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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.)

Background: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging disease. The consequences of SARS-CoV-2 exposure in infants remain unknown. Therefore, this study aims to investigate whether neonates born to mothers with COVID-19 have adverse brain development., Methods: This multicenter observational study was conducted at two designated maternal and children's hospitals in Hubei Province, mainland China from February 1, 2020 to May 15, 2020. Neonates born to mothers with COVID-19 were enrolled. Brain magnetic resonance imaging (MRI) findings, and volumes of grey and white matters, and physical growth parameters were observed at 44 weeks corrected gestational age., Results: Of 72 neonates born to mothers with COVID-19, 8 (11%) were diagnosed with COVID-19, 8 (11%) were critically ill, and no deaths were reported. Among the eight neonates that underwent brain MRI at corrected gestational age of 44 weeks, five neonates were diagnosed with COVID-19. Among these five neonates, three presented abnormal MRI findings including abnormal signal in white matter and delayed myelination in newborn 2, delayed myelination and brain dysplasia in newborn 3, and abnormal signal in the bilateral periventricular in newborn 5. The other three neonates without COVID-19 presented no significantly changes of brain MRI findings and the volumes of grey matter and white matter compared to those of healthy newborns at the equivalent age (P > 0.05). Physical growth parameters for weight, length, and head circumference at gestational age of 44 weeks were all above the 3rd percentile for all neonates., Conclusions: Some of the neonates born to mothers with COVID-19 had abnormal brain MRI findings but these neonates did not appear to have poor physical growth. These findings may provide the information on the follow-up schedule on the neonates exposed to SARS-CoV-2, but further study is required to evaluate the association between the abnormal MRI findings and the exposure to SARS-CoV-2.

Astrocytes are the major glial cells in the central nervous system, but unlike neurons, they do not produce action potentials. For many years, astrocytes were considered supporting cells in the central nervous system (CNS). Technological advances over the last two decades are changing the face of glial research. Accumulating data from recent investigations show that astrocytes display transient calcium spikes and regulate synaptic transmission by releasing transmitters called gliotransmitters. Many new powerful technologies are used to interfere with astrocytic activity, in order to obtain a better understanding of the roles of astrocytes in the brain. Among these technologies, chemogenetics has recently been used frequently. In this review, we will summarize new functions of astrocytes in the brain that have been revealed using this cutting-edge technique. Moreover, we will discuss the possibilities and challenges of manipulating astrocytic activity using this technology., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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.)

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.)

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.)

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.)

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.)

Accumulating data have revealed that abnormal activity of the mTOR (mammalian target of rapamycin) pathway plays an important role in epileptogenesis triggered by various factors. We previously reported that pretreatment with perifosine, an inhibitor of Akt (also called protein kinase B), abolishes the rapamycin-induced paradoxical increase of S6 phosphorylation in a rat model induced by kainic acid (KA). Since Akt is an upstream target in the mTOR signaling pathway, we set out to determine whether perifosine has a preventive effect on epileptogenesis. Here, we explored the effect of perifosine on the model of temporal epilepsy induced by KA in rats and found that pretreatment with perifosine had no effect on the severity or duration of the KA-induced status epilepticus. However, perifosine almost completely inhibited the activation of p-Akt and p-S6 both acutely and chronically following the KA-induced status epilepticus. Perifosine pretreatment suppressed the KA-induced neuronal death and mossy fiber sprouting. The frequency of spontaneous seizures was markedly decreased in rats pretreated with perifosine. Accordingly, rats pretreated with perifosine showed mild impairment in cognitive functions. Collectively, this study provides novel evidence in a KA seizure model that perifosine may be a potential drug for use in anti-epileptogenic therapy.

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.

Personality dimension extraversion describes individual differences in social behaviour and socio-emotional functioning. The intrinsic functional connectivity patterns of the brain are reportedly associated with extraversion. However, whether or not extraversion is associated with functional hubs warrants clarification. Functional hubs are involved in the rapid integration of neural processing, and their dysfunction contributes to the development of neuropsychiatric disorders. In this study, we employed the functional connectivity density (FCD) method for the first time to distinguish the energy-efficient hubs associated with extraversion. The resting-state functional magnetic resonance imaging data of 71 healthy subjects were used in the analysis. Short-range FCD was positively correlated with extraversion in the left cuneus, revealing a link between the local functional activity of this region and extraversion in risk-taking. Long-range FCD was negatively correlated with extraversion in the right superior frontal gyrus and the inferior frontal gyrus. Seed-based resting-state functional connectivity (RSFC) analyses revealed that a decreased long-range FCD in individuals with high extraversion scores showed a low long-range functional connectivity pattern between the medial and dorsolateral prefrontal cortex, middle temporal gyrus, and anterior cingulate cortex. This result suggests that decreased RSFC patterns are responsible for self-esteem, self-evaluation, and inhibitory behaviour system that account for the modulation and shaping of extraversion. Overall, our results emphasize specific brain hubs, and reveal long-range functional connections in relation to extraversion, thereby providing a neurobiological basis of extraversion., (© 2015 The British Psychological Society.)

