Abnormal Degree Centrality of Bilateral Putamen and Left Superior Frontal Gyrus in Schizophrenia with Auditory Hallucinations： A Resting-state Functional Magnetic Resonance Imaging Study

Background： Dysconnectivity hypothesis of schizophrenia has been increasingly emphasized. Recent researches showed that this dysconnectivity might be related to occurrence of auditory hallucination （AH）. However, there is still no consistent conclusion. This study aimed to explore intrinsic dysconnectivity pattern of whole-brain functional networks at voxel level in schizophrenic with AH. Methods： Auditory hallucinated patients group （n = 42 APG）, no hallucinated patients group （n = 42 NPG） and normal controls （n = 84 NCs） were analyzed by resting-state functional magnetic resonance imaging. The functional connectivity metrics index （degree centrality [DC]） across the entire brain networks was calculated and evaluated among three groups. Results： DC decreased in the bilateral putamen and increased in the left superior frontal gyrus in all the patients. However, in APG. the changes of DC were more obvious compared with NPG. Symptomology scores were negatively correlated with the DC of bilateral putamen in all patients. AH score of APG positively correlated with the DC in left superior frontal gyrus but negatively correlated with the DC in bilateral putamen. Conclusion： Our findings corroborated that schizophrenia was characterized by functional dysconnectivity, and the abnormal DC in bilateral putamen and left superior frontal gyrus might be crucial in the occurrence of AH.

Erratum to“The power of many brains:Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration”[Sci Bull 2024;69:1536-1555]

The publisher regrets to note that reference citation errors have occurred in panels b,c,e-l in Fig 2 and the sentence“However,the literature reports both decreased and increased intra-network functional connections among patients with depression[115,116].”The publisher would like to apologise for any inconvenience caused.Fig.2.Principal neuroimaging findings in major depressive disorder.(a)Decreased intra-DMN FC is observed in recurrent MDD patients[35].(b)Eight-week antidepressant treatment reduce extensive large-scale functional networks[107].(c)Reduced global and local efficiency(Eglob/Eloc)are revealed in MDD patients[108].(d)Structural variations of the cortex and subcortical nuclei are found in ENIGMA-MDD studies[82].(e)Accelerated brain aging based on functional MRI is observed in MDD patients[114].(f)Accelerated brain aging based on structural MRI is observed in MDD patients[115].(g)Two subtypes of MDD can be identified through DMN FC[127].(h)A significant schizophrenia PRS by MDD interaction for rostral anterior cingulate cortex thickness is found in the UK Biobank dataset[215].(i)Stability of the four MDD subtypes based on FC[126].(j)The two subtypes of MDD exhibit distinct patterns of FC within and between SMS,DMN,and subcortical structures[130].(k)Performance of the functional connectivity-based classifiers across two multicenter datasets[135].(l)Salient brain regions that serve as important classification features for the graph convolutional network-based classifier[136].Brain-PAD:brain-predicted age difference;DAN:dorsal attention network;DMN:default mode network;FC:functional connectivity;FEDN:first-episode and drug-naïve;FPN:frontoparietal network;GCN:graph convolutional neural network;HC:healthy control;linear-SVM:linear support vector machine;MDD:major depressive disorder;mddrest:REST-meta-MDD dataset;NC:normal control;RACC:rostral anterior cingulate cortex;PRS:polygenic risk score;psymri:PsyMRI dataset;rbf-SVM:radial basis function support vector machine;SCN:subcortical network;SCZ:schizophrenia;SMN:sensorimotor network;SMS:sensory and motor systems;SubC:subcortical network;VAN:ventral attention network;VN:visual network.

众脑之力:通过开放神经影像数据和大规模协作催化神经精神疾病的创新发现

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders.By pooling images from various cohorts,statistical power has increased,enabling the detection of subtle abnormalities and robust associations,and fostering new research methods.Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment.Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies.We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders.However,challenges such as data harmonization across different sites,privacy protection,and effective data sharing must be addressed.With proper governance and open science practices,we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis,treatment selection,and outcome prediction,contributing to optimal brain health.

DPABINet:脑网络和图论分析工具箱

The human brain, a marvel of intricate connections, functions as a complex network comprising structurally and functionally integrated regions. This network orchestrates a multitude of complex patterns through high-level integration and continuous cooperation, essential for overall brain functionality [1].

