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Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learning models while protecting the privacy of medical image data from multiple sites. However, previous studies suggest that domain shift across different sites may influence the performance of federated models. As a solution, we propose a gradient matching federated domain adaptation (GM-FedDA) method for brain image classification, aiming to reduce domain discrepancy with the assistance of a public image dataset and train robust local federated models for target sites. It mainly includes two stages: 1) pretraining stage; we propose a one-common-source adversarial domain adaptation (OCS-ADA) strategy, i.e., adopting ADA with gradient matching loss to pretrain encoders for reducing domain shift at each target site (private data) with the assistance of a common source domain (public data) and 2) fine-tuning stage; we develop a gradient matching federated (GM-Fed) fine-tuning method for updating local federated models pretrained with the OCS-ADA strategy, i.e., pushing the optimization direction of a local federated model toward its specific local minimum by minimizing gradient matching loss between sites. Using fully connected networks as local models, we validate our method with the diagnostic classification tasks of schizophrenia and major depressive disorder based on multisite resting-state functional MRI (fMRI), respectively. Results show that the proposed GM-FedDA method outperforms other commonly used methods, suggesting the potential of our method in brain imaging analysis and other fields, which need to utilize multisite data while preserving data privacy.

Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Antisocial behavior (ASB) is believed to have neural substrates; however, the association between ASB and functional brain networks remains unclear. The temporal variability of the functional connectivity (or dynamic FC) derived from resting-state functional MRI has been suggested as a useful metric for studying abnormal behaviors including ASB. This is the first study using low-frequency fluctuations of the dynamic FC to unravel potential system-level neural correlates with ASB. Specifically, we individually associated the dynamic FC patterns with the ASB scores (measured by Antisocial Process Screening Device) of the male offenders (age: 23.29 ± 3.36 years) based on machine learning. Results showed that the dynamic FCs were associated with individual ASB scores. Moreover, we found that it was mainly the inter-network dynamic FCs that were negatively associated with the ASB severity. Three major high-order cognitive functional networks and the sensorimotor network were found to be more associated with ASB. We further found that impaired behavior in the ASB subjects was mainly associated with decreased FC dynamics in these networks, which may explain why ASB subjects usually have impaired executive control and emotional processing functions. Our study shows that temporal variation of the FC could be a promising tool for ASB assessment, treatment, and prevention., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

This project aims to explore if stronger functional connectivity (FC) exists in the maximal BOLD response of EEG/fMRI analysis when it is concordant with seizure-onset-zone (SOZ). Twenty-six patients with drug-resistant focal epilepsy who had an EEG/fMRI and later underwent stereo-EEG implantation were included. Different types of IEDs were labeled in scalp EEG and IED-related maximal BOLD responses were evaluated separately, each constituting one study. After evaluating concordance between maximal BOLD and SOZ, twenty-seven studies were placed in the concordant group and eight in the discordant group. We evaluated the local connectivity and ipsilaterally distant connectivity difference between the maximal BOLD and the contralateral homotopic region. Significantly stronger local FC was found for the maximal BOLD in the concordant group (p < 0.05, Bonferroni corrected). 52% of the studies in the concordant group and 13% in the discordant group had a significant difference compared to healthy subjects (p < 0.05, uncorrected). The finding suggests that, when concordant with the SOZ, the maximal BOLD is more likely to have stronger local FC compared to its contralateral counterpart. This asymmetry in functional connectivity may help to noninvasively improve the specificity of EEG/fMRI analysis.

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors - derived from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters - as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The neurophysiological basis of spontaneous low-frequency brain activity has become a major theme in the study of neural function in both humans and animal models. In such studies, the anesthesia model was generally adopted. However, the effects of anesthesia on spontaneous activity remain unclear. In this work, we explored the characteristics of cerebral spontaneous low-frequency activities at different depths of anesthesia in mice. Using Fourier transformation and the multitaper analysis methods, spontaneous low-frequency oscillations (LFOs) in the intrinsic signals of different cerebral regions (artery, vein, and cortex) were extracted and analyzed. Under different concentrations of anesthetic, the frequency of spontaneous LFO signals remained stable, while LFO amplitudes increased with the depth of anesthesia. The results imply that the anesthetic impacts the amplitude of spontaneous LFOs but does not alter the oscillation frequency.

Intrinsic functional connectivity (FC) exhibits high variability across individuals, which may account for the diversity of cognitive and behavioural ability. This variability in connectivity could be attributed to individual-specific trait and inter-session state differences (intra-subject differences), as well as a small amount of noise. However, it is still a challenge to perform accurate identification of connectivity traits from FC. Here, we introduced a novel low-rank learning model to solve this problem with a new constraint item that could reduce intra-subject differences. The model could dissociate FC into a substrate (substrate) that delineates functional characteristics common across the population and connectivity traits that are expected to account for individual behavioural differences. Subsequently, we performed a sparse dictionary learning algorithm on the extracted connectivity traits and obtained a dictionary matrix, named connectivity dictionary. We could then predict cognitive behaviours, including fluid intelligence, oral reading recognition, grip strength and anger-aggression, more accurately using the connectivity dictionary than the original FC. The results reflect that we captured individual connectivity traits that more effectively represent cognitive behaviour. Moreover, we found that the functional substrate is significantly correlated with large-scale anatomical brain architecture, and individual differences in connectivity traits are constrained by the connectivity substrate. Our findings may advance our understanding of the relationships among anatomy, function, and behaviour., (Copyright © 2019. Published by Elsevier B.V.)