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.)

Background: there has been a recent increase in the use of connectome-based multivariate pattern analysis (MVPA) of resting-state functional magnetic resonance imaging (fMRI) data aimed at distinguishing patients with major depressive disorder (MDD) from healthy controls (HCs). However, the validity of this method needs to be confirmed in independent samples., Method: we used resting-state fMRI to explore whole-brain functional connectivity (FC) patterns characteristic of MDD and to confirm the effectiveness of MVPA in distinguishing MDD versus HC groups in two independent samples. The first sample set included 29 MDD patients and 33 HCs and second sample set included 46 MDD patients and 57 HCs., Results: for the first sample, we obtained a correct classification rate of 91.9% with a sensitivity of 89.6% and specificity of 93.9%. For the second sample, we observed a correct classification rate of 86.4% with a sensitivity of 84.8% and specificity of 87.7%. With both samples, we found that the majority of consensus FCs used for MDD identification were located in the salience network, default mode network, the cerebellum, visual cortical areas, and the affective network., Limitation: we did not analyze potential structural differences between the groups., Conclusion: results suggest that whole-brain FC patterns can be used to differentiate depressed patients from HCs and provide evidence for the potential use of connectome-based MVPA as a complementary tool in the clinical diagnosis of MDD., (Copyright © 2017 Elsevier B.V. All rights reserved.)

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.

Background: Neuroimaging studies suggest that treatment-naive depression (TD) is characterized by abnormal functional connectivity between specific brain regions. However, the question surrounding the structural basis of functional aberrations in TD patients still remains., Methods: In the present study, diffusion tensor imaging tractography was employed to construct structural connectivity networks in 22 early adult-onset, first-episode TD patients and 19 healthy controls (HC). Graph theory and network-based statistic (NBS) were then employed to investigate systematically the alteration of whole brain structural topological organization and structural connectivity in TD patients., Results: Graph theoretical analysis revealed that, compared with HC, TD patients exhibited altered structural topological measures, including decreased shortest path length, normalized clustering coefficient, normalized shortest path length, and small-worldness, as well as increased global and local efficiency. NBS results further revealed that TD patients showed two altered structural sub-networks. One sub-network mainly involved connections between the right orbitofrontal cortex (OFC) and the right insula, putamen, caudate, hippocampus, fusiform gyrus, inferior temporal gyrus and lingual gyrus. The other sub-network mainly included connections between the left OFC and the left gyrus rectus, insula, putamen, caudate, thalamus, pallidum and middle occipital gyrus., Conclusions: The findings suggest that TD patients exhibit a disruption in the topological organization of structural brain networks. The altered orbitofrontal connectivity may particularly contribute to the manifestation of symptoms in TD patients. The abnormalities may facilitate understanding of the functional disturbances of mood and cognition in the disease., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Recent research has shown that social anxiety disorder (SAD) is accompanied by abnormalities in brain functional connections. However, these findings are based on group comparisons, and, therefore, little is known about whether functional connections could be used in the diagnosis of an individual patient with SAD. Here, we explored the potential of the functional connectivity to be used for SAD diagnosis. Twenty patients with SAD and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. The whole brain was divided into 116 regions based on automated anatomical labeling atlas. The functional connectivity between each pair of regions was computed using Pearson's correlation coefficient and used as classification feature. Multivariate pattern analysis was then used to classify patients from healthy controls. The pattern classifier was designed using linear support vector machine. Experimental results showed a correct classification rate of 82.5 % (p < 0.001) with sensitivity of 85.0 % and specificity of 80.0 %, using a leave-one-out cross-validation method. It was found that the consensus connections used to distinguish SAD were largely located within or across the default mode network, visual network, sensory-motor network, affective network, and cerebellar regions. Specifically, the right orbitofrontal region exhibited the highest weight in classification. The current study demonstrated that functional connectivity had good diagnostic potential for SAD, thus providing evidence for the possible use of whole brain functional connectivity as a complementary tool in clinical diagnosis. In addition, this study confirmed previous work and described novel pathophysiological mechanisms of SAD.

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.

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.

