees of freedom.

Degradation of AF1Q by chaperone-mediated autophagy.

AF1Q, a mixed lineage leukemia gene fusion partner, is identified as a poor prognostic biomarker for pediatric acute myeloid leukemia (AML), adult AML with normal cytogenetic and adult myelodysplastic syndrome. AF1Q is highly regulated during hematopoietic progenitor differentiation and development but its regulatory mechanism has not been defined clearly. In the present study, we used pharmacological and genetic approaches to influence chaperone-mediated autophagy (CMA) and explored the degradation mechanism of AF1Q. Pharmacological inhibitors of lysosomal degradation, such as chloroquine, increased AF1Q levels, whereas activators of CMA, including 6-aminonicotinamide and nutrient starvation, decreased AF1Q levels. AF1Q interacts with HSPA8 and LAMP-2A, which are core components of the CMA machinery. Knockdown of HSPA8 or LAMP-2A increased AF1Q protein levels, whereas overexpression showed the opposite effect. Using an amino acid deletion AF1Q mutation plasmid, we identified that AF1Q had a KFERQ-like motif which was recognized by HSPA8 for CMA-dependent proteolysis. In conclusion, we demonstrate for the first time that AF1Q can be degraded in lysosomes by CMA.

A high performance 3D cluster-based test of unsmoothed fMRI data.

Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness.

Identification of Autism Spectrum Disorder Using Topological Data Analysis.

Autism spectrum disorder (ASD) is a pervasive brain development disease. Recently, the incidence rate of ASD has increased year by year and posed a great threat to the lives and families of individuals with ASD. Therefore, the study of ASD has become very important. A suitable feature representation that preserves the data intrinsic information and also reduces data complexity is very vital to the performance of established models. Topological data analysis (TDA) is an emerging and powerful mathematical tool for characterizing shapes and describing intrinsic information in complex data. In TDA, persistence barcodes or diagrams are usually regarded as visual representations of topological features of data. In this paper, the Regional Homogeneity (ReHo) data of subjects obtained from Autism Brain Imaging Data Exchange (ABIDE) database were used to extract features by using TDA. The average accuracy of cross validation on ABIDE I database was 95.6% that was higher than any other existing methods (the highest accuracy among existing methods was 93.59%). The average accuracy for sampling with the same resolutions with the ABIDE I on the ABIDE II database was 96.5% that was also higher than any other existing methods (the highest accuracy among existing methods was 75.17%).