In this work, we conduct comprehensive comparisons between four variants of independent component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) methods, both at the subject-level, by using synthesized fMRI data with ground-truth. Our results showed that ICA methods perform very well and slightly better than SDL methods when functional networks' spatial overlaps are minor, but ICA methods have difficulty in differentiating functional networks with moderate or significant spatial overlaps. In contrast, the SDL algorithms perform consistently well no matter how functional networks spatially overlap, and importantly, SDL methods are significantly better than ICA methods when spatial overlaps between networks are moderate or severe. This work offers empirical better understanding of ICA and SDL algorithms in inferring functional networks from fMRI data and provides new guidelines and caveats when constructing and interpreting functional networks in the era of fMRI-based connectomics.

Aims/hypotheses: It is now generally accepted that diabetes increases the risk for cognitive impairment, but the precise mechanisms are poorly understood. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to investigate the neural basis of cognitive dysfunction in type 2 diabetes (T2D) patients. Alterations in brain functional connectivity may underlie diabetes-related cognitive dysfunction and brain damage. The aim of this study was to investigate the changes in default mode network (DMN) connectivity in different glucose metabolism status and diabetes duration., Methods: We used a seed-based fMRI analysis to investigate positive and negative DMN connectivity in four groups (39 subjects with normal glucose metabolism [NGM], 23 subjects with impaired glucose metabolism [IGM; i.e., prediabetes], 59 T2D patients with a diabetes duration of <10 years, and 24 T2D patients with a diabetes duration of ≥10 years)., Results: Negative DMN connectivity increased and then regressed with deteriorating glucose metabolism status and extending diabetes duration. DMN connectivity showed a significant correlation with diabetes duration., Conclusion/interpretation: This study suggests that DMN connectivity may exhibit distinct patterns in different glucose metabolism status and diabetes duration, providing some potential neuroimaging evidence for early diagnosis and further understanding of the pathophysiological mechanisms of diabetic brain damage., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Resting-state functional magnetic resonance imaging (fMRI) studies using static functional connectivity (sFC) measures have shown that the brain function is severely disrupted after long-term sleep deprivation (SD). However, increasing evidence has suggested that resting-state functional connectivity (FC) is dynamic and exhibits spontaneous fluctuation on a smaller timescale. The process by which long-term SD can influence dynamic functional connectivity (dFC) remains unclear. In this study, 37 healthy subjects participated in the SD experiment, and they were scanned both during rested wakefulness (RW) and after 36 h of SD. A sliding-window based approach and a spectral clustering algorithm were used to evaluate the effects of SD on dFC based on the 26 qualified subjects' data. The outcomes showed that time-averaging FC across specific regions as well as temporal properties of the FC states, such as the dwell time and transition probability, was strongly influenced after SD in contrast to the RW condition. Based on the occurrences of FC states, we further identified some RW-dominant states characterized by anti-correlation between the default mode network (DMN) and other cortices, and some SD-dominant states marked by significantly decreased thalamocortical connectivity. In particular, the temporal features of these FC states were negatively correlated with the correlation coefficients between the DMN and dorsal attention network (dATN) and demonstrated high potential in classification of sleep state (with 10-fold cross-validation accuracy of 88.6% for dwell time and 88.1% for transition probability). Collectively, our results suggested that the temporal properties of the FC states greatly account for changes in the resting-state brain networks following SD, which provides new insights into the impact of SD on the resting-state functional organization in the human brain., (Copyright © 2017. Published by Elsevier B.V.)

Recently, resting-state functional magnetic resonance imaging (fMRI) studies have been extended to explore fluctuations in correlations over shorter timescales, referred to as dynamic functional connectivity (dFC). However, the impact of global signal regression (GSR) on dFC is not well established, despite the intensive investigations of the influence of GSR on static functional connectivity (sFC). This study aimed to examine the effect of GSR on the performance of the sliding-window correlation, a commonly used method for capturing functional connectivity (FC) dynamics based on resting-state fMRI and simultaneous electroencephalograph (EEG)-fMRI data. The results revealed that the impact of GSR on dFC was spatially heterogeneous, with some susceptible regions including the occipital cortex, sensorimotor area, precuneus, posterior insula and superior temporal gyrus, and that the impact was temporally modulated by the mean global signal (GS) magnitude across windows. Furthermore, GSR substantially changed the connectivity structures of the FC states responding to a high GS magnitude, as well as their temporal features, and even led to the emergence of new FC states. Conversely, those FC states marked by obvious anti-correlation structures associated with the default model network (DMN) were largely unaffected by GSR. Finally, we reported an association between the fluctuations in the windowed magnitude of GS and the time-varying EEG power within subjects, which implied changes in mental states underlying GS dynamics. Overall, this study suggested a potential neuropsychological basis, in addition to nuisance sources, for GS dynamics and highlighted the need for caution in applying GSR to sliding-window correlation analyses. At a minimum, the mental fluctuations of an individual subject, possibly related to ongoing vigilance, should be evaluated during the entire scan when the dynamics of FC is estimated., (Copyright © 2018 Elsevier Inc. All rights reserved.)