This study aims to investigate the neurostructural foundations of the human personality in young adults. High-resolution structural T1-weighted MR images of 71 healthy young individuals were processed using voxel-based morphometric (VBM) approach. Multiple regression analyses were performed to identify the associations between personality traits and gray matter volume (GMV). The Eysenck Personality Questionnaire-Revised, Short Scale for Chinese was chosen to assess the personality traits. This scale includes four dimensions, namely, extraversion, neuroticism, psychoticism, and lie. Particularly, we studied on two dimensions (extraversion and neuroticism) of Eysenck's personality. Our results showed that extraversion was negatively correlated with GMV of the bilateral amygdala, the bilateral parahippocampal gyrus, the right middle temporal gyrus, and the left superior frontal gyrus, all of which are involved in emotional and social cognitive processes. These results might suggest an association between extraversion and affective processing. In addition, a positive correlation was detected between neuroticism and GMV of the right cerebellum, a key brain region for negative affect coordination. Meanwhile, a negative association was revealed between GMV of the left superior frontal gyrus and neuroticism. These results may prove that neuroticism is related to several brain regions involved in regulating negative emotions. Based on those findings, we concluded that brain regions involved in social cognition and affective process accounted for modulation and shaping of personality traits among young individuals. Results of this study may serve as a basis for elucidating the anatomical factors of personality.

Background: Transient ischemic attack (TIA) is usually defined as a neurologic ischemic disorder without permanent cerebral infarction. Studies have showed that patients with TIA can have lasting cognitive functional impairment. Inherent brain activity in the resting state is spatially organized in a set of specific coherent patterns named resting state networks (RSNs), which epitomize the functional architecture of memory, language, attention, visual, auditory and somato-motor networks. Here, we aimed to detect differences in RSNs between TIA patients and healthy controls (HCs)., Methods: Twenty one TIA patients suffered an ischemic event and 21 matched HCs were enrolled in the study. All subjects were investigated using cognitive tests, psychiatric tests and functional magnetic resonance imaging (fMRI). Independent component analysis (ICA) was adopted to acquire the eight brain RSNs. Then one-sample t-tests were calculated in each group to gather the spatial maps of each RSNs, followed by second level analysis to investigate statistical differences on RSNs between twenty one TIA patients and 21 controls. Furthermore, a correlation analysis was performed to explore the relationship between functional connectivity (FC) and cognitive and psychiatric scales in TIA group., Results: Compared with the controls, TIA patients exhibited both decreased and increased functional connectivity in default mode network (DMN) and self-referential network (SRN), and decreased functional connectivity in dorsal attention network (DAN), central-executive network (CEN), core network (CN), somato-motor network (SMN), visual network (VN) and auditory network (AN). There was no correlation between neuropsychological scores and functional connectivity in regions of RSNs., Conclusions: We observed selective impairments of RSN intrinsic FC in TIA patients, whose all eight RSNs had aberrant functional connectivity. These changes indicate that TIA is a disease with widely abnormal brain networks. Our results might put forward a novel way to look into neuro-pathophysiological mechanisms in TIA patients.

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.

Background: The previous resting perfusion or task-based studies have provided evidence of functional changes in the brains of patients with late-life depression (LLD). Little is known, so far, about the changes in the spontaneous brain activity in LLD during the resting state. The aim of this study was to investigate the spontaneous neural activity in first-episode, treatment-naive patients with LLD by using resting-state functional magnetic resonance imaging (fMRI)., Methods: A novel analytical method, coherence-based regional homogeneity (Cohe-ReHo), was used to assess regional spontaneous neural activity during the resting state in 15 first-episode, treatment-naive patients with LLD and 15 age- and gender-matched healthy controls., Results: Compared to the healthy controls, the LLD group showed significantly decreased Cohe-ReHo in left caudate nucleus, right anterior cingulate gyrus, left dorsolateral prefrontal cortex, right angular gyrus, bilateral medial prefrontal cortex, and right precuneus, while significantly increased Cohe-ReHo in left cerebellum posterior lobe, left superior temporal gyrus, bilateral supplementary motor area, and right postcentral gyrus (p<0.005, corrected for multiple comparisons)., Conclusions: These findings indicated abnormal spontaneous neural activity was distributed extensively in first-episode, treatment-naive patients with LLD during the resting state. Our results might supply a novel way to look into the underlying pathophysiology mechanisms of patients with LLD., (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.