DPABISurf:data processing&analysis for brain imaging on surface

Functional magnetic resonance imaging(fMRI)is a prevalent technology in brain research of cognition,emotion,development,and brain disorders.The traditional fMRI analysis is based on volume-based preprocessing pipelines and algorithms,which means that the brain MRI data is to be registered to a 3-dimensional(3D)coordinate[1].However,the relatively low spatial resolution of fMRI may lead to partial-volume-effect(e.g.,a 3D region may contain signals from grey matter,white matter and even cerebrospinal fluid).Given the human brain function is organized in a brain surface mesh manner,therefore,a growing number of studies conducted surface-based preprocessing pipelines and algorithms.Surface-based methods reconstructed the brain grey matter into 2-dimensional cortical surface which better represent the curving structure of the brain.Surface-based method is superior to volume-based method on brain registration,signal–noise ratio and reproducibility of algorithms[2].Specifically,the traditional volume-based approach was reported with a spatial localization that is only 35%of the best surface-based method[2].

The DIRECT consortium and the REST-meta-MDD project:towards neuroimaging biomarkers of major depressive disorder

Despite a growing neuroimaging literature on the pathophysiology of major depressive disorder(MDD),repro-ducible findings are lacking,probably reflecting mostly small sample sizes and heterogeneity in analytic approaches.To address these issues,the Depression Imaging REsearch ConsorTium(DIRECT)was launched.The REST-meta-MDD project,pooling 2428 functional brain images processed with a standardized pipeline across all participating sites,has been the first effort from DIRECT.In this review,we present an overview of the moti-vations,rationale,and principal findings of the studies so far from the REST-meta-MDD project.Findings from the first round of analyses of the pooled repository have included alterations in functional connectivity within the default mode network,in whole-brain topological properties,in dynamic features,and in functional lat-eralization.These well-powered exploratory observations have also provided the basis for future longitudinal hypothesis-driven research.Following these fruitful explorations,DIRECT has proceeded to its second stage of data sharing that seeks to examine ethnicity in brain alterations in MDD by extending the exclusive Chinese original sample to other ethnic groups through international collaborations.A state-of-the-art,surface-based preprocessing pipeline has also been introduced to improve sensitivity.Functional images from patients with bipolar disorder and schizophrenia will be included to identify shared and unique abnormalities across diag-nosis boundaries.In addition,large-scale longitudinal studies targeting brain network alterations following antidepressant treatment,aggregation of diffusion tensor images,and the development of functional magnetic resonance imaging-guided neuromodulation approaches are underway.Through these endeavours,we hope to accelerate the translation of functional neuroimaging findings to clinical use,such as evaluating longitudinal effects of antidepressant medications and developing individualized neuromodulation targets,while building an open repository for the scientific community.

A Connectome Computation System for discovery science of brain

Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce a computational pipeline--namely the Connectome Compu- tation System （CCS）-for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging technologies. The CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping andconnectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. We demonstrate the utility of the CCS based upon a publicly available dataset, the NKI- Rockland Sample, to delineate the normative trajectories of well-known large-scale neural networks across the natural life span （6-85 years of age）. The CCS has been made freely available to the public via GitHub （https：//github.com/ zuoxinian/CCS） and our laboratory＇s Web site （http：//lfcd. psych.ac.cn/ccs.html） to facilitate progress in discovery science in the field of human brain connectomics.

Ventral medial prefrontal functional connectivity and emotion regulation in chronic schizophrenia: A pilot study

People with schizophrenia exhibit impaired social cognitive functions, particularly emotion regulation. Abnormal activations of the ventral medial prefrontal cortex （vMPFC） during emotional tasks have been demonstrated in schizophrenia, suggesting its important role in emotion processing in patients. We used the resting-state functional connectivity approach, setting a functionally relevant region, the vMPFC, as a seed region to examine the intrinsic functional interactions and communication between the vMPFC and other brain regions in schizophrenic patients. We found hypo-connectivity between the vMPFC and the medial frontal cortex, right middle temporal lobe （MTL）, right hippocampus, parahippocampal cortex （PHC） and amygdala. Further, there was a decreased strength of the negative connectivity （or anticorrelation） between the vMPFC and the bilateral dorsal lateral prefrontal cortex （DLPFC） and pre-supplementary motor areas. Among these connectivity alterations, reduced vMPFC-DLPFC connectivity was positively correlated with positive symptoms on the Positive and Negative Syndrome Scale, while vMPFC-right MTL/PHC/amygdala functional connectivity was positively correlated with the performance of emotional regulation in patients. These findings imply that communication and coordination throughout the brain networks are disrupted in schizophrenia. The emotional correlates of vMPFC connectivity suggest a role of the hypo-connectivity between these regions in the neuropathology of abnormal social cognition in chronic schizophrenia.

