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.

Back to the future:Some similarities and many differences between autism spectrum disorder and early onset schizophrenia.Clues to pathophysiology?

In the first issue of the recently launched journal,Medicine Plus,Lu et al.[1]report on a remarkable effort encompassing nearly 600 young participants examined at five centers in China.The focus was on the first episode early-onset schizophrenia(EOS;onset before age 18 years)and autism spectrum disorder(ASD),contrasted to each other and healthy controls(HC).Historically,autism was initially considered a form of infantile schizophrenia,although it was differentiated from early-onset schizophrenia as early as 1955 by Eisenberg and Kanner[2].

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.

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.