In social anxiety disorder (SAD), impairments in limbic/paralimbic structures are associated with emotional dysregulation and inhibition of the medial prefrontal cortex (MPFC). Little is known, however, about alterations in limbic and frontal regions associated with the integrated morphometric, functional, and structural architecture of SAD. Whether altered gray matter volume is associated with altered functional and structural connectivity in SAD. Three techniques were used with 18 SAD patients and 18 healthy controls: voxel-based morphometry; resting-state functional connectivity analysis; and diffusion tensor imaging tractography. SAD patients exhibited significantly decreased gray matter volumes in the right posterior inferior temporal gyrus (ITG) and right parahippocampal/hippocampal gyrus (PHG/HIP). Gray matter volumes in these two regions negatively correlated with the fear factor of the Liebowitz Social Anxiety Scale. In addition, we found increased functional connectivity in SAD patients between the right posterior ITG and the left inferior occipital gyrus, and between the right PHF/HIP and left middle temporal gyrus. SAD patients had increased right MPFC volume, along with enhanced structural connectivity in the genu of the corpus callosum. Reduced limbic/paralimbic volume, together with increased resting-state functional connectivity, suggests the existence of a compensatory mechanism in SAD. Increased MPFC volume, consonant with enhanced structural connectivity, suggests a long-time overgeneralization of structural connectivity and a role of this area in the mediation of clinical severity. Overall, our results may provide a valuable basis for future studies combining morphometric, functional and anatomical data in the search for a comprehensive understanding of the neural circuitry underlying SAD., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The resting state cortical functional connectivity is an important method in current brain researches. In this paper, we propose an approach for analyzing and manipulating the resting state functional magnetic resonance imaging (fMRI) data using spatial independent component analysis (sICA) method, and applying the low-frequency oscillations theory to the choice of component of interest (COI) from the component obtained by sICA method. Firstly, we remove all the inactive voxels and independent voxels via Z value. Then, by making a spectrum analysis, we choose the COI with concentrations of energy between 0.01 and 0.1 Hz. And after that, we obtain the functional connectivity networks using hierarchical clustering.

Mice with inactivation of the Tuberous sclerosis complex-1 (Tsc1) gene in glia (Tsc1 GFAP CKO mice) have deficient astrocyte glutamate transporters and develop seizures, suggesting that abnormal glutamate homeostasis contributes to neurological abnormalities in these mice. We examined the hypothesis that Tsc1 GFAP CKO mice have elevated extracellular brain glutamate levels that may cause neuronal death, abnormal glutamatergic synaptic function, and associated impairments in behavioral learning. In vivo microdialysis documented elevated glutamate levels in hippocampi of Tsc1 GFAP CKO mice and several cell death assays demonstrated neuronal death in hippocampus and neocortex. Impairment of long-term potentiation (LTP) with tetanic stimulation was observed in hippocampal slices from Tsc1 GFAP CKO mice and was reversed by low concentrations of NMDA antagonist, indicating that excessive synaptic glutamate directly inhibited LTP. Finally, Tsc1 GFAP CKO mice exhibited deficits in two hippocampal-dependent learning paradigms. These results suggest that abnormal glutamate homeostasis predisposes to excitotoxic cell death, impaired synaptic plasticity and learning deficits in Tsc1 GFAP CKO mice.

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]

Pur α is an abundant protein in the brain and binds to a (GGN)n sequence, PUR element. It has been shown that Pur α not only interacts with single stranded DNA and RNA, but also with various proteins. In the present study, we tried to search for Pur α-binding proteins (PurBPs) in mouse brain by the overlay assay with GST-Pur α as a ligand. Three PurBPs of 35, 38 and 40 kDa were found mostly in the nuclear extract (N.Ext.) and they were not detected by the pretreatment of N.Ext. with trypsin, but not with RNase or DNase. The three PurBPs disappeared by the addition of ssCRE (single stranded cAMP response element) containing a PUR element, but not by ΔGGN ssCRE (deletion of the PUR element from the ssCRE). The PurBPs were abundantly expressed in the brain as Pur α. We also determined a region in Pur α which is required for the association with the PurBPs by using deletion mutants of Pur α. These biochemical properties of the PurBPs are different from the reported nuclear Pur α-binding proteins such as Sp1 and pRb. [Copyright &y& Elsevier]

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]

Abstract: The natural gradient and fixed-point algorithm are two of the most popular algorithms in independent component analysis (ICA). However, there still remain some problems to be solved in application to the processing of the functional magnetic resonance imaging (fMRI) data. Based on the BFGS quasi-Newton algorithm, this paper presents a novel BFGS-ICA algorithm framework in performing localization of brain activities with fMRI data. The new BFGS-ICA algorithm possesses properties of good convergence and immunity of initial point sensitivity. The convincing results of its application in fMRI show the potential of BFGS-ICA in detection of the brain activities. [Copyright &y& Elsevier]