With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.

Determining gender by examining the human brain is not a simple task because the spatial structure of the human brain is complex, and no obvious differences can be seen by the naked eyes. In this paper, we propose a novel three-dimensional feature descriptor, the three-dimensional weighted histogram of gradient orientation (3D WHGO) to describe this complex spatial structure. The descriptor combines local information for signal intensity and global three-dimensional spatial information for the whole brain. We also improve a framework to address the classification of three-dimensional images based on MRI. This framework, three-dimensional spatial pyramid, uses additional information regarding the spatial relationship between features. The proposed method can be used to distinguish gender at the individual level. We examine our method by using the gender identification of individual magnetic resonance imaging (MRI) scans of a large sample of healthy adults across four research sites, resulting in up to individual-level accuracies under the optimized parameters for distinguishing between females and males. Compared with previous methods, the proposed method obtains higher accuracy, which suggests that this technology has higher discriminative power. With its improved performance in gender identification, the proposed method may have the potential to inform clinical practice and aid in research on neurological and psychiatric disorders.

Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage. Our analysis based on group-level sparse representation revealed that multiple functional networks involved in memory, emotion, attention, and vigilance processing were impaired by SD. Of particular interest, the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited. These results not only further elucidate the impact of SD on brain function but also demonstrate the ability of the proposed approach to provide new insights into the functional organization of the resting-state brain by permitting spatial overlap between networks and facilitating the description of the varied relationships of the overlapping regions with other regions of the brain in the context of different functional systems. Hum Brain Mapp 38:4671-4689, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD.

Internet addiction (IA) is a condition characterized by loss of control over Internet use, leading to a variety of negative psychosocial consequences. Recent neuroimaging studies have begun to identify IA-related changes in specific brain regions and connections. However, whether and how the interactions within and between the large-scale brain networks are disrupted in individuals with IA remain largely unexplored. Using group independent component analysis, we extracted five intrinsic connectivity networks (ICNs) from the resting-state fMRI data of 26 adolescents with IA and 43 controls, including the anterior and posterior default mode network (DMN), left and right fronto-parietal network (FPN), and salience network (SN). We then examined the possible group differences in the functional connectivity within each ICN and between the ICNs. We found that, compared with controls, IA subjects showed: (1) reduced inter-hemispheric functional connectivity of the right FPN, whereas increased intra-hemispheric functional connectivity of the left FPN; (2) reduced functional connectivity in the dorsal medial prefrontal cortex (mPFC) of the anterior DMN; (3) reduced functional connectivity between the SN and anterior DMN. Our findings suggest that IA is associated with imbalanced interactions among the DMN, FPN and SN, which may serve as system-level neural underpinnings for the uncontrollable Internet-using behaviors., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Currently, 422 million adults suffer from diabetes worldwide, leading to tremendous disabilities and a great burden to families and society. Functional and structural MRIs have demonstrated that patients with type 2 diabetes mellitus (T2DM) exhibit abnormalities in brain regions in the cerebral cortex. However, the changes of cerebellar anatomical connections in diabetic patients remains unclear. In the current study, diffusion tensor imaging deterministic tractography and statistical analysis were employed to investigate abnormal cerebellar anatomical connections in diabetic patients. This is the first study to investigate the altered cerebellar anatomical connectivity in T2DM patients. Decreased anatomical connections were found in the cerebellar and cerebro-cerebellar circuits of T2DM patients, providing valuable new insights into the potential neuro-pathophysiology of diabetes-related motor and cognitive deficits., (Copyright © 2017. Published by Elsevier Inc.)

Previous static resting-state functional connectivity (FC) MRI (rs-fcMRI) studies have suggested certain heredity characteristics of schizophrenia. Recently, dynamic rs-fcMRI analysis, which can better characterize the time-varying nature of intrinsic activity and connectivity and may therefore unveil the special connectivity patterns that are always lost in static FC analysis, has shown a potential neuroendophenotype of schizophrenia. In this study, we have extended previous static rs-fcMRI studies to a dynamic study by exploring whether healthy siblings share aberrant dynamic FC patterns with schizophrenic patients, which may imply a potential risk for siblings to develop schizophrenia. We utilized the dynamic rs-fcMRI method using a sliding window approach to evaluate FC discrepancies within transient states across schizophrenic patients, unaffected siblings, and matched healthy controls. Statistical analysis showed five trait-related connections that are related to cingulo-opercular, occipital, and default mode networks, reflecting the shared connectivity alterations between schizophrenic patients and their unaffected siblings. The findings suggested that schizophrenic patients and their unaffected siblings shared common transient functional disconnectivity, implying a potential risk for the healthy siblings of developing schizophrenia.