Concordance among indices of intrinsic brain function： Insights from inter-individual variation and temporal dynamics

Various resting-state fMRI(R-fMRI) measures have been developed to characterize intrinsic brain activity. While each of these measures has gained a growing presence in the literature, questions remain regarding the common and unique aspects these indices capture. The present work provided a comprehensive examination of inter-individual variation and intra-individual temporal variation for commonly used measures, including fractional amplitude of low frequency fluctuations, regional homogeneity,voxel-mirrored homotopic connectivity, network centrality and global signal correlation. Regardless of whether examining intra-individual or inter-individual variation, we found that these definitionally distinct R-fMRI indices tend to exhibit a relatively high degree of covariation, which doesn't exist in phase randomized surrogate data. As a measure of intrinsic brain function, concordance for R-fMRI indices was negatively correlated with age across individuals(i.e., concordance among functional indices decreased with age). To understand the functional significance of concordance, we noted that higher concordance was generally associated with higher strengths of R-fMRI indices, regardless of whether looking through the lens of inter-individual(i.e., high vs. low concordance participants) or intra-individual(i.e., high vs.low concordance states identified via temporal dynamic analyses) differences. We also noted a linear increase in functional concordance together with the R-fMRI indices through the scan, which may suggest a decrease in arousal. The current study demonstrated an enriched picture regarding the relationship among the R-fMRI indices, as well as provided new insights in examining dynamic states within and between individuals.

Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia

Schizophrenia is hypothesized to arise from disrupted brain connectivity. This ＂dysconnectivity hypothesis＂ has generated interest in discovering whether there is anatomical and functional dysconnectivity between the prefrontal cortex （PFC） and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging （DTI） and functional magnetic resonance imaging （fMRI）, make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain in vivo and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia.

The anatomy of reliability: a must read for future human brain mapping

Human brain mapping (HBM)is increasingly becoming a multidisciplinary field where some scientific issues are fundamental for all scientists and applications of using the technology to investigate individual differences.Reliability represents a significant issue for all scientific fields and has particularly been overlooked for decades by the HBM field [1].Meanwhile,recent advances in open science have offered the field big data for developing novel methodological frameworks as well as performing largescale investigations of the brain-mind associations based upon the individual differences assessed with HBM [2].A systematic investigation of reliability seems still far behind these HBM developments. It is critical that reliability is evaluated ahead of these applications, motivating the current commentary on delineation of the anatomy of reliability for future HBM.

Mapping the human brain function in vivo

Mapping of the human brain function in vivo is among the most promising means of uncovering the relationship between brain and behavior. Both the 1000 Functional Connectome Project1 and Human Connectome Project2 have made advancements in the collection, management, and sharing of massive neuroimaging datasets. In China, the government plans to announce the China Brain Project （CBP）, a national brain project aimed at understanding neural mechanisms underlying human cognition with applications of brain disease and brain-inspired computa- tion [1]. Methods for in vivo human brain mapping must be included in the CBP, as they make possible direct assess- ment of brain structure and activity and contribute directly to translational research.

Altered relationship between thickness and intrinsic activity amplitude in generalized tonic–clonic seizures

A thinner cortex has higher potential for binding GABA receptor A which is associated with larger amplitudes of intrinsic brain activity(i BA). However, the relationship between cortical thickness and i BA is unknown in intact and epileptic brains. To this end, we investigated the relationship between cortical thickness measured by highresolution MRI and surface-based i BA derived from resting-state functional MRI in normal controls(n = 82) andpatients with generalized tonic–clonic seizures(GTCS)only(n = 82). We demonstrated that the spatial distribution of cortical thickness negatively correlated with surface-based i BA amplitude at both whole-brain and within independent brain functional networks. In GTCS patients,spatial coupling between thickness and i BA amplitude decreased in the default mode, dorsal attention, and somatomotor networks. In addition, the vertex-wise acrosssubject thickness–i BA amplitude correspondence altered in the frontal and temporal lobes as well as in the precuneus in GTCS patients. The relationship between these two modalities can serve as a brain-based marker for detecting epileptogenic changes.

Altered cerebral activities and functional connectivity in depression:a systematic review of fMRI studies

Background:As one of the leading causes of global disability,major depressive disorder(MDD)places a noticeable burden on individuals and society.Despite the great expectation on finding accurate biomarkers and effective treatment targets of MDD,studies in applying functional magnetic resonance imaging(fMRI)are still faced with challenges,including the representational ambiguity,small sample size,low statistical power,relatively high false positive rates,etc.Thus,reviewing studies with solid methodology may help achieve a consensus on the pathology of MDD.Methods:In this systematic review,we screened fMRI studies on MDD through strict criteria to focus on reliable studies with sufficient sample size,adequate control of head motion,and a proper multiple comparison control strategy.Results:We found consistent evidence regarding the dysfunction within and among the default mode network(DMN),the frontoparietal network(FPN),and other brain regions.However,controversy remains,probably due to the heterogeneity of participants and data processing strategies.Conclusion:Future studies are recommended to apply a comprehensive set of neuro-behavioral measurements,consider the heterogeneity of MDD patients and other potentially confounding factors,apply surface-based neuroscientific network fMRI approaches,and advance research transparency and open science by applying state-ofthe-art pipelines along with open data sharing.