The study aims to investigate the radiation-induced brain functional alterations in nasopharyngeal carcinoma (NPC) patients who received radiotherapy (RT) using functional magnetic resonance imaging (fMRI) and statistic scale.The fMRI data of 35 NPC patients with RT and 24 demographically matched untreated NPC patients were acquired. Montreal Cognitive Assessment (MoCA) was also measured to evaluate their global cognition performance. Multivariate pattern analysis was performed to find the significantly altered functional connections between these 2 groups, while the linear correlation level was detected between the altered functional connections and the MoCA scores.Forty-five notably altered functional connections were found, which were mainly located between 3 brain networks, the cerebellum, sensorimotor, and cingulo-opercular. With strictly false discovery rate correction, 5 altered functional connections were shown to have significant linear correlations with the MoCA scores, that is, the connections between the vermis and hippocampus, cerebellum lobule VI and dorsolateral prefrontal cortex, precuneus and dorsal frontal cortex, cuneus and middle occipital lobe, and insula and cuneus. Besides, the connectivity between the vermis and hippocampus was also significantly correlated with the attention score, 1 of the 7 subscores of the MoCA.The present study provides new insights into the radiation-induced functional connectivity impairments in NPC patients. The results showed that the RT may induce the cognitive impairments, especially the attention alterations. The 45 altered functional connections, especially the 5 altered functional connections that were significantly correlated to the MoCA scores, may serve as the potential biomarkers of the RT-induced brain functional impairments and provide valuable targets for further functional recovery treatment., Competing Interests: The authors have no conflicts of interest to disclose.

An increasing number of neuroimaging studies have suggested that the fluctuations of low-frequency resting-state functional connectivity (FC) are not noise but are instead linked to the shift between distinct cognitive states. However, there is very limited knowledge about whether and how the fluctuations of FC at rest are influenced by long-term training and experience. Here, we investigated how the dynamics of resting-state FC are linked to driving behavior by comparing 20 licensed taxi drivers with 20 healthy non-drivers using a sliding window approach. We found that the driving experience could be effectively decoded with 90% (p<0.001) accuracy by the amplitude of low-frequency fluctuations in some specific connections, based on a multivariate pattern analysis technique. Interestingly, the majority of these connections fell within a set of distributed regions named "the vigilance network". Moreover, the decreased amplitude of the FC fluctuations within the vigilance network in the drivers was negatively correlated with the number of years that they had driven a taxi. Furthermore, temporally quasi-stable functional connectivity segmentation revealed significant differences between the drivers and non-drivers in the dwell time of specific vigilance-related transient brain states, although the brain's repertoire of functional states was preserved. Overall, these results suggested a significant link between the changes in the time-dependent aspects of resting-state FC within the vigilance network and long-term driving experiences. The results not only improve our understanding of how the brain supports driving behavior but also shed new light on the relationship between the dynamics of functional brain networks and individual behaviors., (Copyright © 2015 Elsevier Inc. All rights reserved.)

There has been increasing interest in multivariate pattern analysis (MVPA) as a means of distinguishing psychiatric patients from healthy controls using brain imaging. However, it remains unclear whether MVPA methods can accurately estimate the medication status of psychiatric patients. This study aims to develop an MVPA approach to accurately predict the antidepressant medication status of individuals with major depression on the basis of whole-brain resting-state functional connectivity MRI (rs-fcMRI). We investigated data from rs-fcMRI of 24 medication-naive depressed patients, 16 out of whom subsequently underwent antidepressant treatment and achieved clinical recovery, and 29 demographically similar controls. By training a linear support vector machine classifier and combining it with principal component analysis, the medication-naive patients were identified from the healthy controls with 100% accuracy. In addition, we found reliable correlations between MVPA prediction scores and clinical symptom severity. Moreover, the most discriminative functional connections were located within or across the cerebellum and default mode, affective, and sensorimotor networks, indicating that these networks may play important roles in major depression. Most importantly, only ∼30% of these discriminative connections were normalized in clinically recovered patients after antidepressant treatment. The current study may not only show the feasibility of estimating medication status by MVPA of whole-brain rs-fcMRI data in major depression but also shed new light on the pathological mechanism of this disorder., (Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.)

Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

In this work, abundant anticorrelated networks were successfully detected in rest-stating fMRI-BOLD data from 20 subjects. Spatial independent component analysis (sICA) method was applied at both individual and group levels. At the individual level, for each subject, 30 independent components (IC) were estimated, and each IC was transformed using Z-score mapping. The voxels with >5 and < -5 Z-score were denoted as positive signals (PS) and negative signals (NS) respectively. The correlation coefficients between the mean time series of the PS and NS voxels were computed; if the calculated coefficients were <-0.3, the PS and NS voxels were considered to form an anticorrelated PS-NS network. It was found that 36.5% of the ICs contained an anticorrelated PS-NS network. The spatiotemporal patterns of most PS-NS networks varied from subject to subject, but three networks displaying spatial patterns were comparably consistent among different subjects. For group-level analysis, no anticorrelated PS-NS networks were detected. Our results suggest that future investigations adopt a broader approach for negative BOLD signal characterization. Combined consideration of PS and NS systems help to better elucidate hemodynamic and neuronal brain behavior and further develop understanding of neural mechanisms of brain information processing.

Increasingly more neuroimaging studies have shown that the complex symptoms of schizophrenia are linked to disrupted neural circuits and dysconnectivity of intrinsic connectivity networks. Previous studies have assumed temporal stationarity of resting-state functional connectivity, whereas temporal dynamics have rarely been explored. Here, we utilized resting-state functional MRI with a sliding window approach to measure the amplitude of low-frequency fluctuations (ALFFs) in functional connectivity in 24 patients with schizophrenia and 25 healthy controls. We found that there were significant differences in the ALFFs of specific connections, the majority of which were located between the intrinsic connectivity networks. Importantly, the experimental results of a multivariate pattern analysis of these ALFF measures showed that 81.3% (P<0.0009) of the participants were correctly classified as either schizophrenic patients or healthy controls by leave-one-out cross-validation. Our results show significant abnormality in the dynamics of internetwork functional connectivity in schizophrenia, which contributes toward the characterization and differentiation of schizophrenic patients, and may be used as a potential biomarker.

Statistical analysis on diffusion tensor imaging has been used extensively in mesial temporal lobe epilepsy (mTLE) and most studies report decrease in fractional anisotropy (FA) in multiple white matter regions. However, these findings vary across studies and between regions. Therefore, in this study, we used tract-based spatial statistics along with machine learning approaches to investigate the whole-brain white matter changes between 17 left mTLE patients and 15 right mTLE patients and 34 matched healthy controls. The results showed that the three groups could be distinguished from each other with promising accuracy. Compared with controls, the FA value of the most discriminating voxels was decreased in the ipsilateral limbic system, corpus callosum, and temporal white matter in both patient groups. Compared with right mTLE, left mTLE had decreased FA in the left temporal white matter, whereas right mTLE had decreased FA in the right frontal and temporal white matter, and right posterior corona radiata. These findings not only provide useful information for lateralization of the seizure focus but can also be used as a potential biomarker for the diagnosis and treatment of the mTLE. This may be helpful in assessment of patients with mTLE when no lesion is detected on visual evaluation.

Individual differences in brain metrics, especially connectivity measured with functional MRI, can correlate with differences in motion during data collection. The assumption has been that motion causes artifactual differences in brain connectivity that must and can be corrected. Here we propose that differences in brain connectivity can also represent a neurobiological trait that predisposes to differences in motion. We support this possibility with an analysis of intra- versus intersubject differences in connectivity comparing high- to low-motion subgroups. Intersubject analysis identified a correlate of head motion consisting of reduced distant functional connectivity primarily in the default network in individuals with high head motion. Similar connectivity differences were not found in analysis of intrasubject data. Instead, this correlate of head motion was a stable property in individuals across time. These findings suggest that motion-associated differences in brain connectivity cannot fully be attributed to motion artifacts but rather also reflect individual variability in functional organization.

There is increasing evidence that antisocial personality disorder (ASPD) stems from brain abnormalities. However, there are only a few studies investigating brain structure in ASPD. The aim of this study was to find regional coherence abnormalities in resting-state functional MRI of ASPD. Thirty-two ASPD individuals and 34 controls underwent a resting-state functional MRI scan. The regional homogeneity (ReHo) approach was used to examine whether ASPD was related to alterations in resting-state neural activity. Support vector machine discriminant analysis was used to evaluate the sensitivity/specificity characteristics of the ReHo index in discriminating between the ASPD individuals and controls. The results showed that, compared with controls, ASPD individuals show lower ReHo in the right cerebellum posterior lobe (Crus1) and the right middle frontal gyrus, as well as higher ReHo in the right middle occipital gyrus (BA 19), left inferior temporal gyrus (BA 37), and right inferior occipital gyrus (cuneus, BA 18). All alternation regions reported a predictive accuracy above 70%. To our knowledge, this study was the first to study the change in regional activity coherence in the resting brain of ASPD individuals. These results not only elucidated the pathological mechanism of ASPD from a resting-state functional viewpoint but also showed that these alterations in ReHo may serve as potential markers for the detection of ASPD.

Major depression and schizophrenia are two of the most serious psychiatric disorders and share similar behavioral symptoms. Whether these similar behavioral symptoms underlie any convergent psychiatric pathological mechanisms is not yet clear. To address this issue, this study sought to investigate the whole-brain resting-state functional magnetic resonance imaging (MRI) of major depression and schizophrenia by using multivariate pattern analysis. Thirty-two schizophrenic patients, 19 major depressive disorder patients and 38 healthy controls underwent resting-state functional MRI scanning. A support vector machine in conjunction with intrinsic discriminant analysis was used to solve the multi-classification problem, resulting in a correct classification rate of 80.9% via leave-one-out cross-validation. The depression and schizophrenia groups both showed altered functional connections associated with the medial prefrontal cortex, anterior cingulate cortex, thalamus, hippocampus, and cerebellum. However, the prefrontal cortex, amygdala, and temporal poles were found to be affected differently by major depression and schizophrenia. Our preliminary study suggests that altered connections within or across the default mode network and the cerebellum may account for the common behavioral symptoms between major depression and schizophrenia. In addition, connections associated with the prefrontal cortex and the affective network showed promise as biomarkers for discriminating between the two disorders.

During the development of neuroimaging, numerous analyses were performed to identify population differences, such as studies on age, gender, and diseases. Researchers first normalized the brain image and then identified features that represent key differences between groups. In these studies, the question of whether normalization (a pre-processing step widely used in neuroimaging studies) reduces the diversity of brains was largely ignored. There are a few studies that identify the differences between individuals after normalization. In the current study, we analyzed brain diversity on an individual level, both qualitatively and quantitatively. The main idea was to utilize brain images for identity authentication. First, the brain images were normalized and registered. Then, a pixel-level matching method was developed to compute the identity difference between different images for matching. Finally, by analyzing the performance of the proposed brain recognition strategy, the individual differences in brain images were evaluated. Experimental results on a 150-subject database showed that the proposed approach could achieve a 100% identification ratio, which indicated distinct differences between individuals after normalization. Thus, the results proved that after the normalization stage, brain images retain their main distinguishing information and features. Based on this result, we suggest that diversity (individual differences) should be considered when conducting group analysis, and that this approach may facilitate group pattern classification.

Background: Previous neuroimaging studies have documented changes in the brain of heroin addicts. However, few researches have detailed whether such changes can be amended after short-term abstinence., Methods: We used magnetic resonance imaging (MRI) to investigate gray matter volume in 20 heroin-dependent patients at 3 days and at 1 month after heroin abstinence; 20 normal subjects were also included as controls., Results: Decreased gray matter density in frontal cortex, cingulate and the occipital regions were found in heroin users after three days of abstinence. In contrast, after 1-month abstinence, no significant difference was found in superior frontal gyrus between heroin addicts and controls, but changes in other brain regions, including right middle frontal gyrus, left cingulate gyrus and left inferior occipital gyrus, still remained., Conclusion: Our findings illustrate that abnormal gray matter in some brain regions of heroin addicts can return to normal after one-month abstinence., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Most brain-computer interfaces (BCIs) are non-time-restraint systems. However, the method used to design a real-time BCI paradigm for controlling unstable devices is still a challenging problem. This paper presents a real-time feedback BCI paradigm for controlling an inverted pendulum on a cart (IPC). In this paradigm, sensorimotor rhythms (SMRs) were recorded using 15 active electrodes placed on the surface of the subject's scalp. Subsequently, common spatial pattern (CSP) was used as the basic filter to extract spatial patterns. Finally, linear discriminant analysis (LDA) was used to translate the patterns into control commands that could stabilize the simulated inverted pendulum. Offline trainings were employed to teach the subjects to execute corresponding mental tasks, such as left/right hand motor imagery. Five subjects could successfully balance the online inverted pendulum for more than 35s. The results demonstrated that BCIs are able to control nonlinear unstable devices. Furthermore, the demonstration and extension of real-time continuous control might be useful for the real-life application and generalization of BCI., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

There has been growing interest recently in the use of multivariate pattern analysis (MVPA) to decode information from high-dimensional neuroimaging data. The present study employed a support vector machine-based MVPA approach to identify the complex patterns of sex differences in brain structure and resting-state function. We also aimed to assess the role of anatomy on functional sex differences during rest. One hundred and forty healthy young Chinese adults (70 men and 70 women) underwent structural and resting-state functional MRI scans. Gray matter density and regional homogeneity (ReHo) were used to map brain structure and resting-state function, respectively. After combining these two feature vectors into one union-vector, a pattern classifier was designed using principal component analysis and linear support vector machine to identify brain areas that had distinct characteristics between the groups. We found that: (1) male and female brains were different with a mean classification accuracy of 89%; (2) sex differences in gray matter density were widely distributed in the brain, notably in the occipital lobe and the cerebellum; (3) men primarily showed higher ReHo in their right hemispheres and women tended to show greater ReHo in their left hemispheres; (4) about 50% of brain areas with functional sex differences exhibited significant positive correlations between gray matter density and ReHo. Our results suggest that sex is an important factor that account for interindividual variability in the healthy brain., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis.

Recent resting-state functional connectivity MRI studies using group-level statistical analysis have demonstrated the inheritable characters of schizophrenia. The objective of the present study was to use pattern classification as a means to investigate schizophrenia inheritance based on the whole-brain resting-state functional connectivity at the individual subject level. One-against-one pattern classifications were made amongst three groups (i.e. patients diagnosed with schizophrenia, healthy siblings, and healthy controls after preprocessing), resulting in an 80.4% separation between patients with schizophrenia and healthy controls, a 77.6% separation between schizophrenia patients and their healthy siblings, and a 78.7% separation between healthy siblings and healthy controls, respectively. These results suggest that the healthy siblings of schizophrenia patients have an altered resting-state functional connectivity pattern compared with healthy controls. Thus, healthy siblings may have a potential higher risk for developing schizophrenia compared with the general population. Moreover, this pattern differed from that of schizophrenia patients and may contribute to the normal behavior exhibition of healthy siblings in daily life.

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.

An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivariate pattern analysis (MVPA) enables the exploration of subtle and distributed changes of data obtained from structural MRI images. In this study, a new MVPA approach based on sparse representation has been employed to investigate the anatomical covariance patterns of normal aging. Two groups of participants (group 1:290 participants; group 2:56 participants) were evaluated in this study. These two groups were scanned with two 1.5 T MRI machines. In the first group, we obtained the discriminative patterns using a t-test filter and sparse representation step. We were able to distinguish the young from old cohort with a very high accuracy using only a few voxels of the discriminative patterns (group 1:98.4%; group 2:96.4%). The experimental results showed that the selected voxels may be categorized into two components according to the two steps in the proposed method. The first component focuses on the precentral and postcentral gyri, and the caudate nucleus, which play an important role in sensorimotor tasks. The strongest volume reduction with age was observed in these clusters. The second component is mainly distributed over the cerebellum, thalamus, and right inferior frontal gyrus. These regions are not only critical nodes of the sensorimotor circuitry but also the cognitive circuitry although their volume shows a relative resilience against aging. Considering the voxels selection procedure, we suggest that the aging of the sensorimotor and cognitive brain regions identified in this study has a covarying relationship with each other.

Objective: Brain-computer interface (BCI) provides a mean of communication for the patients that are severely disabled by neuromuscular diseases. The performance of the classical P300 speller, however, declines noticeably in the gaze fixation condition. The speller paradigm presented in this paper aims to release the gaze dependency at the cost of an extra covert visual search task., Methods: Clusters of characters were presented sequentially in the near-central vision as stimulation. Participants fixed their gaze on the center, searched and recognized the target character with covert shift of attention. Random position (RP) and fixed position (FP) presentation modes designed with different searching set size (6 for RP, and ≤ 2 for FP) were examined., Results: Online sessions using 10 stimulus sequences achieved character accuracies of 94.4% and 96.3% for RP and FP mode, respectively. For offline overall evaluation, the peak written symbol rate (WSR) of 1.38 symbols/min was obtained, with corresponding accuracies of 87.8% (RP) and 84.1% (FP). The P300 waveform of RP mode has evident longer latency and larger amplitude. Electrooculogram (EOG) analysis indicated that the performance was independent of gaze shift., Conclusions: The proposed speller could be operated effectively and gaze independently by healthy participants., Significance: The proposed gaze independent BCI approach promises reasonable communication capability for the profoundly paralyzed patients with head or ocular motor impairments., (Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Brain functional asymmetry abnormalities have previously been reported in schizophrenia. In the present study, we hypothesized that the pattern of functional asymmetry in schizophrenia may differ between patients suffering from positive and negative symptoms. We examined the relationship between altered asymmetry of functional connectivity and symptom type (positive/negative) using resting-state functional magnetic resonance imaging. We selected the dorsolateral prefrontal cortex, superior temporal gyrus, middle temporal gyrus and hippocampus as regions of interest, and analyzed functional connectivity patterns between these and other brain regions. Furthermore, a voxel-based two-level asymmetry analysis was conducted to investigate differences in the asymmetry of functional connectivity patterns within and between groups. Our results showed that patients exhibiting positive symptoms had significantly increased leftward asymmetry of functional connectivity. The negative symptom group, in contrast, exhibited increased rightward asymmetry of functional connectivity. The strength of the asymmetry in these regions was found to be significantly correlated with symptom ratings obtained using the Positive and Negative Syndrome Scale. These results suggest that predominantly positive and predominantly negative schizophrenia may have different neural underpinnings, and that certain regions in the frontal and temporal lobes, as well as the cingulate gyrus and precuneus, play important roles in mediating the symptoms of this complex disease. Our study also provided further evidence for the hypothesis that schizophrenia is related to abnormalities in functional brain networks., (Copyright @ 2009 Elsevier Ireland Ltd. All rights reserved.)

Recently, a functional disconnectivity hypothesis of schizophrenia has been proposed for the physiological explanation of behavioral syndromes of this complex mental disorder. In this paper, we aim at further examining whether syndromes of schizophrenia could be decoded by some special spatiotemporal patterns of resting-state functional connectivity. We designed a data-driven classifier based on machine learning to extract highly discriminative functional connectivity features and to discriminate schizophrenic patients from healthy controls. The proposed classifier consisted of two separate steps. First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification. Then, an unsupervised-learning classifier combining low-dimensional embedding and self-organized clustering of fMRI was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a leave-one-out cross-validation strategy. The experimental results demonstrated not only high classification accuracy (93.75% for schizophrenic patients, 75.0% for healthy controls), but also good generalization and stability with respect to the number of extracted features. In addition, some functional connectivities between certain brain regions of the cerebellum and frontal cortex were found to exhibit the highest discriminative power, which might provide further evidence for the cognitive dysmetria hypothesis of schizophrenia. This primary study demonstrated that machine learning could extract exciting new information from the resting-state activity of a brain with schizophrenia, which might have potential ability to improve current diagnosis and treatment evaluation of schizophrenia., (Copyright 2009 Elsevier Inc. All rights reserved.)

Ample evidence from functional magnetic resonance imaging studies has demonstrated functional brain asymmetries in the motor cortex and cerebellum within the task of finger-to-thumb (intratask), but the asymmetries across left and right hand-performed tasks of finger-to-thumb (intertask) are little known. Here, we found that intratask evoked asymmetry responses in the motor cortex and cerebellum, and interestingly in the Extra-Nuclear and insula. Intertask comparisons between left and right hand-activated images and between the asymmetries of intratasks of left and right hand, however, revealed distinct lateralization responses in the temporal cortex, extranuclear, parahippocampal gyrus, cingulate gyrus and prominently in basal ganglia. These results suggest that multiple aspects of functional asymmetries in motor organization are reflected in specific brain regions.

As a special movement disorder, hepatic myelopathy (HM) is characterized by spastic paraperesis and may be secondary to transjugular intrahepatic portosystemic shunt (TIPS). The prediction and diagnosis of HM is difficult due to largely unknown neuropathological underpinnings and a lack of specific biomarkers. We aimed to delve into the alterations in motor system of HM patients' brain and their potential clinical implication. Twenty-three patients with HM and 23 without HM after TIPS and 24 demographically matched healthy controls were enrolled. High-spatial-resolution structural imaging and functional data at rest were acquired. Motor areas were included as seed regions for functional connectivity analysis. Then, we performed brain volume analysis. We found decreased right supplementary motor area (SMA)-seeded functional connectivity with bilateral insula, thalamus and midbrain, left cerebellum and middle temporal gyrus, and right middle cingulate gyrus in HM compared to non-HM patients (p < 0.001). The right insula revealed decreased volume (p < 0.001), and white matter volume reduced in the right corona radiata beneath the right SMA (p < 0.001) in HM relative to non-HM patients. Furthermore, the strength of right SMA-seeded connectivity with insula was positively correlated with folic acid level in HM patients (r = 0.60, p = 0.03), showing an accuracy of 0.87 to distinguish HM from non-HM. Our study demonstrates the HM-specific dysconnectivity with an anatomical basis, and its correlation with laboratory findings and diagnostic value. Detecting these abnormalities might help to predict and diagnose post-TIPS HM. [ABSTRACT FROM AUTHOR]

When analyzing brain imaging data, blind source separation (BSS) techniques critically depend on the level of dimensional reduction. If the reduction level is too slight, the BSS model would be overfitted and become unavailable. Thus, the reduction level must be set relatively heavy. This approach risks discarding useful information and crucially limits the performance of BSS techniques. In this study, a new BSS method that can work well even at a slight reduction level is presented. We proposed the concept of “signal intensity” which measures the significance of the source. Only picking the sources with significant intensity, the new method can avoid the overfitted solutions which are nonexistent artifacts. This approach enables the reduction level to be set slight and retains more useful dimensions in the preliminary reduction. Comparisons between the new and conventional algorithms were performed on both simulated and real data. [ABSTRACT FROM PUBLISHER]

Rationale and Objectives: As a special movement disorder, hepatic myelopathy (HM) is characterized by spastic paraperesis and may be secondary to transjugular intrahepatic portosystemic shunt (TIPS). The prediction and diagnosis of HM is difficult due to largely unknown neuropathological underpinnings and a lack of specific biomarkers. We aimed to delve into the alterations in motor system of HM patients' brain and their potential clinical implication.Material and Methods: Twenty-three patients with HM and 23 without HM after TIPS and 24 demographically matched healthy controls were enrolled. High-spatial-resolution structural imaging and functional data at rest were acquired. Motor areas were included as seed regions for functional connectivity analysis. Then, we performed brain volume analysis.Results: We found decreased right supplementary motor area (SMA)-seeded functional connectivity with bilateral insula, thalamus and midbrain, left cerebellum and middle temporal gyrus, and right middle cingulate gyrus in HM compared to non-HM patients (p < 0.001). The right insula revealed decreased volume (p < 0.001), and white matter volume reduced in the right corona radiata beneath the right SMA (p < 0.001) in HM relative to non-HM patients. Furthermore, the strength of right SMA-seeded connectivity with insula was positively correlated with folic acid level in HM patients (r = 0.60, p = 0.03), showing an accuracy of 0.87 to distinguish HM from non-HM.Conclusion: Our study demonstrates the HM-specific dysconnectivity with an anatomical basis, and its correlation with laboratory findings and diagnostic value. Detecting these abnormalities might help to predict and diagnose post-TIPS HM. [ABSTRACT FROM AUTHOR]